From 0df64834b153e1fa3fe4a989da8b62d3463f2dea Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Fri, 20 Nov 2020 11:59:24 +0000 Subject: [PATCH 001/192] chore: rename g2g_network Add alias for backward compatibility --- R/{g2g_network.R => network_g2g.R} | 15 ++++++++++----- 1 file changed, 10 insertions(+), 5 deletions(-) rename R/{g2g_network.R => network_g2g.R} (91%) diff --git a/R/g2g_network.R b/R/network_g2g.R similarity index 91% rename from R/g2g_network.R rename to R/network_g2g.R index 40d47f32..ddd2eda9 100644 --- a/R/g2g_network.R +++ b/R/network_g2g.R @@ -3,11 +3,10 @@ # Licensed under the MIT License. See LICENSE.txt in the project root for license information. # -------------------------------------------------------------------------------------------- -#' @title Create a network plot with the G2G query +#' @title Create a network plot with the group-to-group query #' #' @description -#' Pass a data frame containing a G2G query and return a network -#' plot. +#' Pass a data frame containing a group-to-group query and return a network plot. #' Automatically handles "Collaborators_within_group" and "Other_collaborators" within query data. #' #' @param data Data frame containing a G2G query. @@ -27,13 +26,13 @@ #' @examples #' \dontrun{ #' g2g_data %>% -#' g2g_network(time_investor = "TimeInvestors_Organization", +#' network_g2g(time_investor = "TimeInvestors_Organization", #' collaborator = "Collaborators_Organization", #' metric = "Collaboration_hours") #' } #' #' @export -g2g_network <- function(data, +network_g2g <- function(data, time_investor, collaborator, metric, @@ -64,6 +63,7 @@ g2g_network <- function(data, } else if(return == "plot"){ + ## Network object mynet_em <- plot_data %>% filter(metric_prop > exc_threshold) %>% @@ -73,6 +73,7 @@ g2g_network <- function(data, ignore.eval = FALSE, names.eval = "weights") + ## Plot object mynet_em %>% GGally::ggnet2(size = 12, color = "lightblue", @@ -96,3 +97,7 @@ g2g_network <- function(data, } } + +#' @rdname network_g2g +#' @export +g2g_network <- network_g2g From 45339580fad93a2a66bba083f3e4951766988d84 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Fri, 20 Nov 2020 16:13:11 +0000 Subject: [PATCH 002/192] feat: add network_p2p - Also add heat_colours for palette --- NAMESPACE | 4 + R/heat_colours.R | 36 +++++++++ R/network_p2p.R | 102 +++++++++++++++++++++++++ man/heat_colours.Rd | 24 ++++++ man/{g2g_network.Rd => network_g2g.Rd} | 23 ++++-- man/network_p2p.Rd | 31 ++++++++ 6 files changed, 214 insertions(+), 6 deletions(-) create mode 100644 R/heat_colours.R create mode 100644 R/network_p2p.R create mode 100644 man/heat_colours.Rd rename man/{g2g_network.Rd => network_g2g.Rd} (70%) create mode 100644 man/network_p2p.Rd diff --git a/NAMESPACE b/NAMESPACE index ad182eb5..472577bf 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -69,6 +69,7 @@ export(flag_extreme) export(flag_outlooktime) export(g2g_network) export(generate_report) +export(heat_colours) export(hr_trend) export(hrvar_count) export(hrvar_count_all) @@ -102,6 +103,8 @@ export(meetingtype_sum) export(meetingtype_summary) export(mgrcoatt_dist) export(mgrrel_matrix) +export(network_g2g) +export(network_p2p) export(one2one_dist) export(one2one_fizz) export(one2one_line) @@ -146,6 +149,7 @@ import(dplyr) import(ggplot2) import(ggraph) import(ggrepel) +import(network) import(reshape2) import(scales) import(tidyselect) diff --git a/R/heat_colours.R b/R/heat_colours.R new file mode 100644 index 00000000..6aa686c5 --- /dev/null +++ b/R/heat_colours.R @@ -0,0 +1,36 @@ +#' @title +#' Generate a vector of `n` contiguous colours, as a red-yellow-green palette. +#' +#' @param n the number of colors (≥ 1) to be in the palette. +#' @param alpha an alpha-transparency level in the range [0,1] (0 means transparent and 1 means opaque) +#' @param rev logical indicating whether the ordering of the colors should be reversed. +#' +#' @examples +#' barplot(rep(10, 50), col = heat_colours(n = 50), border = NA) +#' +#' barplot(rep(10, 50), col = heat_colours(n = 50, alpha = 0.5, rev = TRUE), border = NA) +#' +#' @export +heat_colours <- function (n, alpha, rev = FALSE) { + + ## Move from red to green + pre_h <- seq(from = 0, to = 0.3, length.out = n - 1) + h <- c(1, pre_h) + + ## Less bright + s <- rep(0.69, length(h)) + + ## Increasingly low value (darker) + v <- seq(from = 1, to = 0.8, length.out = n) + + cols <- hsv(h = h, s = s, v = v, alpha = alpha) + + if(rev){ + + rev(cols) + + } else if(rev == FALSE) { + + cols + } +} diff --git a/R/network_p2p.R b/R/network_p2p.R new file mode 100644 index 00000000..dd217777 --- /dev/null +++ b/R/network_p2p.R @@ -0,0 +1,102 @@ +# -------------------------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See LICENSE.txt in the project root for license information. +# -------------------------------------------------------------------------------------------- + +#' @title Create a network plot with the person-to-person query +#' +#' @description +#' Pass a data frame containing a person-to-person query and return a network +#' plot. +#' Automatically handles "Collaborators_within_group" and "Other_collaborators" within query data. +#' +#' @param data Data frame containing a person-to-person query. +#' @param hrvar String containing the label for the HR attribute. +#' @param ... Additional arguments to pass to `GGally::ggnet2()` +#' @param return Character vector specifying what to return, defaults to "plot". +#' Valid inputs are "plot", "network" and "table". "network" returns the `network` +#' object used to generate the network plot. +#' +#' +#' @import ggplot2 +#' @import dplyr +#' @import network +#' +#' @export +network_p2p <- function(data, + hrvar, + return = "plot", + bg_fill = "#000000", + font_col = "#FFFFFF", + ...){ + + data <- + data %>% + filter(StrongTieType > 0) + + tieorigin_var <- paste0("TieOrigin_", hrvar) + tiedestin_var <- paste0("TieDestination_", hrvar) + + + ## Extract edge list + strong_edgelist <- + data %>% + select(TieOrigin_PersonId, TieDestination_PersonId) + + ## Create basic network object + mynet <- + network::network(strong_edgelist, matrix.type="edgelist") + + ## Extract list of nodes from network object + myedges <- data.frame(PersonId = network.vertex.names(mynet)) + + ## Extract attributes + totalAttributes <- + bind_rows(select(data, PersonId = "TieOrigin_PersonId", !!sym(hrvar) := tieorigin_var), + select(data, PersonId = "TieOrigin_PersonId", !!sym(hrvar) := tiedestin_var)) %>% + unique() + + ## Merge list of nodes and attributes + myedges <- merge(myedges, totalAttributes, by = "PersonId", all.x = TRUE) + + ## Add attributes to network object + mynet %v% hrvar <- myedges[[hrvar]] + + ## Palette + pal <- + tibble(!!sym(hrvar) := unique(myedges[[hrvar]])) %>% + mutate(Colours = heat_colours(nrow(.), alpha = 1)) %>% + tibble::deframe() + + if(return == "table"){ + + data + + } else if(return == "network"){ + + mynet + + } else if(return == "plot"){ + + mynet %>% + GGally::ggnet2(size = 1, + color = hrvar, + label = FALSE, + edge.size = .01, + edge.alpha = .1, + node.alpha = .8, + palette = pal, + ...) + + theme_void() + + theme(legend.position = "bottom", + plot.background = element_rect(fill = bg_fill), + text = element_text(colour = font_col), + axis.line = element_blank()) + + + } else { + + stop("Please enter a valid input for `return`.") + + } +} diff --git a/man/heat_colours.Rd b/man/heat_colours.Rd new file mode 100644 index 00000000..154b9533 --- /dev/null +++ b/man/heat_colours.Rd @@ -0,0 +1,24 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/heat_colours.R +\name{heat_colours} +\alias{heat_colours} +\title{Generate a vector of \code{n} contiguous colours, as a red-yellow-green palette.} +\usage{ +heat_colours(n, alpha, rev = FALSE) +} +\arguments{ +\item{n}{the number of colors (≥ 1) to be in the palette.} + +\item{alpha}{an alpha-transparency level in the range \link{0,1} (0 means transparent and 1 means opaque)} + +\item{rev}{logical indicating whether the ordering of the colors should be reversed.} +} +\description{ +Generate a vector of \code{n} contiguous colours, as a red-yellow-green palette. +} +\examples{ +barplot(rep(10, 50), col = heat_colours(n = 50), border = NA) + +barplot(rep(10, 50), col = heat_colours(n = 50, alpha = 0.5, rev = TRUE), border = NA) + +} diff --git a/man/g2g_network.Rd b/man/network_g2g.Rd similarity index 70% rename from man/g2g_network.Rd rename to man/network_g2g.Rd index 2b36d6f1..06780583 100644 --- a/man/g2g_network.Rd +++ b/man/network_g2g.Rd @@ -1,9 +1,21 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/g2g_network.R -\name{g2g_network} +% Please edit documentation in R/network_g2g.R +\name{network_g2g} +\alias{network_g2g} \alias{g2g_network} -\title{Create a network plot with the G2G query} +\title{Create a network plot with the group-to-group query} \usage{ +network_g2g( + data, + time_investor, + collaborator, + metric, + exc_threshold = 0.1, + subtitle = "Collaboration Across Organizations", + return = "plot", + ... +) + g2g_network( data, time_investor, @@ -34,14 +46,13 @@ Valid inputs are "plot" and "table".} \item{...}{Additional arguments to pass to \code{GGally::ggnet2()}} } \description{ -Pass a data frame containing a G2G query and return a network -plot. +Pass a data frame containing a group-to-group query and return a network plot. Automatically handles "Collaborators_within_group" and "Other_collaborators" within query data. } \examples{ \dontrun{ g2g_data \%>\% - g2g_network(time_investor = "TimeInvestors_Organization", + network_g2g(time_investor = "TimeInvestors_Organization", collaborator = "Collaborators_Organization", metric = "Collaboration_hours") } diff --git a/man/network_p2p.Rd b/man/network_p2p.Rd new file mode 100644 index 00000000..12b078b8 --- /dev/null +++ b/man/network_p2p.Rd @@ -0,0 +1,31 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/network_p2p.R +\name{network_p2p} +\alias{network_p2p} +\title{Create a network plot with the person-to-person query} +\usage{ +network_p2p( + data, + hrvar, + return = "plot", + bg_fill = "#000000", + font_col = "#FFFFFF", + ... +) +} +\arguments{ +\item{data}{Data frame containing a person-to-person query.} + +\item{hrvar}{String containing the label for the HR attribute.} + +\item{return}{Character vector specifying what to return, defaults to "plot". +Valid inputs are "plot", "network" and "table". "network" returns the \code{network} +object used to generate the network plot.} + +\item{...}{Additional arguments to pass to \code{GGally::ggnet2()}} +} +\description{ +Pass a data frame containing a person-to-person query and return a network +plot. +Automatically handles "Collaborators_within_group" and "Other_collaborators" within query data. +} From 2eff432e218033fd8339d5a17852401ebe19018d Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Fri, 20 Nov 2020 16:32:24 +0000 Subject: [PATCH 003/192] docs: change network_p2p documentation Default background colour also changed to dark grey --- R/network_p2p.R | 7 +++++-- man/network_p2p.Rd | 9 +++++++-- 2 files changed, 12 insertions(+), 4 deletions(-) diff --git a/R/network_p2p.R b/R/network_p2p.R index dd217777..2fc1de2f 100644 --- a/R/network_p2p.R +++ b/R/network_p2p.R @@ -12,10 +12,13 @@ #' #' @param data Data frame containing a person-to-person query. #' @param hrvar String containing the label for the HR attribute. -#' @param ... Additional arguments to pass to `GGally::ggnet2()` #' @param return Character vector specifying what to return, defaults to "plot". #' Valid inputs are "plot", "network" and "table". "network" returns the `network` #' object used to generate the network plot. +#' @param bg_fill String to specify background fill colour. +#' @param font_col String to specify font colour +#' @param ... Additional arguments to pass to `GGally::ggnet2()`. +#' For instance, you may specify the argument `mode` to change the node placement algorithm. #' #' #' @import ggplot2 @@ -26,7 +29,7 @@ network_p2p <- function(data, hrvar, return = "plot", - bg_fill = "#000000", + bg_fill = "#404040", font_col = "#FFFFFF", ...){ diff --git a/man/network_p2p.Rd b/man/network_p2p.Rd index 12b078b8..96942d5c 100644 --- a/man/network_p2p.Rd +++ b/man/network_p2p.Rd @@ -8,7 +8,7 @@ network_p2p( data, hrvar, return = "plot", - bg_fill = "#000000", + bg_fill = "#404040", font_col = "#FFFFFF", ... ) @@ -22,7 +22,12 @@ network_p2p( Valid inputs are "plot", "network" and "table". "network" returns the \code{network} object used to generate the network plot.} -\item{...}{Additional arguments to pass to \code{GGally::ggnet2()}} +\item{bg_fill}{String to specify background fill colour.} + +\item{font_col}{String to specify font colour} + +\item{...}{Additional arguments to pass to \code{GGally::ggnet2()}. +For instance, you may specify the argument \code{mode} to change the node placement algorithm.} } \description{ Pass a data frame containing a person-to-person query and return a network From 2b510f7f286cd2b4c85595fe416c64caa71412c4 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Fri, 20 Nov 2020 16:54:36 +0000 Subject: [PATCH 004/192] fix: remove theme_void bug Issue with network_p2p --- R/network_p2p.R | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/R/network_p2p.R b/R/network_p2p.R index 2fc1de2f..e1434560 100644 --- a/R/network_p2p.R +++ b/R/network_p2p.R @@ -90,11 +90,12 @@ network_p2p <- function(data, node.alpha = .8, palette = pal, ...) + - theme_void() + theme(legend.position = "bottom", plot.background = element_rect(fill = bg_fill), text = element_text(colour = font_col), - axis.line = element_blank()) + axis.line = element_blank()) + + labs(y = "", + x = "") } else { From 6b922ef2aa0c3a5f789358e4375481209c90c9b1 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Fri, 20 Nov 2020 16:56:52 +0000 Subject: [PATCH 005/192] feat: add argument for legend position --- R/network_p2p.R | 4 +++- man/network_p2p.Rd | 3 +++ 2 files changed, 6 insertions(+), 1 deletion(-) diff --git a/R/network_p2p.R b/R/network_p2p.R index e1434560..bfb96297 100644 --- a/R/network_p2p.R +++ b/R/network_p2p.R @@ -17,6 +17,7 @@ #' object used to generate the network plot. #' @param bg_fill String to specify background fill colour. #' @param font_col String to specify font colour +#' @param legend_pos String to specify position of legend. Defaults to "bottom". See `ggplot2::theme()`. #' @param ... Additional arguments to pass to `GGally::ggnet2()`. #' For instance, you may specify the argument `mode` to change the node placement algorithm. #' @@ -31,6 +32,7 @@ network_p2p <- function(data, return = "plot", bg_fill = "#404040", font_col = "#FFFFFF", + legend_pos = "bottom", ...){ data <- @@ -90,7 +92,7 @@ network_p2p <- function(data, node.alpha = .8, palette = pal, ...) + - theme(legend.position = "bottom", + theme(legend.position = legend_pos, plot.background = element_rect(fill = bg_fill), text = element_text(colour = font_col), axis.line = element_blank()) + diff --git a/man/network_p2p.Rd b/man/network_p2p.Rd index 96942d5c..c687eee8 100644 --- a/man/network_p2p.Rd +++ b/man/network_p2p.Rd @@ -10,6 +10,7 @@ network_p2p( return = "plot", bg_fill = "#404040", font_col = "#FFFFFF", + legend_pos = "bottom", ... ) } @@ -26,6 +27,8 @@ object used to generate the network plot.} \item{font_col}{String to specify font colour} +\item{legend_pos}{String to specify position of legend. Defaults to "bottom". See \code{ggplot2::theme()}.} + \item{...}{Additional arguments to pass to \code{GGally::ggnet2()}. For instance, you may specify the argument \code{mode} to change the node placement algorithm.} } From df53042cfb6d3756c3b75a9efa3f49080f806aab Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Fri, 20 Nov 2020 17:09:24 +0000 Subject: [PATCH 006/192] fix: fix background network_p2p Change background colour and also make legend background to match plot background --- R/network_p2p.R | 3 ++- man/network_p2p.Rd | 2 +- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/R/network_p2p.R b/R/network_p2p.R index bfb96297..8dd9330b 100644 --- a/R/network_p2p.R +++ b/R/network_p2p.R @@ -30,7 +30,7 @@ network_p2p <- function(data, hrvar, return = "plot", - bg_fill = "#404040", + bg_fill = "#000000", font_col = "#FFFFFF", legend_pos = "bottom", ...){ @@ -93,6 +93,7 @@ network_p2p <- function(data, palette = pal, ...) + theme(legend.position = legend_pos, + legend.background = element_rect(fill = bg_fill), plot.background = element_rect(fill = bg_fill), text = element_text(colour = font_col), axis.line = element_blank()) + diff --git a/man/network_p2p.Rd b/man/network_p2p.Rd index c687eee8..82c254be 100644 --- a/man/network_p2p.Rd +++ b/man/network_p2p.Rd @@ -8,7 +8,7 @@ network_p2p( data, hrvar, return = "plot", - bg_fill = "#404040", + bg_fill = "#000000", font_col = "#FFFFFF", legend_pos = "bottom", ... From 003b1abf8245f2adbe9bb2156b8f4c73e657b6c4 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Fri, 20 Nov 2020 17:27:23 +0000 Subject: [PATCH 007/192] feat: change to rainbow palette --- R/network_p2p.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/network_p2p.R b/R/network_p2p.R index 8dd9330b..b260e285 100644 --- a/R/network_p2p.R +++ b/R/network_p2p.R @@ -70,7 +70,7 @@ network_p2p <- function(data, ## Palette pal <- tibble(!!sym(hrvar) := unique(myedges[[hrvar]])) %>% - mutate(Colours = heat_colours(nrow(.), alpha = 1)) %>% + mutate(Colours = rainbow(nrow(.), alpha = 1)) %>% tibble::deframe() if(return == "table"){ From 72c82f42bec3118bc15fc38775cd4409a3a3c015 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Mon, 23 Nov 2020 10:52:20 +0000 Subject: [PATCH 008/192] feat: add direct PDF export option Arguments added for specifying file path --- R/network_p2p.R | 34 +++++++++++++++++++++++++++------- man/network_p2p.Rd | 15 ++++++++++----- 2 files changed, 37 insertions(+), 12 deletions(-) diff --git a/R/network_p2p.R b/R/network_p2p.R index b260e285..bf92bdd4 100644 --- a/R/network_p2p.R +++ b/R/network_p2p.R @@ -12,11 +12,14 @@ #' #' @param data Data frame containing a person-to-person query. #' @param hrvar String containing the label for the HR attribute. -#' @param return Character vector specifying what to return, defaults to "plot". -#' Valid inputs are "plot", "network" and "table". "network" returns the `network` -#' object used to generate the network plot. +#' @param return Character vector specifying what to return, defaults to "pdf". +#' Other valid inputs are "plot", "network" and "table". "network" returns the `network` +#' object used to generate the network plot. The "pdf" option is highly recommended over the "plot" +#' option as the PDF export format has significantly higher performance. The "plot" option is highly +#' computationally intensive and is not generally recommended. +#' @param path File path for saving the PDF output. Defaults to "network_p2p". #' @param bg_fill String to specify background fill colour. -#' @param font_col String to specify font colour +#' @param font_col String to specify font and link colour. #' @param legend_pos String to specify position of legend. Defaults to "bottom". See `ggplot2::theme()`. #' @param ... Additional arguments to pass to `GGally::ggnet2()`. #' For instance, you may specify the argument `mode` to change the node placement algorithm. @@ -29,7 +32,8 @@ #' @export network_p2p <- function(data, hrvar, - return = "plot", + return = "pdf", + path = "network_p2p", bg_fill = "#000000", font_col = "#FFFFFF", legend_pos = "bottom", @@ -81,9 +85,10 @@ network_p2p <- function(data, mynet - } else if(return == "plot"){ + } else if(return %in% c("plot", "pdf")){ - mynet %>% + outputPlot <- + mynet %>% GGally::ggnet2(size = 1, color = hrvar, label = FALSE, @@ -100,6 +105,21 @@ network_p2p <- function(data, labs(y = "", x = "") + ## Inner conditional + if(return == "pdf"){ + + fn <- paste0(path, "_", tstamp(), ".pdf") + ggsave(filename = fn, + plot = outputPlot, + width = 12, + height = 9) + + + } else if(return == "plot"){ + + outputPlot + + } } else { diff --git a/man/network_p2p.Rd b/man/network_p2p.Rd index 82c254be..7c5ac22e 100644 --- a/man/network_p2p.Rd +++ b/man/network_p2p.Rd @@ -7,7 +7,8 @@ network_p2p( data, hrvar, - return = "plot", + return = "pdf", + path = "network_p2p", bg_fill = "#000000", font_col = "#FFFFFF", legend_pos = "bottom", @@ -19,13 +20,17 @@ network_p2p( \item{hrvar}{String containing the label for the HR attribute.} -\item{return}{Character vector specifying what to return, defaults to "plot". -Valid inputs are "plot", "network" and "table". "network" returns the \code{network} -object used to generate the network plot.} +\item{return}{Character vector specifying what to return, defaults to "pdf". +Other valid inputs are "plot", "network" and "table". "network" returns the \code{network} +object used to generate the network plot. The "pdf" option is highly recommended over the "plot" +option as the PDF export format has significantly higher performance. The "plot" option is highly +computationally intensive and is not generally recommended.} + +\item{path}{File path for saving the PDF output. Defaults to "network_p2p".} \item{bg_fill}{String to specify background fill colour.} -\item{font_col}{String to specify font colour} +\item{font_col}{String to specify font and link colour.} \item{legend_pos}{String to specify position of legend. Defaults to "bottom". See \code{ggplot2::theme()}.} From 5be958d7ab733c07bfee9b10560b1e882fb17c45 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Mon, 23 Nov 2020 17:02:05 +0000 Subject: [PATCH 009/192] feat: add palette argument Now possible to specify colour palettes --- R/network_p2p.R | 12 ++++++++++-- man/network_p2p.Rd | 3 +++ 2 files changed, 13 insertions(+), 2 deletions(-) diff --git a/R/network_p2p.R b/R/network_p2p.R index bf92bdd4..df8bf367 100644 --- a/R/network_p2p.R +++ b/R/network_p2p.R @@ -21,6 +21,7 @@ #' @param bg_fill String to specify background fill colour. #' @param font_col String to specify font and link colour. #' @param legend_pos String to specify position of legend. Defaults to "bottom". See `ggplot2::theme()`. +#' @param palette Function for generating a colour palette with a single argument `n`. Uses `rainbow()` by default. #' @param ... Additional arguments to pass to `GGally::ggnet2()`. #' For instance, you may specify the argument `mode` to change the node placement algorithm. #' @@ -37,6 +38,7 @@ network_p2p <- function(data, bg_fill = "#000000", font_col = "#FFFFFF", legend_pos = "bottom", + palette = rainbow, ...){ data <- @@ -72,9 +74,15 @@ network_p2p <- function(data, mynet %v% hrvar <- myedges[[hrvar]] ## Palette + + pal <- tibble(!!sym(hrvar) := unique(myedges[[hrvar]])) + + col_pal <- do.call(what = palette, args = list(nrow(pal))) + + ## named character vector pal <- - tibble(!!sym(hrvar) := unique(myedges[[hrvar]])) %>% - mutate(Colours = rainbow(nrow(.), alpha = 1)) %>% + pal %>% + mutate(Colours = col_pal) %>% tibble::deframe() if(return == "table"){ diff --git a/man/network_p2p.Rd b/man/network_p2p.Rd index 7c5ac22e..2fb607b6 100644 --- a/man/network_p2p.Rd +++ b/man/network_p2p.Rd @@ -12,6 +12,7 @@ network_p2p( bg_fill = "#000000", font_col = "#FFFFFF", legend_pos = "bottom", + palette = rainbow, ... ) } @@ -34,6 +35,8 @@ computationally intensive and is not generally recommended.} \item{legend_pos}{String to specify position of legend. Defaults to "bottom". See \code{ggplot2::theme()}.} +\item{palette}{Function for generating a colour palette with a single argument \code{n}. Uses \code{rainbow()} by default.} + \item{...}{Additional arguments to pass to \code{GGally::ggnet2()}. For instance, you may specify the argument \code{mode} to change the node placement algorithm.} } From dd38bc567435f24e36fee05e03116443650d1c07 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Tue, 24 Nov 2020 11:29:44 +0000 Subject: [PATCH 010/192] docs: update p2p documentation --- R/network_p2p.R | 8 +++++--- man/network_p2p.Rd | 9 ++++++--- 2 files changed, 11 insertions(+), 6 deletions(-) diff --git a/R/network_p2p.R b/R/network_p2p.R index df8bf367..6b09060e 100644 --- a/R/network_p2p.R +++ b/R/network_p2p.R @@ -6,9 +6,8 @@ #' @title Create a network plot with the person-to-person query #' #' @description -#' Pass a data frame containing a person-to-person query and return a network -#' plot. -#' Automatically handles "Collaborators_within_group" and "Other_collaborators" within query data. +#' Pass a data frame containing a person-to-person query and save a network +#' plot as a PDF file. #' #' @param data Data frame containing a person-to-person query. #' @param hrvar String containing the label for the HR attribute. @@ -25,6 +24,9 @@ #' @param ... Additional arguments to pass to `GGally::ggnet2()`. #' For instance, you may specify the argument `mode` to change the node placement algorithm. #' +#' @details +#' For specifying the node placement algorithm, please see the `gplot.layout` documentation for the **sna** +#' package, which provides a full list of the valid functions that can be passed into the `mode` argument. #' #' @import ggplot2 #' @import dplyr diff --git a/man/network_p2p.Rd b/man/network_p2p.Rd index 2fb607b6..30b0b92a 100644 --- a/man/network_p2p.Rd +++ b/man/network_p2p.Rd @@ -41,7 +41,10 @@ computationally intensive and is not generally recommended.} For instance, you may specify the argument \code{mode} to change the node placement algorithm.} } \description{ -Pass a data frame containing a person-to-person query and return a network -plot. -Automatically handles "Collaborators_within_group" and "Other_collaborators" within query data. +Pass a data frame containing a person-to-person query and save a network +plot as a PDF file. +} +\details{ +For specifying the node placement algorithm, please see the \code{gplot.layout} documentation for the \strong{sna} +package, which provides a full list of the valid functions that can be passed into the \code{mode} argument. } From bef84d9e6b89787c4181fa36d27978f4c69a5380 Mon Sep 17 00:00:00 2001 From: moralec Date: Tue, 24 Nov 2020 14:00:11 +0000 Subject: [PATCH 011/192] Update network_p2p.R --- R/network_p2p.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/network_p2p.R b/R/network_p2p.R index 6b09060e..251cc781 100644 --- a/R/network_p2p.R +++ b/R/network_p2p.R @@ -103,7 +103,7 @@ network_p2p <- function(data, color = hrvar, label = FALSE, edge.size = .01, - edge.alpha = .1, + edge.alpha = .5, node.alpha = .8, palette = pal, ...) + From 73993d54bfd4987bab45d42a88c07e7ecb6d5879 Mon Sep 17 00:00:00 2001 From: moralec Date: Tue, 24 Nov 2020 15:38:52 +0000 Subject: [PATCH 012/192] Update network_p2p.R --- R/network_p2p.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/network_p2p.R b/R/network_p2p.R index 251cc781..1439bf0a 100644 --- a/R/network_p2p.R +++ b/R/network_p2p.R @@ -103,7 +103,7 @@ network_p2p <- function(data, color = hrvar, label = FALSE, edge.size = .01, - edge.alpha = .5, + edge.alpha = .2, node.alpha = .8, palette = pal, ...) + From f938584a6e789e0278ebdf3ae348c9c3c19c02ef Mon Sep 17 00:00:00 2001 From: moralec Date: Tue, 24 Nov 2020 15:43:40 +0000 Subject: [PATCH 013/192] Update network_p2p.R --- R/network_p2p.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/network_p2p.R b/R/network_p2p.R index 1439bf0a..ed680247 100644 --- a/R/network_p2p.R +++ b/R/network_p2p.R @@ -103,7 +103,7 @@ network_p2p <- function(data, color = hrvar, label = FALSE, edge.size = .01, - edge.alpha = .2, + edge.alpha = .15, node.alpha = .8, palette = pal, ...) + From 90260163f506c7a58c775620f4a9a18eb606c430 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Wed, 25 Nov 2020 09:15:01 +0000 Subject: [PATCH 014/192] feat: add gallery images Plot examples for the Analyst Guide --- .github/gallery/afterhours_sum.png | Bin 0 -> 162689 bytes .github/gallery/collab_area.png | Bin 0 -> 79653 bytes .github/gallery/collab_sum.png | Bin 0 -> 184165 bytes .github/gallery/email_dist.png | Bin 0 -> 102096 bytes .github/gallery/identify_holidayweeks.png | Bin 0 -> 94117 bytes .github/gallery/meetingtype_dist.png | Bin 0 -> 84220 bytes .github/gallery/mgrrel_matrix.png | Bin 0 -> 111659 bytes .github/gallery/network_g2g.png | Bin 0 -> 160924 bytes .github/gallery/tm_cooc.png | Bin 0 -> 692364 bytes .github/gallery/tm_freq.png | Bin 0 -> 213358 bytes .github/gallery/tm_wordcloud.png | Bin 0 -> 278118 bytes .github/gallery/workpatterns_rank.png | Bin 0 -> 91145 bytes 12 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 .github/gallery/afterhours_sum.png create mode 100644 .github/gallery/collab_area.png create mode 100644 .github/gallery/collab_sum.png 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    + + Cinque Terre + +
    Add a description of the image here
    +
    + +
    + + Forest + +
    Add a description of the image here
    +
    + +
    + + Northern Lights + +
    Add a description of the image here
    +
    + +
    + + Mountains + +
    Add a description of the image here
    +
    + + + + + + ## Going beyond averages Let's continue to [**Distribution Functions**](analyst_guide_distribution.html) to explore how we can analyse distributions from different Workplace Analytics Metrics with unique functions. From bba4271f79b0f5affc97edbc0c417733153f6913 Mon Sep 17 00:00:00 2001 From: moralec Date: Tue, 1 Dec 2020 16:06:19 +0000 Subject: [PATCH 106/192] Update analyst_guide_summary.md --- .github/analyst_guide_summary.md | 37 ++++++++------------------------ 1 file changed, 9 insertions(+), 28 deletions(-) diff --git a/.github/analyst_guide_summary.md b/.github/analyst_guide_summary.md index f5f5eab0..ef604575 100644 --- a/.github/analyst_guide_summary.md +++ b/.github/analyst_guide_summary.md @@ -29,8 +29,6 @@ The `collaboration_summary()` function generates a stacked bar plot summarising sq_data %>% collaboration_summary() ``` - - By changing the `hrvar()` argument, you can change the groups being shown easily: ```{r} @@ -66,7 +64,6 @@ For example, ``` sq_data %>% afterhours_summary() ``` - ## Flexible Function @@ -87,6 +84,10 @@ All plots in *wpa* are [ggplot objects](https://rafalab.github.io/dsbook/ggplot2 sq_data %>% collaboration_summary() + ggtitle("This is a custom title") ``` +## Going beyond averages + + Let's continue to [**Distribution Functions**](analyst_guide_distribution.html) to explore how we can analyse distributions from different Workplace Analytics Metrics with unique functions. + ## Summary Functions: Gallery @@ -119,39 +120,19 @@ div.desc {
    - Cinque Terre + Collaboration Summary -
    Add a description of the image here
    +
    Plot for collaboration_summary()
    - - Forest + + After-Hours Summary -
    Add a description of the image here
    -
    - -
    - - Northern Lights - -
    Add a description of the image here
    -
    - -
    - - Mountains - -
    Add a description of the image here
    +
    Plot from afterhours_summary()
    - -## Going beyond averages - - Let's continue to [**Distribution Functions**](analyst_guide_distribution.html) to explore how we can analyse distributions from different Workplace Analytics Metrics with unique functions. - - From e69a9fa1ac70339d071b7edaa077d20777b24a62 Mon Sep 17 00:00:00 2001 From: moralec Date: Tue, 1 Dec 2020 16:11:07 +0000 Subject: [PATCH 107/192] Updated Analyst Guide Summary --- .github/analyst_guide_summary.md | 9 ++++++++- .github/gallery/keymetrics_scan.png | Bin 0 -> 312927 bytes 2 files changed, 8 insertions(+), 1 deletion(-) create mode 100644 .github/gallery/keymetrics_scan.png diff --git a/.github/analyst_guide_summary.md b/.github/analyst_guide_summary.md index ef604575..20d4b5c6 100644 --- a/.github/analyst_guide_summary.md +++ b/.github/analyst_guide_summary.md @@ -119,7 +119,14 @@ div.desc {
    - + + Key Metrics Scan + +
    Plot for keymetrics_scan()
    +
    + +
    + Collaboration Summary
    Plot for collaboration_summary()
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zo|(n#f~cpQYz^UuMRG$WyLO1>z;|yj@!Tz*+A`_**``cHAK*{v>o2&N+KDw9+_ION zV%OLJ_n~{Zc293zL*L{Y#M<`TaC6QRGDn5e&&sW!H@+4UZS@P`Rjh}k-*rI$i z;;TydoY(TJ$5s;)mT0`!6<>cMUGm0<#8zdEgwfYO5NRGzqofZ&-v{b<*%x3-xL*io zt~x)EZCYKZDg=OQ_Sq5g+9D5{5J%3AhO=L|i;x*u%O(>lJs|U7Lv=Twzo$v2PdRqm zq*yNYyMIgdcM|ZQO=HQN`U=Q!B_u-K zXR71sb#z0(|ANDb5`4}+Bu$H&tM~k+ZDdqhKGr%$uWrn|3fT&_(Ux1B)Di@xcoI+& z+(z{4@d3%30`5Dk=NyI8%GpAvqg`TO-~7Z6RQh&WtHbu#oyx_RVm0m!d4@PWw$)ty z6+iR<*S4l);uG{*ScZL){4MM~WqEtwQ*K?YJ+;E*6?82pvucU0DUAhiQ*THU6@biw zpmx^2(mm%IPIu(>qq8mB>oJ^9ia2D-<+DY&4*=aecsMOT3VhUD{yU5dp(kjO<`N*>aKz5f3`It(X zBpooDh6|zCeG@|#=6K6}>`>rDc}aYDH9p~Nb&>O*N{Vf)!tgm2Eot<|Zff04=np>} zGd|P>*gwoz>hz;pXP-^}a$ket&j32pqZ#6;ATP;wsE zM3~e@e$1M2(jzr@vAo$I8z#ZJeg*Ano@WJASK`qDyIDZww9hMq!-mwyyNoE!O97Sh zZ^E>Iz@ZJ%C4%bBrU71(Lg-CY7l##{0 zi;JsF}}DjcpjS zdrxD3XWwSEhNVM#I=3KMAuI4(Bd>rm*cN(Yg?YfDBTXkRI9Zgc8j zF60uC7HxLIv!;pHA^ao`4Lgz6^e0|z$(7TOw^9kzUu_HcbwAnpZg>==u@>k9NnP0( zJU~>UP#MqEr2UaLXnP24QvI9vUx{NUCAn3&J$Bc!L0 z^=pFL7m9rA&}mLfhd6+~YsJ>Q=iBKpTa^8FPx1EHJIMYM($wL>-%r`!weDQ*7aswe yAAY#|@CZ)=+*T0$|F`}n`Tt)2@81UZ+mB+s&r#J)cP$Q|U~FJ|qx?^o=>GxbygIS~ literal 0 HcmV?d00001 From bd18bdf1c2135139d15b20c2acf8c6e73b324112 Mon Sep 17 00:00:00 2001 From: moralec Date: Tue, 1 Dec 2020 16:12:41 +0000 Subject: [PATCH 108/192] Update analyst_guide_summary.md --- .github/analyst_guide_summary.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/analyst_guide_summary.md b/.github/analyst_guide_summary.md index 20d4b5c6..c94d28c8 100644 --- a/.github/analyst_guide_summary.md +++ b/.github/analyst_guide_summary.md @@ -89,7 +89,7 @@ sq_data %>% collaboration_summary() + ggtitle("This is a custom title") Let's continue to [**Distribution Functions**](analyst_guide_distribution.html) to explore how we can analyse distributions from different Workplace Analytics Metrics with unique functions. -## Summary Functions: Gallery +## Gallery From 385cfa12d1f92a73439b1571befa16ab001ffaf4 Mon Sep 17 00:00:00 2001 From: moralec Date: Tue, 1 Dec 2020 16:16:06 +0000 Subject: [PATCH 109/192] Update analyst_guide_summary.md --- .github/analyst_guide_summary.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/analyst_guide_summary.md b/.github/analyst_guide_summary.md index c94d28c8..63dc880a 100644 --- a/.github/analyst_guide_summary.md +++ b/.github/analyst_guide_summary.md @@ -122,14 +122,14 @@ div.desc { Key Metrics Scan -

    Plot for keymetrics_scan()
    +
    Plot from keymetrics_scan()

    Collaboration Summary -
    Plot for collaboration_summary()
    +
    Plot from collaboration_summary()
    From e03a1844ce1c4284b50090c0547ad33c86418017 Mon Sep 17 00:00:00 2001 From: moralec Date: Tue, 1 Dec 2020 16:16:55 +0000 Subject: [PATCH 110/192] Update analyst_guide_summary.md --- .github/analyst_guide_summary.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/analyst_guide_summary.md b/.github/analyst_guide_summary.md index 63dc880a..817a055b 100644 --- a/.github/analyst_guide_summary.md +++ b/.github/analyst_guide_summary.md @@ -120,7 +120,7 @@ div.desc {
    - Key Metrics Scan + Key Metrics Scan
    Plot from keymetrics_scan()
    From f557a4110f8af97802fceba170ca189680803488 Mon Sep 17 00:00:00 2001 From: moralec Date: Tue, 1 Dec 2020 16:18:43 +0000 Subject: [PATCH 111/192] Update analyst_guide_summary.md --- .github/analyst_guide_summary.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/analyst_guide_summary.md b/.github/analyst_guide_summary.md index 817a055b..6b8d49cb 100644 --- a/.github/analyst_guide_summary.md +++ b/.github/analyst_guide_summary.md @@ -119,8 +119,8 @@ div.desc {
    - - Key Metrics Scan + + Key Metrics Scan
    Plot from keymetrics_scan()
    From 2a56b26362e3739c088f53cad7b6982b9b7dca00 Mon Sep 17 00:00:00 2001 From: moralec Date: Tue, 1 Dec 2020 16:20:26 +0000 Subject: [PATCH 112/192] Update analyst_guide_summary.md --- .github/analyst_guide_summary.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/analyst_guide_summary.md b/.github/analyst_guide_summary.md index 6b8d49cb..d8b00b35 100644 --- a/.github/analyst_guide_summary.md +++ b/.github/analyst_guide_summary.md @@ -133,7 +133,7 @@ div.desc {
    - + After-Hours Summary
    Plot from afterhours_summary()
    From 8e44974f5adc8f30b32bb447ccfb92bacc727bee Mon Sep 17 00:00:00 2001 From: moralec Date: Tue, 1 Dec 2020 16:21:13 +0000 Subject: [PATCH 113/192] Update analyst_guide_summary.md --- .github/analyst_guide_summary.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/analyst_guide_summary.md b/.github/analyst_guide_summary.md index d8b00b35..a6fcf18c 100644 --- a/.github/analyst_guide_summary.md +++ b/.github/analyst_guide_summary.md @@ -122,21 +122,21 @@ div.desc { Key Metrics Scan -
    Plot from keymetrics_scan()
    +
    keymetrics_scan()
    Collaboration Summary -
    Plot from collaboration_summary()
    +
    collaboration_summary()
    After-Hours Summary -
    Plot from afterhours_summary()
    +
    afterhours_summary()
    From 4baf43df63d3f940ae833fb8498c53107f7ee49c Mon Sep 17 00:00:00 2001 From: moralec Date: Tue, 1 Dec 2020 16:21:52 +0000 Subject: [PATCH 114/192] Update analyst_guide_summary.md --- .github/analyst_guide_summary.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/analyst_guide_summary.md b/.github/analyst_guide_summary.md index a6fcf18c..ae0f6b94 100644 --- a/.github/analyst_guide_summary.md +++ b/.github/analyst_guide_summary.md @@ -1,6 +1,6 @@ # Summary functions -This section describes the use of summary functions. These functions allow you to compare averages for many Workplace Analytics metrics, across the groups defined by an organizational attribute. +This section describes the use of summary functions. These functions allow you to compare averages across the groups defined by an organizational attribute, for many Workplace Analytics metrics. ## Averages for key metrics From 2129e8ddf9cf97c8ddf93051bcae827322214d32 Mon Sep 17 00:00:00 2001 From: moralec Date: Tue, 1 Dec 2020 16:22:22 +0000 Subject: [PATCH 115/192] Update analyst_guide_summary.md --- .github/analyst_guide_summary.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/analyst_guide_summary.md b/.github/analyst_guide_summary.md index ae0f6b94..63d5ca57 100644 --- a/.github/analyst_guide_summary.md +++ b/.github/analyst_guide_summary.md @@ -3,7 +3,7 @@ This section describes the use of summary functions. These functions allow you to compare averages across the groups defined by an organizational attribute, for many Workplace Analytics metrics. -## Averages for key metrics +## Exploring key metrics The `keymetrics_scan()` function allows you to produce a summary table with a wide range of metrics from an Standard Person Query. Just like in the function we studied in the previous section, you can specify which HR attribute/variable to use as a grouping variable with the `hrvar` argument, and what output to obtain (either "plot" or "table") with the `return` argument. From 2c103b02269a132d7c151dbcc26fe5e105b8d719 Mon Sep 17 00:00:00 2001 From: moralec Date: Tue, 1 Dec 2020 16:23:18 +0000 Subject: [PATCH 116/192] Update analyst_guide_summary.md --- .github/analyst_guide_summary.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/analyst_guide_summary.md b/.github/analyst_guide_summary.md index 63d5ca57..66d04391 100644 --- a/.github/analyst_guide_summary.md +++ b/.github/analyst_guide_summary.md @@ -65,9 +65,9 @@ For example, sq_data %>% afterhours_summary() ``` -## Flexible Function +## Custom bar charts and tables -For other metrics (including custom ones) the `create_bar()` function is a good way to obtain a summary bar chart for any metric. This function requires you to include a character string containing the name of the metric you want to analyze, e.g. "Generated_workload_email_hours": +For other metrics, the `create_bar()` function is a good way to obtain a summary bar chart for any metric. This function requires you to include a character string containing the name of the metric you want to analyze, e.g. "Generated_workload_email_hours": ``` sq_data %>% create_bar(metric = "Generated_workload_email_hours") From 8273f42544ce867f5213eacee50f01f263afd573 Mon Sep 17 00:00:00 2001 From: moralec Date: Tue, 1 Dec 2020 16:52:13 +0000 Subject: [PATCH 117/192] Added gallery to getting started --- .github/analyst_guide_getting_started.md | 41 +++++++++++++++++++++++ .github/gallery/analysis_scope.png | Bin 0 -> 97228 bytes 2 files changed, 41 insertions(+) create mode 100644 .github/gallery/analysis_scope.png diff --git a/.github/analyst_guide_getting_started.md b/.github/analyst_guide_getting_started.md index 4df7a629..a0fb08ab 100644 --- a/.github/analyst_guide_getting_started.md +++ b/.github/analyst_guide_getting_started.md @@ -125,3 +125,44 @@ person_data %>% analysis_scope() %>% export() # Step 4 ## Ready to learn more? Let's go to the [**Summary Functions**](analyst_guide_summary.html) section, to see how we can analyse different Workplace Analytics Metrics in a similar way. + + + +## Gallery + + + + + + + +
    + + Analysis Scope + +
    analysis_scope()
    +
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zA@kU3K*h=1M%VxMnb#;OT0XUMcVIyUBz{|Ub_=hB3@yN-vb*KFM}sl(d0<)#;I%r# zuPq?nP^G4^K~XFBmQDouj$KY21jAcO)?I)D&r$&Zn6itw`qTj^5fB?LTQXM{v$*#a z@;|15MR4ayoxq2keq-6=UfqlYN%Hw(C48hi=>$_#n(o?CUuW#uK>}QSk%BK%c0JeFVSKs)Elune^=C%^8BkKD7_o!E8z&0Zkn;K|-F-eO+`_Sojtj;U zL#V6hF$;<|K;5zVYQbr=O5S$sBE)fay32rKpj{~5)fJebRw;-qmUl!Mm!Ha~L%O@S^X2n8dG*n25#qpxV;k7@lMu zEBbYAgI`T)S*xgj;2AxGB*RJqIR#OZ6m)hs*eO`1?IZXLTbgSz_kYYKLGP)Al*_6G zUPi0D?ZTW(A$j3&UlH4B>)G0V;?8yS{A(4dHLEk*oDz-%%7uChF}#J#>}6*lE;eCf z)J90s-w%AD!~Acvf3!fXHb85#LCk#%3ybOxh}(?ZCUG>L#Rk4}|7lcpQIVXIBR{t% zGvNvJB*ewo!e`gR?_KT*2{~N2YMF&>VqyJK zZXm$E3G}7K^d0r~nLU!eiMWA<`yDSu5oVFD&wJecOerbo8 Date: Tue, 1 Dec 2020 16:52:38 +0000 Subject: [PATCH 118/192] Update analyst_guide_getting_started.md --- .github/analyst_guide_getting_started.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/analyst_guide_getting_started.md b/.github/analyst_guide_getting_started.md index a0fb08ab..9c58392b 100644 --- a/.github/analyst_guide_getting_started.md +++ b/.github/analyst_guide_getting_started.md @@ -107,7 +107,7 @@ sq_data %>% analysis_scope(hrvar = "Organization") %>% export() ``` -## Summary: Four steps from data to output +## Four steps from data to output The examples above illustrate how the use of **wpa** can be summarized in 4 simple steps: Load the package, read-in query data, run functions and export results. The script below illustrates this funcitonality: From 46a53f1d54bdcfb3a1ef0b321ba8fc98bc1c3301 Mon Sep 17 00:00:00 2001 From: moralec Date: Tue, 1 Dec 2020 16:53:17 +0000 Subject: [PATCH 119/192] Update analyst_guide_summary.md --- .github/analyst_guide_summary.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/analyst_guide_summary.md b/.github/analyst_guide_summary.md index 66d04391..4b38eb4c 100644 --- a/.github/analyst_guide_summary.md +++ b/.github/analyst_guide_summary.md @@ -38,7 +38,7 @@ sq_data %>% collaboration_summary(hrvar = "LevelDesignation") By default, all summary functions exclude groups with less that five individuals. This is also something that can be adjusted, using the `mingroup()` argument: ``` -sq_data %>% collaboration_sum(hrvar = "LevelDesignation", mingroup = 10) +sq_data %>% collaboration_summary(hrvar = "LevelDesignation", mingroup = 10) ``` Finally, you can also use "table" in the `return` argument, to obtain summary table instead of a plot. The `export()` function will copy all contents to the clipboard. From 60c22dc1f0ff286b5ceec4839105dbca24be50d7 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Tue, 1 Dec 2020 21:14:10 +0000 Subject: [PATCH 120/192] docs: update _pkgdown.yml Change labels on navbar --- .github/analyst_guide_summary.md | 2 +- _pkgdown.yml | 8 ++++---- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/.github/analyst_guide_summary.md b/.github/analyst_guide_summary.md index 4b38eb4c..34baa44a 100644 --- a/.github/analyst_guide_summary.md +++ b/.github/analyst_guide_summary.md @@ -78,7 +78,7 @@ The `create_bar()` function also accepts `hrvar`, `return` and `mingroup` argume ## Customizing plots -All plots in *wpa* are [ggplot objects](https://rafalab.github.io/dsbook/ggplot2.html). This means you can customize them by adding ggplot arguments and layers. For instance, you can change the title of a `collaboration_summary()` plot: +All plots in **wpa** are [ggplot objects](https://rafalab.github.io/dsbook/ggplot2.html). This means you can customize them by adding ggplot arguments and layers. For instance, you can change the title of a `collaboration_summary()` plot: ```R sq_data %>% collaboration_summary() + ggtitle("This is a custom title") diff --git a/_pkgdown.yml b/_pkgdown.yml index 73f44774..b6edd0e9 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -28,13 +28,13 @@ navbar: href: analyst_guide.html - text: Getting Started href: analyst_guide_getting_started.html - - text: Summary analysis + - text: Summary Functions href: analyst_guide_summary.html - - text: Distribution analysis + - text: Distribution Functions href: analyst_guide_distribution.html - - text: Trend analysis + - text: Trend Functions href: analyst_guide_trend.html - - text: Network analysis + - text: Network Functions href: analyst_guide_network.html reference: text: Reference From 9775252c013e6001e2232dc80b105330de994ba3 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Wed, 2 Dec 2020 13:56:56 +0000 Subject: [PATCH 121/192] docs: update combine_signals() Update documentation to make it clear that function now works by string matching, and is dynamic. --- R/combine_signals.R | 14 +++++++++----- man/combine_signals.Rd | 14 +++++++++----- 2 files changed, 18 insertions(+), 10 deletions(-) diff --git a/R/combine_signals.R b/R/combine_signals.R index 6bd3402e..db9b7f45 100644 --- a/R/combine_signals.R +++ b/R/combine_signals.R @@ -3,17 +3,21 @@ # Licensed under the MIT License. See LICENSE.txt in the project root for license information. # -------------------------------------------------------------------------------------------- -#' @title Combine signals from Emails and IMs +#' @title Combine signals from the Hourly Collaboration query #' #' @description -#' Takes in an Hourly Collaboration Data, and for each hour sums and stores the -#' signal for `Emails_sent` and `IMs_sent` in `Signals_sent`. This is an internal +#' Takes in an Hourly Collaboration Data, and for each hour sums and aggregates the +#' signals (e.g.`Emails_sent` and `IMs_sent`) in `Signals_sent`. This is an internal #' function used in the Working Patterns functions. #' -#' @param data Data containing Emails_sent and IMs_sent variables +#' @param data Hourly Collaboration query containing signal variables (e.g. `Emails_sent_00_01`) #' @param hr Numeric value between 0 to 23 to iterate through #' @param signals Character vector for specifying which signal types to combine. -#' Defaults to c("Emails_sent", "IMs_sent"). +#' Defaults to c("Emails_sent", "IMs_sent"). Other valid values include "Unscheduled_calls" and +#' "Meetings". +#' +#' @details +#' `combine_signals` uses string matching to aggregate columns. #' #' @export combine_signals <- function(data, diff --git a/man/combine_signals.Rd b/man/combine_signals.Rd index 324c0e39..dd6651cc 100644 --- a/man/combine_signals.Rd +++ b/man/combine_signals.Rd @@ -2,20 +2,24 @@ % Please edit documentation in R/combine_signals.R \name{combine_signals} \alias{combine_signals} -\title{Combine signals from Emails and IMs} +\title{Combine signals from the Hourly Collaboration query} \usage{ combine_signals(data, hr, signals = c("Emails_sent", "IMs_sent")) } \arguments{ -\item{data}{Data containing Emails_sent and IMs_sent variables} +\item{data}{Hourly Collaboration query containing signal variables (e.g. \code{Emails_sent_00_01})} \item{hr}{Numeric value between 0 to 23 to iterate through} \item{signals}{Character vector for specifying which signal types to combine. -Defaults to c("Emails_sent", "IMs_sent").} +Defaults to c("Emails_sent", "IMs_sent"). Other valid values include "Unscheduled_calls" and +"Meetings".} } \description{ -Takes in an Hourly Collaboration Data, and for each hour sums and stores the -signal for \code{Emails_sent} and \code{IMs_sent} in \code{Signals_sent}. This is an internal +Takes in an Hourly Collaboration Data, and for each hour sums and aggregates the +signals (e.g.\code{Emails_sent} and \code{IMs_sent}) in \code{Signals_sent}. This is an internal function used in the Working Patterns functions. } +\details{ +\code{combine_signals} uses string matching to aggregate columns. +} From feda36c3581640a2364848cd06d747159c73de24 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Wed, 2 Dec 2020 14:00:22 +0000 Subject: [PATCH 122/192] feat: add example combine_signals Add example for combine_signals which will also work as an integrated test --- R/combine_signals.R | 8 ++++++++ man/combine_signals.Rd | 9 +++++++++ 2 files changed, 17 insertions(+) diff --git a/R/combine_signals.R b/R/combine_signals.R index db9b7f45..51cfe0d7 100644 --- a/R/combine_signals.R +++ b/R/combine_signals.R @@ -19,6 +19,14 @@ #' @details #' `combine_signals` uses string matching to aggregate columns. #' +#' @examples +#' # Demo using simulated variables +#' sim_data <- +#' data.frame(Emails_sent_09_10 = sample(1:5, size = 10, replace = TRUE), +#' Unscheduled_calls_09_10 = sample(1:5, size = 10, replace = TRUE)) +#' +#' combine_signals(sim_data, hr = 9, signals = c("Emails_sent", "Unscheduled_calls")) +#' #' @export combine_signals <- function(data, hr, diff --git a/man/combine_signals.Rd b/man/combine_signals.Rd index dd6651cc..2dd24dbc 100644 --- a/man/combine_signals.Rd +++ b/man/combine_signals.Rd @@ -23,3 +23,12 @@ function used in the Working Patterns functions. \details{ \code{combine_signals} uses string matching to aggregate columns. } +\examples{ +# Demo using simulated variables +sim_data <- + data.frame(Emails_sent_09_10 = sample(1:5, size = 10, replace = TRUE), + Unscheduled_calls_09_10 = sample(1:5, size = 10, replace = TRUE)) + +combine_signals(sim_data, hr = 9, signals = c("Emails_sent", "Unscheduled_calls")) + +} From 1c730ffa36661738456d1459e8af5f135cafad06 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Thu, 3 Dec 2020 10:10:50 +0000 Subject: [PATCH 123/192] docs: update cheatsheet content --- man/figures/wpa cheatsheet.pdf | Bin 0 -> 201618 bytes man/figures/wpa cheatsheet.png | 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z(KDujMVZEYPQ|1k8|2i1i$7>jPn<>f_$4{lq^+hH!q}8MPgQH=h#7ATe)>xv3QV#x z!s{cYdK*_PuN@tSKLE$CH=i{2fRApJDa>=?Kqfh@{BSKH7rPMuB{$m7!u zKSO@H|NS6ZO8Kg=1vg5MVqjB6Lqp?$aBa2x#Ahrj z^z+>#Z21dUYlLAfUs+oq%x>+SPAg7FbDLh5^{o;Wjw5D;!rfKX297Hp)3(gCWN&c6 zqHn(_q1PDHSBtzmMtgzfsf^(nbwh5m<^5+S1a8IGDLicxg+YQ<_Uh}kZocM+i`P9GX9GB6w@pd%Qbm2{G{^{}2V@2D-gf<7nJ1#FAobw6)@#%3e(Tnw8 Date: Thu, 3 Dec 2020 10:11:11 +0000 Subject: [PATCH 124/192] docs: update cheatsheet references remove timestamps --- .github/analyst_guide.md | 2 +- README.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/analyst_guide.md b/.github/analyst_guide.md index c5d567d5..c99e703d 100644 --- a/.github/analyst_guide.md +++ b/.github/analyst_guide.md @@ -34,7 +34,7 @@ This guide is organized in five key sections: To get the most out of **wpa**, make sure to leaverage these additional resources: -1. Our official [**wpa** cheat sheet](https://github.com/microsoft/wpa/blob/main/man/figures/wpa%20cheatsheet_20201116.pdf). +1. Our official [**wpa** cheat sheet](https://github.com/microsoft/wpa/blob/main/man/figures/wpa%20cheatsheet.pdf). 2. A growing list of [articles](https://microsoft.github.io/wpa/articles/) with detailed walkthroughs, written by multiple contributors. ## Ready to go? diff --git a/README.md b/README.md index ed078033..ad6afb95 100644 --- a/README.md +++ b/README.md @@ -59,7 +59,7 @@ For information on the package structure, please see the relevant section in our Also check out our package cheat sheet for more information: - + --- From af8b1f8fa5e40b22b417c84e992702c2a6d279ca Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Thu, 3 Dec 2020 10:46:54 +0000 Subject: [PATCH 125/192] docs: update developer_guide More elaborate on how to use git --- .github/developer_guide.md | 95 +++++++++++++++++++++++++++++++------- 1 file changed, 79 insertions(+), 16 deletions(-) diff --git a/.github/developer_guide.md b/.github/developer_guide.md index 6ac9598b..fd7b63ca 100644 --- a/.github/developer_guide.md +++ b/.github/developer_guide.md @@ -21,9 +21,9 @@ You should have the following installed before starting: 3. [GitHub Desktop](https://desktop.github.com/) (Highly recommended, unless you are very familiar with git) You should also have the following packages installed. These should all be available from CRAN: -1. {devtools} -2. {tidyverse} -3. {roxygen2} +1. **devtools** +2. **tidyverse** +3. **roxygen2** Once these are all installed, you should be ready to roll! @@ -58,11 +58,27 @@ If everything runs smoothly, run this final line to install the local package. M devtools::install() ``` +#### Note: other installation methods + +You can also install the package directly from GitHub: + +```R +devtools::install_git(url = "https://github.com/microsoft/wpa.git") +``` + +This will install the package from the main branch. If you'd like to install from a particular branch or install vignettes, you can leverage the additional arguments available with `devtools::install_git()`, for example: + +```R +devtools::install_git(url = "https://github.com/microsoft/wpa.git", + branch = "feature/network-functions", + build_vignettes = TRUE) +``` + --- ## To increment or change a package version -Each package release has a corresponding package version (e.g. 1.4.2.) To change that package version number for a particular release, simply go into the `DESCRIPTION` at the project root folder of the {wpa} package. You can edit this with any notepad software or RStudio. +Each package release has a corresponding package version (e.g. 1.4.2.) To change that package version number for a particular release, simply go into the `DESCRIPTION` at the project root folder of the **wpa** package. You can edit this with any notepad software or RStudio. Change the values that come after `Version: ` to change the package version. Prior to incrementing the package version, please align with the main developer team on the agreed version release details. @@ -92,19 +108,66 @@ devtools::build_manual() ## A short guide to adding a function to the package -1. Let us assume that you have written a new function, e.g. a checking function that returns TRUE if PersonId exists, and returns FALSE if it does not). -1. Once you check the function and ensure that it works, save this into a R file under the "R" folder of the package directory. - - Ensure that your current working directory is set to the package directory. You can either open the RStudio session with the package .Rproj file, or run `setwd()` to change you working directory. Run `getwd()` to check what your current working directory is. - - Ensure that the relevant roxygen headers are present (see [here](https://roxygen2.r-lib.org/articles/rd.html) for more reference). - - Whilst it is possible to have more than one function saved in a single R file, we recommend saving them in their own files with their own names for clarity. For instance, the function `collaboration_dist()` would be saved in a file called _collaboration_dist.R_. -1. Follow the steps in the Developer Guide above to generate the documentation (`roxygen2::roxygenise()`), check the package (`devtools::check()`), and install the package (`devtools::install()`).  -1. Once you are sure that everything works, open **GitHub Desktop**, and **check that you have selected the relevant branch**. If you are unsure what your changes are, it is always recommended that you work on a new branch so that you do not introduce any breaking changes to the master branch. -1. _Fetch / pull_ from the branch. This ensures that your local package is updated with changes that others have made to the code. -1. _Commit_ your changes, and add an intuitive commit message so others will know what you have changed to the code. Please see the wiki page of this repo for a style guide on the commit messages. -1. _Push_ your changes to the branch. If there are no errors, you should be able to see the changes on the GitHub repository! +1. Let us assume that you have written a new function, e.g. a checking function that returns TRUE if `PersonId` exists, and returns FALSE if it does not). + +1. Once you check the function and ensure that it works, save this into a R file under the "R" folder of the package directory. + - Ensure that your current working directory is set to the package directory. You can either open the RStudio session with the package .Rproj file, or run `setwd()` to change you working directory. Run `getwd()` to check what your current working directory is. + - Ensure that the relevant roxygen headers are present (see [here](https://roxygen2.r-lib.org/articles/rd.html) for more reference). + - Whilst it is possible to have more than one function saved in a single R file, we recommend saving them in their own files with their own names for clarity. For instance, the function `collaboration_dist()` would be saved in a file called _collaboration_dist.R_. + +1. Run `devtools::load_all()`. This will simulate loading the package with the function in it. Run the function with some example data to ensure that it works. + +1. Follow the steps in the Developer Guide above to generate the documentation (`roxygen2::roxygenise()`), check the package (`devtools::check()`), and install the package (`devtools::install()`). + +1. Once you are sure that everything works, open **GitHub Desktop**, and **check that you have selected the relevant branch**. If you are unsure what your changes are, it is always recommended that you work on a new branch so that you do not introduce any breaking changes to the master branch. + +1. _Fetch/ pull_ from the branch. This ensures that your local package is updated with changes that others have made to the code. + +1. _Commit_ your changes, and add an intuitive commit message so others will know what you have changed to the code. Please see the wiki page of this repo for a style guide on the commit messages. + +1. _Push_ your changes to the branch. + +## Working with GitHub + +### No repository access + +If you are contributing to this repository where you do not have direct access (e.g. as a non-Microsoft contributor), you will have to take the following steps: + +1. Fork the repository. This will create a repository called `/wpa`. + +1. Make required changes to this forked repository. + +1. Once you are happy with the changes, create a pull request to merge the changes with `microsoft/wpa`. + +You can either use **GitHub Desktop** or **git**. + +### With repository access + +If you have repository access, you may still be asked to enter the 2FA process due to the scurity protocols in place. + +If you get an authentication error with GitHub Desktop, please following the following steps to clone the git repository for **wpa** v1.3.0. locally. + +1. Ensure you have git installed. If not, you can download this from [Git - Downloads (git-scm.com)](https://git-scm.com/downloads). + +2. Once installed, open up Git Bash. This should open up a Command Line interface. + +3. Copy the path to the folder in which you want to clone the `wpa` repository; if this doesn't exist yet, you can create a new folder called `wpa` somewhere. An example of this path would be `C:\My Documents\GitHub\wpa`. + +4. Type `cd `, and followed (separated with a space) by the path to the local folder above. You will need to add quotes, so the full command would look something like `cd "C:/My Documents/GitHub/wpa"`. See [How to change folder with git bash? - Stack Overflow](https://stackoverflow.com/questions/8961334/how-to-change-folder-with-git-bash) if you have issues changing directories. + +5. Once Git Bash is showing the correct file path to your `wpa` repository, run the command `git init`. This command initializes your folder as a git repository. In other words, it sets it up to do version control. + +6. Next, run `git remote add origin https://github.com/microsoft/wpa.git`. This will "connect" your local folder with the Git repository. You may get some prompts for authentication somewhere around here. Run `git remote -v` to check whether this has been successfully applied. + +7. Once the authentication through Git Bash is set up, you may now go back to the GitHub Desktop GUI window (phew!). Go to `File > Add local repository` and choose the `wpa` folder that you have just set up. You may be asked to login again, but this should now allow you to fetch or pull from the repository. You are now set up! + + +You can contribute code to the open source repository in the "normal way" once the above is all set up. + +### Git commands + +You may find it helpful [git cheat sheet](https://education.github.com/git-cheat-sheet-education.pdf) for git commands. -## Using GitHub -*It's on its way...* ## Documenting functions with {roxygen2} *It's on its way...* From 8c48b1ac2b9f42edd5e2d398259b3ccb08e26269 Mon Sep 17 00:00:00 2001 From: moralec Date: Thu, 3 Dec 2020 15:02:18 +0000 Subject: [PATCH 126/192] Update analyst_guide_distribution.md --- .github/analyst_guide_distribution.md | 106 +++++++++++++++++++++----- 1 file changed, 86 insertions(+), 20 deletions(-) diff --git a/.github/analyst_guide_distribution.md b/.github/analyst_guide_distribution.md index bac88916..e1b3ff2a 100644 --- a/.github/analyst_guide_distribution.md +++ b/.github/analyst_guide_distribution.md @@ -1,42 +1,108 @@ -# Distribution +# Distribution Functions **Distribution** functions allow you to go beyond aggregated averages, and diagnose whether metrics are skewed, evenly distributed, or contain outliers. -You can use the `fizz` and `dist` family of functions for this. For email hours, you can run the following: +## Grouping individuals + +The `collaboration_dist()` function allows you to produce a 100% stacked bar chart an Standard Person Query, with employees distributed in groups according to their amount of collaboration hours. ```R -sq_data %>% email_dist() # 100% horizontal stacked bar +sq_data %>% collaboration_dist() ``` - - -You can also return a jittered scatter plot ('fizzy drink plot'). +You can specify which HR attribute/variable to use as a grouping variable with the `hrvar` argument, and what output to obtain (either "plot" or "table") with the `return` argument. ```R -sq_data %>% email_fizz() # Fizzy drink plot +sq_data %>% collaboration_dist(return="table") ``` +The package includes a wide range of equivalent functions, that create 100% stacked bar plots for specific metrics. These include: -## Functions - -Other examples of distribution functions include: - -### Dist - -- `collaboration_dist()` +- `email_dist()` - `meeting_dist()` - `one2one_dist()` - `afterhours_dist()` - `workloads_dist()` -### Fizz +## Fizzy Drink Plots +You can also explore distributions by using jittered ('fizzy drink') scatter plot. The `collaboration_fizz()` function illustrate how different individuals are distributed in a common scale: -- `collaboration_fizz()` +```R +sq_data %>% email_fizz() # Fizzy drink plot +``` + +These functions are also flexible, allowing you to specify an HR attribute grouping variable and the `return` argument. + + +```R +sq_data %>% collaboration_fizz(hrvar = "LevelDesignation", return = "table") +``` + +Other examples of distribution fizzy drink plots include: + +- `email_fizz()` - `meeting_fizz()` - `one2one_fizz()` - `afterhours_fizz()` - `workloads_fizz()` -### Flexible functions -- `create_dist()` -- `create_fizz()` -- `create_boxplot()` \ No newline at end of file +## Custom bar charts and tables + +For other metrics, you can obtain the equivalent graphs using the `create_dist()`, `create_fizz()` and `create_boxplot()`. These functions requires you to include a character string containing the name of the metric you want to analyze, e.g. "Generated_workload_email_hours": + + +``` +sq_data %>% create_dist(metric = "Generated_workload_email_hours") + +sq_data %>% create_fizz(metric = "Generated_workload_email_hours") + +sq_data %>% create_boxplot(metric = "Generated_workload_email_hours") + +``` + + +## Exploring changes over time + +In our next section, we will cover [**Trend Functions**](analyst_guide_trend.html) that are useful to explore time dyanmics across a wide range of metrics. + +## Gallery + + + + + + + +

    + + Email Distribution Function + +
    email_dist()
    +
    + + + + + + + From 7cf3d323edfed6812b89a33a3eed08314d195a37 Mon Sep 17 00:00:00 2001 From: moralec Date: Thu, 3 Dec 2020 15:42:45 +0000 Subject: [PATCH 127/192] Update analyst_guide_distribution.md --- .github/analyst_guide_distribution.md | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/.github/analyst_guide_distribution.md b/.github/analyst_guide_distribution.md index e1b3ff2a..9bec8f54 100644 --- a/.github/analyst_guide_distribution.md +++ b/.github/analyst_guide_distribution.md @@ -1,10 +1,10 @@ # Distribution Functions -**Distribution** functions allow you to go beyond aggregated averages, and diagnose whether metrics are skewed, evenly distributed, or contain outliers. +**Distribution** functions allow you to drill deeper into groups, to diagnose if the averages are truly representative of that group, or if they are hiding pockets of individuals with divergent behaviour (outliers). ## Grouping individuals -The `collaboration_dist()` function allows you to produce a 100% stacked bar chart an Standard Person Query, with employees distributed in groups according to their amount of collaboration hours. +The `collaboration_dist()` function produces a 100% stacked bar chart using a Standard Person Query as input. This plot will allow you to see how employees distributed in groups according to specific ranges of collaboration hours. ```R sq_data %>% collaboration_dist() @@ -24,14 +24,13 @@ The package includes a wide range of equivalent functions, that create 100% stac - `workloads_dist()` ## Fizzy Drink Plots -You can also explore distributions by using jittered ('fizzy drink') scatter plot. The `collaboration_fizz()` function illustrate how different individuals are distributed in a common scale: +You can also explore distributions by using jittered ('fizzy drink') scatter plots. The `collaboration_fizz()` function illustrates how different individuals fall in a common scale: ```R sq_data %>% email_fizz() # Fizzy drink plot ``` -These functions are also flexible, allowing you to specify an HR attribute grouping variable and the `return` argument. - +As usual, you to specify an HR attribute grouping variable and create a table with the `return` argument. ```R sq_data %>% collaboration_fizz(hrvar = "LevelDesignation", return = "table") @@ -47,8 +46,9 @@ Other examples of distribution fizzy drink plots include: ## Custom bar charts and tables -For other metrics, you can obtain the equivalent graphs using the `create_dist()`, `create_fizz()` and `create_boxplot()`. These functions requires you to include a character string containing the name of the metric you want to analyze, e.g. "Generated_workload_email_hours": +Not all metrics include their own dist and fizz functions. However, you can obtain the equivalent graphs using the `create_dist()` and `create_fizz()`. These functions requires you to include a character string containing the name of the metric you want to analyze. Additionall, the `create_boxplot()` function allows you to create a box plot. +For example, using "Generated_workload_email_hours": ``` sq_data %>% create_dist(metric = "Generated_workload_email_hours") @@ -59,7 +59,6 @@ sq_data %>% create_boxplot(metric = "Generated_workload_email_hours") ``` - ## Exploring changes over time In our next section, we will cover [**Trend Functions**](analyst_guide_trend.html) that are useful to explore time dyanmics across a wide range of metrics. From 85e75c5b3aa540e0444fb8d2c085d2ef7ebda986 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Thu, 3 Dec 2020 15:53:36 +0000 Subject: [PATCH 128/192] docs: update developer_guide.md Update structure of developer_guide.md --- .github/developer_guide.md | 156 ++++++++++++++++++------------------- 1 file changed, 78 insertions(+), 78 deletions(-) diff --git a/.github/developer_guide.md b/.github/developer_guide.md index fd7b63ca..0be31f48 100644 --- a/.github/developer_guide.md +++ b/.github/developer_guide.md @@ -1,16 +1,8 @@ # :hammer: Developer Guide -This is guide for "developers", or anyone who is looking to contribute code to the {wpa} R package. +This is guide for "developers", or anyone who is looking to contribute code to the **wpa** R package. -## Design Principles - -Please try to adhere to the following package design principles before proceeding with code contribution. - -- **Simple**: the functions ought to be simple and intuitive to maximise adoption. -- **Practical**: the functions should prioritise delivering against the most frequently used outputs and analyses. -- **Consistency**: functions should share a broadly consistent set of input arguments and naming conventions. This will help minimise unexpected results and errors when using the package. -- **Parsimony**: in creating the package, as much of the existing code should be re-used if possible to minimise duplication of work and to make analysis reproducible. -- **Tidy**: the functions from the package are designed to be consistent with tidy principles, and work well with a **dplyr** pipe (`%>%`) workflow. +Please also read our [CONTRIBUTING document](CONTRIBUTING.html) on details regarding our Contributor License Agreement (CLA). ## Pre-requisites @@ -27,30 +19,98 @@ You should also have the following packages installed. These should all be avail Once these are all installed, you should be ready to roll! + + +## Design Principles + +Please try to adhere to the following package design principles before proceeding with code contribution. + +- **Simple**: the functions ought to be simple and intuitive to maximise adoption. +- **Practical**: the functions should prioritise delivering against the most frequently used outputs and analyses. +- **Consistency**: functions should share a broadly consistent set of input arguments and naming conventions. This will help minimise unexpected results and errors when using the package. +- **Parsimony**: in creating the package, as much of the existing code should be re-used if possible to minimise duplication of work and to make analysis reproducible. +- **Tidy**: the functions from the package are designed to be consistent with tidy principles, and work well with a **dplyr** pipe (`%>%`) workflow. + --- ## :hammer: Quick start guide - For developers -### Update package and installing to local directory +### 1. Cloning or forking the package + +To get started, you will need to _fork_ and _clone_ the **wpa** repository on your local machine. You will need to have either **git** or **GitHub Desktop** set up on your local machine (see Pre-requisites). + +Here are the steps if you do not have repository access to `microsoft/wpa` (if you have been granted direct access, you can clone directly): + +1. On the top right of the repository page, click 'Fork'. +1. This will create a repository called `/wpa`. +1. After this, you can either use **GitHub Desktop** or **git** to clone this repository to your local machine. + +### Using GitHub Desktop + +1. Click on the big green `Code` button on the repository page, and click 'Open with GitHub Desktop'. +2. Follow the instructions on GitHub Desktop to clone the repository to a folder on your local machine. + +### Using git + +You may prefer to use **git** because it is more flexible, or because you have repository access and you are trying to directly clone the package from `microsoft/wpa` and are failing to do so with GitHub Desktop. There is a known issue with GitHub Desktop that it could fail with 2FA authentication, in which case you can use **git** to authenticate properly. + +Here are the steps: + +1. Ensure you have git installed. If not, you can download this from [Git - Downloads (git-scm.com)](https://git-scm.com/downloads). +2. Once installed, open up Git Bash. This should open up a Command Line interface. +3. Decide which folder you would like to clone the `wpa` repository, and copy the path to the folder. It is highly recommended that you create a new folder and name it `wpa`. An example of this path would be `C:\My Documents\GitHub\wpa`. +4. Type `cd `, and followed (separated with a space) by the path to the local folder above. You will need to add quotes, so the full command would look something like `cd "C:/My Documents/GitHub/wpa"`. See [How to change folder with git bash? - Stack Overflow](https://stackoverflow.com/questions/8961334/how-to-change-folder-with-git-bash) if you have issues changing directories. +5. Once Git Bash is showing the correct file path to your `wpa` repository, run the command `git init`. This command initializes your folder as a git repository. In other words, it sets it up to do version control. +6. Next, run `git remote add origin https://github.com/microsoft/wpa.git`. This will "connect" your local folder with the Git repository. You may get some prompts for authentication somewhere around here. Run `git remote -v` to check whether this has been successfully applied. +7. You can now use your standard git commands to commit, pull, push, etc. See [git cheat sheet](https://education.github.com/git-cheat-sheet-education.pdf) for some useful git commands. + +If you are in the situation where you are simply trying to overcome the authentication issue with GitHub Desktop, please read this paragraph. Once the authentication through Git Bash is set up, you may now go back to the GitHub Desktop GUI window. Go to `File > Add local repository` and choose the `wpa` folder that you have just set up. You may be asked to login again, but this should now allow you to fetch or pull from the repository. You are now set up! + +### 2. Adding / changing a function + +After creating a copy of the repository on your local machine and having installed the repository version, you can start working with R. + +1. Let us assume that you have written a new function, e.g. a checking function that returns TRUE if `PersonId` exists, and returns FALSE if it does not). + +1. Once you check the function and ensure that it works, save this into a R file under the "R" folder of the package directory. + - Ensure that your current working directory is set to the package directory. You can either open the RStudio session with the package .Rproj file, or run `setwd()` to change you working directory. Run `getwd()` to check what your current working directory is. + - Ensure that the relevant roxygen headers are present (see [here](https://roxygen2.r-lib.org/articles/rd.html) for more reference). + - Whilst it is possible to have more than one function saved in a single R file, we recommend saving them in their own files with their own names for clarity. For instance, the function `collaboration_dist()` would be saved in a file called _collaboration_dist.R_. + +1. Run `devtools::load_all()`. This will simulate loading the package with the function in it. Run the function with some example data to ensure that it works. + +1. Follow the steps in the Developer Guide above to generate the documentation (`roxygen2::roxygenise()`), check the package (`devtools::check()`), and install the package (`devtools::install()`). + +1. Once you are sure that everything works, open **GitHub Desktop**, and **check that you have selected the relevant branch**. If you are unsure what your changes are, it is always recommended that you work on a new branch so that you do not introduce any breaking changes to the master branch. + +1. _Fetch/ pull_ from the branch. This ensures that your local package is updated with changes that others have made to the code. + +1. _Commit_ your changes, and add an intuitive commit message so others will know what you have changed to the code. Please see the wiki page of this repo for a style guide on the commit messages. + +1. _Push_ your changes to the branch. + +### 3. Update package and installing to local directory + +There are certain tricks which are helpful to know with developing or changing an R function, such as how to test the function and how to generate R documentation. This section will provide more information. Once you've made changes to a script, here is how to update the package and install it locally on your computer. Run: -#### 1. Generating documentation +#### 3.1. Generating documentation ```R roxygen2::roxygenise() ``` `roxygenise()` generates documentation files for the R package, and makes them available to the user. -#### 2. R CMD Checks -Next, run this to build and check the package. You will need {devtools} installed. +#### 3.2. R CMD Checks +Next, run this to build and check the package. You will need **devtools** installed. ```R devtools::check() ``` This step runs a R CMD check against the package, to check that there will be no errors and issues if somebody installs the package. This step can take around 5 minutes long, and usually it is worth running this only after you've implemented all the desired changes towards the end. -#### 3. Install the package +#### 3.3. Install the package If everything runs smoothly, run this final line to install the local package. Make sure that your working directory is set to the package! @@ -58,15 +118,15 @@ If everything runs smoothly, run this final line to install the local package. M devtools::install() ``` -#### Note: other installation methods +#### Other installation methods -You can also install the package directly from GitHub: +Note that `devtools::install()` would install the package directly from the directory in your local machine, i.e. the version that you are developing. If you would like to install the stable GitHub version from the `main` branch, you will need to run: ```R devtools::install_git(url = "https://github.com/microsoft/wpa.git") ``` -This will install the package from the main branch. If you'd like to install from a particular branch or install vignettes, you can leverage the additional arguments available with `devtools::install_git()`, for example: +If you'd like to install from a particular branch or install vignettes, you can leverage the additional arguments available with `devtools::install_git()`, for example: ```R devtools::install_git(url = "https://github.com/microsoft/wpa.git", @@ -106,67 +166,7 @@ It is good practice to distribute a package with an updated version of the docum devtools::build_manual() ``` -## A short guide to adding a function to the package -1. Let us assume that you have written a new function, e.g. a checking function that returns TRUE if `PersonId` exists, and returns FALSE if it does not). - -1. Once you check the function and ensure that it works, save this into a R file under the "R" folder of the package directory. - - Ensure that your current working directory is set to the package directory. You can either open the RStudio session with the package .Rproj file, or run `setwd()` to change you working directory. Run `getwd()` to check what your current working directory is. - - Ensure that the relevant roxygen headers are present (see [here](https://roxygen2.r-lib.org/articles/rd.html) for more reference). - - Whilst it is possible to have more than one function saved in a single R file, we recommend saving them in their own files with their own names for clarity. For instance, the function `collaboration_dist()` would be saved in a file called _collaboration_dist.R_. - -1. Run `devtools::load_all()`. This will simulate loading the package with the function in it. Run the function with some example data to ensure that it works. - -1. Follow the steps in the Developer Guide above to generate the documentation (`roxygen2::roxygenise()`), check the package (`devtools::check()`), and install the package (`devtools::install()`). - -1. Once you are sure that everything works, open **GitHub Desktop**, and **check that you have selected the relevant branch**. If you are unsure what your changes are, it is always recommended that you work on a new branch so that you do not introduce any breaking changes to the master branch. - -1. _Fetch/ pull_ from the branch. This ensures that your local package is updated with changes that others have made to the code. - -1. _Commit_ your changes, and add an intuitive commit message so others will know what you have changed to the code. Please see the wiki page of this repo for a style guide on the commit messages. - -1. _Push_ your changes to the branch. - -## Working with GitHub - -### No repository access - -If you are contributing to this repository where you do not have direct access (e.g. as a non-Microsoft contributor), you will have to take the following steps: - -1. Fork the repository. This will create a repository called `/wpa`. - -1. Make required changes to this forked repository. - -1. Once you are happy with the changes, create a pull request to merge the changes with `microsoft/wpa`. - -You can either use **GitHub Desktop** or **git**. - -### With repository access - -If you have repository access, you may still be asked to enter the 2FA process due to the scurity protocols in place. - -If you get an authentication error with GitHub Desktop, please following the following steps to clone the git repository for **wpa** v1.3.0. locally. - -1. Ensure you have git installed. If not, you can download this from [Git - Downloads (git-scm.com)](https://git-scm.com/downloads). - -2. Once installed, open up Git Bash. This should open up a Command Line interface. - -3. Copy the path to the folder in which you want to clone the `wpa` repository; if this doesn't exist yet, you can create a new folder called `wpa` somewhere. An example of this path would be `C:\My Documents\GitHub\wpa`. - -4. Type `cd `, and followed (separated with a space) by the path to the local folder above. You will need to add quotes, so the full command would look something like `cd "C:/My Documents/GitHub/wpa"`. See [How to change folder with git bash? - Stack Overflow](https://stackoverflow.com/questions/8961334/how-to-change-folder-with-git-bash) if you have issues changing directories. - -5. Once Git Bash is showing the correct file path to your `wpa` repository, run the command `git init`. This command initializes your folder as a git repository. In other words, it sets it up to do version control. - -6. Next, run `git remote add origin https://github.com/microsoft/wpa.git`. This will "connect" your local folder with the Git repository. You may get some prompts for authentication somewhere around here. Run `git remote -v` to check whether this has been successfully applied. - -7. Once the authentication through Git Bash is set up, you may now go back to the GitHub Desktop GUI window (phew!). Go to `File > Add local repository` and choose the `wpa` folder that you have just set up. You may be asked to login again, but this should now allow you to fetch or pull from the repository. You are now set up! - - -You can contribute code to the open source repository in the "normal way" once the above is all set up. - -### Git commands - -You may find it helpful [git cheat sheet](https://education.github.com/git-cheat-sheet-education.pdf) for git commands. ## Documenting functions with {roxygen2} From 3232572c33fb46396b15d95ee77bd651c5672211 Mon Sep 17 00:00:00 2001 From: moralec Date: Thu, 3 Dec 2020 16:18:10 +0000 Subject: [PATCH 129/192] Update analyst_guide_distribution.md --- .github/analyst_guide_distribution.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/analyst_guide_distribution.md b/.github/analyst_guide_distribution.md index 9bec8f54..c3fb5084 100644 --- a/.github/analyst_guide_distribution.md +++ b/.github/analyst_guide_distribution.md @@ -4,7 +4,7 @@ ## Grouping individuals -The `collaboration_dist()` function produces a 100% stacked bar chart using a Standard Person Query as input. This plot will allow you to see how employees distributed in groups according to specific ranges of collaboration hours. +The `collaboration_dist()` function produces a 100% stacked bar chart, with all individuals classified in pre-defined groups according to defined ranges of collaboration hours. This function uses a Standard Person Query as input: ```R sq_data %>% collaboration_dist() @@ -13,9 +13,9 @@ sq_data %>% collaboration_dist() You can specify which HR attribute/variable to use as a grouping variable with the `hrvar` argument, and what output to obtain (either "plot" or "table") with the `return` argument. ```R -sq_data %>% collaboration_dist(return="table") +sq_data %>% collaboration_dist(hrvar = "LevelDesignation", return="table") ``` -The package includes a wide range of equivalent functions, that create 100% stacked bar plots for specific metrics. These include: +*wpa* includes a wide range of equivalent functions, that create 100% stacked bar plots for specific metrics. These include: - `email_dist()` - `meeting_dist()` From 93586e3f8cc6334f9643b27bef1f2c967842f3c3 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Thu, 3 Dec 2020 16:39:27 +0000 Subject: [PATCH 130/192] docs: update developer_guide.md --- .github/developer_guide.md | 28 +++++++++++++--------------- 1 file changed, 13 insertions(+), 15 deletions(-) diff --git a/.github/developer_guide.md b/.github/developer_guide.md index 0be31f48..d480954e 100644 --- a/.github/developer_guide.md +++ b/.github/developer_guide.md @@ -17,9 +17,7 @@ You should also have the following packages installed. These should all be avail 2. **tidyverse** 3. **roxygen2** -Once these are all installed, you should be ready to roll! - - +Once these are all installed, you should be ready to get started. ## Design Principles @@ -71,23 +69,12 @@ If you are in the situation where you are simply trying to overcome the authenti After creating a copy of the repository on your local machine and having installed the repository version, you can start working with R. 1. Let us assume that you have written a new function, e.g. a checking function that returns TRUE if `PersonId` exists, and returns FALSE if it does not). - 1. Once you check the function and ensure that it works, save this into a R file under the "R" folder of the package directory. - Ensure that your current working directory is set to the package directory. You can either open the RStudio session with the package .Rproj file, or run `setwd()` to change you working directory. Run `getwd()` to check what your current working directory is. - Ensure that the relevant roxygen headers are present (see [here](https://roxygen2.r-lib.org/articles/rd.html) for more reference). - Whilst it is possible to have more than one function saved in a single R file, we recommend saving them in their own files with their own names for clarity. For instance, the function `collaboration_dist()` would be saved in a file called _collaboration_dist.R_. - 1. Run `devtools::load_all()`. This will simulate loading the package with the function in it. Run the function with some example data to ensure that it works. - -1. Follow the steps in the Developer Guide above to generate the documentation (`roxygen2::roxygenise()`), check the package (`devtools::check()`), and install the package (`devtools::install()`). - -1. Once you are sure that everything works, open **GitHub Desktop**, and **check that you have selected the relevant branch**. If you are unsure what your changes are, it is always recommended that you work on a new branch so that you do not introduce any breaking changes to the master branch. - -1. _Fetch/ pull_ from the branch. This ensures that your local package is updated with changes that others have made to the code. - -1. _Commit_ your changes, and add an intuitive commit message so others will know what you have changed to the code. Please see the wiki page of this repo for a style guide on the commit messages. - -1. _Push_ your changes to the branch. +1. Follow the steps in the next section to generate the documentation (`roxygen2::roxygenise()`), check the package (`devtools::check()`), and install the package (`devtools::install()`). ### 3. Update package and installing to local directory @@ -134,6 +121,17 @@ devtools::install_git(url = "https://github.com/microsoft/wpa.git", build_vignettes = TRUE) ``` +### 4. Pushing changes to GitHub + +Once you are sure that everything works, you can push your changes to GitHub either with git or GitHub Desktop. Depending on the set-up, the method to merge is different: + +- If you are pushing to your forked repository, you can push directly to the master/main branch. +- If you are pushing to the official repository at `microsoft/wpa` (assuming you have access), you are recommended to either create a new branch or push the changes to an existing patch branch that is **not** the main repository. It is recommended that you never push changes directly to the `main` branch so that you do not introduce any breaking changes. + +For commit messages, we would ask you to add an intuitive commit message so others will know what you have changed to the code. Please see the wiki page of this repo for a style guide on the commit messages. + +The next step is to create a pull request to merge your changes to `microsoft/wpa`. The repository itself provides a pull request template on what information you should include in the pull request. A maintainer will reply and review your pull request once it has been successfully submitted. + --- ## To increment or change a package version From 6606a7eec69c167465dc053ef5521e0f9953bfd8 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Thu, 3 Dec 2020 16:44:04 +0000 Subject: [PATCH 131/192] docs: update developer_guide.md --- .github/developer_guide.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/developer_guide.md b/.github/developer_guide.md index d480954e..c814044a 100644 --- a/.github/developer_guide.md +++ b/.github/developer_guide.md @@ -168,7 +168,7 @@ devtools::build_manual() ## Documenting functions with {roxygen2} -*It's on its way...* +We recommend Karl Broman's guide on [Writing documentation with Roxygen2]([Writing documentation with Roxygen2 (kbroman.org)](https://kbroman.org/pkg_primer/pages/docs.html)). ## :link: Useful links From 1bc000aa011aa6c3b1e3d1b02d8e7346321eb0c0 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Tue, 8 Dec 2020 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4cc8449b6c268b0a9c98fbbf26ad1839f88ac3d7 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Tue, 8 Dec 2020 10:38:01 +0000 Subject: [PATCH 133/192] docs: add screenshot for forking repo --- .github/developer_guide.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/.github/developer_guide.md b/.github/developer_guide.md index c814044a..9e30ee15 100644 --- a/.github/developer_guide.md +++ b/.github/developer_guide.md @@ -43,6 +43,10 @@ Here are the steps if you do not have repository access to `microsoft/wpa` (if y 1. This will create a repository called `/wpa`. 1. After this, you can either use **GitHub Desktop** or **git** to clone this repository to your local machine. +When creating a fork on GitHub, you may be asked whether you are looking to contribute to the parent project or for your own purposes (see screenshot). Please select **contribute to the parent project**. + + + ### Using GitHub Desktop 1. Click on the big green `Code` button on the repository page, and click 'Open with GitHub Desktop'. From 4d7cec945dbeae6541cf54f8b28b219370a445a2 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Tue, 8 Dec 2020 23:42:43 +0000 Subject: [PATCH 134/192] feat: add total_cols (#22) Add a 'building block' function for company totals to be shown generally when NULL is supplied to the hrvar argument. --- NAMESPACE | 1 + R/totals_col.R | 33 +++++++++++++++++++++++++++++++++ man/totals_col.Rd | 27 +++++++++++++++++++++++++++ 3 files changed, 61 insertions(+) create mode 100644 R/totals_col.R create mode 100644 man/totals_col.Rd diff --git a/NAMESPACE b/NAMESPACE index 80acf401..468bec7d 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -126,6 +126,7 @@ export(tm_cooc) export(tm_freq) export(tm_wordcloud) export(totals_bind) +export(totals_col) export(totals_reorder) export(track_HR_change) export(tstamp) diff --git a/R/totals_col.R b/R/totals_col.R new file mode 100644 index 00000000..087369cd --- /dev/null +++ b/R/totals_col.R @@ -0,0 +1,33 @@ +# -------------------------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See LICENSE.txt in the project root for license information. +# -------------------------------------------------------------------------------------------- + +#' @title Fabricate a 'Total' HR variable +#' +#' @description Create a 'Total' column of character type comprising exactly of one unique value. +#' This is a convenience function for returning a no-HR attribute view when NULL is supplied to +#' the `hrvar` argument in functions. +#' +#' @examples +#' ## Create a visual without HR attribute breaks +#' sq_data %>% +#' totals_col() %>% +#' collab_fizz(hrvar = "Total") +#' +#' @param data data frame +#' @param target_value Character value defining the name and the value of the "Total" column. +#' Defaults to "Total". An error is returned if an existing variable has the same name as the +#' supplied value. +#' +#' @export +totals_col <- function(data, total_value = "Total"){ + + if(total_value %in% names(data)){ + stop(paste("Column", wrap(total_value, wrapper = "`"), "already exists. Please supply a different + value to `total_value`")) + } + + data %>% + dplyr::mutate(!!sym(total_value) := total_value) +} diff --git a/man/totals_col.Rd b/man/totals_col.Rd new file mode 100644 index 00000000..b7da7128 --- /dev/null +++ b/man/totals_col.Rd @@ -0,0 +1,27 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/totals_col.R +\name{totals_col} +\alias{totals_col} +\title{Fabricate a 'Total' HR variable} +\usage{ +totals_col(data, total_value = "Total") +} +\arguments{ +\item{data}{data frame} + +\item{target_value}{Character value defining the name and the value of the "Total" column. +Defaults to "Total". An error is returned if an existing variable has the same name as the +supplied value.} +} +\description{ +Create a 'Total' column of character type comprising exactly of one unique value. +This is a convenience function for returning a no-HR attribute view when NULL is supplied to +the \code{hrvar} argument in functions. +} +\examples{ +## Create a visual without HR attribute breaks +sq_data \%>\% + totals_col() \%>\% + collab_fizz(hrvar = "Total") + +} From ba493688f4171f915b3e7682ad21238ad4ed6b71 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Tue, 8 Dec 2020 23:45:18 +0000 Subject: [PATCH 135/192] feat: add NULL hrvar handling (#22, #12) Add the capability to handle NULL values passed to `hrvar`. Also unwrapped some dontrun examples. --- R/create_bar.R | 6 ++++++ R/create_boxplot.R | 6 ++++++ R/create_bubble.R | 6 ++++++ R/create_dist.R | 6 ++++++ R/create_fizz.R | 6 ++++++ R/create_line.R | 6 ++++++ R/create_stacked.R | 8 ++++++-- R/create_trend.R | 6 ++++++ R/totals_bind.R | 9 ++------- man/create_stacked.Rd | 2 -- man/totals_bind.Rd | 9 ++------- 11 files changed, 52 insertions(+), 18 deletions(-) diff --git a/R/create_bar.R b/R/create_bar.R index 26fda07d..b77b6e98 100644 --- a/R/create_bar.R +++ b/R/create_bar.R @@ -64,6 +64,12 @@ create_bar <- function(data, data %>% check_inputs(requirements = required_variables) + ## Handling NULL values passed to hrvar + if(is.null(hrvar)){ + data <- totals_col(data) + hrvar <- "Total" + } + ## Clean metric name clean_nm <- gsub(pattern = "_", replacement = " ", x = metric) diff --git a/R/create_boxplot.R b/R/create_boxplot.R index 552bf1fc..816e145f 100644 --- a/R/create_boxplot.R +++ b/R/create_boxplot.R @@ -57,6 +57,12 @@ create_boxplot <- function(data, data %>% check_inputs(requirements = required_variables) + ## Handling NULL values passed to hrvar + if(is.null(hrvar)){ + data <- totals_col(data) + hrvar <- "Total" + } + ## Clean metric name clean_nm <- gsub(pattern = "_", replacement = "", x = metric) diff --git a/R/create_bubble.R b/R/create_bubble.R index 3201932c..4561483c 100644 --- a/R/create_bubble.R +++ b/R/create_bubble.R @@ -66,6 +66,12 @@ create_bubble <- function(data, data %>% check_inputs(requirements = required_variables) + ## Handling NULL values passed to hrvar + if(is.null(hrvar)){ + data <- totals_col(data) + hrvar <- "Total" + } + ## Clean metric names clean_x <- gsub(pattern = "_", replacement = " ", x = metric_x) clean_y <- gsub(pattern = "_", replacement = " ", x = metric_y) diff --git a/R/create_dist.R b/R/create_dist.R index fbd1a4ea..52c035ac 100644 --- a/R/create_dist.R +++ b/R/create_dist.R @@ -66,6 +66,12 @@ create_dist <- function(data, ## Clean metric name clean_nm <- gsub(pattern = "_", replacement = " ", x = metric) + ## Handling NULL values passed to hrvar + if(is.null(hrvar)){ + data <- totals_col(data) + hrvar <- "Total" + } + ## Basic Data for bar plot plot_data <- data %>% diff --git a/R/create_fizz.R b/R/create_fizz.R index ca817687..efab74e5 100644 --- a/R/create_fizz.R +++ b/R/create_fizz.R @@ -57,6 +57,12 @@ create_fizz <- function(data, data %>% check_inputs(requirements = required_variables) + ## Handling NULL values passed to hrvar + if(is.null(hrvar)){ + data <- totals_col(data) + hrvar <- "Total" + } + ## Clean metric name clean_nm <- gsub(pattern = "_", replacement = " ", x = metric) diff --git a/R/create_line.R b/R/create_line.R index 1c8bb962..2784a398 100644 --- a/R/create_line.R +++ b/R/create_line.R @@ -61,6 +61,12 @@ create_line <- function(data, data %>% check_inputs(requirements = required_variables) + ## Handling NULL values passed to hrvar + if(is.null(hrvar)){ + data <- totals_col(data) + hrvar <- "Total" + } + ## Clean metric name clean_nm <- gsub(pattern = "_", replacement = " ", x = metric) diff --git a/R/create_stacked.R b/R/create_stacked.R index 3fd26a1e..f86d17ca 100644 --- a/R/create_stacked.R +++ b/R/create_stacked.R @@ -39,7 +39,6 @@ #' When 'table' is passed, a summary table is returned as a data frame. #' #' @examples -#' \dontrun{ #' sq_data %>% #' create_stacked(hrvar = "LevelDesignation", #' metrics = c("Meeting_hours", "Email_hours"), @@ -60,7 +59,6 @@ #' "Call_hours", #' "Instant_Message_hours"), #' return = "table") -#'} #' @export create_stacked <- function(data, hrvar = "Organization", @@ -85,6 +83,12 @@ create_stacked <- function(data, data %>% check_inputs(requirements = required_variables) + ## Handling NULL values passed to hrvar + if(is.null(hrvar)){ + data <- totals_col(data) + hrvar <- "Total" + } + n_count <- data %>% rename(group = !!sym(hrvar)) %>% # Rename HRvar to `group` diff --git a/R/create_trend.R b/R/create_trend.R index db406c4e..a0be338c 100644 --- a/R/create_trend.R +++ b/R/create_trend.R @@ -49,6 +49,12 @@ create_trend <- function(data, data %>% check_inputs(requirements = required_variables) + ## Handling NULL values passed to hrvar + if(is.null(hrvar)){ + data <- totals_col(data) + hrvar <- "Total" + } + ## Clean metric name clean_nm <- gsub(pattern = "_", replacement = " ", x = metric) diff --git a/R/totals_bind.R b/R/totals_bind.R index 6d6a7898..cabecc1d 100644 --- a/R/totals_bind.R +++ b/R/totals_bind.R @@ -11,14 +11,9 @@ #' row. See example below on usage. #' #' @examples -#' \dontrun{ #' sq_data %>% -#' totals_bind(target_col = "LevelDesignation", -#' target_value = "Total") %>% -#' collab_sum(hrvar = "LevelDesignation", -#' return = "table") -#' -#' } +#' totals_bind(target_col = "LevelDesignation", target_value = "Total") %>% +#' collab_sum(hrvar = "LevelDesignation", return = "table") #' #' @param data data frame #' @param target_col Character value of the column in which to impute "Total". diff --git a/man/create_stacked.Rd b/man/create_stacked.Rd index 4100935c..5f66c1b7 100644 --- a/man/create_stacked.Rd +++ b/man/create_stacked.Rd @@ -48,7 +48,6 @@ Returns a stacked bar plot by default. Additional options available to return a summary table. } \examples{ -\dontrun{ sq_data \%>\% create_stacked(hrvar = "LevelDesignation", metrics = c("Meeting_hours", "Email_hours"), @@ -70,7 +69,6 @@ sq_data \%>\% "Instant_Message_hours"), return = "table") } -} \seealso{ Other Flexible: \code{\link{create_dist}()}, diff --git a/man/totals_bind.Rd b/man/totals_bind.Rd index 8f41513a..38e75c36 100644 --- a/man/totals_bind.Rd +++ b/man/totals_bind.Rd @@ -22,13 +22,8 @@ this is to enable to creation of summary tables with a calculated "Total" row. See example below on usage. } \examples{ -\dontrun{ sq_data \%>\% - totals_bind(target_col = "LevelDesignation", - target_value = "Total") \%>\% - collab_sum(hrvar = "LevelDesignation", - return = "table") - -} + totals_bind(target_col = "LevelDesignation", target_value = "Total") \%>\% + collab_sum(hrvar = "LevelDesignation", return = "table") } From 142f364a534776e7a56fe7e8fe3dcf2a392bda28 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Tue, 8 Dec 2020 23:47:45 +0000 Subject: [PATCH 136/192] docs: totals_col Update argument documentation on totals_col --- R/totals_col.R | 2 +- man/totals_col.Rd | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/R/totals_col.R b/R/totals_col.R index 087369cd..237dad20 100644 --- a/R/totals_col.R +++ b/R/totals_col.R @@ -16,7 +16,7 @@ #' collab_fizz(hrvar = "Total") #' #' @param data data frame -#' @param target_value Character value defining the name and the value of the "Total" column. +#' @param total_value Character value defining the name and the value of the "Total" column. #' Defaults to "Total". An error is returned if an existing variable has the same name as the #' supplied value. #' diff --git a/man/totals_col.Rd b/man/totals_col.Rd index b7da7128..5020e934 100644 --- a/man/totals_col.Rd +++ b/man/totals_col.Rd @@ -9,7 +9,7 @@ totals_col(data, total_value = "Total") \arguments{ \item{data}{data frame} -\item{target_value}{Character value defining the name and the value of the "Total" column. +\item{total_value}{Character value defining the name and the value of the "Total" column. Defaults to "Total". An error is returned if an existing variable has the same name as the supplied value.} } From ec9c034de92f65dd4e8668d1421323a2de19c559 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Wed, 9 Dec 2020 15:14:59 +0000 Subject: [PATCH 137/192] docs: update guides Move content from README.md to be re-directed to getting_started.md --- .github/analyst_guide_getting_started.md | 35 ++++++++++++------ README.md | 47 +++--------------------- 2 files changed, 28 insertions(+), 54 deletions(-) diff --git a/.github/analyst_guide_getting_started.md b/.github/analyst_guide_getting_started.md index 9c58392b..db09f0d6 100644 --- a/.github/analyst_guide_getting_started.md +++ b/.github/analyst_guide_getting_started.md @@ -14,7 +14,9 @@ devtools::install_git(url = "https://github.com/microsoft/wpa.git") ``` -This package is not yet released on CRAN, and therefore `install.packages()` will not work. If you prefer to proceed with a local installation, you can download a installation file [here](https://github.com/microsoft/wpa/releases). +If you are prompted for a message to update your packages, we recommend updating all CRAN packages, unless you are aware that there are any significant breaking changes in the new packages that would be updated. You may need to restart your R Session both **before** and **after** the package updates prior to re-running the above code. + +**wpa** is not yet released on CRAN, and therefore `install.packages()` will not work. If you prefer to proceed with a local installation, you can download a installation file [here](https://github.com/microsoft/wpa/releases). ## Loading the wpa package Once the installation is complete, you can load the package with: @@ -41,17 +43,20 @@ setwd("C:/Users/myuser/Desktop/") person_data <- import_wpa("myquery.csv") ``` -In the code above, `set_wd()` will set the working directory to the Desktop, then `import_wpa()` will read the source CSV. The contents will be saved to the object person_data (using `<-` as an [Assignment Operator](https://stat.ethz.ch/R-manual/R-devel/library/base/html/assignOps.html)). +In the code above, `set_wd()` will set the working directory to the Desktop, then `import_wpa()` will read the source CSV. Note that file paths in R must be provided as a forward-slash (`/`) or escaped back-slash (`\\`). +As an alternative to `set_wd()`, you may also consider using [RStudio Projects](https://martinctc.github.io/blog/rstudio-projects-and-working-directories-a-beginner's-guide/), which enables you to use relative links within the working directory _instead_ of `set_wd()` and full file paths. + +The contents will be saved to the object person_data (using `<-` as an [Assignment Operator](https://stat.ethz.ch/R-manual/R-devel/library/base/html/assignOps.html)). ## Demo data The **wpa** package includes a set of demo Workplace Analytics datasets that you can use to explore the functionality of this package. We will also use them extensively in this guide. The included datasets are: -1. *sq_data*: A Standard Person Query -2. *dv_data*: A Standard Person Query with outliers -3. *mt_data*: A Standard Meeting Query -4. *em_data*: An Hourly Collaboration Query -5. *g2g_data*: A Group-to-Group Query +1. `sq_data`: A Standard Person Query +2. `dv_data`: A Standard Person Query with outliers +3. `mt_data`: A Standard Meeting Query +4. `em_data`: An Hourly Collaboration Query +5. `g2g_data`: A Group-to-Group Query ## Exploring a person query We can explore the sq_data person query using the `analysis_scope()` function. This function create a basic bar plot, with the count of the distinct individuals for different group (groups defined by an HR attribute in your query). @@ -81,13 +86,15 @@ sq_data %>% analysis_scope(hrvar = "TimeZone") We can expand this analysis by using the `dplyr::filter()` function from **dplyr**. This will allows us to drill into a specific subset of the data. This is where the Forward-Pipe Operators (`%>%`) become very useful, as we can write a single line that takes the original data, applies a filter, and then creates the plot: ```R -sq_data %>% filter(LevelDesignation=="Support") %>% analysis_scope(hrvar = "Organization") +sq_data %>% + filter(LevelDesignation == "Support") %>% + analysis_scope(hrvar = "Organization") ``` Most functions in **wpa** create plot by default, but can change their behaviour by adding a `return` argument. If you add `return="table"` to this function it will now produce a table with the count of the distinct individuals by group. ```R -sq_data %>% analysis_scope(hrvar="LevelDesignation", return="table") +sq_data %>% analysis_scope(hrvar = "LevelDesignation", return = "table") ``` ## Function structure @@ -103,13 +110,15 @@ Tables and plots can be saved with the `export()` function. This functions allow One again, adding an additional forward-Pipe operator we can write: ```R -sq_data %>% analysis_scope(hrvar = "Organization") %>% export() +sq_data %>% + analysis_scope(hrvar = "Organization") %>% + export() ``` ## Four steps from data to output -The examples above illustrate how the use of **wpa** can be summarized in 4 simple steps: Load the package, read-in query data, run functions and export results. The script below illustrates this funcitonality: +The examples above illustrate how the use of **wpa** can be summarized in 4 simple steps: Load the package, read-in query data, run functions and export results. The script below illustrates this functionality: ```R library(wpa) # Step 1 @@ -118,7 +127,9 @@ person_data <- import_wpa("myquery.csv") # Step 2 person_data %>% analysis_scope() # Step 3 -person_data %>% analysis_scope() %>% export() # Step 4 +person_data %>% + analysis_scope() %>% + export() # Step 4 ``` diff --git a/README.md b/README.md index ad6afb95..4584813f 100644 --- a/README.md +++ b/README.md @@ -22,56 +22,19 @@ Here is an example of **wpa** in action: --- -## :rocket: Quick start guide - For users +## :rocket: Users -### Installing the package +To get started with the package, please see the following links: -You can install the GitHub version of **wpa** with the following in R: -```R -# Check if devtools is installed, if not then install it -if(!"devtools" %in% installed.packages()){ - install.packages("devtools") -} -devtools::install_git(url = "https://github.com/microsoft/wpa.git") -``` -Note that **wpa** is not yet released on CRAN, and therefore `install.packages()` will not work. +- [Installation and Set-up](https://microsoft.github.io/wpa/analyst_guide_getting_started.html) +- [Full function list](https://microsoft.github.io/wpa/reference/index.html) -### Examples - -The package comes shipped with a sample Standard Query dataset (`sq_data`), so you can start exploring functions without having to read in any data. Most functions in **wpa** share a consistent API, and enable you to return results for both a **plot** or a **table** (data frame): - -```R -collaboration_sum(sq_data, return = "plot") -``` - - -By passing 'table' to the `return` argument, the function will return a data frame with the relevant summary statistics. - -The following illustrates the basic API of standard analysis functions: - - - ---- - -## :package: Package Structure - -For information on the package structure, please see the relevant section in our [Analyst Guide](.github/analyst_guide.md). - -Also check out our package cheat sheet for more information: +Also check out our package cheat sheet for a quick glimpse of what **wpa** offers: --- -## Vignette - -You can browse the vignette by running the following in R: -```R -vignette(topic = "intro-to-wpa", package = "wpa") -``` - ---- - ## :hammer: Developers We welcome contributions to the package! From f76ac28e693425a74bef67e8b6c00f48698f82ce Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Wed, 9 Dec 2020 15:21:59 +0000 Subject: [PATCH 138/192] docs: find out more about functions --- .github/analyst_guide_getting_started.md | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/.github/analyst_guide_getting_started.md b/.github/analyst_guide_getting_started.md index db09f0d6..3978d0db 100644 --- a/.github/analyst_guide_getting_started.md +++ b/.github/analyst_guide_getting_started.md @@ -59,7 +59,7 @@ The **wpa** package includes a set of demo Workplace Analytics datasets that you 5. `g2g_data`: A Group-to-Group Query ## Exploring a person query -We can explore the sq_data person query using the `analysis_scope()` function. This function create a basic bar plot, with the count of the distinct individuals for different group (groups defined by an HR attribute in your query). +We can explore the `sq_data` person query using the `analysis_scope()` function. This function creates a basic bar plot, with the count of the distinct individuals for different group (groups defined by an HR attribute in your query). For example, if we want to know the number of individuals in `sq_data` per organization, we can use: @@ -96,6 +96,14 @@ Most functions in **wpa** create plot by default, but can change their behaviour ```R sq_data %>% analysis_scope(hrvar = "LevelDesignation", return = "table") ``` +If at any point you would like to understand more about the functions, you can: + +- Run in the R console with the function name prefixed with a question mark, e.g. `?analysis_scope` +- View the underlying source code of the function with `View()`, e.g. `View(analysis_scope)` +- Visit the reference page online: https://microsoft.github.io/wpa/reference + + + ## Function structure All functions in **wpa** follow a similar behaviour, including many common arguments. The following illustrates the basic API of standard analysis functions: From 6e87d53568c6bce40f2c1c3e888e8cbea5649056 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Wed, 9 Dec 2020 15:25:16 +0000 Subject: [PATCH 139/192] docs: add link back to query docs --- .github/analyst_guide_getting_started.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/analyst_guide_getting_started.md b/.github/analyst_guide_getting_started.md index 3978d0db..30756a1e 100644 --- a/.github/analyst_guide_getting_started.md +++ b/.github/analyst_guide_getting_started.md @@ -58,6 +58,8 @@ The **wpa** package includes a set of demo Workplace Analytics datasets that you 4. `em_data`: An Hourly Collaboration Query 5. `g2g_data`: A Group-to-Group Query +See [here](https://docs.microsoft.com/en-us/Workplace-Analytics/tutorials/query-basics) for a full documentation of the queries in Workplace Analytics. + ## Exploring a person query We can explore the `sq_data` person query using the `analysis_scope()` function. This function creates a basic bar plot, with the count of the distinct individuals for different group (groups defined by an HR attribute in your query). From f3ab7b3ad128fac313d6da0a0d24f39c73a05664 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Fri, 11 Dec 2020 10:36:42 +0000 Subject: [PATCH 140/192] feat: improve keymetrics_scan (#26) Visual improvements to keymetrics_scan() as per issue #26. Two new functions are exported: us_to_space() and maxmin(), which are used to aid the plotting process. --- NAMESPACE | 3 +- R/keymetrics_scan.R | 68 ++++++++++++++++++++++++++++++++++++--------- man/maxmin.Rd | 23 +++++++++++++++ man/us_to_space.Rd | 18 ++++++++++++ 4 files changed, 98 insertions(+), 14 deletions(-) create mode 100644 man/maxmin.Rd create mode 100644 man/us_to_space.Rd diff --git a/NAMESPACE b/NAMESPACE index 714d62c4..8b9f0cf3 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -87,6 +87,7 @@ export(import_wpa) export(internal_network_plot) export(is_date_format) export(keymetrics_scan) +export(maxmin) export(meeting_dist) export(meeting_fizz) export(meeting_line) @@ -133,6 +134,7 @@ export(totals_col) export(totals_reorder) export(track_HR_change) export(tstamp) +export(us_to_space) export(validation_report) export(workloads_dist) export(workloads_fizz) @@ -150,7 +152,6 @@ import(Information) import(dplyr) import(ggplot2) import(ggraph) -import(ggrepel) import(network) import(reshape2) import(scales) diff --git a/R/keymetrics_scan.R b/R/keymetrics_scan.R index 38ab3cdd..7630f810 100644 --- a/R/keymetrics_scan.R +++ b/R/keymetrics_scan.R @@ -74,34 +74,38 @@ keymetrics_scan <- function(data, mutate(variable = factor(variable)) %>% group_by(variable) %>% # Heatmap by row - mutate(value_rescaled = value/mean(value)) %>% + mutate(value_rescaled = maxmin(value)) %>% ungroup() - # Underscore to space - us_to_space <- function(x){ - gsub(pattern = "_", replacement = " ", x = x) - } - - plot_object <- myTable_long %>% filter(variable != "Employee_Count") %>% ggplot(aes(x = group, y = stats::reorder(variable, desc(variable)))) + - geom_tile(aes(fill = value_rescaled)) + + geom_tile(aes(fill = value_rescaled), + colour = "#FFFFFF", + size = 2) + geom_text(aes(label=round(value, 1)), size = textsize) + - scale_fill_distiller(palette = "Blues", direction = 1) + + # Fill is contingent on max-min scaling + scale_fill_gradient2(low = rgb2hex(216, 24, 42), + mid = rgb2hex(241, 204, 158), + high = rgb2hex(7, 111, 161), + midpoint = 0.5, + breaks = c(0, 0.5, 1), + labels = c("Minimum", "", "Maximum"), + limits = c(0, 1)) + scale_x_discrete(position = "top") + scale_y_discrete(labels = us_to_space) + - theme_light() + - labs(title = "Key Workplace Analytics metrics", + theme_wpa_basic() + + theme(axis.line = element_line(color = "#FFFFFF")) + + labs(title = "Key metrics", subtitle = paste("Weekly average by", camel_clean(hrvar)), y =" ", x =" ", + fill = " ", caption = extract_date_range(data, return = "text")) + theme(axis.text.x = element_text(angle = 90, hjust = 0), - plot.title = element_text(color="grey40", face="bold", size=20)) + - guides(fill=FALSE) + plot.title = element_text(color="grey40", face="bold", size=20)) if(return == "table"){ @@ -121,3 +125,41 @@ keymetrics_scan <- function(data, } } + +#' @title Replace underscore with space +#' +#' @description Convenience function to convert underscores to space +#' +#' @param x String to replace all occurrences of `_` with a single space +#' +#' @examples +#' us_to_space("Meeting_hours_with_manager_1_on_1") +#' +#' @export +us_to_space <- function(x){ + gsub(pattern = "_", replacement = " ", x = x) +} + +#' @title Max-Min Scaling Function +#' +#' @description This function allows you to scale vectors or an entire data frame using the max-min scaling method +#' A numeric vector is always returned. Originally implemented in https://github.com/martinctc/surveytoolbox. +#' +#' @details This is used within `keymetrics_scan()` to enable row-wise heatmapping. +#' +#' @param x Pass a vector or the required columns of a data frame through this argument. +#' @keywords max-min +#' @export +#' @examples +#' numbers <- c(15, 40, 10, 2) +#' maxmin(numbers) +#' @export + +maxmin <- function(x){ + if(any(is.na(x))){ + warning("Warning: vector contains missing values. Those values will return as NA.") + } + maxs <- max(x, na.rm = TRUE) + mins <- min(x, na.rm = TRUE) + as.numeric(scale(x,center=mins,scale=maxs-mins)) +} diff --git a/man/maxmin.Rd b/man/maxmin.Rd new file mode 100644 index 00000000..e1a18bcc --- /dev/null +++ b/man/maxmin.Rd @@ -0,0 +1,23 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/keymetrics_scan.R +\name{maxmin} +\alias{maxmin} +\title{Max-Min Scaling Function} +\usage{ +maxmin(x) +} +\arguments{ +\item{x}{Pass a vector or the required columns of a data frame through this argument.} +} +\description{ +This function allows you to scale vectors or an entire data frame using the max-min scaling method +A numeric vector is always returned. Originally implemented in https://github.com/martinctc/surveytoolbox. +} +\details{ +This is used within \code{keymetrics_scan()} to enable row-wise heatmapping. +} +\examples{ +numbers <- c(15, 40, 10, 2) +maxmin(numbers) +} +\keyword{max-min} diff --git a/man/us_to_space.Rd b/man/us_to_space.Rd new file mode 100644 index 00000000..8145bb26 --- /dev/null +++ b/man/us_to_space.Rd @@ -0,0 +1,18 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/keymetrics_scan.R +\name{us_to_space} +\alias{us_to_space} +\title{Replace underscore with space} +\usage{ +us_to_space(x) +} +\arguments{ +\item{x}{String to replace all occurrences of \verb{_} with a single space} +} +\description{ +Convenience function to convert underscores to space +} +\examples{ +us_to_space("Meeting_hours_with_manager_1_on_1") + +} From 4810739c56afa99871b43e33cdb7d68ed2cc5e6f Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Fri, 11 Dec 2020 10:42:20 +0000 Subject: [PATCH 141/192] feat: adjust title size in theme Global change to title size in plots --- R/themes.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/themes.R b/R/themes.R index f942f36e..d8a0591b 100644 --- a/R/themes.R +++ b/R/themes.R @@ -87,7 +87,7 @@ theme_wpa_basic <- function(font_size = 12){ text_italic <- element_text(size = font_size + 2, colour = text_colour, face = "italic") text_bold <- element_text(size = font_size + 2, colour = text_colour, face = "bold") - text_title <- element_text(size = font_size + 8, colour = text_colour, face = "bold") + text_title <- element_text(size = font_size + 5, colour = text_colour, face = "bold") theme_minimal() + theme(plot.background = element_blank(), From 3af47811e540b0252ba7d8c2ce5e0ed05063383a Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Fri, 11 Dec 2020 10:52:25 +0000 Subject: [PATCH 142/192] feat: flip colours keymetrics_scan --- R/keymetrics_scan.R | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/R/keymetrics_scan.R b/R/keymetrics_scan.R index 7630f810..b7499547 100644 --- a/R/keymetrics_scan.R +++ b/R/keymetrics_scan.R @@ -87,9 +87,9 @@ keymetrics_scan <- function(data, size = 2) + geom_text(aes(label=round(value, 1)), size = textsize) + # Fill is contingent on max-min scaling - scale_fill_gradient2(low = rgb2hex(216, 24, 42), + scale_fill_gradient2(low = rgb2hex(7, 111, 161), mid = rgb2hex(241, 204, 158), - high = rgb2hex(7, 111, 161), + high = rgb2hex(216, 24, 42), midpoint = 0.5, breaks = c(0, 0.5, 1), labels = c("Minimum", "", "Maximum"), From dfef6d7957b9855929fe362557fe941cda3811cd Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Fri, 11 Dec 2020 13:16:22 +0000 Subject: [PATCH 143/192] docs: update FQ names (#20) References to "Collaboration Assessment" now replaced with "Ways of Working Assessment" --- R/check_query.R | 4 ++-- R/collaboration_area.R | 9 ++++++--- R/identify_query.R | 4 ++-- R/import_wpa.R | 6 +++--- R/meetingtype_dist.R | 14 +++++++++----- R/standardise_pq.R | 8 ++++---- man/check_person_query.Rd | 2 +- man/check_query.Rd | 4 ++-- man/check_query_validation.Rd | 2 +- man/collaboration_area.Rd | 10 +++++++--- man/import_wpa.Rd | 2 +- man/meetingtype_dist.Rd | 11 ++++++++--- man/meetingtype_dist_ca.Rd | 4 ++-- man/meetingtype_summary.Rd | 2 +- man/standardise_pq.Rd | 4 ++-- 15 files changed, 51 insertions(+), 35 deletions(-) diff --git a/R/check_query.R b/R/check_query.R index d647b5d6..0d7aacf7 100644 --- a/R/check_query.R +++ b/R/check_query.R @@ -11,11 +11,11 @@ #' #' @details #' This can be used with any person-level query, such as the standard person query, -#' collaboration assessment query, and the hourly collaboration query. When run, +#' Ways of Working assessment query, and the hourly collaboration query. When run, #' this prints diagnostic data to the R console. #' #' @param data A person-level query in the form of a data frame, including the standard person query, -#' collaboration assessment query, and the hourly collaboration query. +#' Ways of Working assessment query, and the hourly collaboration query. #' @param return Character string to specify whether to return a console message ("message"), #' a character string ("text"), or a data frame. Defaults to "message". #' @param validation Logical value to specify whether to return a check used by the `validation_report()`. diff --git a/R/collaboration_area.R b/R/collaboration_area.R index 4478a8d3..569bd316 100644 --- a/R/collaboration_area.R +++ b/R/collaboration_area.R @@ -6,7 +6,7 @@ #' @title Collaboration - Stacked Area Plot #' #' @description -#' Provides an overview analysis of 'Weekly Digital Collaboration'. +#' Provides an overview analysis of Weekly Digital Collaboration. #' Returns an stacked area plot of Email and Meeting Hours by default. #' Additional options available to return a summary table. #' @@ -14,8 +14,8 @@ #' Uses the metrics `Meeting_hours`, `Email_hours`, `Unscheduled_Call_hours`, #' and `Instant_Message_hours`. #' -#' @param data A Standard Query dataset in the form of a data frame. -#' A Collaboration assessment dataset may also be provided, in which +#' @param data A Standard Person Query dataset in the form of a data frame. +#' A Ways of Working assessment dataset may also be provided, in which #' Unscheduled call hours would be included in the output. #' @param hrvar HR Variable by which to split metrics, defaults to "Organization" but accepts any character vector, e.g. "LevelDesignation" #' @param mingroup Numeric value setting the privacy threshold / minimum group size. Defaults to 5. @@ -30,6 +30,9 @@ #' #' @family Collaboration #' +#' @examples +#' collaboration_area(sq_data) +#' #' @return #' Returns a ggplot object by default, where 'plot' is passed in `return`. #' When 'table' is passed, a summary table is returned as a data frame. diff --git a/R/identify_query.R b/R/identify_query.R index 4cb96af5..04d1feb7 100644 --- a/R/identify_query.R +++ b/R/identify_query.R @@ -32,7 +32,7 @@ identify_query <- function(data, threshold = 2){ ## variables to check for in each query type spq_var <- c("PersonId", "Collaboration_hours", "Instant_Message_hours") # Standard Person query - caq_var <- c("PersonId", "Collaboration_hrs", "Instant_message_hours") # Collaboration Assessment query + caq_var <- c("PersonId", "Collaboration_hrs", "Instant_message_hours") # Ways of Working Assessment query smq_var <- c("MeetingId", "Date", "Attendees") # Standard Meeting Query shc_var <- c("PersonId", "Emails_sent_00_01", "IMs_sent_23_24") # Standard Hourly Collaboration @@ -54,7 +54,7 @@ identify_query <- function(data, threshold = 2){ ## Labels qlabels <- c("Person Query", - "Collaboration Assessment Query", + "Ways of Working Assessment Query", "Meeting Query", "Hourly Collaboration Query") diff --git a/R/import_wpa.R b/R/import_wpa.R index f9c22067..41b23527 100644 --- a/R/import_wpa.R +++ b/R/import_wpa.R @@ -19,7 +19,7 @@ #' e.g. "/files/standard query.csv" #' @param standardise logical. If TRUE, `import_wpa()` runs `standardise_pq()` to make a Collaboration #' Assessment query's columns name standard and consistent with a Standard Person Query. Note that this -#' will have no effect if the query being imported is not a Collaboration Assessment query. Defaults +#' will have no effect if the query being imported is not a Ways of Working Assessment query. Defaults #' as FALSE. #' #' @export @@ -43,8 +43,8 @@ import_wpa <- function(x, standardise = FALSE){ } ## Standardise query if `standardise == TRUE` - if(standardise == TRUE & identify_query(return_data) == "Collaboration Assessment Query"){ - message("Standardising column names for a Collaboration Assessment query to + if(standardise == TRUE & identify_query(return_data) == "Ways of Working Assessment Query"){ + message("Standardising column names for a Ways of Working Assessment query to a Person query...") return_data <- standardise_pq(return_data) } diff --git a/R/meetingtype_dist.R b/R/meetingtype_dist.R index ca460b3c..9f961e99 100644 --- a/R/meetingtype_dist.R +++ b/R/meetingtype_dist.R @@ -3,15 +3,15 @@ #' @description #' Calculate the hour distribution of internal meeting types. #' This is a wrapper around `meetingtype_dist_mt()` and -#' `meetingtype_dist_ca()`, depending on whether a Meeting Query or a Collaboration Assessment Query +#' `meetingtype_dist_ca()`, depending on whether a Meeting Query or a Ways of Working Assessment Query #' is passed as an input. #' #' @param data Data frame. If a meeting query, must contain the variables `Attendee` and `DurationHours`. #' @param hrvar Character string to specify the HR attribute to split the data by. -#' Note that this is only applicable if a Collaboration Assessment query is passed to the function. If a Meeting Query +#' Note that this is only applicable if a Ways of Working Assessment query is passed to the function. If a Meeting Query #' is passed instead, this argument is ignored. #' @param mingroup Numeric value setting the privacy threshold / minimum group size. Defaults to 5. -#' Only applicable when using a Collaboration Assessment query. +#' Only applicable when using a Ways of Working Assessment query. #' @param return Character vector specifying what to return, defaults to "plot". #' Valid inputs are "plot" and "table". #' @@ -19,6 +19,10 @@ #' @import dplyr #' @importFrom stats setNames #' +#' @examples +#' ## Implementation using Standard Meeting Query +#' meetingtype_dist(mt_data) +#' #' @export meetingtype_dist <- function(data, @@ -33,10 +37,10 @@ meetingtype_dist <- function(data, } else if("PersonId" %in% names(data)){ - message("Calculating results using a Collaboration Assessment Query...") + message("Calculating results using a Ways of Working Assessment Query...") meetingtype_dist_ca(data, hrvar = hrvar, mingroup = mingroup, return = return) } else { - stop("Please check query type. Must be either a Collaboration Assessment Query or a Meeting Query.") + stop("Please check query type. Must be either a Ways of Working Assessment Query or a Meeting Query.") } } diff --git a/R/standardise_pq.R b/R/standardise_pq.R index 54a7cc85..a7257b3b 100644 --- a/R/standardise_pq.R +++ b/R/standardise_pq.R @@ -7,7 +7,7 @@ #' #' @description #' This function standardises the variable names to a Standard Person -#' Query, where the standard use case is to pass a Collaboration Assessment +#' Query, where the standard use case is to pass a Ways of Working Assessment #' Query to the function. #' #' @details @@ -15,13 +15,13 @@ #' - `Collaboration_hrs` -> `Collaboration_hours` #' - `Instant_message_hours` -> `Instant_Message_hours` #' -#' @param data A Collaboration Assessment query to pass through as a data frame. +#' @param data A Ways of Working Assessment query to pass through as a data frame. #' #' @export standardise_pq <- function(data){ - if(identify_query(data) != "Collaboration Assessment Query"){ - stop("Currently only Collaboration Assessment Query to Standard Person Query + if(identify_query(data) != "Ways of Working Assessment Query"){ + stop("Currently only Ways of Working Assessment Query to Standard Person Query conversions are supported.") } diff --git a/man/check_person_query.Rd b/man/check_person_query.Rd index 2568ae89..b877f631 100644 --- a/man/check_person_query.Rd +++ b/man/check_person_query.Rd @@ -8,7 +8,7 @@ check_person_query(data, return) } \arguments{ \item{data}{A person-level query in the form of a data frame, including the standard person query, -collaboration assessment query, and the hourly collaboration query.} +Ways of Working assessment query, and the hourly collaboration query.} \item{return}{Character string to specify whether to return a console message ("message"), a character string ("text"), or a data frame. Defaults to "message".} diff --git a/man/check_query.Rd b/man/check_query.Rd index bae3f406..b5f3467c 100644 --- a/man/check_query.Rd +++ b/man/check_query.Rd @@ -8,7 +8,7 @@ check_query(data, return = "message", validation = FALSE) } \arguments{ \item{data}{A person-level query in the form of a data frame, including the standard person query, -collaboration assessment query, and the hourly collaboration query.} +Ways of Working assessment query, and the hourly collaboration query.} \item{return}{Character string to specify whether to return a console message ("message"), a character string ("text"), or a data frame. Defaults to "message".} @@ -22,7 +22,7 @@ such as date range, number of employees, HR attributes identified, etc. } \details{ This can be used with any person-level query, such as the standard person query, -collaboration assessment query, and the hourly collaboration query. When run, +Ways of Working assessment query, and the hourly collaboration query. When run, this prints diagnostic data to the R console. } \examples{ diff --git a/man/check_query_validation.Rd b/man/check_query_validation.Rd index 3d9d401f..a7c9724c 100644 --- a/man/check_query_validation.Rd +++ b/man/check_query_validation.Rd @@ -8,7 +8,7 @@ check_query_validation(data, return) } \arguments{ \item{data}{A person-level query in the form of a data frame, including the standard person query, -collaboration assessment query, and the hourly collaboration query.} +Ways of Working assessment query, and the hourly collaboration query.} \item{return}{Character string to specify whether to return a console message ("message"), a character string ("text"), or a data frame. Defaults to "message".} diff --git a/man/collaboration_area.Rd b/man/collaboration_area.Rd index 7802cd03..8ee9f803 100644 --- a/man/collaboration_area.Rd +++ b/man/collaboration_area.Rd @@ -10,8 +10,8 @@ collaboration_area(data, hrvar = "Organization", mingroup = 5, return = "plot") collab_area(data, hrvar = "Organization", mingroup = 5, return = "plot") } \arguments{ -\item{data}{A Standard Query dataset in the form of a data frame. -A Collaboration assessment dataset may also be provided, in which +\item{data}{A Standard Person Query dataset in the form of a data frame. +A Ways of Working assessment dataset may also be provided, in which Unscheduled call hours would be included in the output.} \item{hrvar}{HR Variable by which to split metrics, defaults to "Organization" but accepts any character vector, e.g. "LevelDesignation"} @@ -26,13 +26,17 @@ Returns a ggplot object by default, where 'plot' is passed in \code{return}. When 'table' is passed, a summary table is returned as a data frame. } \description{ -Provides an overview analysis of 'Weekly Digital Collaboration'. +Provides an overview analysis of Weekly Digital Collaboration. Returns an stacked area plot of Email and Meeting Hours by default. Additional options available to return a summary table. } \details{ Uses the metrics \code{Meeting_hours}, \code{Email_hours}, \code{Unscheduled_Call_hours}, and \code{Instant_Message_hours}. +} +\examples{ +collaboration_area(sq_data) + } \seealso{ Other Collaboration: diff --git a/man/import_wpa.Rd b/man/import_wpa.Rd index aa2ea317..0ec3895a 100644 --- a/man/import_wpa.Rd +++ b/man/import_wpa.Rd @@ -13,7 +13,7 @@ e.g. "/files/standard query.csv"} \item{standardise}{logical. If TRUE, \code{import_wpa()} runs \code{standardise_pq()} to make a Collaboration Assessment query's columns name standard and consistent with a Standard Person Query. Note that this -will have no effect if the query being imported is not a Collaboration Assessment query. Defaults +will have no effect if the query being imported is not a Ways of Working Assessment query. Defaults as FALSE.} } \description{ diff --git a/man/meetingtype_dist.Rd b/man/meetingtype_dist.Rd index bde8b6c0..e67ed35b 100644 --- a/man/meetingtype_dist.Rd +++ b/man/meetingtype_dist.Rd @@ -10,11 +10,11 @@ meetingtype_dist(data, hrvar = NULL, mingroup = 5, return = "plot") \item{data}{Data frame. If a meeting query, must contain the variables \code{Attendee} and \code{DurationHours}.} \item{hrvar}{Character string to specify the HR attribute to split the data by. -Note that this is only applicable if a Collaboration Assessment query is passed to the function. If a Meeting Query +Note that this is only applicable if a Ways of Working Assessment query is passed to the function. If a Meeting Query is passed instead, this argument is ignored.} \item{mingroup}{Numeric value setting the privacy threshold / minimum group size. Defaults to 5. -Only applicable when using a Collaboration Assessment query.} +Only applicable when using a Ways of Working Assessment query.} \item{return}{Character vector specifying what to return, defaults to "plot". Valid inputs are "plot" and "table".} @@ -22,6 +22,11 @@ Valid inputs are "plot" and "table".} \description{ Calculate the hour distribution of internal meeting types. This is a wrapper around \code{meetingtype_dist_mt()} and -\code{meetingtype_dist_ca()}, depending on whether a Meeting Query or a Collaboration Assessment Query +\code{meetingtype_dist_ca()}, depending on whether a Meeting Query or a Ways of Working Assessment Query is passed as an input. } +\examples{ +## Implementation using Standard Meeting Query +meetingtype_dist(mt_data) + +} diff --git a/man/meetingtype_dist_ca.Rd b/man/meetingtype_dist_ca.Rd index 8ded09a4..5b396f22 100644 --- a/man/meetingtype_dist_ca.Rd +++ b/man/meetingtype_dist_ca.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/meetingtype_dist_ca.R \name{meetingtype_dist_ca} \alias{meetingtype_dist_ca} -\title{Meeting Type Distribution (Collaboration Assessment Query)} +\title{Meeting Type Distribution (Ways of Working Assessment Query)} \usage{ meetingtype_dist_ca(data, hrvar = NULL, mingroup = 5, return = "plot") } @@ -18,5 +18,5 @@ Valid inputs are "plot" and "table".} } \description{ Calculate the hour distribution of internal meeting types, -using a Collaboration Assessment Query with core WpA variables as an input. +using a Ways of Working Assessment Query with core WpA variables as an input. } diff --git a/man/meetingtype_summary.Rd b/man/meetingtype_summary.Rd index 20a754e8..6867562f 100644 --- a/man/meetingtype_summary.Rd +++ b/man/meetingtype_summary.Rd @@ -15,7 +15,7 @@ meetingtype_summary( meetingtype_sum(data, hrvar = "Organization", mingroup = 5, return = "plot") } \arguments{ -\item{data}{Collaboration Assessment query in the form of a data frame. Requires the following variables: +\item{data}{Ways of Working Assessment query in the form of a data frame. Requires the following variables: \itemize{ \item \code{Bloated_meeting_hours} \item \code{Lengthy_meeting_hours} diff --git a/man/standardise_pq.Rd b/man/standardise_pq.Rd index 6c804f66..35240057 100644 --- a/man/standardise_pq.Rd +++ b/man/standardise_pq.Rd @@ -10,11 +10,11 @@ standardise_pq(data) standardize_pq(data) } \arguments{ -\item{data}{A Collaboration Assessment query to pass through as a data frame.} +\item{data}{A Ways of Working Assessment query to pass through as a data frame.} } \description{ This function standardises the variable names to a Standard Person -Query, where the standard use case is to pass a Collaboration Assessment +Query, where the standard use case is to pass a Ways of Working Assessment Query to the function. } \details{ From 1126bd8abf582babe211fe41f6aa21d5a189a451 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Fri, 11 Dec 2020 13:17:42 +0000 Subject: [PATCH 144/192] chore: one function per file Move maxmin() and us_to_space() to their own R files instead of under keymetrics_scan() --- R/keymetrics_scan.R | 38 -------------------------------------- R/maxmin.R | 23 +++++++++++++++++++++++ R/us_to_space.R | 13 +++++++++++++ man/maxmin.Rd | 2 +- man/us_to_space.Rd | 2 +- 5 files changed, 38 insertions(+), 40 deletions(-) create mode 100644 R/maxmin.R create mode 100644 R/us_to_space.R diff --git a/R/keymetrics_scan.R b/R/keymetrics_scan.R index b7499547..6b71a5ce 100644 --- a/R/keymetrics_scan.R +++ b/R/keymetrics_scan.R @@ -125,41 +125,3 @@ keymetrics_scan <- function(data, } } - -#' @title Replace underscore with space -#' -#' @description Convenience function to convert underscores to space -#' -#' @param x String to replace all occurrences of `_` with a single space -#' -#' @examples -#' us_to_space("Meeting_hours_with_manager_1_on_1") -#' -#' @export -us_to_space <- function(x){ - gsub(pattern = "_", replacement = " ", x = x) -} - -#' @title Max-Min Scaling Function -#' -#' @description This function allows you to scale vectors or an entire data frame using the max-min scaling method -#' A numeric vector is always returned. Originally implemented in https://github.com/martinctc/surveytoolbox. -#' -#' @details This is used within `keymetrics_scan()` to enable row-wise heatmapping. -#' -#' @param x Pass a vector or the required columns of a data frame through this argument. -#' @keywords max-min -#' @export -#' @examples -#' numbers <- c(15, 40, 10, 2) -#' maxmin(numbers) -#' @export - -maxmin <- function(x){ - if(any(is.na(x))){ - warning("Warning: vector contains missing values. Those values will return as NA.") - } - maxs <- max(x, na.rm = TRUE) - mins <- min(x, na.rm = TRUE) - as.numeric(scale(x,center=mins,scale=maxs-mins)) -} diff --git a/R/maxmin.R b/R/maxmin.R new file mode 100644 index 00000000..896160c7 --- /dev/null +++ b/R/maxmin.R @@ -0,0 +1,23 @@ +#' @title Max-Min Scaling Function +#' +#' @description This function allows you to scale vectors or an entire data frame using the max-min scaling method +#' A numeric vector is always returned. Originally implemented in https://github.com/martinctc/surveytoolbox. +#' +#' @details This is used within `keymetrics_scan()` to enable row-wise heatmapping. +#' +#' @param x Pass a vector or the required columns of a data frame through this argument. +#' @keywords max-min +#' @export +#' @examples +#' numbers <- c(15, 40, 10, 2) +#' maxmin(numbers) +#' @export + +maxmin <- function(x){ + if(any(is.na(x))){ + warning("Warning: vector contains missing values. Those values will return as NA.") + } + maxs <- max(x, na.rm = TRUE) + mins <- min(x, na.rm = TRUE) + as.numeric(scale(x,center=mins,scale=maxs-mins)) +} diff --git a/R/us_to_space.R b/R/us_to_space.R new file mode 100644 index 00000000..a77e9537 --- /dev/null +++ b/R/us_to_space.R @@ -0,0 +1,13 @@ +#' @title Replace underscore with space +#' +#' @description Convenience function to convert underscores to space +#' +#' @param x String to replace all occurrences of `_` with a single space +#' +#' @examples +#' us_to_space("Meeting_hours_with_manager_1_on_1") +#' +#' @export +us_to_space <- function(x){ + gsub(pattern = "_", replacement = " ", x = x) +} diff --git a/man/maxmin.Rd b/man/maxmin.Rd index e1a18bcc..fa750b4e 100644 --- a/man/maxmin.Rd +++ b/man/maxmin.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/keymetrics_scan.R +% Please edit documentation in R/maxmin.R \name{maxmin} \alias{maxmin} \title{Max-Min Scaling Function} diff --git a/man/us_to_space.Rd b/man/us_to_space.Rd index 8145bb26..5caa7480 100644 --- a/man/us_to_space.Rd +++ b/man/us_to_space.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/keymetrics_scan.R +% Please edit documentation in R/us_to_space.R \name{us_to_space} \alias{us_to_space} \title{Replace underscore with space} From 4c76fb6b7e74503e2e641463b25de603ab4a17cc Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Fri, 11 Dec 2020 13:18:47 +0000 Subject: [PATCH 145/192] feat: streamline code Use us_to_space() where possible for cleaner code --- R/create_bar.R | 2 +- R/create_boxplot.R | 2 +- R/create_bubble.R | 4 ++-- R/create_dist.R | 2 +- R/create_fizz.R | 2 +- R/create_line.R | 3 +-- R/create_period_scatter.R | 4 ++-- R/create_scatter.R | 6 +++--- R/create_trend.R | 5 ++++- R/flag_extreme.R | 3 +-- R/meetingtype_dist_ca.R | 11 +++++------ R/meetingtype_summary.R | 4 ++-- man/create_trend.Rd | 4 ++++ 13 files changed, 28 insertions(+), 24 deletions(-) diff --git a/R/create_bar.R b/R/create_bar.R index b77b6e98..7b453cc4 100644 --- a/R/create_bar.R +++ b/R/create_bar.R @@ -71,7 +71,7 @@ create_bar <- function(data, } ## Clean metric name - clean_nm <- gsub(pattern = "_", replacement = " ", x = metric) + clean_nm <- us_to_space(metric) ## Data for bar plot plot_data <- diff --git a/R/create_boxplot.R b/R/create_boxplot.R index 816e145f..dc9bfbd7 100644 --- a/R/create_boxplot.R +++ b/R/create_boxplot.R @@ -64,7 +64,7 @@ create_boxplot <- function(data, } ## Clean metric name - clean_nm <- gsub(pattern = "_", replacement = "", x = metric) + clean_nm <- us_to_space(metric) plot_data <- data %>% diff --git a/R/create_bubble.R b/R/create_bubble.R index 4561483c..ba68a95b 100644 --- a/R/create_bubble.R +++ b/R/create_bubble.R @@ -73,8 +73,8 @@ create_bubble <- function(data, } ## Clean metric names - clean_x <- gsub(pattern = "_", replacement = " ", x = metric_x) - clean_y <- gsub(pattern = "_", replacement = " ", x = metric_y) + clean_x <- us_to_space(metric_x) + clean_y <- us_to_space(metric_y) myTable <- data %>% diff --git a/R/create_dist.R b/R/create_dist.R index 52c035ac..64f83421 100644 --- a/R/create_dist.R +++ b/R/create_dist.R @@ -64,7 +64,7 @@ create_dist <- function(data, check_inputs(requirements = required_variables) ## Clean metric name - clean_nm <- gsub(pattern = "_", replacement = " ", x = metric) + clean_nm <- us_to_space(metric) ## Handling NULL values passed to hrvar if(is.null(hrvar)){ diff --git a/R/create_fizz.R b/R/create_fizz.R index efab74e5..650a7e18 100644 --- a/R/create_fizz.R +++ b/R/create_fizz.R @@ -64,7 +64,7 @@ create_fizz <- function(data, } ## Clean metric name - clean_nm <- gsub(pattern = "_", replacement = " ", x = metric) + clean_nm <- us_to_space(metric) ## Plot data plot_data <- diff --git a/R/create_line.R b/R/create_line.R index 2784a398..83a404ed 100644 --- a/R/create_line.R +++ b/R/create_line.R @@ -67,9 +67,8 @@ create_line <- function(data, hrvar <- "Total" } - ## Clean metric name - clean_nm <- gsub(pattern = "_", replacement = " ", x = metric) + clean_nm <- us_to_space(metric) myTable <- data %>% diff --git a/R/create_period_scatter.R b/R/create_period_scatter.R index 59a8d4c0..d7f33a58 100644 --- a/R/create_period_scatter.R +++ b/R/create_period_scatter.R @@ -143,8 +143,8 @@ create_period_scatter <- function(data, "to", daterange_2_end) - clean_x <- gsub(pattern = "_", replacement = " ", x = metric_x) - clean_y <- gsub(pattern = "_", replacement = " ", x = metric_y) + clean_x <- us_to_space(metric_x) + clean_y <- us_to_space(metric_y) plot_title <- paste(clean_x, "and", clean_y, diff --git a/R/create_scatter.R b/R/create_scatter.R index a2fcd38c..3bf3c1be 100644 --- a/R/create_scatter.R +++ b/R/create_scatter.R @@ -74,9 +74,9 @@ create_scatter <- function(data, filter(n < mingroup) %>% pull(!!sym(hrvar)) - - clean_x <- gsub(pattern = "_", replacement = " ", x = metric_x) - clean_y <- gsub(pattern = "_", replacement = " ", x = metric_y) + ## Clean metric names + clean_x <- us_to_space(metric_x) + clean_y <- us_to_space(metric_y) myTable <- data %>% diff --git a/R/create_trend.R b/R/create_trend.R index a0be338c..167c2b67 100644 --- a/R/create_trend.R +++ b/R/create_trend.R @@ -27,6 +27,9 @@ #' #' @family Flexible #' +#' @examples +#' create_trend(sq_data, metric = "Collaboration_hours", hrvar = "LevelDesignation") +#' #' @return #' Returns a ggplot object by default, where 'plot' is passed in `return`. #' When 'table' is passed, a summary table is returned as a data frame. @@ -56,7 +59,7 @@ create_trend <- function(data, } ## Clean metric name - clean_nm <- gsub(pattern = "_", replacement = " ", x = metric) + clean_nm <- us_to_space(metric) myTable <- data %>% diff --git a/R/flag_extreme.R b/R/flag_extreme.R index 50cc2d67..f27cbc87 100644 --- a/R/flag_extreme.R +++ b/R/flag_extreme.R @@ -54,8 +54,7 @@ flag_extreme <- function(data, ## Clean names for pretty printing - metric_nm <- gsub(pattern = "_", replacement = " ", x = metric) - metric_nm <- camel_clean(metric_nm) + metric_nm <- metric_nm %>% us_to_space() %>% camel_clean(metric_nm) ## Define MessageLevel if(person == TRUE){ diff --git a/R/meetingtype_dist_ca.R b/R/meetingtype_dist_ca.R index 043ada15..3d882d7b 100644 --- a/R/meetingtype_dist_ca.R +++ b/R/meetingtype_dist_ca.R @@ -1,8 +1,8 @@ -#' @title Meeting Type Distribution (Collaboration Assessment Query) +#' @title Meeting Type Distribution (Ways of Working Assessment Query) #' #' @description #' Calculate the hour distribution of internal meeting types, -#' using a Collaboration Assessment Query with core WpA variables as an input. +#' using a Ways of Working Assessment Query with core WpA variables as an input. #' #' @param data Meeting Query data frame. Must contain the variables `Attendee` and `DurationHours` #' @param hrvar Character string to specify the HR attribute to split the data by. @@ -32,8 +32,7 @@ meetingtype_dist_ca <- function(data, mt_dist_str_clean <- mt_dist_str %>% gsub(pattern = "_meeting_hours", replacement = "", x = .) %>% - gsub(pattern = "_", replacement = " ", x = .) - + us_to_space() ## Add dummy "Total" column if hrvar = NULL if(is.null(hrvar)){ @@ -53,7 +52,7 @@ meetingtype_dist_ca <- function(data, mutate(Prop = AttendeeMeetingHours / sum(AttendeeMeetingHours), Percent = paste(round(Prop * 100), "%")) %>% mutate(MeetingType = gsub(pattern = "_meeting_hours", replacement = "", x = MeetingType)) %>% - mutate(MeetingType = gsub(pattern = "_", replacement = " ", x = MeetingType)) + mutate(MeetingType = us_to_space(MeetingType)) ## Only for creating the bottom row data myResultsTableTotal <- @@ -86,7 +85,7 @@ meetingtype_dist_ca <- function(data, mutate(Prop = AttendeeMeetingHours / sum(AttendeeMeetingHours), Percent = paste(round(Prop * 100), "%")) %>% mutate(MeetingType = gsub(pattern = "_meeting_hours", replacement = "", x = MeetingType)) %>% - mutate(MeetingType = gsub(pattern = "_", replacement = " ", x = MeetingType)) + mutate(MeetingType = us_to_space(MeetingType)) outputTable <- myResultsTable %>% diff --git a/R/meetingtype_summary.R b/R/meetingtype_summary.R index d283ac95..478e11fb 100644 --- a/R/meetingtype_summary.R +++ b/R/meetingtype_summary.R @@ -4,7 +4,7 @@ #' This function creates a bar chart showing the percentage of meeting hours which are spent in #' long or large meetings. #' -#' @param data Collaboration Assessment query in the form of a data frame. Requires the following variables: +#' @param data Ways of Working Assessment query in the form of a data frame. Requires the following variables: #' - `Bloated_meeting_hours` #' - `Lengthy_meeting_hours` #' - `Workshop_meeting_hours` @@ -43,7 +43,7 @@ meetingtype_summary <- function(data, summarise_at(vars(mt_dist_str), ~sum(., na.rm = TRUE)) %>% gather(MeetingType, AttendeeMeetingHours, -!!sym(hrvar)) %>% mutate(MeetingType = gsub(pattern = "_meeting_hours", replacement = "", x = MeetingType)) %>% - mutate(MeetingType = gsub(pattern = "_", replacement = " ", x = MeetingType)) %>% + mutate(MeetingType = us_to_space(MeetingType)) %>% group_by(!!sym(hrvar)) %>% mutate(AttendeeMeetingHours = AttendeeMeetingHours / sum(AttendeeMeetingHours)) %>% spread(MeetingType, AttendeeMeetingHours) %>% diff --git a/man/create_trend.Rd b/man/create_trend.Rd index 03aaa854..371edb4e 100644 --- a/man/create_trend.Rd +++ b/man/create_trend.Rd @@ -34,6 +34,10 @@ When 'table' is passed, a summary table is returned as a data frame. Provides a week by week view of a selected Workplace Analytics metric. By default returns a week by week heatmap bar plot, highlighting the points in time with most activity. Additional options available to return a summary table. +} +\examples{ +create_trend(sq_data, metric = "Collaboration_hours", hrvar = "LevelDesignation") + } \seealso{ Other Flexible: From 203c953610b869a22f5dd721e19217a440d5cb5b Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Fri, 11 Dec 2020 13:19:50 +0000 Subject: [PATCH 146/192] chore: add importFrom Explicitly specify grDevices import for heat_colours() --- R/heat_colours.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/heat_colours.R b/R/heat_colours.R index 6aa686c5..9bca258c 100644 --- a/R/heat_colours.R +++ b/R/heat_colours.R @@ -23,7 +23,7 @@ heat_colours <- function (n, alpha, rev = FALSE) { ## Increasingly low value (darker) v <- seq(from = 1, to = 0.8, length.out = n) - cols <- hsv(h = h, s = s, v = v, alpha = alpha) + cols <- grDevices::hsv(h = h, s = s, v = v, alpha = alpha) if(rev){ From 9a4a5d82f1a82826086f30e7a6bf7dbe1cf775f3 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Fri, 11 Dec 2020 14:09:30 +0000 Subject: [PATCH 147/192] chore: pass R CMD checks --- .Rbuildignore | 2 ++ NAMESPACE | 1 + R/globals.R | 5 ++++- R/heat_colours.R | 3 ++- R/network_p2p.R | 3 ++- man/heat_colours.Rd | 3 ++- man/network_p2p.Rd | 2 +- 7 files changed, 14 insertions(+), 5 deletions(-) diff --git a/.Rbuildignore b/.Rbuildignore index 4d9ea800..b3bbdfe3 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -12,6 +12,8 @@ ^\.github$ ^man/figures/wpa cheatsheet_20201116.pdf$ ^man/figures/wpa cheatsheet_20201116.png$ +^man/figures/wpa cheatsheet.pdf$ +^man/figures/wpa cheatsheet.png$ ^vignettes/best-practices.Rmd$ ^vignettes/text-mining-capabilities.Rmd$ ^vignettes/IV-report.Rmd$ diff --git a/NAMESPACE b/NAMESPACE index 8b9f0cf3..c224492b 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -161,6 +161,7 @@ importFrom(data.table,"%like%") importFrom(data.table,":=") importFrom(dplyr,`%>%`) importFrom(dplyr,mutate_if) +importFrom(grDevices,rainbow) importFrom(htmltools,HTML) importFrom(igraph,graph_from_data_frame) importFrom(magrittr,"%>%") diff --git a/R/globals.R b/R/globals.R index 9830519d..37680e15 100644 --- a/R/globals.R +++ b/R/globals.R @@ -184,6 +184,9 @@ utils::globalVariables( "value_rescaled", "values", "cleaned_data", - "zscore" + "zscore", + "StrongTieType", + "TieOrigin_PersonId", + "TieDestination_PersonId" ) ) diff --git a/R/heat_colours.R b/R/heat_colours.R index 9bca258c..a79f124a 100644 --- a/R/heat_colours.R +++ b/R/heat_colours.R @@ -2,7 +2,8 @@ #' Generate a vector of `n` contiguous colours, as a red-yellow-green palette. #' #' @param n the number of colors (≥ 1) to be in the palette. -#' @param alpha an alpha-transparency level in the range [0,1] (0 means transparent and 1 means opaque) +#' @param alpha an alpha-transparency level in the range of 0 to 1 +#' (0 means transparent and 1 means opaque) #' @param rev logical indicating whether the ordering of the colors should be reversed. #' #' @examples diff --git a/R/network_p2p.R b/R/network_p2p.R index ed680247..4b768b5b 100644 --- a/R/network_p2p.R +++ b/R/network_p2p.R @@ -31,6 +31,7 @@ #' @import ggplot2 #' @import dplyr #' @import network +#' @importFrom grDevices rainbow #' #' @export network_p2p <- function(data, @@ -40,7 +41,7 @@ network_p2p <- function(data, bg_fill = "#000000", font_col = "#FFFFFF", legend_pos = "bottom", - palette = rainbow, + palette = "rainbow", ...){ data <- diff --git a/man/heat_colours.Rd b/man/heat_colours.Rd index 154b9533..85c461f0 100644 --- a/man/heat_colours.Rd +++ b/man/heat_colours.Rd @@ -9,7 +9,8 @@ heat_colours(n, alpha, rev = FALSE) \arguments{ \item{n}{the number of colors (≥ 1) to be in the palette.} -\item{alpha}{an alpha-transparency level in the range \link{0,1} (0 means transparent and 1 means opaque)} +\item{alpha}{an alpha-transparency level in the range of 0 to 1 +(0 means transparent and 1 means opaque)} \item{rev}{logical indicating whether the ordering of the colors should be reversed.} } diff --git a/man/network_p2p.Rd b/man/network_p2p.Rd index 30b0b92a..8e552f83 100644 --- a/man/network_p2p.Rd +++ b/man/network_p2p.Rd @@ -12,7 +12,7 @@ network_p2p( bg_fill = "#000000", font_col = "#FFFFFF", legend_pos = "bottom", - palette = rainbow, + palette = "rainbow", ... ) } From 6cfae23658553696d81782858eef0c477141d689 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Sat, 12 Dec 2020 13:43:59 +0000 Subject: [PATCH 148/192] docs: update query references (#20) --- R/IV_report.R | 4 ++-- R/coaching_report.R | 4 ++-- R/collaboration_line.R | 2 +- R/collaboration_report.R | 4 ++-- R/collaboration_sum.R | 2 +- R/connectivity_report.R | 4 ++-- R/create_period_scatter.R | 2 +- R/create_stacked.R | 2 +- R/create_trend.R | 2 +- R/dv_data.R | 4 ++-- R/flag_extreme.R | 2 +- R/hr_trend.R | 2 +- R/hrvar_count.R | 2 +- R/hrvar_count_all.R | 2 +- R/identify_holidayweeks.R | 2 +- R/identify_inactiveweeks.R | 2 +- R/identify_nkw.R | 2 +- R/identify_outlier.R | 2 +- R/identify_privacythreshold.R | 2 +- R/identify_tenure.R | 2 +- R/keymetrics_scan.R | 4 ++-- R/mgrcoatt_dist.R | 2 +- R/mgrrel_matrix.R | 2 +- R/remove_outliers.R | 2 +- R/sq_data.R | 4 ++-- R/validation_report.R | 2 +- R/workloads_line.R | 2 +- man/IV_report.Rd | 4 ++-- man/afterhours_trend.Rd | 2 +- man/coaching_report.Rd | 4 ++-- man/collaboration_line.Rd | 2 +- man/collaboration_report.Rd | 4 ++-- man/collaboration_sum.Rd | 2 +- man/collaboration_trend.Rd | 2 +- man/connectivity_report.Rd | 4 ++-- man/create_period_scatter.Rd | 2 +- man/create_stacked.Rd | 2 +- man/create_trend.Rd | 2 +- man/dv_data.Rd | 4 ++-- man/email_trend.Rd | 2 +- man/flag_extreme.Rd | 2 +- man/hr_trend.Rd | 2 +- man/hrvar_count.Rd | 2 +- man/hrvar_count_all.Rd | 2 +- man/identify_holidayweeks.Rd | 2 +- man/identify_inactiveweeks.Rd | 2 +- man/identify_nkw.Rd | 2 +- man/identify_outlier.Rd | 2 +- man/identify_privacythreshold.Rd | 2 +- man/identify_tenure.Rd | 2 +- man/keymetrics_scan.Rd | 4 ++-- man/meeting_trend.Rd | 2 +- man/mgrcoatt_dist.Rd | 2 +- man/mgrrel_matrix.Rd | 2 +- man/one2one_trend.Rd | 2 +- man/remove_outliers.Rd | 2 +- man/sq_data.Rd | 4 ++-- man/validation_report.Rd | 2 +- man/workloads_line.Rd | 2 +- man/workloads_trend.Rd | 2 +- 60 files changed, 74 insertions(+), 74 deletions(-) diff --git a/R/IV_report.R b/R/IV_report.R index 1536d9ac..41d718c6 100644 --- a/R/IV_report.R +++ b/R/IV_report.R @@ -7,9 +7,9 @@ #' #' @description #' The function generates an interactive HTML report using -#' Standard Query data as an input. The report contains a full Information Value analysis, a data exploration technique that helps determine which columns in a data set have predictive power or influence on the value of a specified dependent variable. +#' Standard Person Query data as an input. The report contains a full Information Value analysis, a data exploration technique that helps determine which columns in a data set have predictive power or influence on the value of a specified dependent variable. #' -#' @param data A Standard Query dataset in the form of a data frame. +#' @param data A Standard Person Query dataset in the form of a data frame. #' @param predictors A character vector specifying the columns to be used as predictors. #' Defaults to NULL, where all numeric vectors in the data will be used as predictors. #' @param outcome A string specifying a binary variable, i.e. can only contain diff --git a/R/coaching_report.R b/R/coaching_report.R index ee5a92b5..67cb45c9 100644 --- a/R/coaching_report.R +++ b/R/coaching_report.R @@ -7,12 +7,12 @@ #' #' @description #' The function generates an interactive HTML report using -#' Standard Query data as an input. The report contains a series +#' Standard Person Query data as an input. The report contains a series #' of summary analysis and visualisations relating to key **coaching** #' metrics in Workplace Analytics, specifically relating to the time #' spent between managers and their direct reports. #' -#' @param data A Standard Query dataset in the form of a data frame. +#' @param data A Standard Person Query dataset in the form of a data frame. #' @param hrvar HR Variable by which to split metrics, defaults to "LevelDesignation" #' but accepts any character vector, e.g. "LevelDesignation" #' @param mingroup Numeric value setting the privacy threshold / minimum group size. Defaults to 5. diff --git a/R/collaboration_line.R b/R/collaboration_line.R index d711c4c7..38e332c3 100644 --- a/R/collaboration_line.R +++ b/R/collaboration_line.R @@ -14,7 +14,7 @@ #' @details #' Uses the metric `Collaboration_hours`. #' -#' @param data A Standard Query dataset in the form of a data frame. +#' @param data A Standard Person Query dataset in the form of a data frame. #' @param hrvar HR Variable by which to split metrics, defaults to "Organization" #' but accepts any character vector, e.g. "LevelDesignation" #' @param mingroup Numeric value setting the privacy threshold / minimum group size. Defaults to 5. diff --git a/R/collaboration_report.R b/R/collaboration_report.R index d48ef86f..1921f97e 100644 --- a/R/collaboration_report.R +++ b/R/collaboration_report.R @@ -7,11 +7,11 @@ #' #' @description #' The function generates an interactive HTML report using -#' Standard Query data as an input. The report contains a series +#' Standard Person Query data as an input. The report contains a series #' of summary analysis and visualisations relating to key **collaboration** #' metrics in Workplace Analytics,including email and meeting hours. #' -#' @param data A Standard Query dataset in the form of a data frame. +#' @param data A Standard Person Query dataset in the form of a data frame. #' @param hrvar HR Variable by which to split metrics, defaults to "Organization" #' but accepts any character vector, e.g. "LevelDesignation" #' @param mingroup Numeric value setting the privacy threshold / minimum group size. Defaults to 5. diff --git a/R/collaboration_sum.R b/R/collaboration_sum.R index 371eceb2..2cd43616 100644 --- a/R/collaboration_sum.R +++ b/R/collaboration_sum.R @@ -14,7 +14,7 @@ #' Uses the metrics `Meeting_hours`, `Email_hours`, `Unscheduled_Call_hours`, #' and `Instant_Message_hours`. #' -#' @param data A Standard Query dataset in the form of a data frame. +#' @param data A Standard Person Query dataset in the form of a data frame. #' @param hrvar HR Variable by which to split metrics, defaults to "Organization" but accepts any character vector, e.g. "LevelDesignation" #' @param mingroup Numeric value setting the privacy threshold / minimum group size. Defaults to 5. #' @param return Character vector specifying what to return, defaults to "plot". diff --git a/R/connectivity_report.R b/R/connectivity_report.R index dfc4c24f..40cfbb45 100644 --- a/R/connectivity_report.R +++ b/R/connectivity_report.R @@ -7,12 +7,12 @@ #' #' @description #' The function generates an interactive HTML report using -#' Standard Query data as an input. The report contains a series +#' Standard Person Query data as an input. The report contains a series #' of summary analysis and visualisations relating to key **connectivity** #' metrics in Workplace Analytics, including external/internal network size #' vs breadth. #' -#' @param data A Standard Query dataset in the form of a data frame. +#' @param data A Standard Person Query dataset in the form of a data frame. #' @param hrvar HR Variable by which to split metrics, defaults to "Organization" #' but accepts any character vector, e.g. "LevelDesignation" #' @param mingroup Numeric value setting the privacy threshold / minimum group size. Defaults to 5. diff --git a/R/create_period_scatter.R b/R/create_period_scatter.R index d7f33a58..e82e1136 100644 --- a/R/create_period_scatter.R +++ b/R/create_period_scatter.R @@ -15,7 +15,7 @@ #' This is a general purpose function that powers all the functions #' in the package that produce faceted scatter plots. #' -#' @param data A Standard Query dataset in the form of a data frame. +#' @param data A Standard Person Query dataset in the form of a data frame. #' @param hrvar HR Variable by which to split metrics. Accepts a character vector, #' defaults to "Organization" but accepts any character vector, e.g. "LevelDesignation" #' @param metric_x Character string containing the name of the metric, diff --git a/R/create_stacked.R b/R/create_stacked.R index f86d17ca..719c8064 100644 --- a/R/create_stacked.R +++ b/R/create_stacked.R @@ -12,7 +12,7 @@ #' Returns a stacked bar plot by default. #' Additional options available to return a summary table. #' -#' @param data A Standard Query dataset in the form of a data frame. +#' @param data A Standard Person Query dataset in the form of a data frame. #' @param metrics A character vector to specify variables to be used #' in calculating the "Total" value, e.g. c("Meeting_hours", "Email_hours"). #' The order of the variable names supplied determine the order in which they diff --git a/R/create_trend.R b/R/create_trend.R index 167c2b67..577ce0d7 100644 --- a/R/create_trend.R +++ b/R/create_trend.R @@ -11,7 +11,7 @@ #' Additional options available to return a summary table. #' #' -#' @param data A Standard Query dataset in the form of a data frame. +#' @param data A Standard Person Query dataset in the form of a data frame. #' @param metric Character string containing the name of the metric, #' e.g. "Collaboration_hours" #' @param hrvar HR Variable by which to split metrics, defaults to "Organization" diff --git a/R/dv_data.R b/R/dv_data.R index f5aafe51..4cd26f21 100644 --- a/R/dv_data.R +++ b/R/dv_data.R @@ -3,10 +3,10 @@ # Licensed under the MIT License. See LICENSE.txt in the project root for license information. # -------------------------------------------------------------------------------------------- -#' @title Sample Standard Query dataset for Data Validation +#' @title Sample Standard Person Query dataset for Data Validation #' #' @description -#' A dataset generated from a Standard Query from WpA. +#' A dataset generated from a Standard Person Query from WpA. #' #' @format A data frame with 897 rows and 69 variables: #' \describe{ diff --git a/R/flag_extreme.R b/R/flag_extreme.R index f27cbc87..b642ba28 100644 --- a/R/flag_extreme.R +++ b/R/flag_extreme.R @@ -9,7 +9,7 @@ #' This is used as part of data validation to check if there are extreme values #' in the dataset. #' -#' @param data A Standard Query dataset in the form of a data frame. +#' @param data A Standard Person Query dataset in the form of a data frame. #' @param metric A character string specifying the metric to test. #' @param person A logical value to specify whether to calculate person-averages. #' Defaults to TRUE (person-averages calculated). diff --git a/R/hr_trend.R b/R/hr_trend.R index c7125b8e..1006ce25 100644 --- a/R/hr_trend.R +++ b/R/hr_trend.R @@ -9,7 +9,7 @@ #' employee count over time. Part of a data validation process to check #' for unusual license growth / declines over time. #' -#' @param data A Standard Query dataset in the form of a data frame. +#' @param data A Standard Person Query dataset in the form of a data frame. #' @param return Character vector specifying what to return, defaults to "plot". #' Valid inputs are "plot" and "table". #' diff --git a/R/hrvar_count.R b/R/hrvar_count.R index bb93cc0d..4523c671 100644 --- a/R/hrvar_count.R +++ b/R/hrvar_count.R @@ -10,7 +10,7 @@ #' by the specified HR attribute.The default behaviour is to return a #' bar chart as typically seen in 'Analysis Scope'. #' -#' @param data A Standard Query dataset in the form of a data frame. +#' @param data A Standard Person Query dataset in the form of a data frame. #' @param hrvar HR Variable by which to split metrics, defaults to "Organization" #' but accepts any character vector, e.g. "LevelDesignation". #' If a vector with more than one value is provided, the HR attributes are automatically diff --git a/R/hrvar_count_all.R b/R/hrvar_count_all.R index dcab54d7..7f7cafd9 100644 --- a/R/hrvar_count_all.R +++ b/R/hrvar_count_all.R @@ -12,7 +12,7 @@ #' employees with NAs for that attribute. #' See hrvar_count function for more detail on the specific HR attribute of interest. #' -#' @param data A Standard Query dataset in the form of a data frame. +#' @param data A Standard Person Query dataset in the form of a data frame. #' @param n_var number of HR variables to include in report as rows. Default is set to 10 HR variables. #' @param return String to specify what to return #' @param threshold The max number of unique values allowed for any attribute. Default is 100. diff --git a/R/identify_holidayweeks.R b/R/identify_holidayweeks.R index 69bef91f..d07e4d97 100644 --- a/R/identify_holidayweeks.R +++ b/R/identify_holidayweeks.R @@ -12,7 +12,7 @@ #' #' As best practice, run this function prior to any analysis to remove atypical collaboration weeks from your dataset. #' -#' @param data A Standard Query dataset in the form of a data frame. +#' @param data A Standard Person Query dataset in the form of a data frame. #' @param sd The standard deviation below the mean for collaboration hours that should define an outlier week. Enter a positive number. Default is 1 standard deviation. #' @param return String to specify what to return #' diff --git a/R/identify_inactiveweeks.R b/R/identify_inactiveweeks.R index cf7f8f50..d24b1455 100644 --- a/R/identify_inactiveweeks.R +++ b/R/identify_inactiveweeks.R @@ -11,7 +11,7 @@ #' #' As best practice, run this function prior to any analysis to remove atypical collaboration weeks from your dataset. #' -#' @param data A Standard Query dataset in the form of a data frame. +#' @param data A Standard Person Query dataset in the form of a data frame. #' @param sd The standard deviation below the mean for collaboration hours that should define an outlier week. Enter a positive number. Default is 1 standard deviation. #' @param return String to specify what to return. #' diff --git a/R/identify_nkw.R b/R/identify_nkw.R index 1597147b..16afe5dd 100644 --- a/R/identify_nkw.R +++ b/R/identify_nkw.R @@ -9,7 +9,7 @@ #' This function scans a standard query output to identify employees with consistently low collaboration signals. #' Returns the % of non-knowledge workers identified by Organization, and optionally an edited dataframe with non-knowledge workers removed, or the full dataframe with the kw/nkw flag added. #' -#' @param data A Standard Query dataset in the form of a data frame. +#' @param data A Standard Person Query dataset in the form of a data frame. #' @param collab_threshold The collaboration hours threshold that should be exceeded as an average for the entire analysis period for the employee to be categorized as a knowledge worker ("kw"). #' Enter a positive number. Default is set to 5 collaboration hours. #' @param return String to specify what to return diff --git a/R/identify_outlier.R b/R/identify_outlier.R index 7b793b0b..d76b3304 100644 --- a/R/identify_outlier.R +++ b/R/identify_outlier.R @@ -12,7 +12,7 @@ #' Time as a grouping variable can be overridden with the `group_var` #' argument. #' -#' @param data A Standard Query dataset in the form of a data frame. +#' @param data A Standard Person Query dataset in the form of a data frame. #' @param group_var A string with the name of the grouping variable. #' Defaults to `Date`. #' @param metric Character string containing the name of the metric, diff --git a/R/identify_privacythreshold.R b/R/identify_privacythreshold.R index 9a8b519f..98127694 100644 --- a/R/identify_privacythreshold.R +++ b/R/identify_privacythreshold.R @@ -9,7 +9,7 @@ #' This function scans a standard query output for groups with of employees under the privacy threshold. #' The method consists in reviewing each individual HR attribute, and count the distinct people within each group. #' -#' @param data A Standard Query dataset in the form of a data frame. +#' @param data A Standard Person Query dataset in the form of a data frame. #' @param hrvar A list of HR Variables to consider in the scan. #' Defaults to all HR attributes identified. #' @param mingroup Numeric value setting the privacy threshold / minimum group size. diff --git a/R/identify_tenure.R b/R/identify_tenure.R index 545cca01..3c9ddadc 100644 --- a/R/identify_tenure.R +++ b/R/identify_tenure.R @@ -12,7 +12,7 @@ #' #' @family Data Validation #' -#' @param data A Standard Query dataset in the form of a data frame. +#' @param data A Standard Person Query dataset in the form of a data frame. #' @param end_date A string specifying the name of the date variable representing the latest date. Defaults to "Date". #' @param beg_date A string specifying the name of the date variable representing the hire date. Defaults to "HireDate". #' @param maxten A numeric value representing the maximum tenure. diff --git a/R/keymetrics_scan.R b/R/keymetrics_scan.R index 6b71a5ce..36170a66 100644 --- a/R/keymetrics_scan.R +++ b/R/keymetrics_scan.R @@ -3,12 +3,12 @@ # Licensed under the MIT License. See LICENSE.txt in the project root for license information. # -------------------------------------------------------------------------------------------- -#' @title Run a summary of Key Metrics from the Standard Query data +#' @title Run a summary of Key Metrics from the Standard Person Query data #' #' @description #' Returns a heatmapped table by default, with options to return a table. #' -#' @param data A Standard Query dataset in the form of a data frame. +#' @param data A Standard Person Query dataset in the form of a data frame. #' @param hrvar HR Variable by which to split metrics. Accepts a character vector, e.g. "Organization" #' @param mingroup Numeric value setting the privacy threshold / minimum group size. Defaults to 5. #' @param metrics A character vector containing the variable names to calculate averages of. diff --git a/R/mgrcoatt_dist.R b/R/mgrcoatt_dist.R index e01b1949..45a7e1d2 100644 --- a/R/mgrcoatt_dist.R +++ b/R/mgrcoatt_dist.R @@ -10,7 +10,7 @@ #' Returns a stacked bar plot of different buckets of coattendance. #' Additional options available to return a table with distribution elements. #' -#' @param data A Standard Query dataset in the form of a data frame. +#' @param data A Standard Person Query dataset in the form of a data frame. #' @param hrvar HR Variable by which to split metrics. Accepts a character vector, defaults to "Organization" but accepts any character vector, e.g. "LevelDesignation" #' @param mingroup Numeric value setting the privacy threshold / minimum group size, defaults to 5. #' @param return Character vector specifying what to return, defaults to "plot". diff --git a/R/mgrrel_matrix.R b/R/mgrrel_matrix.R index 8d088534..e9bc0ce2 100644 --- a/R/mgrrel_matrix.R +++ b/R/mgrrel_matrix.R @@ -9,7 +9,7 @@ #' Generate the Manager-Relationship 2x2 matrix, returning a ggplot object by default. #' Additional options available to return a "wide" or "long" summary table. #' -#' @param data Standard Query data to pass through. Accepts a data frame. +#' @param data Standard Person Query data to pass through. Accepts a data frame. #' @param hrvar HR Variable by which to split metrics. Accepts a character vector, #' e.g. "Organization". Defaults to NULL. #' @param return A character vector specifying whether to return a matrix plot or a table. diff --git a/R/remove_outliers.R b/R/remove_outliers.R index 9a5fefbd..71afe10a 100644 --- a/R/remove_outliers.R +++ b/R/remove_outliers.R @@ -21,7 +21,7 @@ #' - `identify_nkw()` #' - `identify_inactiveweeks` #' -#' @param data A Standard Query dataset in the form of a data frame. +#' @param data A Standard Person Query dataset in the form of a data frame. #' @param metric Character string containing the name of the metric, #' e.g. "Collaboration_hours" #' diff --git a/R/sq_data.R b/R/sq_data.R index ce0fc423..f39a53db 100644 --- a/R/sq_data.R +++ b/R/sq_data.R @@ -3,10 +3,10 @@ # Licensed under the MIT License. See LICENSE.txt in the project root for license information. # -------------------------------------------------------------------------------------------- -#' @title Sample Standard Query dataset +#' @title Sample Standard Person Query dataset #' #' @description -#' A dataset generated from a Standard Query from WpA. +#' A dataset generated from a Standard Person Query from WpA. #' #' @format A data frame with 13442 rows and 66 variables: #' \describe{ diff --git a/R/validation_report.R b/R/validation_report.R index e00f459f..d56a216e 100644 --- a/R/validation_report.R +++ b/R/validation_report.R @@ -7,7 +7,7 @@ #' #' @description #' The function generates an interactive HTML report using -#' Standard Query data as an input. The report contains a checks on +#' Standard Person Query data as an input. The report contains a checks on #' Workplace Analytics query outputs, to provide diagnostic information #' for the Analyst pre-analysis. #' diff --git a/R/workloads_line.R b/R/workloads_line.R index ad129f98..0c880df2 100644 --- a/R/workloads_line.R +++ b/R/workloads_line.R @@ -11,7 +11,7 @@ #' with a separate panel per value in the HR attribute. #' Additional options available to return a summary table. #' -#' @param data A Standard Query dataset in the form of a data frame. +#' @param data A Standard Person Query dataset in the form of a data frame. #' @param hrvar HR Variable by which to split metrics, defaults to "Organization" #' but accepts any character vector, e.g. "LevelDesignation" #' @param mingroup Numeric value setting the privacy threshold / minimum group size. Defaults to 5. diff --git a/man/IV_report.Rd b/man/IV_report.Rd index d45b0a52..f4852ac0 100644 --- a/man/IV_report.Rd +++ b/man/IV_report.Rd @@ -14,7 +14,7 @@ IV_report( ) } \arguments{ -\item{data}{A Standard Query dataset in the form of a data frame.} +\item{data}{A Standard Person Query dataset in the form of a data frame.} \item{predictors}{A character vector specifying the columns to be used as predictors. Defaults to NULL, where all numeric vectors in the data will be used as predictors.} @@ -32,7 +32,7 @@ Defaults to TRUE.} } \description{ The function generates an interactive HTML report using -Standard Query data as an input. The report contains a full Information Value analysis, a data exploration technique that helps determine which columns in a data set have predictive power or influence on the value of a specified dependent variable. +Standard Person Query data as an input. The report contains a full Information Value analysis, a data exploration technique that helps determine which columns in a data set have predictive power or influence on the value of a specified dependent variable. } \examples{ \dontrun{ diff --git a/man/afterhours_trend.Rd b/man/afterhours_trend.Rd index c3161ddd..72d5b8e2 100644 --- a/man/afterhours_trend.Rd +++ b/man/afterhours_trend.Rd @@ -7,7 +7,7 @@ afterhours_trend(data, hrvar = "Organization", mingroup = 5, return = "plot") } \arguments{ -\item{data}{A Standard Query dataset in the form of a data frame.} +\item{data}{A Standard Person Query dataset in the form of a data frame.} \item{hrvar}{HR Variable by which to split metrics, defaults to "Organization" but accepts any character vector, e.g. "LevelDesignation"} diff --git a/man/coaching_report.Rd b/man/coaching_report.Rd index 330893c8..456bbafe 100644 --- a/man/coaching_report.Rd +++ b/man/coaching_report.Rd @@ -13,7 +13,7 @@ coaching_report( ) } \arguments{ -\item{data}{A Standard Query dataset in the form of a data frame.} +\item{data}{A Standard Person Query dataset in the form of a data frame.} \item{hrvar}{HR Variable by which to split metrics, defaults to "LevelDesignation" but accepts any character vector, e.g. "LevelDesignation"} @@ -28,7 +28,7 @@ Defaults to TRUE.} } \description{ The function generates an interactive HTML report using -Standard Query data as an input. The report contains a series +Standard Person Query data as an input. The report contains a series of summary analysis and visualisations relating to key \strong{coaching} metrics in Workplace Analytics, specifically relating to the time spent between managers and their direct reports. diff --git a/man/collaboration_line.Rd b/man/collaboration_line.Rd index 2e294975..c64410f8 100644 --- a/man/collaboration_line.Rd +++ b/man/collaboration_line.Rd @@ -10,7 +10,7 @@ collaboration_line(data, hrvar = "Organization", mingroup = 5, return = "plot") collab_line(data, hrvar = "Organization", mingroup = 5, return = "plot") } \arguments{ -\item{data}{A Standard Query dataset in the form of a data frame.} +\item{data}{A Standard Person Query dataset in the form of a data frame.} \item{hrvar}{HR Variable by which to split metrics, defaults to "Organization" but accepts any character vector, e.g. "LevelDesignation"} diff --git a/man/collaboration_report.Rd b/man/collaboration_report.Rd index c294a8b8..8e755d38 100644 --- a/man/collaboration_report.Rd +++ b/man/collaboration_report.Rd @@ -13,7 +13,7 @@ collaboration_report( ) } \arguments{ -\item{data}{A Standard Query dataset in the form of a data frame.} +\item{data}{A Standard Person Query dataset in the form of a data frame.} \item{hrvar}{HR Variable by which to split metrics, defaults to "Organization" but accepts any character vector, e.g. "LevelDesignation"} @@ -28,7 +28,7 @@ Defaults to TRUE.} } \description{ The function generates an interactive HTML report using -Standard Query data as an input. The report contains a series +Standard Person Query data as an input. The report contains a series of summary analysis and visualisations relating to key \strong{collaboration} metrics in Workplace Analytics,including email and meeting hours. } diff --git a/man/collaboration_sum.Rd b/man/collaboration_sum.Rd index 8a9ac58f..5e095deb 100644 --- a/man/collaboration_sum.Rd +++ b/man/collaboration_sum.Rd @@ -21,7 +21,7 @@ collaboration_summary( collab_summary(data, hrvar = "Organization", mingroup = 5, return = "plot") } \arguments{ -\item{data}{A Standard Query dataset in the form of a data frame.} +\item{data}{A Standard Person Query dataset in the form of a data frame.} \item{hrvar}{HR Variable by which to split metrics, defaults to "Organization" but accepts any character vector, e.g. "LevelDesignation"} diff --git a/man/collaboration_trend.Rd b/man/collaboration_trend.Rd index 604d712c..2a8fdca3 100644 --- a/man/collaboration_trend.Rd +++ b/man/collaboration_trend.Rd @@ -12,7 +12,7 @@ collaboration_trend( ) } \arguments{ -\item{data}{A Standard Query dataset in the form of a data frame.} +\item{data}{A Standard Person Query dataset in the form of a data frame.} \item{hrvar}{HR Variable by which to split metrics, defaults to "Organization" but accepts any character vector, e.g. "LevelDesignation"} diff --git a/man/connectivity_report.Rd b/man/connectivity_report.Rd index 41aeaef3..2abd3370 100644 --- a/man/connectivity_report.Rd +++ b/man/connectivity_report.Rd @@ -13,7 +13,7 @@ connectivity_report( ) } \arguments{ -\item{data}{A Standard Query dataset in the form of a data frame.} +\item{data}{A Standard Person Query dataset in the form of a data frame.} \item{hrvar}{HR Variable by which to split metrics, defaults to "Organization" but accepts any character vector, e.g. "LevelDesignation"} @@ -28,7 +28,7 @@ Defaults to TRUE.} } \description{ The function generates an interactive HTML report using -Standard Query data as an input. The report contains a series +Standard Person Query data as an input. The report contains a series of summary analysis and visualisations relating to key \strong{connectivity} metrics in Workplace Analytics, including external/internal network size vs breadth. diff --git a/man/create_period_scatter.Rd b/man/create_period_scatter.Rd index f6061c65..8ee9d9d8 100644 --- a/man/create_period_scatter.Rd +++ b/man/create_period_scatter.Rd @@ -20,7 +20,7 @@ create_period_scatter( ) } \arguments{ -\item{data}{A Standard Query dataset in the form of a data frame.} +\item{data}{A Standard Person Query dataset in the form of a data frame.} \item{hrvar}{HR Variable by which to split metrics. Accepts a character vector, defaults to "Organization" but accepts any character vector, e.g. "LevelDesignation"} diff --git a/man/create_stacked.Rd b/man/create_stacked.Rd index 5f66c1b7..bfeeb36f 100644 --- a/man/create_stacked.Rd +++ b/man/create_stacked.Rd @@ -16,7 +16,7 @@ create_stacked( ) } \arguments{ -\item{data}{A Standard Query dataset in the form of a data frame.} +\item{data}{A Standard Person Query dataset in the form of a data frame.} \item{hrvar}{HR Variable by which to split metrics, defaults to "Organization" but accepts any character vector, e.g. "LevelDesignation"} diff --git a/man/create_trend.Rd b/man/create_trend.Rd index 371edb4e..8134f2d6 100644 --- a/man/create_trend.Rd +++ b/man/create_trend.Rd @@ -13,7 +13,7 @@ create_trend( ) } \arguments{ -\item{data}{A Standard Query dataset in the form of a data frame.} +\item{data}{A Standard Person Query dataset in the form of a data frame.} \item{metric}{Character string containing the name of the metric, e.g. "Collaboration_hours"} diff --git a/man/dv_data.Rd b/man/dv_data.Rd index b5965eec..a7de176f 100644 --- a/man/dv_data.Rd +++ b/man/dv_data.Rd @@ -3,7 +3,7 @@ \docType{data} \name{dv_data} \alias{dv_data} -\title{Sample Standard Query dataset for Data Validation} +\title{Sample Standard Person Query dataset for Data Validation} \format{ A data frame with 897 rows and 69 variables: \describe{ @@ -87,6 +87,6 @@ A data frame with 897 rows and 69 variables: dv_data } \description{ -A dataset generated from a Standard Query from WpA. +A dataset generated from a Standard Person Query from WpA. } \keyword{datasets} diff --git a/man/email_trend.Rd b/man/email_trend.Rd index fe5646d3..08c556e6 100644 --- a/man/email_trend.Rd +++ b/man/email_trend.Rd @@ -7,7 +7,7 @@ email_trend(data, hrvar = "Organization", mingroup = 5, return = "plot") } \arguments{ -\item{data}{A Standard Query dataset in the form of a data frame.} +\item{data}{A Standard Person Query dataset in the form of a data frame.} \item{hrvar}{HR Variable by which to split metrics, defaults to "Organization" but accepts any character vector, e.g. "LevelDesignation"} diff --git a/man/flag_extreme.Rd b/man/flag_extreme.Rd index 3f162184..4a15e11b 100644 --- a/man/flag_extreme.Rd +++ b/man/flag_extreme.Rd @@ -7,7 +7,7 @@ flag_extreme(data, metric, person = TRUE, threshold, return = "message") } \arguments{ -\item{data}{A Standard Query dataset in the form of a data frame.} +\item{data}{A Standard Person Query dataset in the form of a data frame.} \item{metric}{A character string specifying the metric to test.} diff --git a/man/hr_trend.Rd b/man/hr_trend.Rd index f7037157..411bad4b 100644 --- a/man/hr_trend.Rd +++ b/man/hr_trend.Rd @@ -7,7 +7,7 @@ hr_trend(data, return = "plot") } \arguments{ -\item{data}{A Standard Query dataset in the form of a data frame.} +\item{data}{A Standard Person Query dataset in the form of a data frame.} \item{return}{Character vector specifying what to return, defaults to "plot". Valid inputs are "plot" and "table".} diff --git a/man/hrvar_count.Rd b/man/hrvar_count.Rd index dabbd4cf..8ebb5088 100644 --- a/man/hrvar_count.Rd +++ b/man/hrvar_count.Rd @@ -10,7 +10,7 @@ hrvar_count(data, hrvar = "Organization", return = "plot") analysis_scope(data, hrvar = "Organization", return = "plot") } \arguments{ -\item{data}{A Standard Query dataset in the form of a data frame.} +\item{data}{A Standard Person Query dataset in the form of a data frame.} \item{hrvar}{HR Variable by which to split metrics, defaults to "Organization" but accepts any character vector, e.g. "LevelDesignation". diff --git a/man/hrvar_count_all.Rd b/man/hrvar_count_all.Rd index ddbfa9fe..656109eb 100644 --- a/man/hrvar_count_all.Rd +++ b/man/hrvar_count_all.Rd @@ -13,7 +13,7 @@ hrvar_count_all( ) } \arguments{ -\item{data}{A Standard Query dataset in the form of a data frame.} +\item{data}{A Standard Person Query dataset in the form of a data frame.} \item{n_var}{number of HR variables to include in report as rows. Default is set to 10 HR variables.} diff --git a/man/identify_holidayweeks.Rd b/man/identify_holidayweeks.Rd index 31967a1b..a95f7415 100644 --- a/man/identify_holidayweeks.Rd +++ b/man/identify_holidayweeks.Rd @@ -7,7 +7,7 @@ identify_holidayweeks(data, sd = 1, return = "message") } \arguments{ -\item{data}{A Standard Query dataset in the form of a data frame.} +\item{data}{A Standard Person Query dataset in the form of a data frame.} \item{sd}{The standard deviation below the mean for collaboration hours that should define an outlier week. Enter a positive number. Default is 1 standard deviation.} diff --git a/man/identify_inactiveweeks.Rd b/man/identify_inactiveweeks.Rd index 13d66d31..05785e68 100644 --- a/man/identify_inactiveweeks.Rd +++ b/man/identify_inactiveweeks.Rd @@ -7,7 +7,7 @@ identify_inactiveweeks(data, sd = 2, return = "text") } \arguments{ -\item{data}{A Standard Query dataset in the form of a data frame.} +\item{data}{A Standard Person Query dataset in the form of a data frame.} \item{sd}{The standard deviation below the mean for collaboration hours that should define an outlier week. Enter a positive number. Default is 1 standard deviation.} diff --git a/man/identify_nkw.Rd b/man/identify_nkw.Rd index 8d3a6347..15cf322c 100644 --- a/man/identify_nkw.Rd +++ b/man/identify_nkw.Rd @@ -7,7 +7,7 @@ identify_nkw(data, collab_threshold = 5, return = "data_summary") } \arguments{ -\item{data}{A Standard Query dataset in the form of a data frame.} +\item{data}{A Standard Person Query dataset in the form of a data frame.} \item{collab_threshold}{The collaboration hours threshold that should be exceeded as an average for the entire analysis period for the employee to be categorized as a knowledge worker ("kw"). Enter a positive number. Default is set to 5 collaboration hours.} diff --git a/man/identify_outlier.Rd b/man/identify_outlier.Rd index 0d7f5f6f..eda25f69 100644 --- a/man/identify_outlier.Rd +++ b/man/identify_outlier.Rd @@ -7,7 +7,7 @@ identify_outlier(data, group_var = "Date", metric = "Collaboration_hours") } \arguments{ -\item{data}{A Standard Query dataset in the form of a data frame.} +\item{data}{A Standard Person Query dataset in the form of a data frame.} \item{group_var}{A string with the name of the grouping variable. Defaults to \code{Date}.} diff --git a/man/identify_privacythreshold.Rd b/man/identify_privacythreshold.Rd index 9d8ec815..301c67eb 100644 --- a/man/identify_privacythreshold.Rd +++ b/man/identify_privacythreshold.Rd @@ -12,7 +12,7 @@ identify_privacythreshold( ) } \arguments{ -\item{data}{A Standard Query dataset in the form of a data frame.} +\item{data}{A Standard Person Query dataset in the form of a data frame.} \item{hrvar}{A list of HR Variables to consider in the scan. Defaults to all HR attributes identified.} diff --git a/man/identify_tenure.Rd b/man/identify_tenure.Rd index 3cabeed2..b390fdfe 100644 --- a/man/identify_tenure.Rd +++ b/man/identify_tenure.Rd @@ -13,7 +13,7 @@ identify_tenure( ) } \arguments{ -\item{data}{A Standard Query dataset in the form of a data frame.} +\item{data}{A Standard Person Query dataset in the form of a data frame.} \item{end_date}{A string specifying the name of the date variable representing the latest date. Defaults to "Date".} diff --git a/man/keymetrics_scan.Rd b/man/keymetrics_scan.Rd index 3dcb3e98..bc37afdc 100644 --- a/man/keymetrics_scan.Rd +++ b/man/keymetrics_scan.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/keymetrics_scan.R \name{keymetrics_scan} \alias{keymetrics_scan} -\title{Run a summary of Key Metrics from the Standard Query data} +\title{Run a summary of Key Metrics from the Standard Person Query data} \usage{ keymetrics_scan( data, @@ -20,7 +20,7 @@ keymetrics_scan( ) } \arguments{ -\item{data}{A Standard Query dataset in the form of a data frame.} +\item{data}{A Standard Person Query dataset in the form of a data frame.} \item{hrvar}{HR Variable by which to split metrics. Accepts a character vector, e.g. "Organization"} diff --git a/man/meeting_trend.Rd b/man/meeting_trend.Rd index 7a49b50b..2a18cb76 100644 --- a/man/meeting_trend.Rd +++ b/man/meeting_trend.Rd @@ -7,7 +7,7 @@ meeting_trend(data, hrvar = "Organization", mingroup = 5, return = "plot") } \arguments{ -\item{data}{A Standard Query dataset in the form of a data frame.} +\item{data}{A Standard Person Query dataset in the form of a data frame.} \item{hrvar}{HR Variable by which to split metrics, defaults to "Organization" but accepts any character vector, e.g. "LevelDesignation"} diff --git a/man/mgrcoatt_dist.Rd b/man/mgrcoatt_dist.Rd index 77ddefae..54bb3701 100644 --- a/man/mgrcoatt_dist.Rd +++ b/man/mgrcoatt_dist.Rd @@ -7,7 +7,7 @@ mgrcoatt_dist(data, hrvar = "Organization", mingroup = 5, return = "plot") } \arguments{ -\item{data}{A Standard Query dataset in the form of a data frame.} +\item{data}{A Standard Person Query dataset in the form of a data frame.} \item{hrvar}{HR Variable by which to split metrics. Accepts a character vector, defaults to "Organization" but accepts any character vector, e.g. "LevelDesignation"} diff --git a/man/mgrrel_matrix.Rd b/man/mgrrel_matrix.Rd index b7b66a58..6d4651c3 100644 --- a/man/mgrrel_matrix.Rd +++ b/man/mgrrel_matrix.Rd @@ -13,7 +13,7 @@ mgrrel_matrix( ) } \arguments{ -\item{data}{Standard Query data to pass through. Accepts a data frame.} +\item{data}{Standard Person Query data to pass through. Accepts a data frame.} \item{hrvar}{HR Variable by which to split metrics. Accepts a character vector, e.g. "Organization". Defaults to NULL.} diff --git a/man/one2one_trend.Rd b/man/one2one_trend.Rd index e90acf5a..802cb816 100644 --- a/man/one2one_trend.Rd +++ b/man/one2one_trend.Rd @@ -7,7 +7,7 @@ one2one_trend(data, hrvar = "Organization", mingroup = 5, return = "plot") } \arguments{ -\item{data}{A Standard Query dataset in the form of a data frame.} +\item{data}{A Standard Person Query dataset in the form of a data frame.} \item{hrvar}{HR Variable by which to split metrics, defaults to "Organization" but accepts any character vector, e.g. "LevelDesignation"} diff --git a/man/remove_outliers.Rd b/man/remove_outliers.Rd index a755247c..5d04002e 100644 --- a/man/remove_outliers.Rd +++ b/man/remove_outliers.Rd @@ -7,7 +7,7 @@ remove_outliers(data, metric = "Collaboration_hours") } \arguments{ -\item{data}{A Standard Query dataset in the form of a data frame.} +\item{data}{A Standard Person Query dataset in the form of a data frame.} \item{metric}{Character string containing the name of the metric, e.g. "Collaboration_hours"} diff --git a/man/sq_data.Rd b/man/sq_data.Rd index 63689945..d6dd60aa 100644 --- a/man/sq_data.Rd +++ b/man/sq_data.Rd @@ -3,7 +3,7 @@ \docType{data} \name{sq_data} \alias{sq_data} -\title{Sample Standard Query dataset} +\title{Sample Standard Person Query dataset} \format{ A data frame with 13442 rows and 66 variables: \describe{ @@ -85,6 +85,6 @@ the last email or meeting for each day of the work week.} sq_data } \description{ -A dataset generated from a Standard Query from WpA. +A dataset generated from a Standard Person Query from WpA. } \keyword{datasets} diff --git a/man/validation_report.Rd b/man/validation_report.Rd index d80639be..341f6888 100644 --- a/man/validation_report.Rd +++ b/man/validation_report.Rd @@ -27,7 +27,7 @@ Defaults to TRUE.} } \description{ The function generates an interactive HTML report using -Standard Query data as an input. The report contains a checks on +Standard Person Query data as an input. The report contains a checks on Workplace Analytics query outputs, to provide diagnostic information for the Analyst pre-analysis. diff --git a/man/workloads_line.Rd b/man/workloads_line.Rd index 2fb4d31e..06a5138c 100644 --- a/man/workloads_line.Rd +++ b/man/workloads_line.Rd @@ -7,7 +7,7 @@ workloads_line(data, hrvar = "Organization", mingroup = 5, return = "plot") } \arguments{ -\item{data}{A Standard Query dataset in the form of a data frame.} +\item{data}{A Standard Person Query dataset in the form of a data frame.} \item{hrvar}{HR Variable by which to split metrics, defaults to "Organization" but accepts any character vector, e.g. "LevelDesignation"} diff --git a/man/workloads_trend.Rd b/man/workloads_trend.Rd index 1c58b6cc..8585f719 100644 --- a/man/workloads_trend.Rd +++ b/man/workloads_trend.Rd @@ -7,7 +7,7 @@ workloads_trend(data, hrvar = "Organization", mingroup = 5, return = "plot") } \arguments{ -\item{data}{A Standard Query dataset in the form of a data frame.} +\item{data}{A Standard Person Query dataset in the form of a data frame.} \item{hrvar}{HR Variable by which to split metrics, defaults to "Organization" but accepts any character vector, e.g. "LevelDesignation"} From cdfac1f30b1b92cc9935705228e0129e9ee4e4a5 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Sun, 13 Dec 2020 11:12:13 +0000 Subject: [PATCH 149/192] chore: increment package version 1.3.1 --- DESCRIPTION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index e428b8af..55153daf 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Package: wpa Type: Package Title: Tools for Analysing and Visualising Workplace Analytics data -Version: 1.3.0 +Version: 1.3.1 Authors@R: c( person(given = "Martin", family = "Chan", role = c("aut", "cre"), email = "martin.chan@microsoft.com"), person(given = "Carlos", family = "Morales", role = "aut", email = "carlos.morales@microsoft.com"), From 874bba1ec012d4b048b183d2b688d46948b4e469 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Mon, 14 Dec 2020 10:21:31 +0000 Subject: [PATCH 150/192] fix: error with flag_extreme unused argument metric_nm --- R/flag_extreme.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/flag_extreme.R b/R/flag_extreme.R index b642ba28..faf842f7 100644 --- a/R/flag_extreme.R +++ b/R/flag_extreme.R @@ -54,7 +54,7 @@ flag_extreme <- function(data, ## Clean names for pretty printing - metric_nm <- metric_nm %>% us_to_space() %>% camel_clean(metric_nm) + metric_nm <- metric_nm %>% us_to_space() %>% camel_clean() ## Define MessageLevel if(person == TRUE){ From b0d3d0a623a75e79494478dcdb994f9bd59dfd6a Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Mon, 14 Dec 2020 10:52:56 +0000 Subject: [PATCH 151/192] fix: error with flag_extreme --- R/flag_extreme.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/flag_extreme.R b/R/flag_extreme.R index faf842f7..4e396356 100644 --- a/R/flag_extreme.R +++ b/R/flag_extreme.R @@ -54,7 +54,7 @@ flag_extreme <- function(data, ## Clean names for pretty printing - metric_nm <- metric_nm %>% us_to_space() %>% camel_clean() + metric_nm <- metric %>% us_to_space() %>% camel_clean() ## Define MessageLevel if(person == TRUE){ From dcd4ac6870257ad2cb973bc8ed4cd910cfc78f04 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Mon, 14 Dec 2020 11:53:08 +0000 Subject: [PATCH 152/192] docs: unwrap flag_extreme (#12) --- R/flag_extreme.R | 7 +++++-- man/flag_extreme.Rd | 7 +++++-- 2 files changed, 10 insertions(+), 4 deletions(-) diff --git a/R/flag_extreme.R b/R/flag_extreme.R index 4e396356..62c5d2a5 100644 --- a/R/flag_extreme.R +++ b/R/flag_extreme.R @@ -21,12 +21,15 @@ #' @import dplyr #' #' @examples -#' \dontrun{ #' # The threshold values are intentionally set low to trigger messages. #' flag_extreme(sq_data, "Email_hours", threshold = 15) +#' +#' # Return a summary table #' flag_extreme(sq_data, "Email_hours", threshold = 15, return = "table") +#' +#' # Person-week level #' flag_extreme(sq_data, "Email_hours", person = FALSE, threshold = 15) -#' } +#' #' #' @export flag_extreme <- function(data, diff --git a/man/flag_extreme.Rd b/man/flag_extreme.Rd index 4a15e11b..0cae7005 100644 --- a/man/flag_extreme.Rd +++ b/man/flag_extreme.Rd @@ -23,12 +23,15 @@ This is used as part of data validation to check if there are extreme values in the dataset. } \examples{ -\dontrun{ # The threshold values are intentionally set low to trigger messages. flag_extreme(sq_data, "Email_hours", threshold = 15) + +# Return a summary table flag_extreme(sq_data, "Email_hours", threshold = 15, return = "table") + +# Person-week level flag_extreme(sq_data, "Email_hours", person = FALSE, threshold = 15) -} + } \seealso{ From 636d41ea9a6206adddd439984180fbe4deaaf594 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Mon, 14 Dec 2020 12:10:34 +0000 Subject: [PATCH 153/192] feat: add Adobe Analytics Code provided in extra.js; 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"t:" + t : t } function b(n) { if (i = null, n) { for (var t = 0; t < s.length; t++)if (s[t] === n.substring(0, 2)) { i = n; break } i || awa.logger.logWarning("Unsupported app user id: " + n + ". 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awa.ct.captureCorsDisallowed(n) : n.type === "EventTooLong" && awa.ct.captureEventTooLong(n) } function s(n, t) { awa.ct.captureClientError({ errorInfo: n, displayedToUser: t }) } function h(n) { var t = { name: "Ms.Webi.OutgoingRequest", cV: n.cV, data: { baseData: { operationName: n.currentOperationName, targetUri: n.requestUri, latencyMs: n.latencyMs, serviceErrorCode: n.serviceErrorCode || -1, succeeded: n.isSuccess, requestMethod: n.httpMethod, responseContentType: n.contentType, protocolStatusCode: n.httpStatusCode, dependencyOperationName: n.operationName, dependencyOperationVersion: n.operationVersion, dependencyName: n.serviceName, dependencyType: n.serviceType || "WebService", responseSizeBytes: n.responseSize }, baseType: "Ms.Qos.OutgoingServiceRequest", impressionGuid: u.getImpressionGuid(), message: n.errorMessage, retryCount: n.retryCount, customData: n.customData } }; awa.ct.captureQos(t) } var u = awa.ids, n = [], i = "notInit", t = 0; return { sendError: s, sendApiComplete: h, sendJsllEvent: o, SendOrScheduleEvent: e, batchQueuedEvents: r, drainQueuedEvents: f, batchEventsByNumber: function (n) { t = n }, addEventToQueue: function (t) { n.push(t) }, overrideQueueBehavior: function (n) { n && n.domReadyState && (i = n.domReadyState) } } }(); awa.errorHandler = function () { function t(n) { var t = window.onerror; window.onerror = function () { t && t.apply(this, arguments); n.apply(this, arguments) } } function i(n) { return n.stack ? n.stack.substring(0, 1e3) : "" } function r() { var t, r; arguments && (t = "", arguments[1] && (t = awa.sanitizeUrl(arguments[1])), r = { Page: awa.sanitizeUrl(document.location.toString()), Script: t, Message: arguments[0] || "", LineNumber: arguments[2] || 0, StackTrace: arguments[4] ? i(arguments[4]) : "", UserAgent: awa.utils.getUserAgent() || "", Platform: window.navigator.platform || "" }, n.sendError(r, !1)) } function u() { t(r) } var n = awa.vortexEvents; return { init: u } }(); awa.timespanHandler = function () { function t(t, i) { var r = (new Date).getTime(); if (i) return r - n[t]; n[t] = r } var n = {}; return { recordTimeSpan: t } }(), function () { function h(n, r, u) { if (t(u)) { var f = ""; return n.length > 0 && (f += "&"), f + (i.isOfCorrectType("number", u) || i.isOfCorrectType("bool", u) ? r + "=" + u : typeof u == "object" ? r + "=" + JSON.stringify(u) : r + "=" + s + encodeURIComponent(u) + s) } return "" } function w(n, u, f) { if (t(f)) { var e = ""; return n.length > 1 && (e += ","), e + (i.isOfCorrectType("number", f) || i.isOfCorrectType("bool", f) ? r + u + r + ":" + f : typeof f == "object" ? r + u + r + ":" + JSON.stringify(f) : r + u + r + ":" + r + f + r) } return "" } function c(t, i) { for (var o, u, s, c, l, a, v, y, r = "", e = 0; e < nt; e++)r += h(r, f[e], t[f[e]]); o = t.data; for (u in o) if (u == "baseData") { s = o[u]; for (c in s) r += h(r, p + c, s[c]) } else r += h(r, yt + u, o[u]); l = t.ext; for (a in l) { v = l[a]; for (y in v) r += h(r, "ext" + p + a + p + y, v[y]) } return r += "&" + ot(), n.shareAuthStatus === !0 && n.authMethod == 2 && awa.getSignedInStatus() === !0 && (r += "&" + wt(i)), pt(r), r } function ut(n) { for (var r, u, t = "{", i = 0; i < nt; i++)t += w(t, f[i], n[f[i]]); return r = n.data, r && (t += w(t, "data", r)), u = n.ext, u && (t += w(t, "ext", u)), t += "}", awa.requestBody = t, t } function pt(n) { tt = n } function ft(n, t) { var i = new Image; i.onload = function () { i.width === 1 && u(n, t) }; i.onerror = function () { awa.logger.logWarning("Telemetry image beacon not sent properly. Sending through POST."); k(t) }; i.src = n } function et(i) { var r = "", e = "", f = "", u, o; if (i.length) for (f = i[0].ext.javascript.msfpc, u = 0; u < i.length; u++)u > 0 && (r += "\n"), r += ut(i[u]); else f = i.ext.javascript.msfpc, r = ut(i); return o = n.endpoint + "/t.req?" + r, e = "?" + ot(), e += t(f) ? "&ext-javascript-msfpc=" + encodeURIComponent(s + f + s) : "", { qp: e, requestBody: r, requestSrc: o } } function ot() { return "$mscomCookies=" + n.mscomCookies } function wt(n) { return "$nr=" + t(n.Bounced) } function k(t) { var f = et(t), i, r; y === "xhr" ? (i = new XMLHttpRequest, i.open("POST", n.endpoint + f.qp, !0), i.withCredentials = !0, i.onload = function () { i.status === 200 ? u(f.requestSrc, t) : rt(i.statusText, i.status) }, i.onerror = function () { rt(i.statusText, i.status) }, i.send(f.requestBody)) : y === "XDomainRequest" ? (r = new window.XDomainRequest, r.onload = function () { u(f.requestSrc, t) }, r.onerror = function () { vt(r) }, r.open("POST", n.endpoint), r.send(f.requestBody)) : awa.logger.logError("No event is sent.") } function st(t) { var i = et(t); navigator.sendBeacon(n.endpoint + i.qp, i.requestBody) ? u(i.requestSrc, t) : k(t) } function bt(n, t, i) { var f = document.head || document.getElementsByTagName("head")[0] || document.documentElement, r = document.createElement("script"); r.onload = function () { u(n, t, !0) }; r.onerror = function () { i ? awa.logger.logError("No event is sent.") : (o(), awa.writeEvent(t, !0)) }; r.async = !0; r.src = n; f.appendChild(r) } function kt() { try { if (n.callback && n.callback.userConsentDetailsCallback) { var t = n.callback.userConsentDetailsCallback(); if (t) return JSON.stringify({ Required: t.Required ? t.Required : !1, Analytics: t.Analytics ? t.Analytics : !1, SocialMedia: t.SocialMedia ? t.SocialMedia : !1, Advertising: t.Advertising ? t.Advertising : !1 }) } } catch (i) { } return null } function dt(i) { var r, u, f, e; return i ? (r = i.protocol + "//" + i.hostname + (t(i.port) ? ":" + i.port : "") + i.pathname, n.urlCollectHash && (r += i.hash), n.urlCollectQuery && (u = i.search, u || (f = i.hash, e = f.indexOf("?"), e != -1 && (u = f.slice(e))), r += u), r) : "" } function gt() { var n = i.getUserAgent(), t = n.indexOf("MSIE "); return t > 0 ? parseInt(n.substring(t + 5, n.indexOf(".", t)), 10) < 9 : !1 } var a, v, b, d, l, g; awa._schemas = awa._schemas || []; var i = awa.utils, t = i.isValueAssigned, ht = /([^?#]*)(?:\?([^#]*))?(?:#(.*))*/, n = { endpoint: "https://web.vortex.data.microsoft.com/collect/v1", sendMode: 1, batchSize: 10, authMethod: 0, validateEvents: !0, sendEvents: !0, logLevel: awa.verbosityLevels.NONE, syncMuid: !1, provisionMsfpc: !0, useDefaultContentName: !0, useShortNameForContentBlob: !1, debounceMs: { scroll: 600, resize: 3e3 }, muidDomain: "microsoft.com", biBlobAttributeTag: "data-m", isLoggedIn: !1, shareAuthStatus: !1, cookiesToCollect: ["Bounced"], mscomCookies: !1, userConsentCookieName: "MSCC", userConsented: undefined, useBeacon: !0, urlCollectHash: !1, urlCollectQuery: !0, initCv: !1, enabledFeatures: !0, ix: { a: !1, g: !1 }, autoCapture: { pageView: !0, onLoad: !0, onUnload: !0, click: !0, scroll: !1, resize: !1, jsError: !0, addin: !0, perf: !0, assets: !1, lineage: !1, invalidEvents: !1, msTags: !0, awaTags: !0, eventTooLong: !0, corsDisallowed: !0 }, callback: { pageName: null, pageActionPageTags: null, pageViewPageTags: null, contentUpdatePageTags: null, pageActionContentTags: null, signedinStatus: null, jsllEventCallback: null, pageUnloadTimings: null, userConsentDetailsCallback: null }, coreData: { referrerUri: undefined, requestUri: "", appId: window.location.hostname, pageName: "", title: "", pageType: "", env: "", product: "", market: "", serverImpressionGuid: "", expId: "", pageTags: {} } }; awa.getConfig = function () { return n }; a = []; for (v in n) typeof n[v] == "object" && a.push(v); var f = ["ver", "name", "time", "flags", "os", "appId", "cV", "deviceClass"], nt = f.length, ct = gt(), lt = ct ? 4095 : 15e3, tt = "", e = !1, it = t(navigator) && t(navigator.sendBeacon); awa.experimentIdTag = "expId"; awa.isAvailable = !0; awa.requestBody = ""; awa.getQueryStringParameters = function () { return tt }; Object.freeze && Object.freeze(awa.verbosityLevels); awa.capabilitiesCheck(); var y = window.XDomainRequest ? "XDomainRequest" : "xhr", at = function () { return y === "XDomainRequest" && n.endpoint && n.endpoint.indexOf(window.location.protocol) !== 0 ? (awa.logger.logError("Unable to write event: CORS requests are not supported cross-protocol in this browser"), !1) : !0 }, o = function () { e = !0; n.sendMode == 1 && (awa.vortexEvents.overrideQueueBehavior({ domReadyState: "complete" }), awa.vortexEvents.drainQueuedEvents()) }; awa.firstEventDone = o; awa.firstEventDoneTasks = function () { awa.vortexEvents.drainQueuedEvents() }; var rt = function (n, t) { awa.logger.logError("Failure sending data to vortex: " + n + "Error: " + t.toString()) }, u = function (t, i, r) { awa.OnSuccessfulVortexRequest && awa.OnSuccessfulVortexRequest(t); i && typeof n.callback.jsllEventCallback == "function" && n.callback.jsllEventCallback(i); e === !1 && (e = !0, n.sendMode !== 1 || r || o()); awa.logger.logInformation("Success sending data to vortex") }, vt = function (n) { awa.logger.logError("Failure sending data to vortex using XDomainRequest. Status code:" + n.status) }, s = "'", r = '"', p = "-", yt = "*"; for (awa.init = function (t) { if (!awa.isInitialized) { awa.isInitialized = !0; t && (i.removeNonObjectsAndInvalidElements(t, a), n = i.extend(!0, n, t)); n.coreData.referrerUri = n.coreData.referrerUri ? n.coreData.referrerUri : awa.sanitizeUrl(document.referrer); var r = n.sendMode; r == 0 && (e = !0, awa.vortexEvents.overrideQueueBehavior({ domReadyState: "complete" })); r >= 1 && awa.vortexEvents.overrideQueueBehavior({ domReadyState: "notComplete" }); r === 2 && awa.vortexEvents.batchEventsByNumber(n.batchSize); awa.consoleVerbosity = n.logLevel; awa.ct.initialize(n); awa.ix.init(n); n.initCv && awa.cv.init() } }, b = function (r, u) { var o, s, f, e, h, c; awa.translateEventFromIntermediateStructure && (r = awa.translateEventFromIntermediateStructure(r)); o = r.cV; s = r.appId; r.ver = "2.1"; r.cV = t(o) ? o : awa.cv.getValue(); r.time = t(r.time) ? r.time : i.dateToISOString(new Date); r.os = i.getOs(); r.deviceClass = awa.ids.getDeviceClass() || undefined; r.appId = i.getAppIdWithPrefix(s ? s : n.coreData.appId); f = window.location.hostname; t(f) || (f = window.location.protocol == "file:" ? "local" : f); e = { ver: "1.1", libVer: "4.3.4", domain: f, msfpc: u.MSFPC, userConsent: t(n.userConsented) ? n.userConsented : t(u[n.userConsentCookieName]) ? !0 : !1, consentDetails: kt(), anid: u.ANON ? u.ANON.slice(0, 34) : undefined }; h = r.serviceName ? r.serviceName : n.coreData.serviceName; h && (e.serviceName = h); r.ext ? r.ext.javascript = e : r.ext = { javascript: e }; c = awa.ids.getMuidUserId(u); c && (r.ext.user = { localId: c }); var p = n.coreData.env, y = r.ext.app ? r.ext.app.env : undefined, l = t(y) ? y : p, a = n.coreData.expId ? awa.ids.readExpIdFromCoreData(n.coreData.expId) : awa.ids.readExpIdFromCookie(u), v = awa.ids.getAppUserId(); return (t(l) || t(a) || t(v)) && (r.ext.app || (r.ext.app = {}), l && (r.ext.app.env = l), a && (r.ext.app.expId = a), v && (r.ext.app.userId = v)), n.validateEvents && awa.isEventValid && !awa.isEventValid(r) ? undefined : (awa._validateAndTranslateEvent(r), r) }, awa.writeEvent = function (i, r) { var h, l, v, y, o, a, w; if (!awa.isInitialized) { awa.vortexEvents.addEventToQueue(i); return } var s = !0, f = awa.cookie.getAllCookies(), p = t(f.MSFPC); if (Object.prototype.toString.call(i) === "[object Array]") { for (h = [], l = 0; l < i.length; l++)v = b(i[l], f), v && h.push(v); if (h.length != 0) i = h, s = !1; else return } else if (i = b(i, f), !i) return; if (n.sendEvents) { if (y = JSON.stringify(i).length + n.endpoint.length, s && y + 7 > lt && (awa.logger.logWarning("event is too large to send using get -- using post instead"), n.autoCapture.eventTooLong && (o = {}, o.eventName = i.name, o.payloadLength = y, awa.ct.captureEventTooLong(o)), s = !1), !s && !at()) { n.autoCapture.corsDisallowed && (o = {}, o.eventName = i.name, o.type = "CorsDisallowed", awa.ct.captureCorsDisallowed(o)); return } if (s) if (e === !1 || n.provisionMsfpc && !p || n.shareAuthStatus === !0) { if (p = !0, a = "/t.js", n.shareAuthStatus === !0 && awa.getSignedInStatus() === !0) if (n.authMethod === 1) { if (w = t(f.Bounced), w) { ft(n.endpoint + "/asm.gif?" + c(i, f), i); return } awa.vortexEvents.overrideQueueBehavior({ domReadyState: "notComplete" }); a = "/asm.js" } else n.authMethod === 2 && (a = "/asa.js"); bt(n.endpoint + a + "?" + c(i, f), i, r) } else it && n.useBeacon === !0 ? st(i) : ft(n.endpoint + "/t.gif?" + c(i, f), i); else it && n.useBeacon === !0 && n.shareAuthStatus === !1 ? st(i) : k(i) } else u(c(i, f), i) }, awa.sanitizeUrl = function (t) { var i, r; if (t) { if (!n.urlCollectQuery || !n.urlCollectHash) { if (i = "", r = t.match(ht), r.length > 0) { var e = r[1], u = r[2], f = r[3]; i = e || ""; n.urlCollectQuery && u && (i += u); n.urlCollectHash && f && (i += f) } return i } return t } return "" }, awa.sanitizeCurrentUrl = function () { return window.location ? dt(window.location) : "" }, awa.getSignedInStatus = function () { return n.callback && typeof n.callback.signedinStatus == "function" ? n.callback.signedinStatus() : n.isLoggedIn }, awa.sendEventThroughIframe = function (t) { var r = document.createElement("iframe"), u = Math.floor(Math.random() * Math.floor(1e4)); r.id = "telframe" + u; r.style.display = "none"; n.authMethod == 1 && (r.addEventListener ? r.addEventListener("load", o) : r.attachEvent("onload", o)); r.src = t; awa.cookie.setCookie("Bounced", i.dateToISOString(new Date), .5 / 24); document.body.appendChild(r) }, awa._registerSchemas = function (n) { for (var t = 0; t < n.length; t++)awa._schemas[n[t].name] = n[t] }, awa.console = window.console || {}, awa.console.fallback = awa.console.log || function () { }, d = ["log", "info", "warn", "error", "trace"], l = 0; l < d.length; l++)g = d[l], awa.console[g] || (awa.console[g] = awa.console.fallback); awa.extendCoreData = function (t) { i.removeInvalidElements(t); n.coreData = i.extend(!0, n.coreData, t) }; awa._validateAndTranslateEvent = function (n) { return { event: n } } }(); awa._registerSchemas([{ name: "Ms.Cll.Javascript.CorsDisallowed", "Ms.Cll.Javascript.CorsDisallowed": { part: "C", def: { fields: [{ req: !0, name: "eventName", type: "string" }, { name: "pageName", type: "string" }, { name: "market", type: "string" }, { name: "uri", type: "string" }, { name: "impressionGuid", type: "string" }, { name: "serverImpressionGuid", type: "string" }] } } }, { name: "Ms.Cll.Javascript.EventTooLong", "Ms.Cll.Javascript.EventTooLong": { part: "C", def: { fields: [{ req: !0, name: "eventName", type: "string" }, { req: !0, name: "payloadLength", type: "int32" }, { name: "pageName", type: "string" }, { name: "market", type: "string" }, { name: "uri", type: "string" }, { name: "impressionGuid", type: "string" }, { name: "serverImpressionGuid", type: "string" }] } } }, { name: "Ms.Cll.Javascript.Error", "Ms.Telemetry.Error": { part: "B", def: { fields: [{ name: "errorName", type: "string" }, { name: "errorMessage", type: "string" }, { name: "severity", type: "int32" }, { name: "errorType", type: "string" }, { name: "errorCode", type: "int32" }, { name: "lineNumber", type: "uint32" }, { name: "isDisplayed", type: "bool" }, { name: "errorLocation", type: "string" }, { name: "errorMethod", type: "string" }] } }, "Ms.Cll.Javascript.Error": { part: "C", def: { fields: [{ req: !0, name: "code", type: "int32" }, { req: !0, name: "message", type: "string" }, { name: "pageName", type: "string" }, { name: "market", type: "string" }, { name: "uri", type: "string" }, { name: "impressionGuid", type: "string" }, { name: "serverImpressionGuid", type: "string" }] } } }, { name: "Ms.Cll.Javascript.EventDropped", "Ms.Cll.Javascript.EventDropped": { part: "C", def: { fields: [{ req: !0, name: "eventName", type: "string" }, { name: "pageName", type: "string" }, { name: "market", type: "string" }, { name: "uri", type: "string" }, { name: "impressionGuid", type: "string" }, { name: "serverImpressionGuid", type: "string" }, { name: "droppedInfo", type: "string" }] } } }, { name: "Ms.Webi.ClientError", "Ms.Webi.ClientError": { part: "B", def: { fields: [{ req: !0, name: "errorInfo", type: "string" }, { name: "wasDisplayed", type: "bool" }, { name: "impressionGuid", type: "string" }, { name: "serverImpressionGuid", type: "string" }, { name: "pageName", type: "string" }, { name: "market", type: "string" }, { name: "uri", type: "string" }, { name: "errorName", type: "string" }, { name: "errorMessage", type: "string" }, { name: "severity", type: "int32" }, { name: "errorType", type: "string" }, { name: "errorCode", type: "int32" }, { name: "lineNumber", type: "uint32" }, { name: "isDisplayed", type: "bool" }, { name: "errorLocation", type: "string" }, { name: "errorMethod", type: "string" }] } } }, { name: "Ms.Webi.OutgoingRequest", "Ms.Qos.OutgoingServiceRequest": { part: "B", def: { fields: [{ req: !0, name: "operationName", type: "string" }, { name: "targetUri", type: "string" }, { req: !0, name: "latencyMs", type: "int32" }, { name: "serviceErrorCode", type: "int32" }, { req: !0, name: "succeeded", type: "bool" }, { name: "requestMethod", type: "string" }, { name: "responseContentType", type: "string" }, { name: "protocol", type: "string" }, { name: "protocolStatusCode", type: "string" }, { req: !0, name: "dependencyOperationName", type: "string" }, { name: "dependencyOperationVersion", type: "string" }, { req: !0, name: "dependencyName", type: "string" }, { name: "dependencyType", type: "string" }, { name: "responseSizeBytes", type: "int32" }, { name: "requestStatus", type: "int32" }] } }, "Ms.Webi.OutgoingRequest": { part: "C", def: { fields: [{ name: "impressionGuid", type: "string" }, { name: "message", type: "string" }, { name: "retryCount", type: "int32" }, { name: "customData", type: "string" }, { name: "serverImpressionGuid", type: "string" }, { name: "pageName", type: "string" }, { name: "market", type: "string" }, { name: "uri", type: "string" }] } } }, { name: "Ms.Webi.PageView", "Ms.Content.PageView": { part: "B", def: { fields: [{ name: "ver", type: "string" }, { req: !0, name: "impressionGuid", type: "string" }, { req: !0, name: "pageName", type: "string" }, { name: "uri", type: "string" }, { name: "referrerUri", type: "string" }, { name: "pageType", type: "string" }, { name: "pageTags", type: "string" }, { name: "product", type: "string" }, { name: "screenState", type: "int32" }, { name: "actionType", type: "string" }, { name: "behavior", type: "int32" }, { name: "resHeight", type: "int32" }, { name: "resWidth", type: "int32" }, { name: "vpHeight", type: "int32" }, { name: "vpWidth", type: "int32" }, { name: "market", type: "string" }] } }, "Ms.Webi.PageView": { part: "C", def: { fields: [{ name: "cookieEnabled", type: "bool" }, { name: "flashInstalled", type: "bool" }, { name: "flashVersion", type: "string" }, { name: "cookies", type: "string" }, { name: "isJs", type: "bool" }, { name: "title", type: "string" }, { name: "isLoggedIn", type: "bool" }, { name: "isManual", type: "bool" }, { name: "serverImpressionGuid", type: "string" }] } } }, { name: "Ms.Webi.ContentUpdate", "Ms.Content.ContentUpdate": { part: "B", def: { fields: [{ name: "ver", type: "string" }, { req: !0, name: "impressionGuid", type: "string" }, { req: !0, name: "pageName", type: "string" }, { name: "uri", type: "string" }, { name: "pageTags", type: "string" }, { name: "pageHeight", type: "int32" }, { name: "vpHeight", type: "int32" }, { name: "vpWidth", type: "int32" }, { name: "market", type: "string" }, { name: "actionType", type: "string" }, { name: "behavior", type: "int32" }, { name: "vScrollOffset", type: "int32" }, { name: "hScrollOffset", type: "int32" }, { name: "contentVer", type: "string" }, { req: !0, name: "content", type: "string" }] } }, "Ms.Webi.ContentUpdate": { part: "C", def: { fields: [{ name: "pageLoadTime", type: "int32" }, { name: "title", type: "string" }, { name: "isJs", type: "bool" }, { name: "cookieEnabled", type: "bool" }, { name: "isLoggedIn", type: "bool" }, { name: "isManual", type: "bool" }, { name: "isDomComplete", type: "bool" }, { name: "serverImpressionGuid", type: "string" }, { name: "timings", type: "string" }] } } }, { name: "Ms.Webi.PageAction", "Ms.Content.PageAction": { part: "B", def: { fields: [{ name: "ver", type: "string" }, { req: !0, name: "impressionGuid", type: "string" }, { req: !0, name: "pageName", type: "string" }, { name: "uri", type: "string" }, { name: "destUri", type: "string" }, { name: "market", type: "string" }, { name: "pageType", type: "string" }, { name: "pageTags", type: "string" }, { name: "product", type: "string" }, { name: "screenState", type: "int32" }, { name: "actionType", type: "string" }, { name: "behavior", type: "int32" }, { name: "contentVer", type: "string" }, { name: "content", type: "string" }] } }, "Ms.Webi.PageAction": { part: "C", def: { fields: [{ name: "timeToAction", type: "int32" }, { name: "cookieEnabled", type: "bool" }, { name: "cookies", type: "string" }, { name: "isJs", type: "bool" }, { name: "title", type: "string" }, { name: "referrerUri", type: "string" }, { name: "isLoggedIn", type: "bool" }, { name: "isManual", type: "bool" }, { name: "serverImpressionGuid", type: "string" }] } } }, { name: "Ms.Webi.PageUnload", "Ms.Content.PageUnload": { part: "B", def: { fields: [{ name: "ver", type: "string" }, { req: !0, name: "impressionGuid", type: "string" }, { req: !0, name: "pageName", type: "string" }, { name: "uri", type: "string" }] } }, "Ms.Webi.PageUnload": { part: "C", def: { fields: [{ name: "dwellTime", type: "int32" }, { name: "scrollDepth", type: "string" }, { name: "serverImpressionGuid", type: "string" }, { name: "timings", type: "string" }, { name: "vScrollOffset", type: "int32" }, { name: "pageHeight", type: "int32" }, { name: "vpHeight", type: "int32" }, { name: "pageLoadTime", type: "int32" }] } } }]); awa.isEventValid = function (n) { function r(n, t) { var i = n.eventName + " - " + n.droppedInfo; t ? (c("Dropped event " + i), awa.ct.captureEventDrop(n)) : c("Dropped field from " + i) } function a(n, t) { for (var i = 0; i < n.length; i++)if (n[i] == t) return !0; return !1 } function l(n, i) { for (var e, u, y, f, h = [], l = i.length, o = 0; o < l; o++) { e = i[o]; u = e.name; h.push(u); var v = e.type, c = n[u], s = e.req; if (awa.utils.isValueAssigned(c)) { if (!awa.utils.isOfCorrectType(v, c)) { if (r({ eventName: t, droppedInfo: "Incorrect type: " + u }, s), s === !0) return !1; delete n[u] } } else if (s === !0) return r({ eventName: t, droppedInfo: "Missing field: " + u }, !0), !1 } y = 0; for (f in n) f == "baseData" || f == "baseType" || a(h, f) || (r({ eventName: t, droppedInfo: "Unexpected field " + f }, !1), delete n[f]); return !0 } var c = awa.logger.logWarning, e, o, s, h; if (n) { var t = n.name, v = awa._schemas, u = n.data.baseType, f = t, i = v[t]; if (i) { if (u && (e = n.data.baseData, o = i[u] ? i[u].def.fields : undefined, e && o && !l(e, o)) || f && (s = n.data, h = i[f] ? i[f].def.fields : undefined, s && h && !l(s, h))) return !1 } else return r({ eventName: t, droppedInfo: "Not registered in JSLL" }, !0), !1; return !0 } return !1 }; awa.translateEventFromIntermediateStructure = function (n) { var u = n.name, t = n.data, i = awa.utils, e, r, o, f, s, h; if (i.isValueAssigned(t[u])) { e = i.extend(!0, t[u]); delete t[u]; r = []; for (o in t) r.push(o); return f = i.extend(!0, t[r[0]]), delete t[r[0]], delete n.content, s = i.extend(!0, JSON.stringify(f) !== "{}" ? { baseData: f } : {}, e, { baseType: r[0] }), h = i.extend(!0, n, { data: s }), h } return n }; awa.ix = function () { function u(n) { r = n.ix } function f() { var r = "%010"; return n(t) || n(i) ? "https://dpm.demdex.net/id?d_orgid=EA76ADE95776D2EC7F000101@AdobeOrg&d_cid=" + (n(t) ? "88170%01" + t + r : "") + (n(i) ? (n(t) ? "%01" : "") + "88169%01" + i + r : "") + "&d_ver=2" : undefined } function e() { return n(t) ? "https://ad.doubleclick.net/ddm/activity/src=6952136;type=store0;cat=jsll;u58=" + t + ";match_id=" + t + ";dc_lat=;dc_rdid=;tag_for_child_directed_treatment=;ord=1?" : undefined } function o(u) { var o, s, h; u && (t = u.mc1, i = u.anid, (n(t) || n(i)) && (r.a && (o = new XMLHttpRequest, o.onreadystatechange = function () { o.readyState == 4 && o.status == 200 && awa.ct.captureSwap(JSON.parse(o.responseText)) }, s = f(), n(s) && (o.open("GET", s, !0), o.setRequestHeader("Accept", "application/json; charset=utf-8"), o.send())), r.g && (s = e(), n(s) && (h = new Image, h.src = s)))) } var r = {}, t = "", i = "", n = awa.utils.isValueAssigned; return { init: u, set: o } }(); awa.ct = function () { function ui(u) { var f, e, o, s, h; awa.isAvailable && (n = u, r = n.autoCapture, i = n.useShortNameForContentBlob === !0 ? g.shortNames : g.longNames, r.pageView && ct({ isAuto: !0 }), r.click && (window.addEventListener ? (f = navigator.appVersion.indexOf("MSIE") !== -1 ? "click" : "mousedown", window.addEventListener(f, p, !1), window.addEventListener("keyup", p, !1)) : document.attachEvent && (document.attachEvent("onclick", p), document.attachEvent("keyup", p))), r.jsError && ii.init(), r.scroll && (e = ht(null, function () { b({ isAuto: !0, actionType: awa.actionType.SCROLL }) }, n.debounceMs.scroll), t.attachEvent(window, "scroll", e)), r.resize && (o = ht(function () { b({ isAuto: !0, actionType: awa.actionType.RESIZE }) }, null, n.debounceMs.resize), t.attachEvent(window, "resize", o)), (r.onUnload || n.manualPageUnload) && (s = function () { var n = gt(); a.v = a.v > n.v ? a.v : n.v }, t.attachEvent(window, "scroll", s)), r.onUnload && t.addPageUnloadEventListener(at), n.sendMode == 2 && (h = awa.vortexEvents.batchQueuedEvents, t.addPageUnloadEventListener(h)), n.authMethod == 2 && n.shareAuthStatus == !0 && awa.getSignedInStatus() && t.attachEvent(window, "message", fi), (n.syncMuid || n.autoCapture.onLoad) && st(awa.ct.domReadyTasksWrapper)) } function fi(n) { var i, r; if ((n.origin == "https://web.vortex.data.microsoft.com" || n.origin == "https://login.microsoftonline.com") && n.data) try { i = JSON.parse(n.data); i && i.Action == "firstEventDone" && awa.firstEventDone(); i && i.BouncedCookieExpires && (r = "Bounced=" + t.dateToISOString(new Date) + "; expires=" + new Date(i.BouncedCookieExpires).toUTCString() + "; path=/;", window.document.cookie = r) } catch (u) { } } function st(n) { /in/.test(document.readyState) ? setTimeout(function () { awa.ct.onDomReadyDo(n) }, 100) : n.call() } function ei() { n.syncMuid && si(); r.onLoad && (document.readyState === "complete" ? lt() : t.attachEvent(window, "load", lt)) } function oi() { awa.ct.domReadyTasks() } function u() { if (n.callback && typeof n.callback.pageName == "function") return n.callback.pageName(); if (n.coreData.pageName) return n.coreData.pageName; var i = window.location.pathname, t = i.split("/"); return t && t[t.length - 1] !== "" ? t[t.length - 1] : "Undefined" } function nt() { return n.coreData.title ? n.coreData.title : document.title.substring(0, 150) } function si() { var r = t.getMuidHost(n.muidDomain || "microsoft.com"), u, i; r ? (u = (window.location.protocol || "http:") + "//" + r + "/c.gif?DI=4050&did=1&t=", i = document.createElement("IMG"), i.style.display = "none", i.src = u, i.hidden = "", i["aria-hidden"] = "true", i.role = "presentation") : awa.logger.logWarning("Unable to get a muid host for the configured muidDomain '" + n.muidDomain + "'. Unable to sync muid") } function hi(n) { return n.which ? n.which === 3 : n.button ? n.button === 2 : void 0 } function ci(n) { return n.which ? n.which === 1 : n.button ? n.button === 1 : void 0 } function li(n) { return n.which ? n.which === 2 : n.button ? n.button === 4 : void 0 } function ai(n) { if (n.keyCode) return n.keyCode === 13 } function vi(n) { if (n.keyCode) return n.keyCode === 32 } function yi(n) { if (n.keyCode) return n.keyCode === 195 } function pi(n) { if (n.keyCode) return n.keyCode === 207 } function p(n) { var u = { A: !0, BUTTON: !0, AREA: !0, INPUT: !0 }, i = n || window.event, t = i.srcElement || i.target, r = { isAuto: !0 }, f = hi(i), e; if (f) r.actionType = c.CLICKRIGHT; else if (ci(i)) r.actionType = c.CLICKLEFT; else if (ai(i)) r.actionType = c.KEYBOARDENTER; else if (vi(i)) r.actionType = c.KEYBOARDSPACE; else if (li(i)) r.actionType = c.CLICKMIDDLE; else if (yi(i)) r.actionType = c.GAMEPADA; else if (pi(i)) r.actionType = c.GAMEPADMENU; else return; while (t && t.tagName) if (t.control && u[t.control.tagName.toUpperCase()] && (t = t.control), u[t.tagName.toUpperCase()]) { e = t.tagName.toUpperCase() === "INPUT" ? ot[t.type.toUpperCase()] : !0; e && (w(t, et) || tt(t, r, f)); break } else { t = t.parentElement || t.parentNode; continue } } function w(n, i) { var r = t.findClosestByAttribute(n, i); return t.isValueAssigned(r) ? !0 : !1 } function ht(n, t, i) { var r; return function () { var u = this, f = arguments, e = function () { r = null; t && t.apply(u, f) }, o = !r; clearTimeout(r); r = setTimeout(e, i); o && n && n.apply(u, f) } } function ct(i) { var a, l, p; ki(); i = i || {}; var w = k.getAllCookies(), r = {}, c = awa.utils.extend(!0, n.coreData.pageTags); c.metaTags = e(r, !0); it(c, w); typeof n.callback.pageViewPageTags == "function" && (c = t.extend(!0, c, n.callback.pageViewPageTags())); a = vr(); l = { name: "Ms.Webi.PageView", data: { baseData: { ver: y, impressionGuid: h.getPageViewImpressionGuid(), pageName: i.pageName || u(), uri: i.uri || o(), referrerUri: i.referrerUri || n.coreData.referrerUri, market: r.market, pageType: r.pageType, product: r.product, resHeight: a.h, resWidth: a.w, pageTags: JSON.stringify(t.extend(!0, c, i.pageTags)), actionType: i.actionType || r.actionType, behavior: v(i.behavior) || (r.behavior ? r.behavior : awa.behavior.UNDEFINED) }, baseType: "Ms.Content.PageView", cookieEnabled: rt(), cookies: bt(w), isJs: !0, title: i.title || nt(), isLoggedIn: awa.getSignedInStatus(), serverImpressionGuid: r.serverImpressionGuid, isManual: !0 }, flags: r.isSensitive ? s : undefined }; n.autoCapture.addin && (p = ar(), l.data.flashInstalled = p.installed || !1, l.data.flashVersion = p.version || ""); f(l, i, r.env, !0) } function lt() { b({ isAuto: !0, isDomComplete: !0 }) } function b(i) { var c, l, ft, a, p, w, b, tt, d, g, et; if (i = i || {}, c = {}, l = awa.utils.extend(!0, n.coreData.pageTags), l.metaTags = e(c), ft = k.getAllCookies(), it(l, ft), i && i.isDomComplete && r.perf === !0 && window.performance) { if (a = {}, window.performance.timing) { if (window.performance.getEntriesByType) if (p = window.performance.getEntriesByType("paint"), p) { for (w = 0; w < p.length; w++)a[p[w].name] = p[w].startTime; for (b in window.performance.timing) b != "toJSON" && (a[b] = window.performance.timing[b]) } else a = window.performance.timing; l.timing = t.stringifyField("timing", a) } r.assets === !0 && (tt = t.getPerformanceData(), tt && (l.AssetPerformance = t.stringifyField("AssetPerformance", tt))) } typeof n.callback.contentUpdatePageTags == "function" && (l = t.extend(!0, l, n.callback.contentUpdatePageTags())); d = dt(); g = {}; t.isValueAssigned(i.vScrollOffset) && t.isValueAssigned(i.hScrollOffset) || (g = gt()); et = { name: "Ms.Webi.ContentUpdate", data: { baseData: { ver: y, impressionGuid: h.getImpressionGuid(), pageName: i.pageName || u(), uri: i.uri || o(), market: c.market, pageTags: JSON.stringify(t.extend(!0, l, i.pageTags)), pageHeight: i.pageHeight || document.body.scrollHeight, vpHeight: d.h, vpWidth: d.w, actionType: i.actionType || c.actionType, behavior: v(i.behavior) || (c.behavior ? c.behavior : awa.behavior.UNDEFINED), vScrollOffset: i.vScrollOffset || g.v, hScrollOffset: i.hScrollOffset || g.h, contentVer: ut, content: vt(i.content) || pt(fr(d)) }, baseType: "Ms.Content.ContentUpdate", timings: JSON.stringify(i.timings), title: i.title || nt(), cookieEnabled: rt(), isJs: !0, isManual: !0, isDomComplete: !1, isLoggedIn: awa.getSignedInStatus(), serverImpressionGuid: c.serverImpressionGuid }, flags: c.isSensitive ? s : undefined }; f(et, i, c.env, !1) } function wi(n) { tt(null, n) } function bi(n, t) { tt(n, t) } function tt(i, c, l) { var g, d, ot; if (c = c || {}, g = c.isAuto ? !1 : w(i, et), i && w(i, ft) && !g) { awa.logger.logWarning("The element has do-not-track attribute set. PageAction will not be sent."); return } var tt = k.getAllCookies(), a = {}, b = awa.utils.extend(!0, n.coreData.pageTags), p = {}; i = t.returnDomObjectIfjQuery(i); i && (a.targetUri = cr(i), p = wt(i, !0), r.msTags && (p = t.extend(p, lr(i))), l && (a.behavior = awa.behavior.CONTEXTMENU), p.bhvr && (a.behavior = v(t.extractFieldFromObject(p, "bhvr")))); b.metaTags = e(a); it(b, tt); typeof n.callback.pageActionPageTags == "function" && (b = t.extend(!0, b, n.callback.pageActionPageTags(i))); d = n.callback.pageActionContentTags; ot = { name: "Ms.Webi.PageAction", data: { baseData: { ver: y, impressionGuid: h.getImpressionGuid(), pageName: c.pageName || u(), uri: c.uri || o(), pageTags: t.stringifyField("pageTags", t.extend(!0, b, c.pageTags)), contentVer: ut, market: a.market, destUri: c.targetUri || a.targetUri, pageType: a.pageType, product: a.product, actionType: c.actionType || a.actionType, behavior: v(c.behavior) || (a.behavior ? a.behavior : awa.behavior.UNDEFINED), content: vt(c.content) || t.bracketIt(JSON.stringify(t.extend(p, typeof d == "function" ? d(i) : {}, c && c.contentTags ? c.contentTags : {}))) }, baseType: "Ms.Content.PageAction", timeToAction: yt(), cookieEnabled: rt(), cookies: bt(tt), isJs: !0, title: c.title || nt(), isLoggedIn: awa.getSignedInStatus(), isManual: !0, referrerUri: c.referrerUri || n.coreData.referrerUri, serverImpressionGuid: a.serverImpressionGuid }, flags: a.isSensitive ? s : undefined }; f(ot, c, a.env, !0) } function ki() { awa.timespanHandler.recordTimeSpan("dwellTime", !1); a.v = 0; d = !1 } function at(t) { var i; if (!d) { d = !0; t = t || {}; t.isDomComplete = !0; i = {}; e(i); var r = n.callback.pageUnloadTimings, o = document.body.scrollHeight, c = { name: "Ms.Webi.PageUnload", data: { baseData: { ver: y, impressionGuid: h.getImpressionGuid(), pageName: t.pageName || u() }, baseType: "Ms.Content.PageUnload", dwellTime: awa.timespanHandler.recordTimeSpan("dwellTime", !0), scrollDepth: t.scrollDepth || a.v.toString() + "/" + o.toString(), vScrollOffset: t.vScrollOffset || a.v, pageHeight: t.pageHeight || o, vpHeight: dt().h, serverImpressionGuid: i.serverImpressionGuid, timings: r ? r() : undefined }, flags: i.isSensitive ? s : undefined }; f(c, t, i.env, !0) } } function di(n) { var t, i; n && (t = {}, e(t), i = { name: "Ms.Webi.ClientError", data: { errorInfo: JSON.stringify(n.errorInfo), wasDisplayed: n.displayedToUser || !1, impressionGuid: h.getImpressionGuid(), pageName: n.pageName || u(), uri: n.uri || o(), market: t.market, serverImpressionGuid: t.serverImpressionGuid }, flags: t.isSensitive ? s : undefined }, f(i, n, t.env, !1)) } function gi(n) { var t, i; n && (t = {}, e(t), i = { name: "Ms.Cll.Javascript.CorsDisallowed", data: { eventName: n.eventName, pageName: u(), uri: n.uri || o(), market: t.market, impressionGuid: h.getImpressionGuid(), serverImpressionGuid: t.serverImpressionGuid }, flags: t.isSensitive ? s : undefined }, f(i, n, t.env, !1)) } function nr(n) { var t, i; n && (t = {}, e(t), i = { name: "Ms.Cll.Javascript.EventTooLong", data: { eventName: n.eventName, payloadLength: n.payloadLength, pageName: n.pageName || u(), uri: n.uri || o(), market: t.market, impressionGuid: h.getImpressionGuid(), serverImpressionGuid: t.serverImpressionGuid }, flags: t.isSensitive ? s : undefined }, f(i, n, t.env, !1)) } function tr(n) { var t, i; n && r.invalidEvents && (t = {}, e(t), i = { name: "Ms.Cll.Javascript.EventDropped", data: { eventName: n.eventName, droppedInfo: n.droppedInfo, pageName: n.pageName || u(), uri: n.uri || o(), market: t.market, impressionGuid: h.getImpressionGuid(), serverImpressionGuid: t.serverImpressionGuid }, flags: t.isSensitive ? s : undefined }, f(i, n, t.env, !1)) } function ir() { return } function rr(n) { var i = {}, t; e(i); t = n.data; t.serverImpressionGuid = i.serverImpressionGuid; t.market = i.market; t.pageName = u(); t.uri = o(); f(n, undefined, i.env, !1) } function ur(n, i, r) { r.appId && (n.appId = r.appId); r.serviceName && (n.serviceName = r.serviceName); i.isManual = r.isAuto !== !0 ? !0 : undefined; r.isDomComplete && (i.isDomComplete = r.isDomComplete, i.pageLoadTime = r.pageLoadTime || t.getPageLoadTime()) } function vt(n) { return t.isValueAssigned(n) ? Object.prototype.toString.call(n) === "[object Array]" ? JSON.stringify(n) : t.bracketIt(JSON.stringify(n)) : undefined } function f(n, i, r, u) { n.time = t.dateToISOString(new Date); var f = n.data; i && ur(n, f, i); r && (n.ext = { app: { env: r } }); ri.SendOrScheduleEvent(n, u) } function yt() { if (window.performance && window.performance.timing) { var n = window.performance.timing.navigationStart; if (n !== 0) return (new Date).getTime() - n } return -1 } function fr(n) { return { top: 0, bottom: n.h, left: 0, right: n.w } } function pt(r) { var u = [], f = document.querySelectorAll(t.bracketIt(i.areaName) + "," + t.bracketIt(i.slotNumber) + "," + t.bracketIt(n.biBlobAttributeTag)); return er(f, u, r), JSON.stringify(u) } function er(n, i, r) { var u, f, e; if (n) for (u = 0; u < n.length; u++)f = n[u], w(f, ft) || t.isElementTrulyVisible(f, r) && (e = wt(f, !1), e && i.push(e)) } function or(t) { for (var e = [], o = [], l = ">", a = n.biBlobAttributeTag, v = "data-module-id", s = undefined, r, u, h, c, f; t && t.getAttribute;)h = t.getAttribute(a) || t[a], c = t.getAttribute(v) || t[v], h ? (f = JSON.parse(h), f && (r = f.cN || f.cT, u = f.id || undefined, (r || u) && (e.push(r), c && (s = r), o.push(u)))) : (r = t.getAttribute(i.contentName) || t.getAttribute(i.contentType), u = t.getAttribute(i.id) || undefined, (r || u) && (e.push(r), c && (s = r), o.push(u))), t = t.parentElement; return { lineage: e.join(l), lineageById: o.join(l), containerName: s } } function wt(r, u) { var f, c, l, o, a, s, h, e, v; if (!r) return ""; if (f = {}, c = awa.utils.findClosestByAttribute(r, n.biBlobAttributeTag), c && (l = c.getAttribute(n.biBlobAttributeTag)), l) f = JSON.parse(l); else for (o = t.findClosestByAttribute(r, t.bracketIt(i.id)), o = t.returnDomObjectIfjQuery(r), a = t.findClosestByAttribute(r, i.areaName), s = awa.utils.extend({}, hr(a)), f = { id: o.getAttribute(i.id) || r.id || "", aN: s.areaName, sN: o.getAttribute(i.slotNumber), cN: o.getAttribute(i.contentName) || sr(r) || o.getAttribute("alt") || "", cS: o.getAttribute(i.contentSource) || s.contentSource, tN: s.templateName, pid: o.getAttribute(i.productId), cT: o.getAttribute(i.contentType) || s.type }, f.id && f.aN && f.sN && f.cN || awa.logger.logWarning("Invalid content blob. Missing required attributes (id, aN/area, sN/slot), cN/contentName. Content information will still be collected!"), i.isShortNames || (f = { contentId: f.id, areaName: f.aN, slotNumber: f.sN, contentName: f.cN, contentSource: f.cS, templateName: f.tN, productId: f.pid, contentType: f.cT }), h = 0; h < o.attributes.length; h++)(e = o.attributes[h], e.name !== i.id && e.name !== i.areaName && e.name !== i.slotNumber && e.name !== i.contentName && e.name !== i.contentSource && e.name !== i.templateName && e.name !== i.productId && e.name !== i.contentType && e.name.indexOf("data-bi-") !== -1) && (v = e.name.replace("data-bi-", ""), f[v] = e.value); return awa.utils.removeInvalidElements(f), n.autoCapture.lineage && u && (f = awa.utils.extend(f, or(r))), f } function sr(t) { if (n.useDefaultContentName === !1 || ni(t) || !t.tagName) return ""; var i; switch (t.tagName) { case "A": i = document.all ? t.innerText || t.innerHTML : t.text || t.innerHTML; case "IMG": case "AREA": i = t.alt; default: i = t.value || t.name || t.alt || t.innerText || t.id }return i.substring(0, ti) } function hr(n) { return n = t.returnDomObjectIfjQuery(n), n ? 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(o++, e = awa.cv.isValid(n.cV) ? awa.cv.incrementExternal(e) : awa.cv.increment(), setTimeout(function () { c().done(f.resolve).fail(f.reject) }, 50)) : f.reject(r, u), f.promise() }) }, c().then(function (n, t, i) { return i }, function (t) { return w(t, n) }) } awa.logger.logError("jQuery is not defined, cannot use service module") }; return { post: function (t) { return n(awa.utils.extend({}, t, { method: "POST" })) }, get: function (t) { return n(awa.utils.extend({ maxRetry: 1 }, t, { method: "GET" })) }, put: function (t) { return n(awa.utils.extend({}, t, { method: "PUT" })) }, del: function (t) { return n(awa.utils.extend({}, t, { method: "DELETE" })) }, patch: function (t) { return n(awa.utils.extend({}, t, { method: "PATCH" })) }, doNotChangeSupportCors: function () { r = !1 }, alwaysAddCvToRequestHeader: function () { u = !0 }, setTargetUriOverride: f } }(); + +var config = { + autoCapture: { + lineage: true + }, + coreData: { + appId: "JS:JSLLTest" + } +}; +awa.init(config); \ No newline at end of file From f0d64e372d5602079006da65c5b4a82144bd1207 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Mon, 14 Dec 2020 18:15:48 +0000 Subject: [PATCH 154/192] feat: add plot options create_rank (#27) --- R/create_rank.R | 114 ++++++++++++++++++++++++++++++++++++++++++++- man/create_rank.Rd | 9 +++- 2 files changed, 121 insertions(+), 2 deletions(-) diff --git a/R/create_rank.R b/R/create_rank.R index aa0080f2..6b4dab69 100644 --- a/R/create_rank.R +++ b/R/create_rank.R @@ -19,6 +19,9 @@ #' @param return A character vector specifying what to return. #' Valid values include "table" (default). Features are being considered for alternative return options but are currently #' unavailable. +#' @param plot_mode Numeric vector to determine which plot mode to return: +#' 1. Top and bottom five groups across the data population are highlighted +#' 2. Top and bottom groups _per_ organizational attribute are highlighted #' #' @import dplyr #' @import ggplot2 @@ -38,7 +41,8 @@ create_rank <- function(data, metric, hrvar = extract_hr(data), mingroup = 5, - return = "table"){ + return = "table", + plot_mode = 1){ results <- create_bar(data, @@ -73,7 +77,115 @@ create_rank <- function(data, select(hrvar, everything()) if(return == "table"){ + return(output) + + } else if(return == "plot"){ + + # Company average + avg_ch <- + data %>% + create_bar(hrvar = NULL, metric = metric, return = "table") %>% + pull(metric) + + if(plot_mode == 1){ + + # Main plot + output %>% + mutate(Rank = rev(rank(!!sym(metric), ties.method = "max"))) %>% + mutate(Group = + case_when(Rank %in% 1:5 ~ "Top 5", + Rank %in% nrow(.):(nrow(.) - 5) ~ "Bottom 5", + TRUE ~ "Middle")) %>% + group_by(hrvar) %>% + mutate(OrgGroup = + case_when(!!sym(metric) == max(!!sym(metric), na.rm = TRUE) ~ "Top", + !!sym(metric) == min(!!sym(metric), na.rm = TRUE) ~ "Bottom", + TRUE ~ "Middle")) %>% + mutate(top_group = max(!!sym(metric), na.rm = TRUE)) %>% + ungroup() %>% + ggplot(aes(x = !!sym(metric), + y = reorder(hrvar, top_group))) + # Sort by top group + geom_point(aes(fill = Group, + size = n), + colour = "black", + pch = 21, + alpha = 0.8) + + labs(title = us_to_space(metric), + subtitle = "Group averages by organizational attribute", + y = "Organizational attributes", + x = us_to_space(metric)) + + ggrepel::geom_text_repel(aes(x = !!sym(metric), + y = hrvar, + label = ifelse(Group %in% c("Top 5", "Bottom 5"), group, "")), + size = 3) + + scale_x_continuous(position = "top") + + scale_fill_manual(name = "Group", + values = c("#FE7F4F", + "white", + rgb2hex(68,151,169)), + guide = "legend") + + theme_wpa_basic() + + scale_size(guide = "none", range = c(1, 15)) + + theme( + panel.grid.major.x = element_blank(), + panel.grid.major.y = element_line(colour = "darkgrey"), + panel.grid.minor.y = element_line(colour = "black"), + strip.placement = "outside", + strip.background = element_blank(), + strip.text = element_blank() + ) + + geom_vline(xintercept = avg_ch, colour = "red") + + } else if(plot_mode == 2){ + + output %>% + group_by(hrvar) %>% + mutate(OrgGroup = + case_when(!!sym(metric) == max(!!sym(metric), na.rm = TRUE) ~ "Top", + !!sym(metric) == min(!!sym(metric), na.rm = TRUE) ~ "Bottom", + TRUE ~ "Middle")) %>% + mutate(top_group = max(!!sym(metric), na.rm = TRUE)) %>% + ungroup() %>% + ggplot(aes(x = !!sym(metric), + y = reorder(hrvar, top_group))) + # Sort by top group + geom_point(aes(fill = OrgGroup, + size = n), + colour = "black", + pch = 21, + alpha = 0.8) + + labs(title = us_to_space(metric), + subtitle = "Group averages by organizational attribute", + y = "Organizational attributes", + x = us_to_space(metric)) + + ggrepel::geom_text_repel(aes(x = !!sym(metric), + y = hrvar, + label = ifelse(OrgGroup %in% c("Top", "Bottom"), group, "")), + size = 3) + + scale_x_continuous(position = "top") + + scale_fill_manual(name = "Group", + values = c("#FE7F4F", + "white", + rgb2hex(68,151,169)), + guide = "legend") + + theme_wpa_basic() + + scale_size(guide = "none", range = c(1, 8)) + + theme( + panel.grid.major.x = element_blank(), + panel.grid.major.y = element_line(colour = "darkgrey"), + panel.grid.minor.y = element_line(colour = "black"), + strip.placement = "outside", + strip.background = element_blank(), + strip.text = element_blank() + ) + + geom_vline(xintercept = avg_ch, colour = "red") + + } else { + + stop("Invalid plot_mode argument.") + + } + } else { stop("Invalid `return` argument.") } diff --git a/man/create_rank.Rd b/man/create_rank.Rd index b45c55b4..aaad45b1 100644 --- a/man/create_rank.Rd +++ b/man/create_rank.Rd @@ -9,7 +9,8 @@ create_rank( metric, hrvar = extract_hr(data), mingroup = 5, - return = "table" + return = "table", + plot_mode = 1 ) } \arguments{ @@ -27,6 +28,12 @@ Defaults to 5.} \item{return}{A character vector specifying what to return. Valid values include "table" (default). Features are being considered for alternative return options but are currently unavailable.} + +\item{plot_mode}{Numeric vector to determine which plot mode to return: +\enumerate{ +\item Top and bottom five groups across the data population are highlighted +\item Top and bottom groups \emph{per} organizational attribute are highlighted +}} } \value{ Returns a ggplot object by default, where 'plot' is passed in \code{return}. From 396fcdcd9249d7bc420950c02e5861622ee7f73c Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Mon, 14 Dec 2020 18:18:11 +0000 Subject: [PATCH 155/192] fix: flip colours for create_rank() --- R/create_rank.R | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/R/create_rank.R b/R/create_rank.R index 6b4dab69..bfee5cb8 100644 --- a/R/create_rank.R +++ b/R/create_rank.R @@ -121,9 +121,9 @@ create_rank <- function(data, size = 3) + scale_x_continuous(position = "top") + scale_fill_manual(name = "Group", - values = c("#FE7F4F", + values = c(rgb2hex(68,151,169), "white", - rgb2hex(68,151,169)), + "#FE7F4F"), guide = "legend") + theme_wpa_basic() + scale_size(guide = "none", range = c(1, 15)) + @@ -164,9 +164,9 @@ create_rank <- function(data, size = 3) + scale_x_continuous(position = "top") + scale_fill_manual(name = "Group", - values = c("#FE7F4F", + values = c(rgb2hex(68,151,169), "white", - rgb2hex(68,151,169)), + "#FE7F4F"), guide = "legend") + theme_wpa_basic() + scale_size(guide = "none", range = c(1, 8)) + From f27f94ce1f808442442961402d3fac6444b97b4c Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Tue, 15 Dec 2020 09:45:18 +0000 Subject: [PATCH 156/192] fix: hrvar_count_all - `threshold` argument previously had no effect on underlying `extract_hr()` function - Add examples for test purposes --- R/hrvar_count_all.R | 6 +++++- man/hrvar_count_all.Rd | 5 +++++ 2 files changed, 10 insertions(+), 1 deletion(-) diff --git a/R/hrvar_count_all.R b/R/hrvar_count_all.R index 7f7cafd9..57571afe 100644 --- a/R/hrvar_count_all.R +++ b/R/hrvar_count_all.R @@ -22,6 +22,10 @@ #' #' @family Data Validation #' +#' @examples +#' # Return a summary table of all HR attributes +#' hrvar_count_all(sq_data, return = "table") +#' #' @return #' Returns an error message by default, where 'text' is passed in `return`. #' When 'table' is passed, a summary table listing the number of distinct fields and percentage of NAs for the specified number of HR attributes will be returned. @@ -36,7 +40,7 @@ hrvar_count_all <- function(data, ){ ## Character vector of HR attributes - extracted_chr <- extract_hr(data, return = "names") + extracted_chr <- extract_hr(data, return = "names", max_unique = threshold) summary_table_n <- data %>% diff --git a/man/hrvar_count_all.Rd b/man/hrvar_count_all.Rd index 656109eb..5e907ba8 100644 --- a/man/hrvar_count_all.Rd +++ b/man/hrvar_count_all.Rd @@ -34,6 +34,11 @@ in the WpA Sources page. This function will return a summary table with the count of distinct fields per HR attribute and the percentage of employees with NAs for that attribute. See hrvar_count function for more detail on the specific HR attribute of interest. +} +\examples{ +# Return a summary table of all HR attributes +hrvar_count_all(sq_data, return = "table") + } \seealso{ Other Data Validation: From 2e7c2f522f374156ecd1f5c5d9a65722f5dea320 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Tue, 15 Dec 2020 23:11:01 +0000 Subject: [PATCH 157/192] feat: change bar colours (#27) --- R/create_rank.R | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/R/create_rank.R b/R/create_rank.R index bfee5cb8..197f41ef 100644 --- a/R/create_rank.R +++ b/R/create_rank.R @@ -129,8 +129,7 @@ create_rank <- function(data, scale_size(guide = "none", range = c(1, 15)) + theme( panel.grid.major.x = element_blank(), - panel.grid.major.y = element_line(colour = "darkgrey"), - panel.grid.minor.y = element_line(colour = "black"), + panel.grid.major.y = element_line(colour = "#D9E7F7", size = 3), # lightblue bar strip.placement = "outside", strip.background = element_blank(), strip.text = element_blank() @@ -172,8 +171,7 @@ create_rank <- function(data, scale_size(guide = "none", range = c(1, 8)) + theme( panel.grid.major.x = element_blank(), - panel.grid.major.y = element_line(colour = "darkgrey"), - panel.grid.minor.y = element_line(colour = "black"), + panel.grid.major.y = element_line(colour = "#D9E7F7", size = 3), # lightblue bar strip.placement = "outside", strip.background = element_blank(), strip.text = element_blank() From a118c360b9c9a115a071b22dbbd19310d539064b Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Tue, 15 Dec 2020 23:19:52 +0000 Subject: [PATCH 158/192] docs: consistent return options (#31) --- R/identify_holidayweeks.R | 21 ++++++++++++++------- man/identify_holidayweeks.Rd | 17 ++++++++++------- 2 files changed, 24 insertions(+), 14 deletions(-) diff --git a/R/identify_holidayweeks.R b/R/identify_holidayweeks.R index d07e4d97..3f817ff9 100644 --- a/R/identify_holidayweeks.R +++ b/R/identify_holidayweeks.R @@ -14,7 +14,13 @@ #' #' @param data A Standard Person Query dataset in the form of a data frame. #' @param sd The standard deviation below the mean for collaboration hours that should define an outlier week. Enter a positive number. Default is 1 standard deviation. -#' @param return String to specify what to return +#' @param return String to specify what to return. Returns a message by default (`return` = "message"). +#' Valid options are: +#' - When 'message' is passed, a message is printed identifying holiday weeks. +#' - When 'data' is passed, a dataset with outlier weeks flagged in a new column is returned as a dataframe. +#' - When 'data_cleaned' is passed, a dataset with outlier weeks removed is returned as a dataframe. +#' - when 'data_dirty' is passed, a dataset with only outlier weeks is returned as a dataframe. +#' - when 'plot' is passed, a pot with holiday weeks highlighted is returned as a dataframe. #' #' @import dplyr #' @import ggplot2 @@ -22,13 +28,14 @@ #' #' @family Data Validation #' +#' @examples +#' # Return a message by default +#' identify_holidayweeks(sq_data) +#' +#' # Return a plot +#' identify_holidayweeks(sq_data, return = "plot") +#' #' @return -#' Returns a message by default (`return` = "message"). -#' When 'message' is passed, a message is printed identifying holiday weeks. -#' When 'data' is passed, a dataset with outlier weeks flagged in a new column is returned as a dataframe. -#' When 'data_cleaned' is passed, a dataset with outlier weeks removed is returned as a dataframe. -#' when 'data_dirty' is passed, a dataset with only outlier weeks is returned as a dataframe. -#' when 'plot' is passed, a pot with holiday weeks highlighted is returned as a dataframe. #' #' @export identify_holidayweeks <- function(data, sd = 1, return = "message"){ diff --git a/man/identify_holidayweeks.Rd b/man/identify_holidayweeks.Rd index a95f7415..f83a5fa7 100644 --- a/man/identify_holidayweeks.Rd +++ b/man/identify_holidayweeks.Rd @@ -11,15 +11,18 @@ identify_holidayweeks(data, sd = 1, return = "message") \item{sd}{The standard deviation below the mean for collaboration hours that should define an outlier week. Enter a positive number. Default is 1 standard deviation.} -\item{return}{String to specify what to return} +\item{return}{String to specify what to return. Returns a message by default (\code{return} = "message"). +Valid options are: +\itemize{ +\item When 'message' is passed, a message is printed identifying holiday weeks. +\item When 'data' is passed, a dataset with outlier weeks flagged in a new column is returned as a dataframe. +\item When 'data_cleaned' is passed, a dataset with outlier weeks removed is returned as a dataframe. +\item when 'data_dirty' is passed, a dataset with only outlier weeks is returned as a dataframe. +\item when 'plot' is passed, a pot with holiday weeks highlighted is returned as a dataframe. +}} } \value{ -Returns a message by default (\code{return} = "message"). -When 'message' is passed, a message is printed identifying holiday weeks. -When 'data' is passed, a dataset with outlier weeks flagged in a new column is returned as a dataframe. -When 'data_cleaned' is passed, a dataset with outlier weeks removed is returned as a dataframe. -when 'data_dirty' is passed, a dataset with only outlier weeks is returned as a dataframe. -when 'plot' is passed, a pot with holiday weeks highlighted is returned as a dataframe. + } \description{ This function scans a standard query output for weeks where collaboration hours is far outside the mean. From ca217a8b6afbe8f92555f9397950960b024874e6 Mon Sep 17 00:00:00 2001 From: moralec Date: Wed, 16 Dec 2020 10:42:40 +0000 Subject: [PATCH 159/192] Update create_rank.R --- R/create_rank.R | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/R/create_rank.R b/R/create_rank.R index 197f41ef..d16d616a 100644 --- a/R/create_rank.R +++ b/R/create_rank.R @@ -112,9 +112,9 @@ create_rank <- function(data, pch = 21, alpha = 0.8) + labs(title = us_to_space(metric), - subtitle = "Group averages by organizational attribute", - y = "Organizational attributes", - x = us_to_space(metric)) + + subtitle = "Lowest and highest values, by org. attribute", + y = "", + x = "") + ggrepel::geom_text_repel(aes(x = !!sym(metric), y = hrvar, label = ifelse(Group %in% c("Top 5", "Bottom 5"), group, "")), @@ -128,13 +128,15 @@ create_rank <- function(data, theme_wpa_basic() + scale_size(guide = "none", range = c(1, 15)) + theme( - panel.grid.major.x = element_blank(), + axis.line=element_blank(), + panel.grid.major.x = element_blank(), panel.grid.major.y = element_line(colour = "#D9E7F7", size = 3), # lightblue bar + panel.grid.minor.x = element_line(color="gray"), strip.placement = "outside", strip.background = element_blank(), strip.text = element_blank() ) + - geom_vline(xintercept = avg_ch, colour = "red") + geom_vline(xintercept = avg_ch, colour = "red") } else if(plot_mode == 2){ From b9800867affa06ef451944e696b9963596f9c594 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Wed, 16 Dec 2020 11:36:26 +0000 Subject: [PATCH 160/192] feat: add create_sankey() --- NAMESPACE | 1 + R/create_sankey.R | 67 ++++++++++++++++++++++++++++++++++++++++++++ man/create_sankey.Rd | 35 +++++++++++++++++++++++ 3 files changed, 103 insertions(+) create mode 100644 R/create_sankey.R create mode 100644 man/create_sankey.Rd diff --git a/NAMESPACE b/NAMESPACE index c224492b..1e38e56d 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -49,6 +49,7 @@ export(create_line) export(create_line_asis) export(create_period_scatter) export(create_rank) +export(create_sankey) export(create_scatter) export(create_stacked) export(create_trend) diff --git a/R/create_sankey.R b/R/create_sankey.R new file mode 100644 index 00000000..d53992b7 --- /dev/null +++ b/R/create_sankey.R @@ -0,0 +1,67 @@ +#' @title Create a sankey chart from a two-column count table +#' +#' @description +#' Create a networkD3 style sankey chart based on a long count table +#' with two variables. The input data should have three columns, where +#' each row is a unique group: +#' 1. Variable 1 +#' 2. Variable 2 +#' 3. Count +#' +#' @param data Data frame of the long count table. +#' @param var1 String containing the name of the variable to be shown on the left. +#' @param var2 String containing the name of the variable to be shown on the right. +#' @param count String containing the name of the count variable. +#' +#' @import dplyr +#' +#' @examples +#' \donttest{ +#' sq_data %>% +#' dplyr::count(Organization, FunctionType) %>% +#' create_sankey(var1 = "Organization", var2 = "FunctionType") +#' } +#' +#' @export +create_sankey <- function(data, var1, var2, count = "n"){ + + ## Rename + data$pre_group <- data[[var1]] + data$group <- data[[var2]] + + ## Set up `nodes` + group_source <- unique(data$pre_group) + group_target <- paste0(unique(data$group), " ") + + groups <- c(group_source, group_target) + + nodes_source <- tibble(name = group_source) + nodes_target <- tibble(name = group_target) + nodes <- rbind(nodes_source, nodes_target) %>% mutate(node = 0:(nrow(.) - 1)) + + ## Set up `links` + links <- + data %>% + mutate(group = paste0(group, " ")) %>% + select(source = "pre_group", + target = "group", + value = count) + + nodes_source <- nodes_source %>% select(name) # Make `nodes` a single column data frame + nodes_target <- nodes_target %>% select(name) # Make `nodes` a single column data frame + + links <- + links %>% + left_join(nodes %>% rename(IDsource = "node"), by = c("source" = "name")) %>% + left_join(nodes %>% rename(IDtarget = "node"), by = c("target" = "name")) + + + networkD3::sankeyNetwork(Links = as.data.frame(links), + Nodes = as.data.frame(nodes), + Source = 'IDsource', # Change reference to IDsource + Target = 'IDtarget', # Change reference to IDtarget + Value = 'value', + NodeID = 'name', + units="count", + sinksRight = FALSE) +} diff --git a/man/create_sankey.Rd b/man/create_sankey.Rd new file mode 100644 index 00000000..05b76504 --- /dev/null +++ b/man/create_sankey.Rd @@ -0,0 +1,35 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/create_sankey.R +\name{create_sankey} +\alias{create_sankey} +\title{Create a sankey chart from a two-column count table} +\usage{ +create_sankey(data, var1, var2, count = "n") +} +\arguments{ +\item{data}{Data frame of the long count table.} + +\item{var1}{String containing the name of the variable to be shown on the left.} + +\item{var2}{String containing the name of the variable to be shown on the right.} + +\item{count}{String containing the name of the count variable.} +} +\description{ +Create a networkD3 style sankey chart based on a long count table +with two variables. The input data should have three columns, where +each row is a unique group: +\enumerate{ +\item Variable 1 +\item Variable 2 +\item Count +} +} +\examples{ +\donttest{ +sq_data \%>\% + dplyr::count(Organization, FunctionType) \%>\% + create_sankey(var1 = "Organization", var2 = "FunctionType") +} + +} From d3289124602f19f38cec3b62fc5b87c56a4f29b2 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Wed, 16 Dec 2020 11:48:32 +0000 Subject: [PATCH 161/192] feat: add network_leiden() --- DESCRIPTION | 3 +- NAMESPACE | 1 + R/network_leiden.R | 118 ++++++++++++++++++++++++++++++++++++++++++ man/network_leiden.Rd | 29 +++++++++++ 4 files changed, 150 insertions(+), 1 deletion(-) create mode 100644 R/network_leiden.R create mode 100644 man/network_leiden.Rd diff --git a/DESCRIPTION b/DESCRIPTION index 55153daf..3f0cc220 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -66,5 +66,6 @@ RoxygenNote: 7.1.1 Roxygen: list(markdown = TRUE) VignetteBuilder: knitr Suggests: - extrafont + extrafont, + leiden Language: en-US diff --git a/NAMESPACE b/NAMESPACE index 1e38e56d..702d4fe2 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -107,6 +107,7 @@ export(meetingtype_summary) export(mgrcoatt_dist) export(mgrrel_matrix) export(network_g2g) +export(network_leiden) export(network_p2p) export(one2one_dist) export(one2one_fizz) diff --git a/R/network_leiden.R b/R/network_leiden.R new file mode 100644 index 00000000..5ab92abd --- /dev/null +++ b/R/network_leiden.R @@ -0,0 +1,118 @@ +#' @title Implement the Leiden community detection on a Person to Person network query +#' +#' @description +#' Take a P2P network query and implement the Leiden community detection method. To run +#' this function, you will require all the pre-requisites of the **leiden** package installed, +#' which includes Python and **reticulate**. +#' +#' @param data Data frame containing a Person to Person query. +#' @param hrvar String containing the HR attribute to be matched in the dataset. +#' @param res Resolution parameter to be passed to `leiden::leiden()`. Defaults to 0.5. +#' @param return String specifying what output to return. Valid return options include: +#' - 'plot-leiden': return a network plot coloured by leiden communities. +#' - 'plot-hrvar': return a network plot coloured by HR attribute. +#' - 'plot-sankey': return a sankey plot combining communities and HR attribute. +#' - 'table': return a vertex summary table with counts in communities and HR attribute. +#' - 'data': return a vertex data file that matches vertices with communities and HR attributes. +#' +#' @import dplyr +#' +#' @export +network_leiden <- function(data, hrvar, res = 0.5, return){ + + ## Set variables + TO_hrvar <- paste0("TieOrigin_", hrvar) + TD_hrvar <- paste0("TieDestination_", hrvar) + + ## Set edges df + edges <- + data %>% + select(from = "TieOrigin_PersonId", + to = "TieDestination_PersonId", + weight = "StrongTieScore") + + ## Vertices data frame to provide meta-data + vert_ft <- + rbind( + # TieOrigin + edges %>% + left_join(select(p2p_demo, TieOrigin_PersonId, TO_hrvar), + by = c("from" = "TieOrigin_PersonId")) %>% + select(node = "from", !!sym(hrvar) := TO_hrvar), + + # TieDestination + edges %>% + left_join(select(p2p_demo, TieDestination_PersonId, TD_hrvar), + by = c("to" = "TieDestination_PersonId")) %>% + select(node = "to", !!sym(hrvar) := TD_hrvar) + ) + + ## Create igraph object + g_raw <- + igraph::graph_from_data_frame(edges, + directed = TRUE, # Directed, but FALSE for visualization + vertices = unique(vert_ft)) # remove duplicates + + ## Return a numeric vector of partitions / clusters / modules + ## Set a low resolution parameter to have fewer groups + ld <- leiden::leiden(g_raw, resolution_parameter = res) # create partitions + + ## Add cluster + g <- + g_raw %>% + # Add leiden partitions to graph object + igraph::set_vertex_attr("cluster", value = as.character(ld)) + + ## Create vertex table + vertex_tb <- + g %>% + igraph::get.vertex.attribute() %>% + as_tibble() + + ## Return + if(return == "plot-leiden"){ + g %>% + ggraph::ggraph(layout = "stress") + + ggraph::geom_edge_link(colour = "lightgrey", edge_width = 0.1) + + ggraph::geom_node_point(aes(colour = cluster), alpha = 0.8) + + theme_void() + + theme(legend.position = "bottom", + # legend.background = element_rect(fill = "black"), + # plot.background = element_rect(fill = "black"), + # text = element_text(colour = "white"), + axis.line = element_blank()) + + labs(title = "Person to person collaboration with Community Detection", + subtitle = "Based on Leiden algorithm and Strong Tie Score", + y = "", + x = "") + } else if(return == "plot-hrvar"){ + g %>% + ggraph::ggraph(layout = "stress") + + ggraph::geom_edge_link(colour = "lightgrey", edge_width = 0.1) + + ggraph::geom_node_point(aes(colour = !!sym(hrvar)), alpha = 0.8) + + theme_void() + + theme(legend.position = "bottom", + axis.line = element_blank()) + + labs(title = "Person to person collaboration", + subtitle = paste0("Showing ", hrvar), + y = "", + x = "") + + } else if(return == "table"){ + + vertex_tb %>% + count(!!sym(hrvar), cluster) + + } else if(return == "data"){ + + vertex_tb + + } else if(return == "plot-sankey"){ + + create_sankey(data = vertex_tb %>% count(!!sym(hrvar), cluster), + var1 = hrvar, + var2 = "cluster", + count = "n") + + } +} diff --git a/man/network_leiden.Rd b/man/network_leiden.Rd new file mode 100644 index 00000000..fd417126 --- /dev/null +++ b/man/network_leiden.Rd @@ -0,0 +1,29 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/network_leiden.R +\name{network_leiden} +\alias{network_leiden} +\title{Implement the Leiden community detection on a Person to Person network query} +\usage{ +network_leiden(data, hrvar, res = 0.5, return) +} +\arguments{ +\item{data}{Data frame containing a Person to Person query.} + +\item{hrvar}{String containing the HR attribute to be matched in the dataset.} + +\item{res}{Resolution parameter to be passed to \code{leiden::leiden()}. Defaults to 0.5.} + +\item{return}{String specifying what output to return. Valid return options include: +\itemize{ +\item 'plot-leiden': return a network plot coloured by leiden communities. +\item 'plot-hrvar': return a network plot coloured by HR attribute. +\item 'plot-sankey': return a sankey plot combining communities and HR attribute. +\item 'table': return a vertex summary table with counts in communities and HR attribute. +\item 'data': return a vertex data file that matches vertices with communities and HR attributes. +}} +} +\description{ +Take a P2P network query and implement the Leiden community detection method. To run +this function, you will require all the pre-requisites of the \strong{leiden} package installed, +which includes Python and \strong{reticulate}. +} From 01f6204ef3ee4d50f3b8db96fb258d60c8346612 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Wed, 16 Dec 2020 11:49:55 +0000 Subject: [PATCH 162/192] docs: update Rd files --- man/identify_holidayweeks.Rd | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/man/identify_holidayweeks.Rd b/man/identify_holidayweeks.Rd index f83a5fa7..20513c89 100644 --- a/man/identify_holidayweeks.Rd +++ b/man/identify_holidayweeks.Rd @@ -30,6 +30,14 @@ Returns a list of weeks that appear to be holiday weeks and optionally an edited By default, missing values are excluded. As best practice, run this function prior to any analysis to remove atypical collaboration weeks from your dataset. +} +\examples{ +# Return a message by default +identify_holidayweeks(sq_data) + +# Return a plot +identify_holidayweeks(sq_data, return = "plot") + } \seealso{ Other Data Validation: From 305d0d885a6bbd3a9013c253a3eb73425017f09e Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Wed, 16 Dec 2020 12:38:01 +0000 Subject: [PATCH 163/192] fix: bug with old variables --- R/network_leiden.R | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/R/network_leiden.R b/R/network_leiden.R index 5ab92abd..ace6e5d7 100644 --- a/R/network_leiden.R +++ b/R/network_leiden.R @@ -36,13 +36,13 @@ network_leiden <- function(data, hrvar, res = 0.5, return){ rbind( # TieOrigin edges %>% - left_join(select(p2p_demo, TieOrigin_PersonId, TO_hrvar), + left_join(select(data, TieOrigin_PersonId, TO_hrvar), by = c("from" = "TieOrigin_PersonId")) %>% select(node = "from", !!sym(hrvar) := TO_hrvar), # TieDestination edges %>% - left_join(select(p2p_demo, TieDestination_PersonId, TD_hrvar), + left_join(select(data, TieDestination_PersonId, TD_hrvar), by = c("to" = "TieDestination_PersonId")) %>% select(node = "to", !!sym(hrvar) := TD_hrvar) ) From b9bd6aafc896424fc53c5dec945079ab7e35e738 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Wed, 16 Dec 2020 13:42:50 +0000 Subject: [PATCH 164/192] feat: add AppId to Adobe Analytics --- pkgdown/extra.js | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pkgdown/extra.js b/pkgdown/extra.js index 3ecd37af..521c8878 100644 --- a/pkgdown/extra.js +++ b/pkgdown/extra.js @@ -5,7 +5,7 @@ var config = { lineage: true }, coreData: { - appId: "JS:JSLLTest" + appId: "wpaGitHubSite" } }; -awa.init(config); \ No newline at end of file +awa.init(config); From 5c0c4fe3f8716d3c2984c19ab8b44c694714b102 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Wed, 16 Dec 2020 14:07:29 +0000 Subject: [PATCH 165/192] docs: add return error catch option --- R/network_leiden.R | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/R/network_leiden.R b/R/network_leiden.R index ace6e5d7..2771487a 100644 --- a/R/network_leiden.R +++ b/R/network_leiden.R @@ -114,5 +114,9 @@ network_leiden <- function(data, hrvar, res = 0.5, return){ var2 = "cluster", count = "n") + } else { + + stop("Please enter a valid input for `return`.") + } } From 86477c43d3061e0f0e0227ad73f71c4e209ffcd2 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Thu, 17 Dec 2020 10:48:24 +0000 Subject: [PATCH 166/192] feat: add network_describe --- NAMESPACE | 3 + R/network_describe.R | 123 ++++++++++++++++++++++++++++++++++++++++ man/network_describe.Rd | 23 ++++++++ 3 files changed, 149 insertions(+) create mode 100644 R/network_describe.R create mode 100644 man/network_describe.Rd diff --git a/NAMESPACE b/NAMESPACE index 702d4fe2..665a3010 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -106,8 +106,10 @@ export(meetingtype_sum) export(meetingtype_summary) export(mgrcoatt_dist) export(mgrrel_matrix) +export(network_describe) export(network_g2g) export(network_leiden) +export(network_louvain) export(network_p2p) export(one2one_dist) export(one2one_fizz) @@ -157,6 +159,7 @@ import(ggraph) import(network) import(reshape2) import(scales) +import(tidyr) import(tidyselect) importFrom(data.table,"%between%") importFrom(data.table,"%like%") diff --git a/R/network_describe.R b/R/network_describe.R new file mode 100644 index 00000000..07ded275 --- /dev/null +++ b/R/network_describe.R @@ -0,0 +1,123 @@ +# -------------------------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See LICENSE.txt in the project root for license information. +# -------------------------------------------------------------------------------------------- + +#' @title Uncover HR attributes which best represent a population for a Person to Person query +#' +#' @author Tannaz Sattari Tabrizi +#' +#' @description +#' Returns a data frame that gives a percentage of the group combinations that best represent +#' the population provided. Uses a person to person query. +#' +#' @param data Data frame for a person to person query. +#' @param hrvar Character vector of length 3 containing the HR attributes to be used. +#' +#' @import dplyr +#' @import tidyr +#' +#' @export +network_describe <- function(data, hrvar = c("Organization", "LevelDesignation", "FunctionType")){ + + if(length(hrvar) != 3){ + stop("Please provide a character vector of length 3 for `hrvar`") + } + + ## De-duplicated data containing only TieOrigins + filtered_Data <- unique(select(data, starts_with("TieOrigin_"))) + + ## Select features + features <- select(filtered_Data, paste0("TieOrigin_", hrvar)) + + ## Feature set: 1 + max_percentages_1f <- + features %>% + colnames() %>% + purrr::map(function(c){ + + agg <- + features %>% + group_by_at(.vars=vars(c)) %>% + summarise(count = n(), .groups = "drop") %>% + mutate(percentage = count / sum(count, na.rm = TRUE)) + + agg %>% + arrange(desc(percentage)) %>% + slice(1) %>% # Extract first row + mutate(feature_1 = c, + feature_1_value = !!sym(c)) %>% + select(feature_1, feature_1_value, Percentage = "percentage") + }) %>% + bind_rows() + + ## Feature set: 2 + max_percentages_2f <- + list(c1 = colnames(features), + c2 = colnames(features)) %>% + expand.grid(stringsAsFactors = FALSE) %>% + filter(c1 != c2) %>% + purrr::pmap(function(c1, c2){ + agg <- + features %>% + group_by_at(.vars=vars(c1, c2)) %>% + summarise(count = n(), .groups = "drop") %>% + mutate(percentage = count / sum(count, na.rm = TRUE)) + + agg %>% + arrange(desc(percentage)) %>% + slice(1) %>% # Extract first row + mutate(feature_1 = c1, + feature_1_value = !!sym(as.character(c1)), + feature_2 = c2, + feature_2_value = !!sym(as.character(c2))) %>% + select(feature_1, + feature_1_value, + feature_2, + feature_2_value, + Percentage = "percentage") + }) %>% + bind_rows() + + + ## Feature set: 3 + max_percentages_3f <- + list(c1 = colnames(features), + c2 = colnames(features), + c3 = colnames(features)) %>% + expand.grid(stringsAsFactors = FALSE) %>% + filter(c1 != c2, + c2 != c3, + c3 != c1) %>% + purrr::pmap(function(c1, c2, c3){ + agg <- + features %>% + group_by_at(.vars=vars(c1, c2, c3)) %>% + summarise(count = n(), .groups = "drop") %>% + mutate(percentage = count / sum(count, na.rm = TRUE)) + + agg %>% + arrange(desc(percentage)) %>% + slice(1) %>% # Extract first row + mutate(feature_1 = c1, + feature_1_value = !!sym(c1), + feature_2 = c2, + feature_2_value = !!sym(c2), + feature_3 = c3, + feature_3_value = !!sym(c3)) %>% + select(feature_1, + feature_1_value, + feature_2, + feature_2_value, + feature_3, + feature_3_value, + Percentage = "percentage") + }) %>% + bind_rows() + + list(max_percentages_1f, + max_percentages_2f, + max_percentages_3f) %>% + bind_rows() %>% + select(starts_with("feature"), Percentage) +} diff --git a/man/network_describe.Rd b/man/network_describe.Rd new file mode 100644 index 00000000..916439a4 --- /dev/null +++ b/man/network_describe.Rd @@ -0,0 +1,23 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/network_describe.R +\name{network_describe} +\alias{network_describe} +\title{Uncover HR attributes which best represent a population for a Person to Person query} +\usage{ +network_describe( + data, + hrvar = c("Organization", "LevelDesignation", "FunctionType") +) +} +\arguments{ +\item{data}{Data frame for a person to person query.} + +\item{hrvar}{Character vector of length 3 containing the HR attributes to be used.} +} +\description{ +Returns a data frame that gives a percentage of the group combinations that best represent +the population provided. Uses a person to person query. +} +\author{ +Tannaz Sattari Tabrizi \href{mailto:Tannaz.Sattari@microsoft.com}{Tannaz.Sattari@microsoft.com} +} From b8063d153dc28bff896eba23c2e1ea591bed6afb Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Thu, 17 Dec 2020 10:49:11 +0000 Subject: [PATCH 167/192] feat: add network_louvain --- R/network_louvain.R | 113 +++++++++++++++++++++++++++++++++++++++++ man/network_louvain.Rd | 19 +++++++ 2 files changed, 132 insertions(+) create mode 100644 R/network_louvain.R create mode 100644 man/network_louvain.Rd diff --git a/R/network_louvain.R b/R/network_louvain.R new file mode 100644 index 00000000..d29edab0 --- /dev/null +++ b/R/network_louvain.R @@ -0,0 +1,113 @@ +# -------------------------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See LICENSE.txt in the project root for license information. +# -------------------------------------------------------------------------------------------- + +#' @title Implement the Louvain community detection on a Person to Person network query +#' +#' @description +#' Take a P2P network query and implement the Louvain community detection method. The +#' **igraph** implementation of the Louvain method is used. +#' +#' @param data Data frame containing a Person to Person query. +#' @param hrvar String containing the HR attribute to be matched in the dataset. +#' @param return String specifying what output to return. +#' +#' @import ggraph +#' @import dplyr +#' +#' @export +network_louvain <- function(data, hrvar, return){ + + ## Set variables + TO_hrvar <- paste0("TieOrigin_", hrvar) + TD_hrvar <- paste0("TieDestination_", hrvar) + + ## Set edges df + edges <- + data %>% + select(from = "TieOrigin_PersonId", + to = "TieDestination_PersonId", + weight = "StrongTieScore") + + ## Vertices data frame to provide meta-data + vert_ft <- + rbind( + # TieOrigin + edges %>% + left_join(select(data, TieOrigin_PersonId, TO_hrvar), + by = c("from" = "TieOrigin_PersonId")) %>% + select(node = "from", !!sym(hrvar) := TO_hrvar), + + # TieDestination + edges %>% + left_join(select(data, TieDestination_PersonId, TD_hrvar), + by = c("to" = "TieDestination_PersonId")) %>% + select(node = "to", !!sym(hrvar) := TD_hrvar) + ) + + ## Create igraph object + g_raw <- + igraph::graph_from_data_frame(edges, + directed = FALSE, # Set to undirected for clustering + vertices = unique(vert_ft)) # remove duplicates + + ## Return a numeric vector of partitions / clusters / modules + ## Set a low resolution parameter to have fewer groups + lc <- igraph::cluster_louvain(g_raw) + + ## Add cluster + g <- + g_raw %>% + # Add louvain partitions to graph object + igraph::set_vertex_attr("cluster", value = igraph::membership(lc)) # Return membership - diff from Leiden + + ## Create vertex table + vertex_tb <- + g %>% + igraph::get.vertex.attribute() %>% + as_tibble() %>% + count(!!sym(hrvar), cluster) + + ## Return + if(return == "plot-louvain"){ + g %>% + ggraph::ggraph(layout = "stress") + + ggraph::geom_edge_link(colour = "lightgrey", edge_width = 0.1) + + ggraph::geom_node_point(aes(colour = cluster), alpha = 0.8) + + theme_void() + + theme(legend.position = "bottom", + # legend.background = element_rect(fill = "black"), + # plot.background = element_rect(fill = "black"), + # text = element_text(colour = "white"), + axis.line = element_blank()) + + labs(title = "Person to person collaboration with Community Detection", + subtitle = "Based on louvain algorithm and Strong Tie Score", + y = "", + x = "") + } else if(return == "plot-hrvar"){ + g %>% + ggraph::ggraph(layout = "stress") + + ggraph::geom_edge_link(colour = "lightgrey", edge_width = 0.1) + + ggraph::geom_node_point(aes(colour = !!sym(hrvar)), alpha = 0.8) + + theme_void() + + theme(legend.position = "bottom", + axis.line = element_blank()) + + labs(title = "Person to person collaboration", + subtitle = paste0("Showing ", hrvar), + y = "", + x = "") + + } else if(return == "table"){ + + vertex_tb + + } else if(return == "plot-sankey"){ + + create_sankey(data = vertex_tb, + var1 = hrvar, + var2 = "cluster", + count = "n") + + } +} diff --git a/man/network_louvain.Rd b/man/network_louvain.Rd new file mode 100644 index 00000000..08625549 --- /dev/null +++ b/man/network_louvain.Rd @@ -0,0 +1,19 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/network_louvain.R +\name{network_louvain} +\alias{network_louvain} +\title{Implement the Louvain community detection on a Person to Person network query} +\usage{ +network_louvain(data, hrvar, return) +} +\arguments{ +\item{data}{Data frame containing a Person to Person query.} + +\item{hrvar}{String containing the HR attribute to be matched in the dataset.} + +\item{return}{String specifying what output to return.} +} +\description{ +Take a P2P network query and implement the Louvain community detection method. The +\strong{igraph} implementation of the Louvain method is used. +} From 69f27c39fdd10949c980531d9675b599e6bf22cb Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Thu, 17 Dec 2020 10:49:27 +0000 Subject: [PATCH 168/192] docs: add copyright header --- R/create_sankey.R | 5 +++++ R/network_leiden.R | 5 +++++ 2 files changed, 10 insertions(+) diff --git a/R/create_sankey.R b/R/create_sankey.R index d53992b7..7d88f37d 100644 --- a/R/create_sankey.R +++ b/R/create_sankey.R @@ -1,3 +1,8 @@ +# -------------------------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See LICENSE.txt in the project root for license information. +# -------------------------------------------------------------------------------------------- + #' @title Create a sankey chart from a two-column count table #' #' @description diff --git a/R/network_leiden.R b/R/network_leiden.R index 2771487a..15ea7906 100644 --- a/R/network_leiden.R +++ b/R/network_leiden.R @@ -1,3 +1,8 @@ +# -------------------------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See LICENSE.txt in the project root for license information. +# -------------------------------------------------------------------------------------------- + #' @title Implement the Leiden community detection on a Person to Person network query #' #' @description From 52dee349d5fcb2c996fb1486c66aeef9225a5728 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Thu, 17 Dec 2020 15:42:46 +0000 Subject: [PATCH 169/192] feat: add network return option `network_g2g()` can return a network option. Also now improved documentation for returning options. --- R/network_g2g.R | 50 +++++++++++++++++++++++++++------------------- man/network_g2g.Rd | 7 ++++++- 2 files changed, 36 insertions(+), 21 deletions(-) diff --git a/R/network_g2g.R b/R/network_g2g.R index ddd2eda9..f89a33a2 100644 --- a/R/network_g2g.R +++ b/R/network_g2g.R @@ -17,8 +17,10 @@ #' @param subtitle String to override default plot subtitle. #' @param ... Additional arguments to pass to `GGally::ggnet2()` #' @param return Character vector specifying what to return, defaults to "plot". -#' Valid inputs are "plot" and "table". -#' +#' Valid inputs include: +#' - "plot": return a network plot. +#' - "table": return a raw data table used to plot the network. +#' - "network": return a network object generated by `network::network()` #' #' @import ggplot2 #' @import dplyr @@ -61,7 +63,7 @@ network_g2g <- function(data, plot_data - } else if(return == "plot"){ + } else if(return %in% c("plot", "network")){ ## Network object mynet_em <- @@ -73,23 +75,31 @@ network_g2g <- function(data, ignore.eval = FALSE, names.eval = "weights") - ## Plot object - mynet_em %>% - GGally::ggnet2(size = 12, - color = "lightblue", - label = TRUE, - label.size = 4, - label.color = "black", - edge.size = "weights", - edge.alpha = .5, - ...) + - ggtitle("Group to Group Collaboration", - subtitle = subtitle) + - xlab(label = "") + - ylab(label = "") + - theme_wpa_basic() + - labs(caption = paste("Displays only collaboration above ", exc_threshold * 100, "% of node's total collaboration", sep = "")) + - theme(axis.line = element_blank()) + if(return == "network"){ + + mynet_em # Return network object + + } else { + + ## Plot object + mynet_em %>% + GGally::ggnet2(size = 12, + color = "lightblue", + label = TRUE, + label.size = 4, + label.color = "black", + edge.size = "weights", + edge.alpha = .5, + ...) + + ggtitle("Group to Group Collaboration", + subtitle = subtitle) + + xlab(label = "") + + ylab(label = "") + + theme_wpa_basic() + + labs(caption = paste("Displays only collaboration above ", exc_threshold * 100, "% of node's total collaboration", sep = "")) + + theme(axis.line = element_blank()) + + } } else { diff --git a/man/network_g2g.Rd b/man/network_g2g.Rd index 06780583..cd06320c 100644 --- a/man/network_g2g.Rd +++ b/man/network_g2g.Rd @@ -41,7 +41,12 @@ g2g_network( \item{subtitle}{String to override default plot subtitle.} \item{return}{Character vector specifying what to return, defaults to "plot". -Valid inputs are "plot" and "table".} +Valid inputs include: +\itemize{ +\item "plot": return a network plot. +\item "table": return a raw data table used to plot the network. +\item "network": return a network object generated by \code{network::network()} +}} \item{...}{Additional arguments to pass to \code{GGally::ggnet2()}} } From 320e97af8dae19821621d3364070fe0e028e0f07 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Thu, 17 Dec 2020 16:42:58 +0000 Subject: [PATCH 170/192] docs: add example for network_g2g --- R/network_g2g.R | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git a/R/network_g2g.R b/R/network_g2g.R index f89a33a2..99749bf8 100644 --- a/R/network_g2g.R +++ b/R/network_g2g.R @@ -27,10 +27,27 @@ #' #' @examples #' \dontrun{ +#' ## Return a network plot #' g2g_data %>% #' network_g2g(time_investor = "TimeInvestors_Organization", #' collaborator = "Collaborators_Organization", #' metric = "Collaboration_hours") +#' +#' ## Export the underlying network object for more customization +#' g2g_data %>% +#' network_g2g(time_investor = "TimeInvestors_Organization", +#' collaborator = "Collaborators_Organization", +#' metric = "Collaboration_hours", +#' return = "network") %>% +#' GGally::ggnet2(size = 20, +#' color = "#ed786f", +#' label = TRUE, +#' label.size = 2, +#' label.color = "black", +#' edge.size = "weights", +#' edge.alpha = .5) + +#' theme(plot.background = element_rect(fill = "black"), +#' text = element_text(colour = "white")) #' } #' #' @export From 07089f3891f89b9b4b4da068d14c2535d8ea4512 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Thu, 17 Dec 2020 16:43:33 +0000 Subject: [PATCH 171/192] docs: update Rd files --- man/network_g2g.Rd | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git a/man/network_g2g.Rd b/man/network_g2g.Rd index cd06320c..b199893d 100644 --- a/man/network_g2g.Rd +++ b/man/network_g2g.Rd @@ -56,10 +56,27 @@ Automatically handles "Collaborators_within_group" and "Other_collaborators" wit } \examples{ \dontrun{ +## Return a network plot g2g_data \%>\% network_g2g(time_investor = "TimeInvestors_Organization", collaborator = "Collaborators_Organization", metric = "Collaboration_hours") + +## Export the underlying network object for more customization +g2g_data \%>\% + network_g2g(time_investor = "TimeInvestors_Organization", + collaborator = "Collaborators_Organization", + metric = "Collaboration_hours", + return = "network") \%>\% + GGally::ggnet2(size = 20, + color = "#ed786f", + label = TRUE, + label.size = 2, + label.color = "black", + edge.size = "weights", + edge.alpha = .5) + + theme(plot.background = element_rect(fill = "black"), + text = element_text(colour = "white")) } } From c2b72b786bb3da93429e7c00d762583764943cdf Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Thu, 17 Dec 2020 17:30:06 +0000 Subject: [PATCH 172/192] feat: font sizes (#29) - Plot Title: 14 - Plot Subtitle plus axis titles: 12 - Axis texts, labels, data numbers and footnotes: 10 --- R/themes.R | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/R/themes.R b/R/themes.R index d8a0591b..0d49dd33 100644 --- a/R/themes.R +++ b/R/themes.R @@ -83,11 +83,11 @@ theme_wpa_basic <- function(font_size = 12){ text_small_dark <- element_text(size = font_size - 2, colour = text_colour, face = "plain") text_small_light <- element_text(size = font_size - 2, colour = "#FFFFFF", face = "plain") - text_normal <- element_text(size = font_size + 2, colour = text_colour, face = "plain") - text_italic <- element_text(size = font_size + 2, colour = text_colour, face = "italic") + text_normal <- element_text(size = font_size + 0, colour = text_colour, face = "plain") + text_italic <- element_text(size = font_size + 0, colour = text_colour, face = "italic") - text_bold <- element_text(size = font_size + 2, colour = text_colour, face = "bold") - text_title <- element_text(size = font_size + 5, colour = text_colour, face = "bold") + text_bold <- element_text(size = font_size + 0, colour = text_colour, face = "bold") + text_title <- element_text(size = font_size + 2, colour = text_colour, face = "bold") theme_minimal() + theme(plot.background = element_blank(), From 912883983d09509a07ce6646c2d544eb80633337 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Thu, 17 Dec 2020 17:42:24 +0000 Subject: [PATCH 173/192] feat: add hrvar_threshold (#28) --- R/validation_report.R | 6 +++++- man/validation_report.Rd | 5 +++++ 2 files changed, 10 insertions(+), 1 deletion(-) diff --git a/R/validation_report.R b/R/validation_report.R index d56a216e..9c33df7a 100644 --- a/R/validation_report.R +++ b/R/validation_report.R @@ -20,6 +20,9 @@ #' @param hrvar HR Variable by which to split metrics, defaults to "Organization" #' but accepts any character vector, e.g. "Organization" #' @param path Pass the file path and the desired file name, _excluding the file extension_. +#' @param hrvar_threshold Numeric value determining the maximum number of unique values +#' to be allowed to qualify as a HR variable. This is passed directly to the `threshold` +#' argument within `hrvar_count_all()`. #' @param timestamp Logical vector specifying whether to include a timestamp in the file name. #' Defaults to TRUE. #' @@ -41,6 +44,7 @@ validation_report <- function(data, meeting_data = NULL, hrvar = "Organization", path = "validation report", + hrvar_threshold = 150, timestamp = TRUE){ ## Create timestamped path (if applicable) @@ -114,7 +118,7 @@ validation_report <- function(data, read_preamble("organizational_data_quality.md"), #13, Header - 2. Organizational Data Quality read_preamble("attributes_available.md"),#14 - data %>% hrvar_count_all(return = "table"), + data %>% hrvar_count_all(return = "table", threshold = hrvar_threshold), read_preamble("groups_under_privacy_threshold_1.md"), #16, Header - 2.2 Groups under Privacy Threshold paste(">", data %>% identify_privacythreshold(return="text")), diff --git a/man/validation_report.Rd b/man/validation_report.Rd index 341f6888..fa905a01 100644 --- a/man/validation_report.Rd +++ b/man/validation_report.Rd @@ -9,6 +9,7 @@ validation_report( meeting_data = NULL, hrvar = "Organization", path = "validation report", + hrvar_threshold = 150, timestamp = TRUE ) } @@ -22,6 +23,10 @@ but accepts any character vector, e.g. "Organization"} \item{path}{Pass the file path and the desired file name, \emph{excluding the file extension}.} +\item{hrvar_threshold}{Numeric value determining the maximum number of unique values +to be allowed to qualify as a HR variable. This is passed directly to the \code{threshold} +argument within \code{hrvar_count_all()}.} + \item{timestamp}{Logical vector specifying whether to include a timestamp in the file name. Defaults to TRUE.} } From 6ad8fff18468e50daf05640da4ea4640bddb784b Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Thu, 17 Dec 2020 17:57:15 +0000 Subject: [PATCH 174/192] feat: remove italic subtitle (#29) --- R/themes.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/themes.R b/R/themes.R index 0d49dd33..7c0e9232 100644 --- a/R/themes.R +++ b/R/themes.R @@ -94,7 +94,7 @@ theme_wpa_basic <- function(font_size = 12){ # plot.background = element_rect(fill = bg_colour), text = text_normal, plot.title = text_title, - plot.subtitle = text_italic, + plot.subtitle = text_normal, axis.title = text_normal, axis.text = text_small_dark, From 01167625446bf2acc9ad4bf13d2765480cb48764 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Thu, 17 Dec 2020 22:21:56 +0000 Subject: [PATCH 175/192] refactor: use igraph migrate implementation of network_g2g() to igraph --- R/network_g2g.R | 55 ++++++++++++++-------------------------------- man/network_g2g.Rd | 26 +++------------------- 2 files changed, 20 insertions(+), 61 deletions(-) diff --git a/R/network_g2g.R b/R/network_g2g.R index 99749bf8..ff00328d 100644 --- a/R/network_g2g.R +++ b/R/network_g2g.R @@ -15,12 +15,11 @@ #' @param metric String containing the variable name for metric. #' @param exc_threshold Exclusion threshold to apply. #' @param subtitle String to override default plot subtitle. -#' @param ... Additional arguments to pass to `GGally::ggnet2()` #' @param return Character vector specifying what to return, defaults to "plot". #' Valid inputs include: #' - "plot": return a network plot. #' - "table": return a raw data table used to plot the network. -#' - "network": return a network object generated by `network::network()` +#' - "network": return an **igraph** object #' #' @import ggplot2 #' @import dplyr @@ -32,22 +31,6 @@ #' network_g2g(time_investor = "TimeInvestors_Organization", #' collaborator = "Collaborators_Organization", #' metric = "Collaboration_hours") -#' -#' ## Export the underlying network object for more customization -#' g2g_data %>% -#' network_g2g(time_investor = "TimeInvestors_Organization", -#' collaborator = "Collaborators_Organization", -#' metric = "Collaboration_hours", -#' return = "network") %>% -#' GGally::ggnet2(size = 20, -#' color = "#ed786f", -#' label = TRUE, -#' label.size = 2, -#' label.color = "black", -#' edge.size = "weights", -#' edge.alpha = .5) + -#' theme(plot.background = element_rect(fill = "black"), -#' text = element_text(colour = "white")) #' } #' #' @export @@ -57,8 +40,7 @@ network_g2g <- function(data, metric, exc_threshold = 0.1, subtitle = "Collaboration Across Organizations", - return = "plot", - ...){ + return = "plot"){ plot_data <- data %>% @@ -88,33 +70,30 @@ network_g2g <- function(data, filter(metric_prop > exc_threshold) %>% mutate_at(vars(TimeInvestorOrg, CollaboratorOrg), ~sub(pattern = " ", replacement = "\n", x = .)) %>% mutate(metric_prop = metric_prop * 10) %>% - network::network(matrix.type = "edgelist", - ignore.eval = FALSE, - names.eval = "weights") + igraph::graph_from_data_frame(directed = FALSE) if(return == "network"){ - mynet_em # Return network object + mynet_em # Return igraph object } else { ## Plot object mynet_em %>% - GGally::ggnet2(size = 12, - color = "lightblue", - label = TRUE, - label.size = 4, - label.color = "black", - edge.size = "weights", - edge.alpha = .5, - ...) + - ggtitle("Group to Group Collaboration", - subtitle = subtitle) + - xlab(label = "") + - ylab(label = "") + + ggraph::ggraph(layout = "fr") + + ggraph::geom_edge_link(aes(edge_width = metric_prop * 1), edge_alpha = 0.5, edge_colour = "grey") + + ggraph::geom_node_point(size = 20, colour = "lightblue") + + ggraph::geom_node_text(aes(label = name), size = 3, repel = FALSE) + + ggplot2::theme(panel.background = ggplot2::element_rect(fill = 'white'), legend.position = "none") + theme_wpa_basic() + - labs(caption = paste("Displays only collaboration above ", exc_threshold * 100, "% of node's total collaboration", sep = "")) + - theme(axis.line = element_blank()) + labs(title = "Group to Group Collaboration", + subtitle = subtitle, + x = "", + y = "", + caption = paste("Displays only collaboration above ", exc_threshold * 100, "% of node's total collaboration", sep = "")) + + theme(axis.line = element_blank(), + axis.text = element_blank(), + legend.position = "none") } diff --git a/man/network_g2g.Rd b/man/network_g2g.Rd index b199893d..c900b446 100644 --- a/man/network_g2g.Rd +++ b/man/network_g2g.Rd @@ -12,8 +12,7 @@ network_g2g( metric, exc_threshold = 0.1, subtitle = "Collaboration Across Organizations", - return = "plot", - ... + return = "plot" ) g2g_network( @@ -23,8 +22,7 @@ g2g_network( metric, exc_threshold = 0.1, subtitle = "Collaboration Across Organizations", - return = "plot", - ... + return = "plot" ) } \arguments{ @@ -45,10 +43,8 @@ Valid inputs include: \itemize{ \item "plot": return a network plot. \item "table": return a raw data table used to plot the network. -\item "network": return a network object generated by \code{network::network()} +\item "network": return an \strong{igraph} object }} - -\item{...}{Additional arguments to pass to \code{GGally::ggnet2()}} } \description{ Pass a data frame containing a group-to-group query and return a network plot. @@ -61,22 +57,6 @@ g2g_data \%>\% network_g2g(time_investor = "TimeInvestors_Organization", collaborator = "Collaborators_Organization", metric = "Collaboration_hours") - -## Export the underlying network object for more customization -g2g_data \%>\% - network_g2g(time_investor = "TimeInvestors_Organization", - collaborator = "Collaborators_Organization", - metric = "Collaboration_hours", - return = "network") \%>\% - GGally::ggnet2(size = 20, - color = "#ed786f", - label = TRUE, - label.size = 2, - label.color = "black", - edge.size = "weights", - edge.alpha = .5) + - theme(plot.background = element_rect(fill = "black"), - text = element_text(colour = "white")) } } From ef7123ee4849f0cfa95a802335b607ebb569a08b Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Fri, 18 Dec 2020 11:05:41 +0000 Subject: [PATCH 176/192] docs: unwrap examples g2g --- R/network_g2g.R | 2 -- man/network_g2g.Rd | 2 -- 2 files changed, 4 deletions(-) diff --git a/R/network_g2g.R b/R/network_g2g.R index ff00328d..3131c8d0 100644 --- a/R/network_g2g.R +++ b/R/network_g2g.R @@ -25,13 +25,11 @@ #' @import dplyr #' #' @examples -#' \dontrun{ #' ## Return a network plot #' g2g_data %>% #' network_g2g(time_investor = "TimeInvestors_Organization", #' collaborator = "Collaborators_Organization", #' metric = "Collaboration_hours") -#' } #' #' @export network_g2g <- function(data, diff --git a/man/network_g2g.Rd b/man/network_g2g.Rd index c900b446..b5c8716f 100644 --- a/man/network_g2g.Rd +++ b/man/network_g2g.Rd @@ -51,12 +51,10 @@ Pass a data frame containing a group-to-group query and return a network plot. Automatically handles "Collaborators_within_group" and "Other_collaborators" within query data. } \examples{ -\dontrun{ ## Return a network plot g2g_data \%>\% network_g2g(time_investor = "TimeInvestors_Organization", collaborator = "Collaborators_Organization", metric = "Collaboration_hours") -} } From 8574145fc15eed25d3e40a49ee42bf137f97a30e Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Fri, 18 Dec 2020 11:06:06 +0000 Subject: [PATCH 177/192] refactor: migrate igraph network_p2p --- NAMESPACE | 1 - R/network_p2p.R | 118 ++++++++++++++++++++++++++------------------- man/network_p2p.Rd | 43 ++++++++++++----- 3 files changed, 101 insertions(+), 61 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index c224492b..906046af 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -152,7 +152,6 @@ import(Information) import(dplyr) import(ggplot2) import(ggraph) -import(network) import(reshape2) import(scales) import(tidyselect) diff --git a/R/network_p2p.R b/R/network_p2p.R index 4b768b5b..dd446413 100644 --- a/R/network_p2p.R +++ b/R/network_p2p.R @@ -12,25 +12,40 @@ #' @param data Data frame containing a person-to-person query. #' @param hrvar String containing the label for the HR attribute. #' @param return Character vector specifying what to return, defaults to "pdf". -#' Other valid inputs are "plot", "network" and "table". "network" returns the `network` -#' object used to generate the network plot. The "pdf" option is highly recommended over the "plot" -#' option as the PDF export format has significantly higher performance. The "plot" option is highly -#' computationally intensive and is not generally recommended. +#' Valid inputs are: +#' - "pdf": saves the network plot as a PDF in the specified path. See `path`. This is the recommended +#' output format as large networks can be slow in other formats. +#' - "plot": returns a ggplot object of the network plot. It is not recommended that you run this without +#' assigning the output to an object as plotting to the R console can be slow for large networks. +#' - "table": returns the edgelist data frame used in the network. +#' - "network": returns the igraph object used to create the network plot. #' @param path File path for saving the PDF output. Defaults to "network_p2p". #' @param bg_fill String to specify background fill colour. #' @param font_col String to specify font and link colour. #' @param legend_pos String to specify position of legend. Defaults to "bottom". See `ggplot2::theme()`. -#' @param palette Function for generating a colour palette with a single argument `n`. Uses `rainbow()` by default. -#' @param ... Additional arguments to pass to `GGally::ggnet2()`. -#' For instance, you may specify the argument `mode` to change the node placement algorithm. +#' @param palette Function for generating a colour palette with a single argument `n`. Uses "rainbow" by default. +#' @param node_alpha A numeric value between 0 and 1 to specify the transparency of the nodes. +#' @param algorithm String to specify the node placement algorithm to be used. Defaults to "fr" for the force-directed +#' algorithm of Fruchterman and Reingold. See for a +#' full list of options. #' -#' @details -#' For specifying the node placement algorithm, please see the `gplot.layout` documentation for the **sna** -#' package, which provides a full list of the valid functions that can be passed into the `mode` argument. +#' @examples +#' ## Simulate simple P2P network +#' sim_net <- +#' data.frame(TieOrigin_PersonId = sample(seq(1, 100), size = 100, replace = TRUE), +#' TieDestination_PersonId = sample(seq(1, 100), size = 100, replace = TRUE)) %>% +#' dplyr::mutate(TieOrigin_Organization = ifelse(TieOrigin_PersonId >= 50, "A", "B"), +#' TieDestination_Organization = ifelse(TieDestination_PersonId >= 50, "A", "B"), +#' StrongTieScore = rep(1, 100)) +#' +#' ## Run plot +#' ## ONLY return 'plot' instead of 'pdf' when data size is small +#' network_p2p(data = sim_net, +#' hrvar = "Organization", +#' return = "plot") #' #' @import ggplot2 #' @import dplyr -#' @import network #' @importFrom grDevices rainbow #' #' @export @@ -42,44 +57,52 @@ network_p2p <- function(data, font_col = "#FFFFFF", legend_pos = "bottom", palette = "rainbow", - ...){ - - data <- - data %>% - filter(StrongTieType > 0) + node_alpha = 0.7, + algorithm = "fr"){ + ## No filtering tieorigin_var <- paste0("TieOrigin_", hrvar) tiedestin_var <- paste0("TieDestination_", hrvar) - - ## Extract edge list - strong_edgelist <- + ## Set edges df + edges <- data %>% - select(TieOrigin_PersonId, TieDestination_PersonId) + select(from = "TieOrigin_PersonId", + to = "TieDestination_PersonId", + weight = "StrongTieScore") %>% + select(-weight) # Overwrite - no info on edge - ## Create basic network object - mynet <- - network::network(strong_edgelist, matrix.type="edgelist") + ## Vertices data frame to provide meta-data + vert_ft <- + rbind( + # TieOrigin + edges %>% + left_join(select(data, TieOrigin_PersonId, tieorigin_var), + by = c("from" = "TieOrigin_PersonId")) %>% + select(node = "from", !!sym(hrvar) := tieorigin_var), - ## Extract list of nodes from network object - myedges <- data.frame(PersonId = network.vertex.names(mynet)) + # TieDestination + edges %>% + left_join(select(data, TieDestination_PersonId, tiedestin_var), + by = c("to" = "TieDestination_PersonId")) %>% + select(node = "to", !!sym(hrvar) := tiedestin_var) + ) - ## Extract attributes - totalAttributes <- - bind_rows(select(data, PersonId = "TieOrigin_PersonId", !!sym(hrvar) := tieorigin_var), - select(data, PersonId = "TieOrigin_PersonId", !!sym(hrvar) := tiedestin_var)) %>% - unique() - - ## Merge list of nodes and attributes - myedges <- merge(myedges, totalAttributes, by = "PersonId", all.x = TRUE) - - ## Add attributes to network object - mynet %v% hrvar <- myedges[[hrvar]] + ## Create igraph object + g <- + igraph::graph_from_data_frame(edges, + directed = FALSE, # Directed, but FALSE for visualization + vertices = unique(vert_ft)) %>% # remove duplicates + igraph::simplify() ## Palette + ## Create tibble + pal <- + tibble(!!sym(hrvar) := g %>% + igraph::get.vertex.attribute(hrvar) %>% + unique()) - pal <- tibble(!!sym(hrvar) := unique(myedges[[hrvar]])) - + ## Apply palette function col_pal <- do.call(what = palette, args = list(nrow(pal))) ## named character vector @@ -90,24 +113,21 @@ network_p2p <- function(data, if(return == "table"){ - data + edges } else if(return == "network"){ - mynet + g } else if(return %in% c("plot", "pdf")){ outputPlot <- - mynet %>% - GGally::ggnet2(size = 1, - color = hrvar, - label = FALSE, - edge.size = .01, - edge.alpha = .15, - node.alpha = .8, - palette = pal, - ...) + + g %>% + ggraph::ggraph(layout = "igraph", algorithm = algorithm) + + ggraph::geom_edge_link(colour = "lightgrey", edge_width = 0.01, alpha = 0.15) + + ggraph::geom_node_point(aes(colour = !!sym(hrvar)), alpha = node_alpha) + + scale_colour_discrete(type = pal) + + theme_void() + theme(legend.position = legend_pos, legend.background = element_rect(fill = bg_fill), plot.background = element_rect(fill = bg_fill), diff --git a/man/network_p2p.Rd b/man/network_p2p.Rd index 8e552f83..3b90e0ec 100644 --- a/man/network_p2p.Rd +++ b/man/network_p2p.Rd @@ -13,7 +13,8 @@ network_p2p( font_col = "#FFFFFF", legend_pos = "bottom", palette = "rainbow", - ... + node_alpha = 0.7, + algorithm = "fr" ) } \arguments{ @@ -22,10 +23,15 @@ network_p2p( \item{hrvar}{String containing the label for the HR attribute.} \item{return}{Character vector specifying what to return, defaults to "pdf". -Other valid inputs are "plot", "network" and "table". "network" returns the \code{network} -object used to generate the network plot. The "pdf" option is highly recommended over the "plot" -option as the PDF export format has significantly higher performance. The "plot" option is highly -computationally intensive and is not generally recommended.} +Valid inputs are: +\itemize{ +\item "pdf": saves the network plot as a PDF in the specified path. See \code{path}. This is the recommended +output format as large networks can be slow in other formats. +\item "plot": returns a ggplot object of the network plot. It is not recommended that you run this without +assigning the output to an object as plotting to the R console can be slow for large networks. +\item "table": returns the edgelist data frame used in the network. +\item "network": returns the igraph object used to create the network plot. +}} \item{path}{File path for saving the PDF output. Defaults to "network_p2p".} @@ -35,16 +41,31 @@ computationally intensive and is not generally recommended.} \item{legend_pos}{String to specify position of legend. Defaults to "bottom". See \code{ggplot2::theme()}.} -\item{palette}{Function for generating a colour palette with a single argument \code{n}. Uses \code{rainbow()} by default.} +\item{palette}{Function for generating a colour palette with a single argument \code{n}. Uses "rainbow" by default.} -\item{...}{Additional arguments to pass to \code{GGally::ggnet2()}. -For instance, you may specify the argument \code{mode} to change the node placement algorithm.} +\item{node_alpha}{A numeric value between 0 and 1 to specify the transparency of the nodes.} + +\item{algorithm}{String to specify the node placement algorithm to be used. Defaults to "fr" for the force-directed +algorithm of Fruchterman and Reingold. See \url{https://rdrr.io/cran/ggraph/man/layout_tbl_graph_igraph.html} for a +full list of options.} } \description{ Pass a data frame containing a person-to-person query and save a network plot as a PDF file. } -\details{ -For specifying the node placement algorithm, please see the \code{gplot.layout} documentation for the \strong{sna} -package, which provides a full list of the valid functions that can be passed into the \code{mode} argument. +\examples{ +## Simulate simple P2P network +sim_net <- + data.frame(TieOrigin_PersonId = sample(seq(1, 100), size = 100, replace = TRUE), + TieDestination_PersonId = sample(seq(1, 100), size = 100, replace = TRUE)) \%>\% + dplyr::mutate(TieOrigin_Organization = ifelse(TieOrigin_PersonId >= 50, "A", "B"), + TieDestination_Organization = ifelse(TieDestination_PersonId >= 50, "A", "B"), + StrongTieScore = rep(1, 100)) + +## Run plot +## ONLY return 'plot' instead of 'pdf' when data size is small +network_p2p(data = sim_net, + hrvar = "Organization", + return = "plot") + } From b76989852ad01b50be1a9d10b265ba38472c1537 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Fri, 18 Dec 2020 11:26:36 +0000 Subject: [PATCH 178/192] perf: remove GGally and network (#16) --- DESCRIPTION | 2 -- 1 file changed, 2 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 55153daf..955e6728 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -57,8 +57,6 @@ Imports: widyr, proxy, rlang, - GGally, - network, ggwordcloud, methods, data.table From 626c95fed163cc77dad79dc9bfcc6efb13354961 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Fri, 18 Dec 2020 12:59:29 +0000 Subject: [PATCH 179/192] chore: remove empty return value --- R/identify_holidayweeks.R | 1 - man/identify_holidayweeks.Rd | 4 +--- 2 files changed, 1 insertion(+), 4 deletions(-) diff --git a/R/identify_holidayweeks.R b/R/identify_holidayweeks.R index 3f817ff9..4029d179 100644 --- a/R/identify_holidayweeks.R +++ b/R/identify_holidayweeks.R @@ -35,7 +35,6 @@ #' # Return a plot #' identify_holidayweeks(sq_data, return = "plot") #' -#' @return #' #' @export identify_holidayweeks <- function(data, sd = 1, return = "message"){ diff --git a/man/identify_holidayweeks.Rd b/man/identify_holidayweeks.Rd index 20513c89..9efacff8 100644 --- a/man/identify_holidayweeks.Rd +++ b/man/identify_holidayweeks.Rd @@ -20,9 +20,6 @@ Valid options are: \item when 'data_dirty' is passed, a dataset with only outlier weeks is returned as a dataframe. \item when 'plot' is passed, a pot with holiday weeks highlighted is returned as a dataframe. }} -} -\value{ - } \description{ This function scans a standard query output for weeks where collaboration hours is far outside the mean. @@ -38,6 +35,7 @@ identify_holidayweeks(sq_data) # Return a plot identify_holidayweeks(sq_data, return = "plot") + } \seealso{ Other Data Validation: From 6386f9965165ee077b8abd2690699e99360d1cb2 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Fri, 18 Dec 2020 13:45:35 +0000 Subject: [PATCH 180/192] feat: add pairwise_count A data.table implementation of the original widyr::pairwise_count() function --- NAMESPACE | 3 ++- R/pairwise_count.R | 48 +++++++++++++++++++++++++++++++++++++++++++ man/pairwise_count.Rd | 20 ++++++++++++++++++ 3 files changed, 70 insertions(+), 1 deletion(-) create mode 100644 R/pairwise_count.R create mode 100644 man/pairwise_count.Rd diff --git a/NAMESPACE b/NAMESPACE index 906046af..0347e5d5 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -114,6 +114,7 @@ export(one2one_rank) export(one2one_sum) export(one2one_summary) export(one2one_trend) +export(pairwise_count) export(period_change) export(personas_hclust) export(read_preamble) @@ -149,6 +150,7 @@ export(workpatterns_rank) export(wrap) import(DT) import(Information) +import(data.table) import(dplyr) import(ggplot2) import(ggraph) @@ -190,4 +192,3 @@ importFrom(tidyselect,all_of) importFrom(tidytext,unnest_tokens) importFrom(utils,write.csv) importFrom(utils,write.table) -importFrom(widyr,pairwise_count) diff --git a/R/pairwise_count.R b/R/pairwise_count.R new file mode 100644 index 00000000..8e0b715b --- /dev/null +++ b/R/pairwise_count.R @@ -0,0 +1,48 @@ +#' @title Perform a pairwise count of words by id +#' +#' @description This is a **data.table** implementation that mimics the output of +#' `widyr::pairwise_count()` to reduce package dependency. This is used internally +#' within `tm_cooc()`. +#' +#' @param data Data frame output from `tm_clean()`. +#' @param id String to represent the id variable. Defaults to "word". +#' @param word String to represent the word variable. Defaults to "word". +#' +#' @import data.table +#' +#' @export +pairwise_count <- function(data, + id = "line", + word = "word"){ + + data <- + data %>% + dplyr::rename(word := !!sym(word), + id := !!sym(id)) + + + DT <- data.table::as.data.table(data) + + # convert to character + DT[, word := as.character(word)] + + # subset those with >1 per id + DT2 <- DT[, N := .N, by = id][N>1] + + # create all combinations of 2 + # return as a data.table with these as columns `V1` and `V2` + # then count the numbers in each id + out_data <- + DT2[, rbindlist(utils::combn(word,2, + FUN = function(x) as.data.table(as.list(x)), + simplify = FALSE)), by = id] %>% + .[, .N, by = list(V1,V2)] + + # format and sort + out_data %>% + dplyr::as_tibble() %>% + dplyr::rename(item1 = "V1", + item2 = "V2", + n = "N") %>% + dplyr::arrange(desc(n)) +} diff --git a/man/pairwise_count.Rd b/man/pairwise_count.Rd new file mode 100644 index 00000000..fb588a4a --- /dev/null +++ b/man/pairwise_count.Rd @@ -0,0 +1,20 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/pairwise_count.R +\name{pairwise_count} +\alias{pairwise_count} +\title{Perform a pairwise count of words by id} +\usage{ +pairwise_count(data, id = "line", word = "word") +} +\arguments{ +\item{data}{Data frame output from \code{tm_clean()}.} + +\item{id}{String to represent the id variable. Defaults to "word".} + +\item{word}{String to represent the word variable. Defaults to "word".} +} +\description{ +This is a \strong{data.table} implementation that mimics the output of +\code{widyr::pairwise_count()} to reduce package dependency. This is used internally +within \code{tm_cooc()}. +} From 9b74d2175fe582389dd8a68e8eaf2ef9475e6931 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Fri, 18 Dec 2020 13:45:58 +0000 Subject: [PATCH 181/192] refactor: remove widyr dependency --- R/tm_cooc.R | 14 ++++++-------- man/tm_cooc.Rd | 8 +++++--- 2 files changed, 11 insertions(+), 11 deletions(-) diff --git a/R/tm_cooc.R b/R/tm_cooc.R index dad63ec6..f41891bc 100644 --- a/R/tm_cooc.R +++ b/R/tm_cooc.R @@ -26,15 +26,16 @@ #' @import ggplot2 #' @import ggraph #' @importFrom igraph graph_from_data_frame -#' @importFrom widyr pairwise_count #' @importFrom tidytext unnest_tokens #' #' @family Text-mining #' #' @examples -#' \dontrun{ -#' tm_cooc(mt_data,lmult = 0.01) -#' } +#' # Demo using a subset of `mt_data` +#' mt_data %>% +#' dplyr::slice(1:20) %>% +#' tm_cooc(lmult = 0.01) +#' #' @export tm_cooc <- function(data, stopwords = NULL, @@ -50,10 +51,7 @@ tm_cooc <- function(data, # Calculate frequency of pairs title_word_pairs <- text_df %>% - widyr::pairwise_count(word, - line, - sort = TRUE, - upper = FALSE) + pairwise_count(id = "line", word = "word") # Graph networks set.seed(seed) diff --git a/man/tm_cooc.Rd b/man/tm_cooc.Rd index ebbf4723..f923d0c3 100644 --- a/man/tm_cooc.Rd +++ b/man/tm_cooc.Rd @@ -29,9 +29,11 @@ There is an option to remove stopwords by passing a data frame into the \code{st argument. } \examples{ -\dontrun{ -tm_cooc(mt_data,lmult = 0.01) -} +# Demo using a subset of `mt_data` +mt_data \%>\% + dplyr::slice(1:20) \%>\% + tm_cooc(lmult = 0.01) + } \seealso{ Other Text-mining: From 7da6aab430adb4cc2b3bf0a0c56ba53364614675 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Fri, 18 Dec 2020 13:46:48 +0000 Subject: [PATCH 182/192] refactor: remove widyr from DESCRIPTION --- DESCRIPTION | 1 - 1 file changed, 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 955e6728..6fe74fd0 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -54,7 +54,6 @@ Imports: tidytext, ggraph, igraph, - widyr, proxy, rlang, ggwordcloud, From 3b5d858c1cd5609a2bab279d22692d8a73527556 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Fri, 18 Dec 2020 17:46:45 +0000 Subject: [PATCH 183/192] feat: network_louvain Improve formatting, return options, and documentation --- R/network_louvain.R | 61 ++++++++++++++++++++++++++++++------------ man/network_louvain.Rd | 29 ++++++++++++++++++-- 2 files changed, 71 insertions(+), 19 deletions(-) diff --git a/R/network_louvain.R b/R/network_louvain.R index d29edab0..d1013d91 100644 --- a/R/network_louvain.R +++ b/R/network_louvain.R @@ -11,13 +11,30 @@ #' #' @param data Data frame containing a Person to Person query. #' @param hrvar String containing the HR attribute to be matched in the dataset. -#' @param return String specifying what output to return. +#' @param bg_fill String to specify background fill colour. +#' @param font_col String to specify font and link colour. +#' @param node_alpha A numeric value between 0 and 1 to specify the transparency of the nodes. +#' @param algorithm String to specify the node placement algorithm to be used. Defaults to "fr" for the force-directed +#' algorithm of Fruchterman and Reingold. See for a +#' full list of options. +#' @param return String specifying what output to return.Valid return options include: +#' - 'plot-louvain': return a network plot coloured by louvain communities. +#' - 'plot-hrvar': return a network plot coloured by HR attribute. +#' - 'plot-sankey': return a sankey plot combining communities and HR attribute. +#' - 'table': return a vertex summary table with counts in communities and HR attribute. +#' - 'data': return a vertex data file that matches vertices with communities and HR attributes. #' #' @import ggraph #' @import dplyr #' #' @export -network_louvain <- function(data, hrvar, return){ +network_louvain <- function(data, + hrvar, + bg_fill = "#000000", + font_col = "#FFFFFF", + node_alpha = 0.8, + algorithm = "mds", + return){ ## Set variables TO_hrvar <- paste0("TieOrigin_", hrvar) @@ -60,38 +77,43 @@ network_louvain <- function(data, hrvar, return){ g <- g_raw %>% # Add louvain partitions to graph object - igraph::set_vertex_attr("cluster", value = igraph::membership(lc)) # Return membership - diff from Leiden + igraph::set_vertex_attr("cluster", value = as.character(igraph::membership(lc))) %>% # Return membership - diff from Leiden + igraph::simplify() ## Create vertex table vertex_tb <- g %>% igraph::get.vertex.attribute() %>% - as_tibble() %>% - count(!!sym(hrvar), cluster) + as_tibble() + + g_layout <- + g %>% + ggraph::ggraph(layout = "igraph", algorithm = algorithm) ## Return if(return == "plot-louvain"){ - g %>% - ggraph::ggraph(layout = "stress") + - ggraph::geom_edge_link(colour = "lightgrey", edge_width = 0.1) + - ggraph::geom_node_point(aes(colour = cluster), alpha = 0.8) + + g_layout + + ggraph::geom_edge_link(colour = "lightgrey", edge_width = 0.01, alpha = 0.15) + + ggraph::geom_node_point(aes(colour = cluster), alpha = node_alpha) + theme_void() + theme(legend.position = "bottom", - # legend.background = element_rect(fill = "black"), - # plot.background = element_rect(fill = "black"), - # text = element_text(colour = "white"), + legend.background = element_rect(fill = bg_fill), + plot.background = element_rect(fill = bg_fill), + text = element_text(colour = font_col), axis.line = element_blank()) + labs(title = "Person to person collaboration with Community Detection", - subtitle = "Based on louvain algorithm and Strong Tie Score", + subtitle = "Based on Louvain algorithm and Strong Tie Score", y = "", x = "") } else if(return == "plot-hrvar"){ - g %>% - ggraph::ggraph(layout = "stress") + - ggraph::geom_edge_link(colour = "lightgrey", edge_width = 0.1) + - ggraph::geom_node_point(aes(colour = !!sym(hrvar)), alpha = 0.8) + + g_layout + + ggraph::geom_edge_link(colour = "lightgrey", edge_width = 0.01, alpha = 0.15) + + ggraph::geom_node_point(aes(colour = !!sym(hrvar)), alpha = node_alpha) + theme_void() + theme(legend.position = "bottom", + legend.background = element_rect(fill = bg_fill), + plot.background = element_rect(fill = bg_fill), + text = element_text(colour = font_col), axis.line = element_blank()) + labs(title = "Person to person collaboration", subtitle = paste0("Showing ", hrvar), @@ -100,6 +122,11 @@ network_louvain <- function(data, hrvar, return){ } else if(return == "table"){ + vertex_tb %>% + count(!!sym(hrvar), cluster) + + } else if(return == "data"){ + vertex_tb } else if(return == "plot-sankey"){ diff --git a/man/network_louvain.Rd b/man/network_louvain.Rd index 08625549..986ae3c9 100644 --- a/man/network_louvain.Rd +++ b/man/network_louvain.Rd @@ -4,14 +4,39 @@ \alias{network_louvain} \title{Implement the Louvain community detection on a Person to Person network query} \usage{ -network_louvain(data, hrvar, return) +network_louvain( + data, + hrvar, + bg_fill = "#000000", + font_col = "#FFFFFF", + node_alpha = 0.8, + algorithm = "mds", + return +) } \arguments{ \item{data}{Data frame containing a Person to Person query.} \item{hrvar}{String containing the HR attribute to be matched in the dataset.} -\item{return}{String specifying what output to return.} +\item{bg_fill}{String to specify background fill colour.} + +\item{font_col}{String to specify font and link colour.} + +\item{node_alpha}{A numeric value between 0 and 1 to specify the transparency of the nodes.} + +\item{algorithm}{String to specify the node placement algorithm to be used. Defaults to "fr" for the force-directed +algorithm of Fruchterman and Reingold. See \url{https://rdrr.io/cran/ggraph/man/layout_tbl_graph_igraph.html} for a +full list of options.} + +\item{return}{String specifying what output to return.Valid return options include: +\itemize{ +\item 'plot-louvain': return a network plot coloured by louvain communities. +\item 'plot-hrvar': return a network plot coloured by HR attribute. +\item 'plot-sankey': return a sankey plot combining communities and HR attribute. +\item 'table': return a vertex summary table with counts in communities and HR attribute. +\item 'data': return a vertex data file that matches vertices with communities and HR attributes. +}} } \description{ Take a P2P network query and implement the Louvain community detection method. The From d6d778024c31e00a5044e9cc794e118a522e72cf Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Fri, 18 Dec 2020 17:47:21 +0000 Subject: [PATCH 184/192] feat: network_leiden Improve formatting, performance, and return options --- R/network_leiden.R | 45 ++++++++++++++++++++++++++++++------------- man/network_leiden.Rd | 21 +++++++++++++++++++- 2 files changed, 52 insertions(+), 14 deletions(-) diff --git a/R/network_leiden.R b/R/network_leiden.R index 15ea7906..29428e41 100644 --- a/R/network_leiden.R +++ b/R/network_leiden.R @@ -12,6 +12,12 @@ #' #' @param data Data frame containing a Person to Person query. #' @param hrvar String containing the HR attribute to be matched in the dataset. +#' @param bg_fill String to specify background fill colour. +#' @param font_col String to specify font and link colour. +#' @param node_alpha A numeric value between 0 and 1 to specify the transparency of the nodes. +#' @param algorithm String to specify the node placement algorithm to be used. Defaults to "fr" for the force-directed +#' algorithm of Fruchterman and Reingold. See for a +#' full list of options. #' @param res Resolution parameter to be passed to `leiden::leiden()`. Defaults to 0.5. #' @param return String specifying what output to return. Valid return options include: #' - 'plot-leiden': return a network plot coloured by leiden communities. @@ -23,7 +29,14 @@ #' @import dplyr #' #' @export -network_leiden <- function(data, hrvar, res = 0.5, return){ +network_leiden <- function(data, + hrvar, + bg_fill = "#000000", + font_col = "#FFFFFF", + algorithm = "mds", + node_alpha = 0.8, + res = 0.5, + return){ ## Set variables TO_hrvar <- paste0("TieOrigin_", hrvar) @@ -66,7 +79,8 @@ network_leiden <- function(data, hrvar, res = 0.5, return){ g <- g_raw %>% # Add leiden partitions to graph object - igraph::set_vertex_attr("cluster", value = as.character(ld)) + igraph::set_vertex_attr("cluster", value = as.character(ld)) %>% + igraph::simplify() ## Create vertex table vertex_tb <- @@ -74,29 +88,34 @@ network_leiden <- function(data, hrvar, res = 0.5, return){ igraph::get.vertex.attribute() %>% as_tibble() + g_layout <- + g %>% + ggraph::ggraph(layout = "igraph", algorithm = algorithm) + ## Return if(return == "plot-leiden"){ - g %>% - ggraph::ggraph(layout = "stress") + - ggraph::geom_edge_link(colour = "lightgrey", edge_width = 0.1) + - ggraph::geom_node_point(aes(colour = cluster), alpha = 0.8) + + g_layout + + ggraph::geom_edge_link(colour = "lightgrey", edge_width = 0.01, alpha = 0.15) + + ggraph::geom_node_point(aes(colour = cluster), alpha = node_alpha) + theme_void() + theme(legend.position = "bottom", - # legend.background = element_rect(fill = "black"), - # plot.background = element_rect(fill = "black"), - # text = element_text(colour = "white"), + legend.background = element_rect(fill = bg_fill), + plot.background = element_rect(fill = bg_fill), + text = element_text(colour = font_col), axis.line = element_blank()) + labs(title = "Person to person collaboration with Community Detection", subtitle = "Based on Leiden algorithm and Strong Tie Score", y = "", x = "") } else if(return == "plot-hrvar"){ - g %>% - ggraph::ggraph(layout = "stress") + - ggraph::geom_edge_link(colour = "lightgrey", edge_width = 0.1) + - ggraph::geom_node_point(aes(colour = !!sym(hrvar)), alpha = 0.8) + + g_layout + + ggraph::geom_edge_link(colour = "lightgrey", edge_width = 0.01, alpha = 0.15) + + ggraph::geom_node_point(aes(colour = !!sym(hrvar)), alpha = node_alpha) + theme_void() + theme(legend.position = "bottom", + legend.background = element_rect(fill = bg_fill), + plot.background = element_rect(fill = bg_fill), + text = element_text(colour = font_col), axis.line = element_blank()) + labs(title = "Person to person collaboration", subtitle = paste0("Showing ", hrvar), diff --git a/man/network_leiden.Rd b/man/network_leiden.Rd index fd417126..1e5221a4 100644 --- a/man/network_leiden.Rd +++ b/man/network_leiden.Rd @@ -4,13 +4,32 @@ \alias{network_leiden} \title{Implement the Leiden community detection on a Person to Person network query} \usage{ -network_leiden(data, hrvar, res = 0.5, return) +network_leiden( + data, + hrvar, + bg_fill = "#000000", + font_col = "#FFFFFF", + algorithm = "mds", + node_alpha = 0.8, + res = 0.5, + return +) } \arguments{ \item{data}{Data frame containing a Person to Person query.} \item{hrvar}{String containing the HR attribute to be matched in the dataset.} +\item{bg_fill}{String to specify background fill colour.} + +\item{font_col}{String to specify font and link colour.} + +\item{algorithm}{String to specify the node placement algorithm to be used. Defaults to "fr" for the force-directed +algorithm of Fruchterman and Reingold. See \url{https://rdrr.io/cran/ggraph/man/layout_tbl_graph_igraph.html} for a +full list of options.} + +\item{node_alpha}{A numeric value between 0 and 1 to specify the transparency of the nodes.} + \item{res}{Resolution parameter to be passed to \code{leiden::leiden()}. Defaults to 0.5.} \item{return}{String specifying what output to return. Valid return options include: From fcece6fda3e81f15993c46f619678822e326dec6 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Fri, 18 Dec 2020 17:47:40 +0000 Subject: [PATCH 185/192] refactor: network_describe --- R/network_describe.R | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/R/network_describe.R b/R/network_describe.R index 07ded275..01e38212 100644 --- a/R/network_describe.R +++ b/R/network_describe.R @@ -21,7 +21,9 @@ network_describe <- function(data, hrvar = c("Organization", "LevelDesignation", "FunctionType")){ if(length(hrvar) != 3){ + stop("Please provide a character vector of length 3 for `hrvar`") + } ## De-duplicated data containing only TieOrigins @@ -38,7 +40,7 @@ network_describe <- function(data, hrvar = c("Organization", "LevelDesignation", agg <- features %>% - group_by_at(.vars=vars(c)) %>% + group_by_at(.vars = vars(c)) %>% summarise(count = n(), .groups = "drop") %>% mutate(percentage = count / sum(count, na.rm = TRUE)) From e582cc4684cb9a41956c31f897c81ca6cca174c9 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Fri, 18 Dec 2020 18:14:58 +0000 Subject: [PATCH 186/192] feat: add describe option network_louvain() and network_leiden() can now return a description outputs using network_describe() --- R/network_leiden.R | 22 ++++++++++++++++++++++ R/network_louvain.R | 26 ++++++++++++++++++++++++++ man/network_leiden.Rd | 2 ++ man/network_louvain.Rd | 2 ++ 4 files changed, 52 insertions(+) diff --git a/R/network_leiden.R b/R/network_leiden.R index 29428e41..a0a49b91 100644 --- a/R/network_leiden.R +++ b/R/network_leiden.R @@ -25,6 +25,7 @@ #' - 'plot-sankey': return a sankey plot combining communities and HR attribute. #' - 'table': return a vertex summary table with counts in communities and HR attribute. #' - 'data': return a vertex data file that matches vertices with communities and HR attributes. +#' - 'describe': returns a list of data frames which describe each of the identified communities. #' #' @import dplyr #' @@ -36,6 +37,7 @@ network_leiden <- function(data, algorithm = "mds", node_alpha = 0.8, res = 0.5, + desc_hrvar = c("Organization", "LevelDesignation", "FunctionType"), return){ ## Set variables @@ -138,6 +140,26 @@ network_leiden <- function(data, var2 = "cluster", count = "n") + } else if(return == "describe"){ + + describe_tb <- + vertex_tb %>% + left_join(select(data, starts_with("TieOrigin_")), + by = c("name" = "TieOrigin_PersonId")) + + desc_str <- + describe_tb %>% + pull(cluster) %>% + unique() + + desc_str %>% + purrr::map(function(x){ + describe_tb %>% + filter(cluster == x) %>% + network_describe(hrvar = desc_hrvar) + }) %>% + setNames(nm = desc_str) + } else { stop("Please enter a valid input for `return`.") diff --git a/R/network_louvain.R b/R/network_louvain.R index d1013d91..755b12b4 100644 --- a/R/network_louvain.R +++ b/R/network_louvain.R @@ -23,6 +23,7 @@ #' - 'plot-sankey': return a sankey plot combining communities and HR attribute. #' - 'table': return a vertex summary table with counts in communities and HR attribute. #' - 'data': return a vertex data file that matches vertices with communities and HR attributes. +#' - 'describe': returns a list of data frames which describe each of the identified communities. #' #' @import ggraph #' @import dplyr @@ -34,6 +35,7 @@ network_louvain <- function(data, font_col = "#FFFFFF", node_alpha = 0.8, algorithm = "mds", + desc_hrvar = c("Organization", "LevelDesignation", "FunctionType"), return){ ## Set variables @@ -136,5 +138,29 @@ network_louvain <- function(data, var2 = "cluster", count = "n") + } else if(return == "describe"){ + + describe_tb <- + vertex_tb %>% + left_join(select(data, starts_with("TieOrigin_")), + by = c("name" = "TieOrigin_PersonId")) + + desc_str <- + describe_tb %>% + pull(cluster) %>% + unique() + + desc_str %>% + purrr::map(function(x){ + describe_tb %>% + filter(cluster == x) %>% + network_describe(hrvar = desc_hrvar) + }) %>% + setNames(nm = desc_str) + + } else { + + stop("Please enter a valid input for `return`.") + } } diff --git a/man/network_leiden.Rd b/man/network_leiden.Rd index 1e5221a4..f3aa2e50 100644 --- a/man/network_leiden.Rd +++ b/man/network_leiden.Rd @@ -12,6 +12,7 @@ network_leiden( algorithm = "mds", node_alpha = 0.8, res = 0.5, + desc_hrvar = c("Organization", "LevelDesignation", "FunctionType"), return ) } @@ -39,6 +40,7 @@ full list of options.} \item 'plot-sankey': return a sankey plot combining communities and HR attribute. \item 'table': return a vertex summary table with counts in communities and HR attribute. \item 'data': return a vertex data file that matches vertices with communities and HR attributes. +\item 'describe': returns a list of data frames which describe each of the identified communities. }} } \description{ diff --git a/man/network_louvain.Rd b/man/network_louvain.Rd index 986ae3c9..2afc10a1 100644 --- a/man/network_louvain.Rd +++ b/man/network_louvain.Rd @@ -11,6 +11,7 @@ network_louvain( font_col = "#FFFFFF", node_alpha = 0.8, algorithm = "mds", + desc_hrvar = c("Organization", "LevelDesignation", "FunctionType"), return ) } @@ -36,6 +37,7 @@ full list of options.} \item 'plot-sankey': return a sankey plot combining communities and HR attribute. \item 'table': return a vertex summary table with counts in communities and HR attribute. \item 'data': return a vertex data file that matches vertices with communities and HR attributes. +\item 'describe': returns a list of data frames which describe each of the identified communities. }} } \description{ From 276d4efa71bbe01d104f1e460a478f7c5ae0f89e Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Fri, 18 Dec 2020 19:13:54 +0000 Subject: [PATCH 187/192] feat: add network return option --- R/network_leiden.R | 8 +++++++- R/network_louvain.R | 6 ++++++ 2 files changed, 13 insertions(+), 1 deletion(-) diff --git a/R/network_leiden.R b/R/network_leiden.R index a0a49b91..bd6bb98e 100644 --- a/R/network_leiden.R +++ b/R/network_leiden.R @@ -25,7 +25,8 @@ #' - 'plot-sankey': return a sankey plot combining communities and HR attribute. #' - 'table': return a vertex summary table with counts in communities and HR attribute. #' - 'data': return a vertex data file that matches vertices with communities and HR attributes. -#' - 'describe': returns a list of data frames which describe each of the identified communities. +#' - 'describe': return a list of data frames which describe each of the identified communities. +#' - 'network': return igraph object. #' #' @import dplyr #' @@ -133,6 +134,11 @@ network_leiden <- function(data, vertex_tb + } else if(return == "network"){ + + g + + } else if(return == "plot-sankey"){ create_sankey(data = vertex_tb %>% count(!!sym(hrvar), cluster), diff --git a/R/network_louvain.R b/R/network_louvain.R index 755b12b4..4235c893 100644 --- a/R/network_louvain.R +++ b/R/network_louvain.R @@ -24,6 +24,7 @@ #' - 'table': return a vertex summary table with counts in communities and HR attribute. #' - 'data': return a vertex data file that matches vertices with communities and HR attributes. #' - 'describe': returns a list of data frames which describe each of the identified communities. +#' - 'network': return igraph object. #' #' @import ggraph #' @import dplyr @@ -131,6 +132,11 @@ network_louvain <- function(data, vertex_tb + } else if(return == "network"){ + + g + + } else if(return == "plot-sankey"){ create_sankey(data = vertex_tb, From 2e319b28dd5971b2c22dd9bbc5ec14603adae898 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Fri, 18 Dec 2020 19:15:18 +0000 Subject: [PATCH 188/192] docs: update Rd files --- man/network_leiden.Rd | 3 ++- man/network_louvain.Rd | 1 + 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/man/network_leiden.Rd b/man/network_leiden.Rd index f3aa2e50..eb252e45 100644 --- a/man/network_leiden.Rd +++ b/man/network_leiden.Rd @@ -40,7 +40,8 @@ full list of options.} \item 'plot-sankey': return a sankey plot combining communities and HR attribute. \item 'table': return a vertex summary table with counts in communities and HR attribute. \item 'data': return a vertex data file that matches vertices with communities and HR attributes. -\item 'describe': returns a list of data frames which describe each of the identified communities. +\item 'describe': return a list of data frames which describe each of the identified communities. +\item 'network': return igraph object. }} } \description{ diff --git a/man/network_louvain.Rd b/man/network_louvain.Rd index 2afc10a1..89159a6a 100644 --- a/man/network_louvain.Rd +++ b/man/network_louvain.Rd @@ -38,6 +38,7 @@ full list of options.} \item 'table': return a vertex summary table with counts in communities and HR attribute. \item 'data': return a vertex data file that matches vertices with communities and HR attributes. \item 'describe': returns a list of data frames which describe each of the identified communities. +\item 'network': return igraph object. }} } \description{ From d4470a4d53df82be1e7d1dcd41fb239fc6afdda2 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Tue, 5 Jan 2021 10:45:56 +0000 Subject: [PATCH 189/192] feat: default save as PDF output --- R/network_leiden.R | 51 +++++++++++++++++++++++++++++++++++++----- R/network_louvain.R | 50 ++++++++++++++++++++++++++++++++++++----- man/network_leiden.Rd | 13 ++++++++--- man/network_louvain.Rd | 13 ++++++++--- 4 files changed, 111 insertions(+), 16 deletions(-) diff --git a/R/network_leiden.R b/R/network_leiden.R index bd6bb98e..40af3ea8 100644 --- a/R/network_leiden.R +++ b/R/network_leiden.R @@ -15,9 +15,16 @@ #' @param bg_fill String to specify background fill colour. #' @param font_col String to specify font and link colour. #' @param node_alpha A numeric value between 0 and 1 to specify the transparency of the nodes. -#' @param algorithm String to specify the node placement algorithm to be used. Defaults to "fr" for the force-directed -#' algorithm of Fruchterman and Reingold. See for a -#' full list of options. +#' @param path File path for saving the PDF output. Defaults to "network_p2p_leiden". +#' Since the network outputs are computationally intensive, the default behaviour is to save time by +#' saving the plot output directly as a PDF in the specified path. To override this behaviour and return +#' a plot object instead, you can pass `NULL` to `path`. What is passed to `path` makes no difference +#' if returning anything other than "plot-leiden" or "plot-hrvar". +#' +#' @param algorithm String to specify the node placement algorithm to be used. Defaults to "mds" to perform +#' a multidimensional scaling of nodes using a shortest path, which is also a deterministic method. +#' See for a full list of options. +#' #' @param res Resolution parameter to be passed to `leiden::leiden()`. Defaults to 0.5. #' @param return String specifying what output to return. Valid return options include: #' - 'plot-leiden': return a network plot coloured by leiden communities. @@ -36,6 +43,7 @@ network_leiden <- function(data, bg_fill = "#000000", font_col = "#FFFFFF", algorithm = "mds", + path = "network_p2p_leiden", node_alpha = 0.8, res = 0.5, desc_hrvar = c("Organization", "LevelDesignation", "FunctionType"), @@ -97,7 +105,9 @@ network_leiden <- function(data, ## Return if(return == "plot-leiden"){ - g_layout + + + plot_output <- + g_layout + ggraph::geom_edge_link(colour = "lightgrey", edge_width = 0.01, alpha = 0.15) + ggraph::geom_node_point(aes(colour = cluster), alpha = node_alpha) + theme_void() + @@ -110,8 +120,25 @@ network_leiden <- function(data, subtitle = "Based on Leiden algorithm and Strong Tie Score", y = "", x = "") + + # Default PDF output unless NULL supplied to path + if(is.null(path)){ + + plot_output + + } else { + + ggsave(paste0(path, tstamp(), ".pdf"), + plot = plot_output, + width = 16, + height = 9) + + } + } else if(return == "plot-hrvar"){ - g_layout + + + plot_output <- + g_layout + ggraph::geom_edge_link(colour = "lightgrey", edge_width = 0.01, alpha = 0.15) + ggraph::geom_node_point(aes(colour = !!sym(hrvar)), alpha = node_alpha) + theme_void() + @@ -125,6 +152,20 @@ network_leiden <- function(data, y = "", x = "") + # Default PDF output unless NULL supplied to path + if(is.null(path)){ + + plot_output + + } else { + + ggsave(paste0(path, tstamp(), ".pdf"), + plot = plot_output, + width = 16, + height = 9) + + } + } else if(return == "table"){ vertex_tb %>% diff --git a/R/network_louvain.R b/R/network_louvain.R index 4235c893..42509056 100644 --- a/R/network_louvain.R +++ b/R/network_louvain.R @@ -14,9 +14,15 @@ #' @param bg_fill String to specify background fill colour. #' @param font_col String to specify font and link colour. #' @param node_alpha A numeric value between 0 and 1 to specify the transparency of the nodes. -#' @param algorithm String to specify the node placement algorithm to be used. Defaults to "fr" for the force-directed -#' algorithm of Fruchterman and Reingold. See for a -#' full list of options. +#' @param algorithm String to specify the node placement algorithm to be used. Defaults to "mds" to perform +#' a multidimensional scaling of nodes using a shortest path, which is also a deterministic method. +#' See for a full list of options. +#' @param path File path for saving the PDF output. Defaults to "network_p2p_louvain". +#' Since the network outputs are computationally intensive, the default behaviour is to save time by +#' saving the plot output directly as a PDF in the specified path. To override this behaviour and return +#' a plot object instead, you can pass `NULL` to `path`. What is passed to `path` makes no difference +#' if returning anything other than "plot-louvain" or "plot-hrvar". +#' #' @param return String specifying what output to return.Valid return options include: #' - 'plot-louvain': return a network plot coloured by louvain communities. #' - 'plot-hrvar': return a network plot coloured by HR attribute. @@ -36,6 +42,7 @@ network_louvain <- function(data, font_col = "#FFFFFF", node_alpha = 0.8, algorithm = "mds", + path = "network_p2p_louvain", desc_hrvar = c("Organization", "LevelDesignation", "FunctionType"), return){ @@ -95,7 +102,9 @@ network_louvain <- function(data, ## Return if(return == "plot-louvain"){ - g_layout + + + plot_output <- + g_layout + ggraph::geom_edge_link(colour = "lightgrey", edge_width = 0.01, alpha = 0.15) + ggraph::geom_node_point(aes(colour = cluster), alpha = node_alpha) + theme_void() + @@ -108,8 +117,25 @@ network_louvain <- function(data, subtitle = "Based on Louvain algorithm and Strong Tie Score", y = "", x = "") + + # Default PDF output unless NULL supplied to path + if(is.null(path)){ + + plot_output + + } else { + + ggsave(paste0(path, tstamp(), ".pdf"), + plot = plot_output, + width = 16, + height = 9) + + } + } else if(return == "plot-hrvar"){ - g_layout + + + plot_output <- + g_layout + ggraph::geom_edge_link(colour = "lightgrey", edge_width = 0.01, alpha = 0.15) + ggraph::geom_node_point(aes(colour = !!sym(hrvar)), alpha = node_alpha) + theme_void() + @@ -123,6 +149,20 @@ network_louvain <- function(data, y = "", x = "") + # Default PDF output unless NULL supplied to path + if(is.null(path)){ + + plot_output + + } else { + + ggsave(paste0(path, tstamp(), ".pdf"), + plot = plot_output, + width = 16, + height = 9) + + } + } else if(return == "table"){ vertex_tb %>% diff --git a/man/network_leiden.Rd b/man/network_leiden.Rd index eb252e45..f7cf123c 100644 --- a/man/network_leiden.Rd +++ b/man/network_leiden.Rd @@ -10,6 +10,7 @@ network_leiden( bg_fill = "#000000", font_col = "#FFFFFF", algorithm = "mds", + path = "network_p2p_leiden", node_alpha = 0.8, res = 0.5, desc_hrvar = c("Organization", "LevelDesignation", "FunctionType"), @@ -25,9 +26,15 @@ network_leiden( \item{font_col}{String to specify font and link colour.} -\item{algorithm}{String to specify the node placement algorithm to be used. Defaults to "fr" for the force-directed -algorithm of Fruchterman and Reingold. See \url{https://rdrr.io/cran/ggraph/man/layout_tbl_graph_igraph.html} for a -full list of options.} +\item{algorithm}{String to specify the node placement algorithm to be used. Defaults to "mds" to perform +a multidimensional scaling of nodes using a shortest path, which is also a deterministic method. +See \url{https://rdrr.io/cran/ggraph/man/layout_tbl_graph_igraph.html} for a full list of options.} + +\item{path}{File path for saving the PDF output. Defaults to "network_p2p_leiden". +Since the network outputs are computationally intensive, the default behaviour is to save time by +saving the plot output directly as a PDF in the specified path. To override this behaviour and return +a plot object instead, you can pass \code{NULL} to \code{path}. What is passed to \code{path} makes no difference +if returning anything other than "plot-leiden" or "plot-hrvar".} \item{node_alpha}{A numeric value between 0 and 1 to specify the transparency of the nodes.} diff --git a/man/network_louvain.Rd b/man/network_louvain.Rd index 89159a6a..cbee1861 100644 --- a/man/network_louvain.Rd +++ b/man/network_louvain.Rd @@ -11,6 +11,7 @@ network_louvain( font_col = "#FFFFFF", node_alpha = 0.8, algorithm = "mds", + path = "network_p2p_louvain", desc_hrvar = c("Organization", "LevelDesignation", "FunctionType"), return ) @@ -26,9 +27,15 @@ network_louvain( \item{node_alpha}{A numeric value between 0 and 1 to specify the transparency of the nodes.} -\item{algorithm}{String to specify the node placement algorithm to be used. Defaults to "fr" for the force-directed -algorithm of Fruchterman and Reingold. See \url{https://rdrr.io/cran/ggraph/man/layout_tbl_graph_igraph.html} for a -full list of options.} +\item{algorithm}{String to specify the node placement algorithm to be used. Defaults to "mds" to perform +a multidimensional scaling of nodes using a shortest path, which is also a deterministic method. +See \url{https://rdrr.io/cran/ggraph/man/layout_tbl_graph_igraph.html} for a full list of options.} + +\item{path}{File path for saving the PDF output. Defaults to "network_p2p_louvain". +Since the network outputs are computationally intensive, the default behaviour is to save time by +saving the plot output directly as a PDF in the specified path. To override this behaviour and return +a plot object instead, you can pass \code{NULL} to \code{path}. What is passed to \code{path} makes no difference +if returning anything other than "plot-louvain" or "plot-hrvar".} \item{return}{String specifying what output to return.Valid return options include: \itemize{ From 0b9e35ccef97752e3552408b0591123e760aa273 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Tue, 5 Jan 2021 14:31:47 +0000 Subject: [PATCH 190/192] perf: set pch = 16 for faster rendering --- R/network_leiden.R | 8 ++++++-- R/network_louvain.R | 10 +++++++--- 2 files changed, 13 insertions(+), 5 deletions(-) diff --git a/R/network_leiden.R b/R/network_leiden.R index 40af3ea8..52195d30 100644 --- a/R/network_leiden.R +++ b/R/network_leiden.R @@ -109,7 +109,9 @@ network_leiden <- function(data, plot_output <- g_layout + ggraph::geom_edge_link(colour = "lightgrey", edge_width = 0.01, alpha = 0.15) + - ggraph::geom_node_point(aes(colour = cluster), alpha = node_alpha) + + ggraph::geom_node_point(aes(colour = cluster), + alpha = node_alpha, + pch = 16) + theme_void() + theme(legend.position = "bottom", legend.background = element_rect(fill = bg_fill), @@ -140,7 +142,9 @@ network_leiden <- function(data, plot_output <- g_layout + ggraph::geom_edge_link(colour = "lightgrey", edge_width = 0.01, alpha = 0.15) + - ggraph::geom_node_point(aes(colour = !!sym(hrvar)), alpha = node_alpha) + + ggraph::geom_node_point(aes(colour = !!sym(hrvar)), + alpha = node_alpha, + pch = 16) + theme_void() + theme(legend.position = "bottom", legend.background = element_rect(fill = bg_fill), diff --git a/R/network_louvain.R b/R/network_louvain.R index 42509056..85b6d897 100644 --- a/R/network_louvain.R +++ b/R/network_louvain.R @@ -106,7 +106,9 @@ network_louvain <- function(data, plot_output <- g_layout + ggraph::geom_edge_link(colour = "lightgrey", edge_width = 0.01, alpha = 0.15) + - ggraph::geom_node_point(aes(colour = cluster), alpha = node_alpha) + + ggraph::geom_node_point(aes(colour = cluster), + alpha = node_alpha, + pch = 16) + theme_void() + theme(legend.position = "bottom", legend.background = element_rect(fill = bg_fill), @@ -137,7 +139,9 @@ network_louvain <- function(data, plot_output <- g_layout + ggraph::geom_edge_link(colour = "lightgrey", edge_width = 0.01, alpha = 0.15) + - ggraph::geom_node_point(aes(colour = !!sym(hrvar)), alpha = node_alpha) + + ggraph::geom_node_point(aes(colour = !!sym(hrvar)), + alpha = node_alpha, + pch = 16) + theme_void() + theme(legend.position = "bottom", legend.background = element_rect(fill = bg_fill), @@ -179,7 +183,7 @@ network_louvain <- function(data, } else if(return == "plot-sankey"){ - create_sankey(data = vertex_tb, + create_sankey(data = vertex_tb %>% count(!!sym(hrvar), cluster), var1 = hrvar, var2 = "cluster", count = "n") From fa9db7b199a8f0ac950e5b302792d8fc13991f14 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Wed, 6 Jan 2021 09:50:29 +0000 Subject: [PATCH 191/192] perf: speed up joins - Speed up the data joins for creating vertex meta-data. - Add missing argument documentation for `desc_hrvar` --- R/network_leiden.R | 6 ++++++ R/network_louvain.R | 6 ++++++ man/network_leiden.Rd | 3 +++ man/network_louvain.Rd | 3 +++ 4 files changed, 18 insertions(+) diff --git a/R/network_leiden.R b/R/network_leiden.R index 52195d30..99163c88 100644 --- a/R/network_leiden.R +++ b/R/network_leiden.R @@ -26,6 +26,8 @@ #' See for a full list of options. #' #' @param res Resolution parameter to be passed to `leiden::leiden()`. Defaults to 0.5. +#' @param desc_hrvar Character vector of length 3 containing the HR attributes to use when returning the +#' "describe" output. See `network_describe()`. #' @param return String specifying what output to return. Valid return options include: #' - 'plot-leiden': return a network plot coloured by leiden communities. #' - 'plot-hrvar': return a network plot coloured by HR attribute. @@ -65,12 +67,16 @@ network_leiden <- function(data, rbind( # TieOrigin edges %>% + select(from) %>% # Single column + unique() %>% # Remove duplications left_join(select(data, TieOrigin_PersonId, TO_hrvar), by = c("from" = "TieOrigin_PersonId")) %>% select(node = "from", !!sym(hrvar) := TO_hrvar), # TieDestination edges %>% + select(to) %>% # Single column + unique() %>% # Remove duplications left_join(select(data, TieDestination_PersonId, TD_hrvar), by = c("to" = "TieDestination_PersonId")) %>% select(node = "to", !!sym(hrvar) := TD_hrvar) diff --git a/R/network_louvain.R b/R/network_louvain.R index 85b6d897..f8714bbf 100644 --- a/R/network_louvain.R +++ b/R/network_louvain.R @@ -22,6 +22,8 @@ #' saving the plot output directly as a PDF in the specified path. To override this behaviour and return #' a plot object instead, you can pass `NULL` to `path`. What is passed to `path` makes no difference #' if returning anything other than "plot-louvain" or "plot-hrvar". +#' @param desc_hrvar Character vector of length 3 containing the HR attributes to use when returning the +#' "describe" output. See `network_describe()`. #' #' @param return String specifying what output to return.Valid return options include: #' - 'plot-louvain': return a network plot coloured by louvain communities. @@ -62,12 +64,16 @@ network_louvain <- function(data, rbind( # TieOrigin edges %>% + select(from) %>% # Single column + unique() %>% # Remove duplications left_join(select(data, TieOrigin_PersonId, TO_hrvar), by = c("from" = "TieOrigin_PersonId")) %>% select(node = "from", !!sym(hrvar) := TO_hrvar), # TieDestination edges %>% + select(to) %>% # Single column + unique() %>% # Remove duplications left_join(select(data, TieDestination_PersonId, TD_hrvar), by = c("to" = "TieDestination_PersonId")) %>% select(node = "to", !!sym(hrvar) := TD_hrvar) diff --git a/man/network_leiden.Rd b/man/network_leiden.Rd index f7cf123c..7fe24514 100644 --- a/man/network_leiden.Rd +++ b/man/network_leiden.Rd @@ -40,6 +40,9 @@ if returning anything other than "plot-leiden" or "plot-hrvar".} \item{res}{Resolution parameter to be passed to \code{leiden::leiden()}. Defaults to 0.5.} +\item{desc_hrvar}{Character vector of length 3 containing the HR attributes to use when returning the +"describe" output. See \code{network_describe()}.} + \item{return}{String specifying what output to return. Valid return options include: \itemize{ \item 'plot-leiden': return a network plot coloured by leiden communities. diff --git a/man/network_louvain.Rd b/man/network_louvain.Rd index cbee1861..c5b060e5 100644 --- a/man/network_louvain.Rd +++ b/man/network_louvain.Rd @@ -37,6 +37,9 @@ saving the plot output directly as a PDF in the specified path. To override this a plot object instead, you can pass \code{NULL} to \code{path}. What is passed to \code{path} makes no difference if returning anything other than "plot-louvain" or "plot-hrvar".} +\item{desc_hrvar}{Character vector of length 3 containing the HR attributes to use when returning the +"describe" output. See \code{network_describe()}.} + \item{return}{String specifying what output to return.Valid return options include: \itemize{ \item 'plot-louvain': return a network plot coloured by louvain communities. From e9a60a8c903bc8ae105dd1db2bd3853957ca0bfe Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Wed, 6 Jan 2021 10:49:46 +0000 Subject: [PATCH 192/192] fix: import changes Import individual functions for pairwise_count rather than the entire data.table library. This should remove start up warning messages with namespace conflicts between dplyr and data.table --- NAMESPACE | 3 ++- R/pairwise_count.R | 13 +++++++++++-- man/pairwise_count.Rd | 9 ++++++++- 3 files changed, 21 insertions(+), 4 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index 65a75edd..bb223eb1 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -154,7 +154,6 @@ export(workpatterns_rank) export(wrap) import(DT) import(Information) -import(data.table) import(dplyr) import(ggplot2) import(ggraph) @@ -165,6 +164,8 @@ import(tidyselect) importFrom(data.table,"%between%") importFrom(data.table,"%like%") importFrom(data.table,":=") +importFrom(data.table,as.data.table) +importFrom(data.table,rbindlist) importFrom(dplyr,`%>%`) importFrom(dplyr,mutate_if) importFrom(grDevices,rainbow) diff --git a/R/pairwise_count.R b/R/pairwise_count.R index 8e0b715b..a2e2ad44 100644 --- a/R/pairwise_count.R +++ b/R/pairwise_count.R @@ -5,16 +5,25 @@ #' within `tm_cooc()`. #' #' @param data Data frame output from `tm_clean()`. -#' @param id String to represent the id variable. Defaults to "word". +#' @param id String to represent the id variable. Defaults to "line". #' @param word String to represent the word variable. Defaults to "word". #' -#' @import data.table +#' @importFrom data.table ":=" "%like%" "%between%" rbindlist as.data.table +#' +#' @examples +#' td <- data.frame(line = c(1, 1, 2, 2), +#' word = c("work", "meeting", "catch", "up")) +#' +#' pairwise_count(td, id = "line", word = "word") #' #' @export pairwise_count <- function(data, id = "line", word = "word"){ + # Make sure data.table knows we know we're using it + .datatable.aware = TRUE + data <- data %>% dplyr::rename(word := !!sym(word), diff --git a/man/pairwise_count.Rd b/man/pairwise_count.Rd index fb588a4a..32e8b642 100644 --- a/man/pairwise_count.Rd +++ b/man/pairwise_count.Rd @@ -9,7 +9,7 @@ pairwise_count(data, id = "line", word = "word") \arguments{ \item{data}{Data frame output from \code{tm_clean()}.} -\item{id}{String to represent the id variable. Defaults to "word".} +\item{id}{String to represent the id variable. Defaults to "line".} \item{word}{String to represent the word variable. Defaults to "word".} } @@ -18,3 +18,10 @@ This is a \strong{data.table} implementation that mimics the output of \code{widyr::pairwise_count()} to reduce package dependency. This is used internally within \code{tm_cooc()}. } +\examples{ +td <- data.frame(line = c(1, 1, 2, 2), + word = c("work", "meeting", "catch", "up")) + +pairwise_count(td, id = "line", word = "word") + +}

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zDIBnm5|ec%!EB<65u+5Dy(i`*8r*BR1W*yxXvv54Ve%i+PcMMsIPakvY}~YDb=U-E z+IlLc%B*~tKmtjv5LDcVtwkg38@}-QlJwT$&O!eD;+uYk;F72DCtvf#h08NWuE>RU l(3gS8JlJ;dKQl`4Hx6t_4}6gGXjjni&Cbf(-My1t|1XZvKqdeH literal 0 HcmV?d00001 From b18b4fd9507d012285ebc53d2a599aefd5c9a133 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Wed, 25 Nov 2020 10:23:19 +0000 Subject: [PATCH 015/192] docs: update example in README --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index a0348f8b..8081b96b 100644 --- a/README.md +++ b/README.md @@ -40,7 +40,7 @@ The package comes shipped with a sample Standard Query dataset (`sq_data`), so y ```R collaboration_sum(sq_data, return = "plot") ``` - + By passing 'table' to the `return` argument, the function will return a data frame with the relevant summary statistics. From 32eb1a1f6b3f53fac5884377447e53ab01ab1eda Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Wed, 25 Nov 2020 10:23:46 +0000 Subject: [PATCH 016/192] feat: update analyst guide --- .github/analyst_guide_distribution.md | 41 ++++++++++++++++++++++++++- .github/analyst_guide_summary.md | 23 +++++++++++++-- 2 files changed, 61 insertions(+), 3 deletions(-) diff --git a/.github/analyst_guide_distribution.md b/.github/analyst_guide_distribution.md index 064684ba..bac88916 100644 --- a/.github/analyst_guide_distribution.md +++ b/.github/analyst_guide_distribution.md @@ -1,3 +1,42 @@ # Distribution -_Coming soon..._ \ No newline at end of file +**Distribution** functions allow you to go beyond aggregated averages, and diagnose whether metrics are skewed, evenly distributed, or contain outliers. + +You can use the `fizz` and `dist` family of functions for this. For email hours, you can run the following: + +```R +sq_data %>% email_dist() # 100% horizontal stacked bar +``` + + + +You can also return a jittered scatter plot ('fizzy drink plot'). + +```R +sq_data %>% email_fizz() # Fizzy drink plot +``` + +## Functions + +Other examples of distribution functions include: + +### Dist + +- `collaboration_dist()` +- `meeting_dist()` +- `one2one_dist()` +- `afterhours_dist()` +- `workloads_dist()` + +### Fizz + +- `collaboration_fizz()` +- `meeting_fizz()` +- `one2one_fizz()` +- `afterhours_fizz()` +- `workloads_fizz()` + +### Flexible functions +- `create_dist()` +- `create_fizz()` +- `create_boxplot()` \ No newline at end of file diff --git a/.github/analyst_guide_summary.md b/.github/analyst_guide_summary.md index 53dd80b4..585e863e 100644 --- a/.github/analyst_guide_summary.md +++ b/.github/analyst_guide_summary.md @@ -8,12 +8,30 @@ An instance of a summary function in action would be: sq_data %>% collaboration_summary() ``` + + +This returns a plot that you can either as a SVG (vector) or PNG (scalar) image by passing the output to `export()`. Since the plot output is a ggplot object, you can also make further customizations to it by appending ggplot layers, for instance: + +```R +sq_data %>% + collaboration_summary() + + ggtitle("This is a custom title") +``` + You can use return a summary table rather than a plot: ```R sq_data %>% collaboration_summary(return = "table") ``` +To export the results to a clipboard, you can pass the outputs to `export()` again: + +```R +sq_data %>% + collaboration_summary(return = "table") %>% + export() +``` + There is also an option to change the threshold for excluding group size: ``` @@ -23,8 +41,6 @@ sq_data %>% return = "table") ``` - - Other similar functions include: - `email_summary()` @@ -32,3 +48,6 @@ Other similar functions include: - `one2one_summary()` - `workloads_summary()` - `afterhours_summary()` + +See `create_bar()` for creating summary functions with other Workplace Analytics metrics. + From 1b605c76c58beffc81e2c72376b02d9e4c6ef92a Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Wed, 25 Nov 2020 10:24:09 +0000 Subject: [PATCH 017/192] chore: edit format for package structure --- .github/package_structure.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/package_structure.md b/.github/package_structure.md index 5af85b74..204998db 100644 --- a/.github/package_structure.md +++ b/.github/package_structure.md @@ -1,4 +1,4 @@ -## :package: Package Structure +# :package: Package Structure There are four main types of functions in **wpa**: 1. Standard Analysis From 6937c1d52b348d917219c4c9edceaa4573314ab8 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Wed, 25 Nov 2020 11:12:28 +0000 Subject: [PATCH 018/192] feat: clean code create_fizz --- R/create_fizz.R | 12 ++---------- 1 file changed, 2 insertions(+), 10 deletions(-) diff --git a/R/create_fizz.R b/R/create_fizz.R index 693bb492..ca817687 100644 --- a/R/create_fizz.R +++ b/R/create_fizz.R @@ -58,7 +58,7 @@ create_fizz <- function(data, check_inputs(requirements = required_variables) ## Clean metric name - clean_nm <- gsub(pattern = "_", replacement = "", x = metric) + clean_nm <- gsub(pattern = "_", replacement = " ", x = metric) ## Plot data plot_data <- @@ -93,15 +93,7 @@ create_fizz <- function(data, ylim(0, max_point) + annotate("text", x = plot_legend$group, y = 0, label = plot_legend$Employee_Count) + scale_x_discrete(labels = scales::wrap_format(10)) + - theme_classic() + - theme(axis.text=element_text(size=12), - axis.text.x = element_text(angle = 30, hjust = 1), - plot.title = element_text(color="grey40", face="bold", size=18), - plot.subtitle = element_text(size=14), - legend.position = "top", - legend.justification = "right", - legend.title=element_text(size=14), - legend.text=element_text(size=14)) + + theme_wpa_basic() + labs(title = clean_nm, subtitle = paste("Distribution of", tolower(clean_nm), From 933b939bd61a0ba655cd3a81694283de052724ca Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Wed, 25 Nov 2020 11:25:33 +0000 Subject: [PATCH 019/192] feat: add content for trend analyst guide --- .github/analyst_guide_trend.md | 35 +++++++++++++++++++++++++++++++++- 1 file changed, 34 insertions(+), 1 deletion(-) diff --git a/.github/analyst_guide_trend.md b/.github/analyst_guide_trend.md index 5ff30178..3df6226c 100644 --- a/.github/analyst_guide_trend.md +++ b/.github/analyst_guide_trend.md @@ -1,3 +1,36 @@ # Trend -_Coming soon..._ \ No newline at end of file +**Trend** functions analyze Workplace Analytics metrics over time. + + + +## Functions + +Trend functions include: + +### Trend + +- `collaboration_trend()` +- `meeting_trend()` +- `email_trend()` +- `one2one_trend()` +- `afterhours_trend()` +- `workloads_trend()` + +### Line + +- `collaboration_line()` +- `meeting_line()` +- `email_line()` +- `one2one_line()` +- `afterhours_line()` +- `workloads_line()` + +### Flexible functions + +- `create_trend()` +- `create_line()` + +### Other + +- `collab_area()` \ No newline at end of file From 7496d3c42817681cbbdd16c5041b754a552e4f24 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Wed, 25 Nov 2020 14:11:28 +0000 Subject: [PATCH 020/192] feat: update pkgdown site --- .github/reporting_security_issues.md | 4 ++-- README.md | 9 ++++++++- 2 files changed, 10 insertions(+), 3 deletions(-) diff --git a/.github/reporting_security_issues.md b/.github/reporting_security_issues.md index 34b15843..45ab14be 100644 --- a/.github/reporting_security_issues.md +++ b/.github/reporting_security_issues.md @@ -1,4 +1,4 @@ -## Security +# Security Microsoft takes the security of our software products and services seriously, which includes all source code repositories managed through our GitHub organizations, which include [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet), [Xamarin](https://github.com/xamarin), and [our GitHub organizations](https://opensource.microsoft.com/). @@ -34,4 +34,4 @@ We prefer all communications to be in English. ## Policy -Microsoft follows the principle of [Coordinated Vulnerability Disclosure](https://www.microsoft.com/en-us/msrc/cvd). \ No newline at end of file +Microsoft follows the principle of [Coordinated Vulnerability Disclosure](https://www.microsoft.com/en-us/msrc/cvd). diff --git a/README.md b/README.md index 8081b96b..54a7e814 100644 --- a/README.md +++ b/README.md @@ -9,8 +9,11 @@ This is an R package for analyzing and visualizing data from [Microsoft Workplac ## With the **wpa** package, you can... 1. **Run prebuilt analysis and visualizations** off Workplace Analytics data with settings for HR variables, privacy threshold, etc. + 2. **Generate prebuilt interactive HTML reports**, which cover specific areas e.g. collaboration, connectivity + 3. Leverage **advanced analytics functions**, such as text mining and hierarchical clustering, which are built for Workplace Analytics metrics + 4. Integrate analysis of Workplace Analytics data with your R workflow seamlessly Here is an example of **wpa** in action: @@ -40,7 +43,7 @@ The package comes shipped with a sample Standard Query dataset (`sq_data`), so y ```R collaboration_sum(sq_data, return = "plot") ``` - + By passing 'table' to the `return` argument, the function will return a data frame with the relevant summary statistics. @@ -95,3 +98,7 @@ We would ask you to please read the [Microsoft Open Source Code of Conduct](http **Trademarks** This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow [Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general). Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies. + +--- + +Finding this project useful? Star this repository to keep us going! :star: From 2b717090f1ae4c98d828141d797dc503964e89c4 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Wed, 25 Nov 2020 14:14:02 +0000 Subject: [PATCH 021/192] feat: add contributors list yaml --- .github/workflows/contributors.yaml | 2 ++ 1 file changed, 2 insertions(+) create mode 100644 .github/workflows/contributors.yaml diff --git a/.github/workflows/contributors.yaml b/.github/workflows/contributors.yaml new file mode 100644 index 00000000..46034fe9 --- /dev/null +++ b/.github/workflows/contributors.yaml @@ -0,0 +1,2 @@ +- name: Contributors List + uses: bubkoo/contributors-list@v1.0.6 From 3a6aaf928d132e717c155928fdeac0ea2ba06965 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Wed, 25 Nov 2020 14:21:56 +0000 Subject: [PATCH 022/192] fix: update contributors.yaml --- .github/workflows/contributors.yaml | 42 +++++++++++++++++++++++++++-- 1 file changed, 40 insertions(+), 2 deletions(-) diff --git a/.github/workflows/contributors.yaml b/.github/workflows/contributors.yaml index 46034fe9..8794e39f 100644 --- a/.github/workflows/contributors.yaml +++ b/.github/workflows/contributors.yaml @@ -1,2 +1,40 @@ -- name: Contributors List - uses: bubkoo/contributors-list@v1.0.6 +name: Contributors +on: + schedule: + - cron: '0 1 * * 0' # At 01:00 on Sunday. + push: + branches: + - master +jobs: + contributors: + runs-on: ubuntu-latest + steps: + - uses: bubkoo/contributors-list@v1 + with: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + includeBots: false + svgTemplate: > + + + + Contributors + + {{{ contributors }}} + + + Bots + + {{{ bots }}} + + + Collaborators + + {{{ collaborators }}} + + \ No newline at end of file From def59790e0e430e044f96121b73c538766607499 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Wed, 25 Nov 2020 14:35:32 +0000 Subject: [PATCH 023/192] fix: update contributors.yaml run on pushes to main rather than master --- .github/workflows/contributors.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/contributors.yaml b/.github/workflows/contributors.yaml index 8794e39f..117de238 100644 --- a/.github/workflows/contributors.yaml +++ b/.github/workflows/contributors.yaml @@ -4,7 +4,7 @@ on: - cron: '0 1 * * 0' # At 01:00 on Sunday. push: branches: - - master + - main jobs: contributors: runs-on: ubuntu-latest @@ -37,4 +37,4 @@ jobs: {{{ collaborators }}} - \ No newline at end of file + From ad1051bfbb2386246f0619c5bdc899d3dc3c7925 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Wed, 25 Nov 2020 14:35:51 +0000 Subject: [PATCH 024/192] chore: update contributors [skip ci] --- CONTRIBUTORS.svg | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) create mode 100644 CONTRIBUTORS.svg diff --git a/CONTRIBUTORS.svg b/CONTRIBUTORS.svg new file mode 100644 index 00000000..49435083 --- /dev/null +++ b/CONTRIBUTORS.svg @@ -0,0 +1,23 @@ + + + + Contributors + + + + + + + + + + Bots + + + + + Collaborators + + + + \ No newline at end of file From 9b030105a0588e5052b5051607131e5651347508 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Wed, 25 Nov 2020 14:49:09 +0000 Subject: [PATCH 025/192] feat: update CONTRIBUTORS.svg --- .Rbuildignore | 1 + .github/workflows/contributors.yaml | 19 ++----------------- README.md | 10 +++++++++- 3 files changed, 12 insertions(+), 18 deletions(-) diff --git a/.Rbuildignore b/.Rbuildignore index 4f107364..4a61c3ce 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -14,3 +14,4 @@ ^man/figures/wpa cheatsheet_20201116.png$ ^vignettes/best-practices.Rmd$ ^vignettes/text-mining-capabilities.Rmd$ +^CONTRIBUTORS.svg$ diff --git a/.github/workflows/contributors.yaml b/.github/workflows/contributors.yaml index 117de238..2d231b94 100644 --- a/.github/workflows/contributors.yaml +++ b/.github/workflows/contributors.yaml @@ -12,29 +12,14 @@ jobs: - uses: bubkoo/contributors-list@v1 with: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} - includeBots: false svgTemplate: > - - Contributors - - {{{ contributors }}} - - - Bots - - {{{ bots }}} - - - Collaborators - - {{{ collaborators }}} - + {{{ collaborators }}} diff --git a/README.md b/README.md index 54a7e814..bc16e652 100644 --- a/README.md +++ b/README.md @@ -101,4 +101,12 @@ This project may contain trademarks or logos for projects, products, or services --- -Finding this project useful? Star this repository to keep us going! :star: +## Finding this project useful? + +:star: Please star this repository to keep us going! + +Contributors to the GitHub repo: + +![](CONTRIBUTORS.svg) + +See full list [here](https://microsoft.github.io/wpa/authors.html). From b6a5f2ec15fd19f23f0d69742e488b0d15652608 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Wed, 25 Nov 2020 14:49:34 +0000 Subject: [PATCH 026/192] chore: update contributors [skip ci] --- CONTRIBUTORS.svg | 23 ++--------------------- 1 file changed, 2 insertions(+), 21 deletions(-) diff --git a/CONTRIBUTORS.svg b/CONTRIBUTORS.svg index 49435083..fb5827f4 100644 --- a/CONTRIBUTORS.svg +++ b/CONTRIBUTORS.svg @@ -1,23 +1,4 @@ - + - - Contributors - - - - - - - - - - Bots - - - - - Collaborators - - - + \ No newline at end of file From 6ca74e1801dd2f5ad4f85c855f0a0a123317951f Mon Sep 17 00:00:00 2001 From: moralec Date: Wed, 25 Nov 2020 15:46:49 +0000 Subject: [PATCH 027/192] Update create_IV.R --- R/create_IV.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/create_IV.R b/R/create_IV.R index 304ce419..c9449cc9 100644 --- a/R/create_IV.R +++ b/R/create_IV.R @@ -87,7 +87,7 @@ create_IV <- function(data, subtitle = "Showing top 12 only") } else if(return == "plot-WOE"){ - Information::plot_infotables(IV, IV$Summary$Variable[], same_scale=TRUE) %>% grDevices::recordPlot() + Information::plot_infotables(IV, IV$Summary$Variable[1:9], same_scale=TRUE) %>% grDevices::recordPlot() } else if(return == "list"){ output_list From 96f36fbe3d5cc5594aa7e718c2e9df97ef023d52 Mon Sep 17 00:00:00 2001 From: moralec Date: Wed, 25 Nov 2020 15:47:29 +0000 Subject: [PATCH 028/192] Update create_IV.R --- R/create_IV.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/create_IV.R b/R/create_IV.R index 304ce419..c9449cc9 100644 --- a/R/create_IV.R +++ b/R/create_IV.R @@ -87,7 +87,7 @@ create_IV <- function(data, subtitle = "Showing top 12 only") } else if(return == "plot-WOE"){ - Information::plot_infotables(IV, IV$Summary$Variable[], same_scale=TRUE) %>% grDevices::recordPlot() + Information::plot_infotables(IV, IV$Summary$Variable[1:9], same_scale=TRUE) %>% grDevices::recordPlot() } else if(return == "list"){ output_list From 43f0b8071af005cabe1d1b74a218f98f7c9357e4 Mon Sep 17 00:00:00 2001 From: moralec Date: Wed, 25 Nov 2020 15:56:23 +0000 Subject: [PATCH 029/192] Update create_IV.R --- R/create_IV.R | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/R/create_IV.R b/R/create_IV.R index c9449cc9..c5572cf4 100644 --- a/R/create_IV.R +++ b/R/create_IV.R @@ -87,7 +87,13 @@ create_IV <- function(data, subtitle = "Showing top 12 only") } else if(return == "plot-WOE"){ + if (length(IV$Summary$Variable[]) >9) { Information::plot_infotables(IV, IV$Summary$Variable[1:9], same_scale=TRUE) %>% grDevices::recordPlot() + } + + else { + Information::plot_infotables(IV, IV$Summary$Variable[], same_scale=TRUE) %>% grDevices::recordPlot() + } } else if(return == "list"){ output_list From efbc6d6ea88f79cf2f7fc1e1be49ab422dd75346 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Wed, 25 Nov 2020 16:45:13 +0000 Subject: [PATCH 030/192] docs: add family for IV functions --- R/IV_by_Period.R | 1 + R/IV_report.R | 10 +++++++++- man/IV_by_period.Rd | 7 ++++++- man/IV_report.Rd | 13 +++++++++++++ 4 files changed, 29 insertions(+), 2 deletions(-) diff --git a/R/IV_by_Period.R b/R/IV_by_Period.R index bf174638..5da5bd4c 100644 --- a/R/IV_by_Period.R +++ b/R/IV_by_Period.R @@ -23,6 +23,7 @@ #' #' #' @family Flexible Input +#' @family Information Value #' #' @export diff --git a/R/IV_report.R b/R/IV_report.R index d80bac20..1536d9ac 100644 --- a/R/IV_report.R +++ b/R/IV_report.R @@ -20,13 +20,21 @@ #' @param timestamp Logical vector specifying whether to include a timestamp in the file name. #' Defaults to TRUE. #' +#' @examples +#' \dontrun{ +#' sq_data %>% +#' mutate(X = ifelse(Collaboration_hours > 12, 1, 0)) %>% # Simulate binary variable +#' IV_report(outcome = "CH_binary", predictors = c("Email_hours", "Workweek_span")) +#' } +#' #' @family Reports +#' @family Information Value #' #' @export IV_report <- function(data, predictors = NULL, outcome, - bins= 5, + bins = 5, path = "IV report", timestamp = TRUE){ diff --git a/man/IV_by_period.Rd b/man/IV_by_period.Rd index dc3a730b..3ebc2b06 100644 --- a/man/IV_by_period.Rd +++ b/man/IV_by_period.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/IV_by_Period.R +% Please edit documentation in R/IV_by_period.R \name{IV_by_period} \alias{IV_by_period} \title{Identify the WPA metrics that have the biggest change between two periods.} @@ -37,5 +37,10 @@ This function uses the Information Value algorithm to predict which WPA metrics \seealso{ Other Flexible Input: \code{\link{period_change}()} + +Other Information Value: +\code{\link{IV_report}()}, +\code{\link{create_IV}()} } \concept{Flexible Input} +\concept{Information Value} diff --git a/man/IV_report.Rd b/man/IV_report.Rd index a741a322..d45b0a52 100644 --- a/man/IV_report.Rd +++ b/man/IV_report.Rd @@ -33,6 +33,14 @@ Defaults to TRUE.} \description{ The function generates an interactive HTML report using Standard Query data as an input. The report contains a full Information Value analysis, a data exploration technique that helps determine which columns in a data set have predictive power or influence on the value of a specified dependent variable. +} +\examples{ +\dontrun{ +sq_data \%>\% + mutate(X = ifelse(Collaboration_hours > 12, 1, 0)) \%>\% # Simulate binary variable + IV_report(outcome = "CH_binary", predictors = c("Email_hours", "Workweek_span")) +} + } \seealso{ Other Reports: @@ -42,5 +50,10 @@ Other Reports: \code{\link{connectivity_report}()}, \code{\link{generate_report}()}, \code{\link{validation_report}()} + +Other Information Value: +\code{\link{IV_by_period}()}, +\code{\link{create_IV}()} } +\concept{Information Value} \concept{Reports} From c9fa33a7adb587e4a79a61ba3f4b88642e9a36ba Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Wed, 25 Nov 2020 16:45:38 +0000 Subject: [PATCH 031/192] feat: add create_bubble --- NAMESPACE | 2 +- R/create_bubble.R | 114 +++++++++++++++++++++++++++++++++++++++++++ man/create_bubble.Rd | 71 +++++++++++++++++++++++++++ 3 files changed, 186 insertions(+), 1 deletion(-) create mode 100644 R/create_bubble.R create mode 100644 man/create_bubble.Rd diff --git a/NAMESPACE b/NAMESPACE index ad182eb5..80acf401 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -41,6 +41,7 @@ export(create_IV) export(create_bar) export(create_bar_asis) export(create_boxplot) +export(create_bubble) export(create_dist) export(create_dt) export(create_fizz) @@ -145,7 +146,6 @@ import(Information) import(dplyr) import(ggplot2) import(ggraph) -import(ggrepel) import(reshape2) import(scales) import(tidyselect) diff --git a/R/create_bubble.R b/R/create_bubble.R new file mode 100644 index 00000000..3201932c --- /dev/null +++ b/R/create_bubble.R @@ -0,0 +1,114 @@ +# -------------------------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See LICENSE.txt in the project root for license information. +# -------------------------------------------------------------------------------------------- + +#' @title Create a bubble plot with two selected Workplace Analytics metrics (General Purpose), with size +#' representing the number of employees in the group. +#' +#' @description +#' Returns a bubble plot of two selected metrics, using size to map the number of employees. +#' +#' @details +#' This is a general purpose function that powers all the functions +#' in the package that produce bubble plots. +#' +#' @param data A Standard Person Query dataset in the form of a data frame. +#' @param metric_x Character string containing the name of the metric, +#' e.g. "Collaboration_hours" +#' @param metric_y Character string containing the name of the metric, +#' e.g. "Collaboration_hours" +#' @param hrvar HR Variable by which to split metrics, defaults to "Organization" +#' but accepts any character vector, e.g. "LevelDesignation" +#' @param mingroup Numeric value setting the privacy threshold / minimum group size. Defaults to 5. +#' @param return Character vector specifying what to return, defaults to "plot". +#' Valid inputs are "plot" and "table". +#' @param bubble_size A numeric vector of length two to specify the size range of the bubbles +#' +#' @import dplyr +#' @import ggplot2 +#' @import scales +#' +#' @family General +#' +#' @examples +#' create_bubble(sq_data, +#' "Internal_network_size", +#' "External_network_size", +#' "Organization") +#' +#' create_bubble(sq_data, +#' "Generated_workload_call_hours", +#' "Generated_workload_email_hours", +#' "Organization", mingroup = 100, return = "plot") +#' +#' @return +#' Returns a ggplot object by default, where 'plot' is passed in `return`. +#' When 'table' is passed, a summary table is returned as a data frame. +#' +#' @export +create_bubble <- function(data, + metric_x, + metric_y, + hrvar = "Organization", + mingroup = 5, + return = "plot", + bubble_size = c(1, 10)){ + + ## Check inputs + required_variables <- c(hrvar, + metric_x, + metric_y, + "PersonId") + + ## Error message if variables are not present + ## Nothing happens if all present + data %>% + check_inputs(requirements = required_variables) + + ## Clean metric names + clean_x <- gsub(pattern = "_", replacement = " ", x = metric_x) + clean_y <- gsub(pattern = "_", replacement = " ", x = metric_y) + + myTable <- + data %>% + group_by(PersonId, !!sym(hrvar)) %>% + summarise_at(vars(!!sym(metric_x), !!sym(metric_y)), ~mean(., na.rm = TRUE)) %>% + group_by(!!sym(hrvar)) %>% + summarise_at(vars(!!sym(metric_x), !!sym(metric_y)), ~mean(., na.rm = TRUE)) %>% + ungroup() %>% + left_join(hrvar_count(data, hrvar = hrvar, return = "table"), + by = hrvar) %>% + filter(n >= mingroup) + + plot_object <- + myTable %>% + ggplot(aes(x = !!sym(metric_x), + y = !!sym(metric_y), + label = !!sym(hrvar))) + + geom_point(alpha = 0.5, color = rgb2hex(0, 120, 212), aes(size = n)) + + ggrepel::geom_text_repel(size = 3) + + labs(title = paste0(clean_x, " and ", clean_y), + subtitle = paste("By", camel_clean(hrvar)), + caption = paste("Total employees =", sum(myTable$n), "|", + extract_date_range(data, return = "text"))) + + xlab(clean_x) + + ylab(clean_y) + + scale_size(range = bubble_size) + + theme_wpa_basic() + + if(return == "table"){ + + return(myTable) + + } else if(return == "plot"){ + + return(plot_object) + + } else { + + stop("Please enter a valid input for `return`.") + + } +} + diff --git a/man/create_bubble.Rd b/man/create_bubble.Rd new file mode 100644 index 00000000..a0e01307 --- /dev/null +++ b/man/create_bubble.Rd @@ -0,0 +1,71 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/create_bubble.R +\name{create_bubble} +\alias{create_bubble} +\title{Create a bubble plot with two selected Workplace Analytics metrics (General Purpose), with size +representing the number of employees in the group.} +\usage{ +create_bubble( + data, + metric_x, + metric_y, + hrvar = "Organization", + mingroup = 5, + return = "plot", + bubble_size = c(1, 10) +) +} +\arguments{ +\item{data}{A Standard Person Query dataset in the form of a data frame.} + +\item{metric_x}{Character string containing the name of the metric, +e.g. "Collaboration_hours"} + +\item{metric_y}{Character string containing the name of the metric, +e.g. "Collaboration_hours"} + +\item{hrvar}{HR Variable by which to split metrics, defaults to "Organization" +but accepts any character vector, e.g. "LevelDesignation"} + +\item{mingroup}{Numeric value setting the privacy threshold / minimum group size. Defaults to 5.} + +\item{return}{Character vector specifying what to return, defaults to "plot". +Valid inputs are "plot" and "table".} + +\item{bubble_size}{A numeric vector of length two to specify the size range of the bubbles} +} +\value{ +Returns a ggplot object by default, where 'plot' is passed in \code{return}. +When 'table' is passed, a summary table is returned as a data frame. +} +\description{ +Returns a bubble plot of two selected metrics, using size to map the number of employees. +} +\details{ +This is a general purpose function that powers all the functions +in the package that produce bubble plots. +} +\examples{ +create_bubble(sq_data, +"Internal_network_size", +"External_network_size", +"Organization") + +create_bubble(sq_data, +"Generated_workload_call_hours", +"Generated_workload_email_hours", +"Organization", mingroup = 100, return = "plot") + +} +\seealso{ +Other General: +\code{\link{create_bar_asis}()}, +\code{\link{create_bar}()}, +\code{\link{create_boxplot}()}, +\code{\link{create_scatter}()}, +\code{\link{cut_hour}()}, +\code{\link{extract_hr}()}, +\code{\link{hrvar_count}()}, +\code{\link{identify_outlier}()} +} +\concept{General} From d5bb7616cde2137a5fc7aeddb78d1dd8f7653df2 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Wed, 25 Nov 2020 16:46:40 +0000 Subject: [PATCH 032/192] feat: use DRY dependencies All of the following now depend on create_bubble: - internal_network_plot - external_network_plot - meeting_quality --- R/external_network_plot.R | 74 ++++-------------------- R/internal_network_plot.R | 82 +++++--------------------- R/meeting_quality.R | 108 +++++++++-------------------------- man/external_network_plot.Rd | 13 ++--- man/internal_network_plot.Rd | 14 ++--- man/meeting_quality.Rd | 16 +++--- 6 files changed, 71 insertions(+), 236 deletions(-) diff --git a/R/external_network_plot.R b/R/external_network_plot.R index 9447722b..4592ff35 100644 --- a/R/external_network_plot.R +++ b/R/external_network_plot.R @@ -10,24 +10,11 @@ #' External Network Breadth as the vertical axis and External Network Size as the #' horizontal axis. #' -#' @param data Person Query as a dataframe including date column named "Date" -#' This function assumes the data format is MM/DD/YYYY as is standard in a WpA query output. -#' @param hrvar WpA variable for an HR variable to group networks by -#' For example, "Layer" -#' @param mingroup Numeric vector for minimum group size for aggregation -#' @param return Character vector specifying what to return, defaults to "plot". -#' Valid inputs are "plot" and "table". -#' @param bubble_size A numeric vector of length two to specify the size range of the bubbles -#' -#' @import dplyr -#' @import reshape2 -#' @import ggplot2 -#' @import ggrepel +#' @inheritParams create_bubble #' #' @examples -#' \dontrun{external_network_plot(sq_data) -# -#' } +#' ## Return a plot +#' sq_data %>% external_network_plot(return = "plot") #' #' @family Connectivity #' @@ -38,52 +25,15 @@ external_network_plot <- function(data, mingroup = 5, return = "plot", bubble_size = c(1, 8)){ - plot_data <- - data %>% - rename(group = !!sym(hrvar)) - plot_data <- - plot_data %>% - group_by(group) %>% - summarize(Ext_network_size = mean(External_network_size), - Ext_network_breadth = mean(Networking_outside_company), - Employee_count = n_distinct(PersonId) - ) %>% - filter(Employee_count >= mingroup) - - summary_table <- plot_data %>% arrange(., desc(Ext_network_size)) - - ext_network_plot <- ggplot(plot_data, - aes(x=Ext_network_size, - y=Ext_network_breadth) - ) + - geom_point(aes(size=Employee_count), - color = rgb2hex(0, 120, 212), - alpha = 0.5) + - geom_text_repel(label=plot_data$group) + - scale_x_continuous(name = "External Network Size") + - scale_y_continuous(name = "External Network Breadth") + - scale_size(range = bubble_size) + - theme_wpa_basic() + - labs(title = paste("External network metrics by", hrvar), - subtitle = paste("Network size is number of people, breadth is number of organizations"), - size = "Size") + - labs(caption = paste("Total employees =", sum(plot_data$Employee_count), "|", extract_date_range(data, return = "text"))) - - if(return == "table"){ - - summary_table %>% - as_tibble() %>% - return() - - } else if(return == "plot"){ - - return(ext_network_plot) - - } else { - - stop("Please enter a valid input for `return`.") - - } + data %>% + rename(`External Network Size` = "External_network_size", + `External Network Breadth` = "Networking_outside_company") %>% + create_bubble(hrvar = hrvar, + mingroup = mingroup, + metric_x = "External Network Size", + metric_y = "External Network Breadth", + return = return, + bubble_size = bubble_size) } diff --git a/R/internal_network_plot.R b/R/internal_network_plot.R index 2b1fb4af..68ba24c0 100644 --- a/R/internal_network_plot.R +++ b/R/internal_network_plot.R @@ -10,24 +10,11 @@ #' Internal Network Breadth as the vertical axis and Internal Network Size as the #' horizontal axis. #' -#' @param data Person Query as a dataframe including date column named "Date" -#' This function assumes the data format is MM/DD/YYYY as is standard in a WpA query output. -#' @param hrvar WpA variable for an HR variable to group networks by -#' For example, "Layer" -#' @param mingroup Numeric vector for minimum group size for aggregation -#' @param return Character vector specifying what to return, defaults to "plot". -#' Valid inputs are "plot" and "table". -#' @param bubble_size A numeric vector of length two to specify the size range of the bubbles -#' -#' @import dplyr -#' @import reshape2 -#' @import ggplot2 -#' @import ggrepel +#' @inheritParams create_bubble #' #' @examples -#' \dontrun{ -#' internal_network_plot(sq_data) -#' } +#' ## Return a plot +#' sq_data %>% internal_network_plot(return = "plot") #' #' @family Connectivity #' @@ -37,60 +24,17 @@ internal_network_plot <- function(data, hrvar = "Organization", mingroup = 5, return = "plot", - bubble_size = c(1, 8)) - { - plot_data <- - data %>% - rename(group = !!sym(hrvar)) + bubble_size = c(1, 8)) { - plot_data <- - plot_data %>% - group_by(group) %>% - summarize(Int_network_size = mean(Internal_network_size), - Int_network_breadth = mean(Networking_outside_organization), - Employee_count = n_distinct(PersonId) - ) %>% - filter(Employee_count >= mingroup) + data %>% + rename(`Internal Network Size` = "Internal_network_size", + `Internal Network Breadth` = "Networking_outside_organization") %>% + create_bubble(hrvar = hrvar, + mingroup = mingroup, + metric_x = "Internal Network Size", + metric_y = "Internal Network Breadth", + return = return, + bubble_size = bubble_size) - # Create summary table - summary_table <- plot_data %>% arrange(., desc(Int_network_size)) - - # Create plot - int_network_plot <-ggplot(plot_data, - aes(x=Int_network_size, - y=Int_network_breadth) - ) + - geom_point(aes(size=Employee_count), - color = rgb2hex(0, 120, 212), - alpha = 0.5) + - geom_text_repel(label=plot_data$group) + - scale_x_continuous(name = "Internal Network Size") + - scale_y_continuous(name = "Internal Network Breadth") + - scale_size(range = bubble_size) + - theme_wpa_basic() + - labs( - title = paste("Internal network metrics by", hrvar), - subtitle = paste("Network size is number of people, breadth is number of organizations"), - size = "Size" - ) + - labs(caption = paste("Total employees =", sum(plot_data$Employee_count), "|", extract_date_range(data, return = "text"))) - - - - if(return == "table"){ - - summary_table %>% - as_tibble() %>% - return() - - } else if(return == "plot"){ - - return(int_network_plot) - - } else { - - stop("Please enter a valid input for `return`.") - - } } diff --git a/R/meeting_quality.R b/R/meeting_quality.R index 80a1c3c0..b7a9d253 100644 --- a/R/meeting_quality.R +++ b/R/meeting_quality.R @@ -8,16 +8,12 @@ #' @description Return an analysis of Meeting Quality with a bubble plot, using a Standard Person #' Query as an input. #' -#' @param data A Standard Query dataset in the form of a data frame. -#' @param hrvar HR Variable by which to split metrics. Accepts a character vector, e.g. "Organization" -#' @param mingroup Numeric value setting the privacy threshold / minimum group size. Defaults to 5. -#' @param return Character vector specifying what to return, defaults to "plot". -#' Valid inputs are "plot" or "table". +#' @inheritParams create_bubble #' #' @import dplyr -#' @import ggplot2 -#' @import reshape2 -#' @family Meeting Culture +#' +#' @examples +#' meeting_quality(sq_data, return = "plot") #' #' @return #' Returns a ggplot object by default, where 'plot' is passed in `return`. @@ -30,85 +26,35 @@ meeting_quality <- function(data, mingroup = 5, return = "plot"){ - ## Date range data frame - myPeriod <- extract_date_range(data) - - ## Prepare Table - data %>% - rename(group = !!sym(hrvar)) %>% # Rename hrvar to `group` - group_by(PersonId, group) %>% - summarize(Meeting_count = mean(Meetings), - Meeting_hours = mean(Meeting_hours), - Low_quality_meeting_hours = mean(Low_quality_meeting_hours), - perc_after_hours_m = 100*mean(After_hours_meeting_hours)/mean(Meeting_hours), - perc_low_quality = 100*mean(Low_quality_meeting_hours)/mean(Meeting_hours), - perc_Multitasking = 100*mean(Multitasking_meeting_hours)/mean(Meeting_hours), - perc_Redundant = 100*mean(Redundant_meeting_hours__organizational_)/mean(Meeting_hours), - perc_conflicting = 100*mean(Conflicting_meeting_hours)/mean(Meeting_hours)) %>% - group_by(group) %>% - summarise_at(vars(Meeting_count, - Meeting_hours, - Low_quality_meeting_hours, - perc_after_hours_m, - perc_low_quality, - perc_Multitasking, - perc_Redundant, - perc_conflicting), - ~mean(.)) %>% - left_join(data %>% - rename(group = !!sym(hrvar)) %>% - group_by(group) %>% - summarise(Employee_Count = n_distinct(PersonId)), - by = "group") %>% - filter(Employee_Count >= mingroup) -> myTable - - myTable_wide <- myTable %>% - reshape2::melt(id.vars = "group") %>% - reshape2::dcast(variable ~ group) - - myTable_long <- reshape2::melt(myTable, id.vars=c("group")) - myTable_plot <- myTable %>% select(group, perc_low_quality, Meeting_hours) - -## Bar plot - plot_object <- - myTable_plot %>% - ggplot(aes(x = perc_low_quality, y = Meeting_hours, size=2)) + - geom_point(stat = "identity", - fill = "#203864", alpha=0.1) + - geom_text(aes(label = group), - hjust = 0.2, - color = "black", - fontface = "bold", - size = 4)+ - ylim(min(myTable_plot$Meeting_hours),max(myTable_plot$Meeting_hours) + 2) + - xlim(min(myTable_plot$perc_low_quality),max(myTable_plot$perc_low_quality) + 2) + - theme_wpa_basic() + - theme(axis.text=element_text(size=12), - plot.title = element_text(color="grey40", face="bold", size=18), - plot.subtitle = element_text(size=14), - legend.position = "none", - legend.justification = "right", - legend.title=element_text(size=14), - legend.text=element_text(size=14)) + - labs(title = "Meeting Quality", - subtitle = paste("Meeting time and low-quality percentage by", tolower(hrvar))) + - ylab("Average weekly meeting hours") + - xlab("Average weekly percentage of low-quality meeting hours") + - labs(caption = paste("Data from week of", myPeriod$Start, "to week of", myPeriod$End)) - + ## Wrapper around summary table if(return == "table"){ - myTable %>% - as_tibble() %>% - return() + meeting_chr <- + c("After_hours_meeting_hours", + "Low_quality_meeting_hours", + "Conflicting_meeting_hours", + "Multitasking_meeting_hours", + "Meetings", + "Meeting_hours") - } else if(return == "plot"){ - return(plot_object) + data %>% + rename(group = !!sym(hrvar)) %>% # Rename hrvar to `group` + group_by(PersonId, group) %>% + summarise_at(vars(meeting_chr), ~mean(., na.rm = TRUE)) %>% + group_by(group) %>% + summarise_at(vars(meeting_chr), ~mean(., na.rm = TRUE)) %>% + left_join(hrvar_count(data, hrvar, return = "table"), by = c("group" = hrvar)) %>% + filter(n >= mingroup) } else { - stop("Please enter a valid input for `return`.") - + data %>% + mutate(Percentage_of_Low_quality_meeting_hours = Low_quality_meeting_hours / Meeting_hours) %>% + create_bubble(hrvar = hrvar, + mingroup = mingroup, + metric_x = "Percentage_of_Low_quality_meeting_hours", + metric_y = "Meeting_hours", + return = return) } } diff --git a/man/external_network_plot.Rd b/man/external_network_plot.Rd index abddbf7c..ab6c10c1 100644 --- a/man/external_network_plot.Rd +++ b/man/external_network_plot.Rd @@ -13,13 +13,12 @@ external_network_plot( ) } \arguments{ -\item{data}{Person Query as a dataframe including date column named "Date" -This function assumes the data format is MM/DD/YYYY as is standard in a WpA query output.} +\item{data}{A Standard Person Query dataset in the form of a data frame.} -\item{hrvar}{WpA variable for an HR variable to group networks by -For example, "Layer"} +\item{hrvar}{HR Variable by which to split metrics, defaults to "Organization" +but accepts any character vector, e.g. "LevelDesignation"} -\item{mingroup}{Numeric vector for minimum group size for aggregation} +\item{mingroup}{Numeric value setting the privacy threshold / minimum group size. Defaults to 5.} \item{return}{Character vector specifying what to return, defaults to "plot". Valid inputs are "plot" and "table".} @@ -32,8 +31,8 @@ External Network Breadth as the vertical axis and External Network Size as the horizontal axis. } \examples{ -\dontrun{external_network_plot(sq_data) -} +## Return a plot +sq_data \%>\% external_network_plot(return = "plot") } \seealso{ diff --git a/man/internal_network_plot.Rd b/man/internal_network_plot.Rd index 063101fe..0dfb0246 100644 --- a/man/internal_network_plot.Rd +++ b/man/internal_network_plot.Rd @@ -13,13 +13,12 @@ internal_network_plot( ) } \arguments{ -\item{data}{Person Query as a dataframe including date column named "Date" -This function assumes the data format is MM/DD/YYYY as is standard in a WpA query output.} +\item{data}{A Standard Person Query dataset in the form of a data frame.} -\item{hrvar}{WpA variable for an HR variable to group networks by -For example, "Layer"} +\item{hrvar}{HR Variable by which to split metrics, defaults to "Organization" +but accepts any character vector, e.g. "LevelDesignation"} -\item{mingroup}{Numeric vector for minimum group size for aggregation} +\item{mingroup}{Numeric value setting the privacy threshold / minimum group size. Defaults to 5.} \item{return}{Character vector specifying what to return, defaults to "plot". Valid inputs are "plot" and "table".} @@ -32,9 +31,8 @@ Internal Network Breadth as the vertical axis and Internal Network Size as the horizontal axis. } \examples{ -\dontrun{ -internal_network_plot(sq_data) -} +## Return a plot +sq_data \%>\% internal_network_plot(return = "plot") } \seealso{ diff --git a/man/meeting_quality.Rd b/man/meeting_quality.Rd index ba421f74..9ef643f8 100644 --- a/man/meeting_quality.Rd +++ b/man/meeting_quality.Rd @@ -7,14 +7,15 @@ meeting_quality(data, hrvar = "Organization", mingroup = 5, return = "plot") } \arguments{ -\item{data}{A Standard Query dataset in the form of a data frame.} +\item{data}{A Standard Person Query dataset in the form of a data frame.} -\item{hrvar}{HR Variable by which to split metrics. Accepts a character vector, e.g. "Organization"} +\item{hrvar}{HR Variable by which to split metrics, defaults to "Organization" +but accepts any character vector, e.g. "LevelDesignation"} \item{mingroup}{Numeric value setting the privacy threshold / minimum group size. Defaults to 5.} \item{return}{Character vector specifying what to return, defaults to "plot". -Valid inputs are "plot" or "table".} +Valid inputs are "plot" and "table".} } \value{ Returns a ggplot object by default, where 'plot' is passed in \code{return}. @@ -24,10 +25,7 @@ When 'table' is passed, a summary table is returned as a data frame. Return an analysis of Meeting Quality with a bubble plot, using a Standard Person Query as an input. } -\seealso{ -Other Meeting Culture: -\code{\link{meeting_dist}()}, -\code{\link{mgrcoatt_dist}()}, -\code{\link{mgrrel_matrix}()} +\examples{ +meeting_quality(sq_data, return = "plot") + } -\concept{Meeting Culture} From 0ccfc5e59768de809190de5648d1d4a317ef2bc3 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Wed, 25 Nov 2020 16:47:15 +0000 Subject: [PATCH 033/192] feat: change to theme_wpa_basic Legend title now displays by default --- R/themes.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/themes.R b/R/themes.R index 6407d7ff..f942f36e 100644 --- a/R/themes.R +++ b/R/themes.R @@ -106,7 +106,7 @@ theme_wpa_basic <- function(font_size = 12){ axis.ticks = element_blank(), legend.position = "bottom", - legend.title = element_blank(), + # legend.title = element_blank(), panel.grid = element_blank(), strip.background = element_rect(fill = bg_colour2, colour = bg_colour2), From 494789a445bec33dd20728f3311691da8719e829 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Wed, 25 Nov 2020 16:47:26 +0000 Subject: [PATCH 034/192] docs: roxygen2 run --- man/create_IV.Rd | 5 +++++ man/create_bar.Rd | 1 + man/create_bar_asis.Rd | 1 + man/create_boxplot.Rd | 1 + man/create_scatter.Rd | 1 + man/cut_hour.Rd | 1 + man/extract_hr.Rd | 1 + man/hrvar_count.Rd | 1 + man/identify_outlier.Rd | 1 + man/meeting_dist.Rd | 1 - man/mgrcoatt_dist.Rd | 1 - man/mgrrel_matrix.Rd | 1 - 12 files changed, 13 insertions(+), 3 deletions(-) diff --git a/man/create_IV.Rd b/man/create_IV.Rd index b56f3d83..84600764 100644 --- a/man/create_IV.Rd +++ b/man/create_IV.Rd @@ -38,5 +38,10 @@ sq_data \%>\% return = "list") } +} +\seealso{ +Other Information Value: +\code{\link{IV_by_period}()}, +\code{\link{IV_report}()} } \concept{Information Value} diff --git a/man/create_bar.Rd b/man/create_bar.Rd index 47532f12..69eca8a8 100644 --- a/man/create_bar.Rd +++ b/man/create_bar.Rd @@ -58,6 +58,7 @@ create_bar(sq_data, metric = "Collaboration_hours", hrvar = "LevelDesignation", Other General: \code{\link{create_bar_asis}()}, \code{\link{create_boxplot}()}, +\code{\link{create_bubble}()}, \code{\link{create_scatter}()}, \code{\link{cut_hour}()}, \code{\link{extract_hr}()}, diff --git a/man/create_bar_asis.Rd b/man/create_bar_asis.Rd index af417f62..7315a56d 100644 --- a/man/create_bar_asis.Rd +++ b/man/create_bar_asis.Rd @@ -51,6 +51,7 @@ into the function. Other General: \code{\link{create_bar}()}, \code{\link{create_boxplot}()}, +\code{\link{create_bubble}()}, \code{\link{create_scatter}()}, \code{\link{cut_hour}()}, \code{\link{extract_hr}()}, diff --git a/man/create_boxplot.Rd b/man/create_boxplot.Rd index b9cc68d6..154df0e0 100644 --- a/man/create_boxplot.Rd +++ b/man/create_boxplot.Rd @@ -47,6 +47,7 @@ create_boxplot(sq_data, metric = "Collaboration_hours", hrvar = "LevelDesignatio Other General: \code{\link{create_bar_asis}()}, \code{\link{create_bar}()}, +\code{\link{create_bubble}()}, \code{\link{create_scatter}()}, \code{\link{cut_hour}()}, \code{\link{extract_hr}()}, diff --git a/man/create_scatter.Rd b/man/create_scatter.Rd index 0c94dd68..99fefa84 100644 --- a/man/create_scatter.Rd +++ b/man/create_scatter.Rd @@ -61,6 +61,7 @@ Other General: \code{\link{create_bar_asis}()}, \code{\link{create_bar}()}, \code{\link{create_boxplot}()}, +\code{\link{create_bubble}()}, \code{\link{cut_hour}()}, \code{\link{extract_hr}()}, \code{\link{hrvar_count}()}, diff --git a/man/cut_hour.Rd b/man/cut_hour.Rd index 3d768ee6..ea387236 100644 --- a/man/cut_hour.Rd +++ b/man/cut_hour.Rd @@ -31,6 +31,7 @@ Other General: \code{\link{create_bar_asis}()}, \code{\link{create_bar}()}, \code{\link{create_boxplot}()}, +\code{\link{create_bubble}()}, \code{\link{create_scatter}()}, \code{\link{extract_hr}()}, \code{\link{hrvar_count}()}, diff --git a/man/extract_hr.Rd b/man/extract_hr.Rd index 9f9a44ff..f2ea1a95 100644 --- a/man/extract_hr.Rd +++ b/man/extract_hr.Rd @@ -33,6 +33,7 @@ Other General: \code{\link{create_bar_asis}()}, \code{\link{create_bar}()}, \code{\link{create_boxplot}()}, +\code{\link{create_bubble}()}, \code{\link{create_scatter}()}, \code{\link{cut_hour}()}, \code{\link{hrvar_count}()}, diff --git a/man/hrvar_count.Rd b/man/hrvar_count.Rd index 7170dfce..dabbd4cf 100644 --- a/man/hrvar_count.Rd +++ b/man/hrvar_count.Rd @@ -38,6 +38,7 @@ Other General: \code{\link{create_bar_asis}()}, \code{\link{create_bar}()}, \code{\link{create_boxplot}()}, +\code{\link{create_bubble}()}, \code{\link{create_scatter}()}, \code{\link{cut_hour}()}, \code{\link{extract_hr}()}, diff --git a/man/identify_outlier.Rd b/man/identify_outlier.Rd index 7d67a326..0d7f5f6f 100644 --- a/man/identify_outlier.Rd +++ b/man/identify_outlier.Rd @@ -36,6 +36,7 @@ Other General: \code{\link{create_bar_asis}()}, \code{\link{create_bar}()}, \code{\link{create_boxplot}()}, +\code{\link{create_bubble}()}, \code{\link{create_scatter}()}, \code{\link{cut_hour}()}, \code{\link{extract_hr}()}, diff --git a/man/meeting_dist.Rd b/man/meeting_dist.Rd index d3778c42..ab702d56 100644 --- a/man/meeting_dist.Rd +++ b/man/meeting_dist.Rd @@ -44,7 +44,6 @@ meeting_dist(sq_data, hrvar = "LevelDesignation", cut = c(4, 7, 9)) } \seealso{ Other Meeting Culture: -\code{\link{meeting_quality}()}, \code{\link{mgrcoatt_dist}()}, \code{\link{mgrrel_matrix}()} } diff --git a/man/mgrcoatt_dist.Rd b/man/mgrcoatt_dist.Rd index 6224ea56..77ddefae 100644 --- a/man/mgrcoatt_dist.Rd +++ b/man/mgrcoatt_dist.Rd @@ -36,7 +36,6 @@ Other Managerial Relations: Other Meeting Culture: \code{\link{meeting_dist}()}, -\code{\link{meeting_quality}()}, \code{\link{mgrrel_matrix}()} } \concept{Managerial Relations} diff --git a/man/mgrrel_matrix.Rd b/man/mgrrel_matrix.Rd index 81693872..b7b66a58 100644 --- a/man/mgrrel_matrix.Rd +++ b/man/mgrrel_matrix.Rd @@ -46,7 +46,6 @@ Other Managerial Relations: Other Meeting Culture: \code{\link{meeting_dist}()}, -\code{\link{meeting_quality}()}, \code{\link{mgrcoatt_dist}()} } \concept{Managerial Relations} From c2adab1a0b811798b88e46d2ac4258a0842426ac Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Wed, 25 Nov 2020 16:54:12 +0000 Subject: [PATCH 035/192] docs: fix broken link in README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index bc16e652..ed078033 100644 --- a/README.md +++ b/README.md @@ -107,6 +107,6 @@ This project may contain trademarks or logos for projects, products, or services Contributors to the GitHub repo: -![](CONTRIBUTORS.svg) +![](https://raw.githubusercontent.com/microsoft/wpa/main/CONTRIBUTORS.svg) See full list [here](https://microsoft.github.io/wpa/authors.html). From 8ece45dda7f1c0086ebb8fda73cc89192b6fd871 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Wed, 25 Nov 2020 17:39:39 +0000 Subject: [PATCH 036/192] feat: add best practices article to navbar --- _pkgdown.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/_pkgdown.yml b/_pkgdown.yml index b79ba414..f3a6e602 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -61,4 +61,6 @@ navbar: href: articles/intro-to-wpa.html - text: Text-mining Meeting Subject lines href: articles/text-mining-capabilities.html + - text: Best Practices when using wpa + href: articles/best-practices.html From c88e37c825a114aeb04744b1c68ef05ea802ec9b Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Wed, 25 Nov 2020 18:42:08 +0000 Subject: [PATCH 037/192] feat: add IV article --- .Rbuildignore | 1 + _pkgdown.yml | 2 + vignettes/IV-report.Rmd | 287 +++++++++++++++++++++++++++++++++++++ vignettes/intro-to-wpa.Rmd | 2 +- 4 files changed, 291 insertions(+), 1 deletion(-) create mode 100644 vignettes/IV-report.Rmd diff --git a/.Rbuildignore b/.Rbuildignore index 4a61c3ce..4d9ea800 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -14,4 +14,5 @@ ^man/figures/wpa cheatsheet_20201116.png$ ^vignettes/best-practices.Rmd$ ^vignettes/text-mining-capabilities.Rmd$ +^vignettes/IV-report.Rmd$ ^CONTRIBUTORS.svg$ diff --git a/_pkgdown.yml b/_pkgdown.yml index f3a6e602..baba71fe 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -63,4 +63,6 @@ navbar: href: articles/text-mining-capabilities.html - text: Best Practices when using wpa href: articles/best-practices.html + - text: Introduction to Information Value + - href: articles/IV-report.html diff --git a/vignettes/IV-report.Rmd b/vignettes/IV-report.Rmd new file mode 100644 index 00000000..b7408ea3 --- /dev/null +++ b/vignettes/IV-report.Rmd @@ -0,0 +1,287 @@ +--- +title: "How to identify potential predictors for survey results using information value" +author: "Simone Liebal" +date: "2020-11-25" +tags: +- introduction +- collaboration +categories: R +--- + +```{r setup, include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>", + fig.width=8, + fig.height=8 +) +``` + +## About + +This post will give you a short introduction to the data exploration technique **information Value (IV) ** and how to use it as part of the [**wpa** R package](https://microsoft.github.io/wpa). + +After setting the stage, we will have a look at how this can be used to identify potential predictors for survey responses. + +## What is Information Value? + +**Information Value (IV)** refers to a data exploration technique that helps determine which variables in a dataset have predictive power or influence on the value of a dependent variable. This technique delivers an IV score, which in itself tells you whether one variable **A** is a good predictor for another (say **B**). If **A** has a low information value, it is potentially not be a good predictor for variable **B**, and vice versa. Unlike other methods of measuring relationship between variables, such as correlation or simple linear regression, the IV score is calculated with in terms of how well a specific variable **A** can distinguish between a _binary_ response in a target variable **B**. + +### Or said differently: + +Imagine you have you have a simple yes/no question, e.g. "Are you satisfied with your current work-life-balance?". The answer to that question, which only has two possible values, is defined as variable **B**, and each value in that variable as **b**. We also have a variable **A**, which is the average weekly total of after-hours collaboration time, and each value in **A** is denoted as **a**. + +Let's assume the values for **a** and **b** are captured for 10000 employees, and are contained in the two variable-columns **A** and **B**. On a row-wise basis, each **a** in **A** links to a **b** in **B**. Based on these values, the information value for **A** can be calculated. + +For this purpose, data is separated into different buckets or _bins_, with the same quantity of data (responses) in each of them. Here we are choosing 5 bins, therefore 20% of responses in each of them. + +The numbers and distributions in this example are exaggerated for illustrative purposes: + +|Avg. weekly after-hours|Total # of responses|# of negative responses|# of positive responses| +|:---------------------:|-------------------:|----------------------:|----------------------:| +|0-2 | 2000| 200| 1800| +|2-4 | 2000| 350| 1650| +|4-6 | 2000| 800| 1200| +|6-8 | 2000| 1200| 800| +|8-10 | 2000| 1400| 600| + +The information value is then calculated using the following formula: + +$$IV=∑(ProportionPositive_i-ProportionNegative_i)*ln(\frac{ProportionPositive_i}{ProportionNegative_i})$$ + +A component in calculating IV is the **Weight of Evidence (WoE)**, which is the logarithmic part within the formula above: + +$$WeightOfEvidence=ln(\frac{ProportionPositive_i}{ProportionNegative_i})$$ + +WOE describes the relationship between a _predictor variable_ and a _binary target variable_ for each bin, while IV measures the strength of that total relationship across all the bins. + +Applying this to the table above, we get the following: + +|Avg. weekly after-hours|Total # of responses|# of negative responses|# of positive responses| WOE| +|:---------------------:|-------------------:|----------------------:|----------------------:|-------:| +|0-2 | 2000| 200| 1800| 1.438| +|2-4 | 2000| 350| 1650| 0.791| +|4-6 | 2000| 800| 1200| -0.354| +|6-8 | 2000| 1200| 800| -1.165| +|8-10 | 2000| 1400| 600| -1.607| + +The first two bins (`0-2` and `2-4`) show a positive WOE value. This means the percentage of _positive responses_ is higher than the percentage of _negative responses_. For the other three bins (`4-6`, `6-8`, and `8-10`), we can observe a negative WOE value, which means that the percentage of "negative responses" is higher than the percentage of "positive responses". Therefore, respondents with 0 to 4h in average weekly after-hours seem to answer more positively on the work-life-balance question. + +With the WOE calculated, we can apply the full IV calculation to the table: + +|Avg. weekly after-hours|Total # of responses|# of negative responses|# of positive responses| WOE| IV| +|:---------------------:|-------------------:|----------------------:|----------------------:|-------:|-------:| +|0-2 | 2000| 200| 1800| 1.438| 0.3621| +|2-4 | 2000| 350| 1650| 0.791| 0.1309| +|4-6 | 2000| 800| 1200| -0.354| 0.0331| +|6-8 | 2000| 1200| 800| -1.165| 0.3772| +|8-10 | 2000| 1400| 600| -1.607| 0.7053| + +In terms of predictiveness you can use the following table as a reference: + +| IV value | predictiveness | +|:---------------:|:----------------------------:| +| Less than 0.02 | Not useful for prediction | +| 0.02 to 0.1 | Weak predictive power | +| 0.1 to 0.3 | Medium predictive power | +| 0.3 to 1.0 | Strong predictive power | +| > 1.0 | Very strong predictive power | + + +Therefore in this example, after-hours seem to have a strong predictive power regarding the answer to "Are you satisfied with your current work-life-balance?". This data exploration technique builds the foundation for predictor analysis using the [**wpa** R package](https://microsoft.github.io/wpa). + +## Let's get started + +As of writing this article, there are two IV functions included as part of the **wpa** R package - `create_IV()` and `IV_report()`. +To run either of those, you will need to ensure that the package has been successfully installed for your version of R. For more information on the installation process, please check out https://microsoft.github.io/wpa. + +## Putting it into practice + +### `create_IV()` function + +Let's start with `create_IV()`. +For data input, the `create_IV()` function uses a standard person query dataset. The **wpa** package already brings a set of built-in datasets for testing purposes. The ready-to-use standard person query dataset is called `sq_data` and will be used for this section of the post. + +To get started, open a new session in R and load the following packages: + +```{r message=FALSE, warning=FALSE} +library(wpa) +library(tidyverse) # For data manipulation +``` + +The general format looks like this: + +```{r message=FALSE, warning=FALSE, eval=FALSE} +create_IV( + data, + predictors = NULL, + outcome, + bins = 5, + return = "plot") +``` + +At minimum, you need to supply the `data` argument, which is a Person Query dataset in the form of a data frame and the variable you want to calculate the IV for (`outcome`) to run the report. If only these two are defined, the remaining arguments will use default values. + +Additionally, it's recommended to use the `predictors` argument to specify specific columns, that you want to include. Otherwise, all numeric vectors in the data will be included, which might lead to a rather big output. + +You can also adjust what the output will look like with the `return` argument - here you have a choice of: + +* "plot" (returns a bar plot) +* "summary" (returns a summary table) +* "list" (returns a list of outputs for all input variables) +* "plot-WOE" (returns a WOE plot) + +`bins` allows you to define the amount of same-sized bins the data is separated into. + +Using the `sq_data` dataset, you can have a look at the predictors focus hours, email hours and meeting hours in correlation to collaboration hours: + + +```{r message=FALSE, warning=FALSE} +sq_data %>% + mutate(X = ifelse(Collaboration_hours > 12, 1, 0)) %>% + create_IV( + outcome = "X", + predictors = c("Email_hours", "Meeting_hours", "Total_focus_hours"), + return = "plot") +``` + + +You may see, that all three are very strong predictors. Initially it might come as a bit of a surprise, that `Total_focus_hours` is actually the strongest predictor of those three, even though focus hours (in the context of Workplace Analytics) are a block of at least two hours without any meetings - so pretty much the opposite of `Collaboration_hours`. + +This is where WOE comes into play, as it describes the **relationship** between a predictor variable and a binary target variable: + +```{r message=FALSE, warning=FALSE} +sq_data %>% + mutate(X = ifelse(Collaboration_hours > 12, 1, 0)) %>% + create_IV(outcome = "X", + predictors = c("Email_hours", "Meeting_hours", "Total_focus_hours"), + return = "plot-WOE") +``` + +You may see, that meeting hours and email hours seem to have direct relationship with collaboration hours: + ++ Focusing on email hours as an example - we have the buckets with the lower email hours to the left and the higher ones to the right. + ++ The two buckets on the left have a negative WOE value. This tells us, that less email hours seem to lead to less collaboration hours. + ++ The two buckets on the right have a positive WOE value. This tells us, that more email hours seem to lead to more collaboration hours. + +But what about the focus hours? + ++ Focusing on the WOE chart for focus hours above, we can see an inverse relationship. Again, we have low amount of focus hours on the left and high amount of focus hours on the right. + ++ The two buckets on the left have a strong positive WOE value. So, less focus hours seems to have a correlation with more collaboration hours and vice versa. + + +### `IV_report()` function +Another option is the `IV_report()` function, which generates an interactive HTML report, again using Standard Query data as an input. The report contains a full Information Value analysis. + +This function follows a similar format as the `create_IV()` function above, with the difference that the `return` is already predefined as an HTML report. Instead it consists of two other additional arguments - `path`, which defines the file path and the desired file name and `timestamp`, which is a true/false variable that defines whether to include a time stamp in the file name. + +The general format is the following: + +```{r message=FALSE, warning=FALSE, eval=FALSE} +IV_report( + data, + predictors = NULL, + outcome, + bins = 5, + path = "IV_report", + timestamp = TRUE) +``` + + +## Using the IV report with survey results + +The IV report can be leveraged as a powerful option to identify potential predictors for survey responses. In this section we will focus on the non-free-text responses. Our example will use survey responses on a scale from 1-5 (1 being most disagreeable - 5 being most agreeable). + +To get started, the survey responses need to uploaded as an additional column (per question) to the organizational file, that is uploaded via the Workplace Analytics portal. You can find more information on the upload of that data [here](https://docs.microsoft.com/en-us/workplace-analytics/setup/upload-organizational-data). + +After creating a new standard person query, which includes the new column, the data can be imported using the `import_wpa()` function into a new R session with the above mentioned libraries already imported. + +```{r message=FALSE, warning=FALSE, eval=FALSE} +raw_data <- import_wpa(myfile) +``` + +The query data might include multiple lines per person per time period. In this example, we only have the current survey responses available. To simplify the analysis, these lines can be grouped by person ID with the result of only having one line per person going forward. + + +```{r message=FALSE, warning=FALSE, eval=FALSE} +survey_data <- raw_data %>% + group_by(PersonId) %>% + summarise_if(is.numeric, mean, na.rm = TRUE) +``` + + +### Dealing with non-numeric values + +The values used for the creation of IV and IV report need to be numeric. However when uploading the survey responses using the org-data file, these responses might be formatted as a string, even if they seem to be numeric at first glance. + +Therefore, they need to be converted: + +```{r message=FALSE, warning=FALSE, eval=FALSE} +survey_data$survey_response_1 <- + ifelse(survey_data$survey_response_1 == "5", 5, + ifelse(survey_data$survey_response_1 == "4", 4, + ifelse(survey_data$survey_response_1 == "3", 3, + ifelse(survey_data$survey_response_1 == "2", 2, + ifelse(survey_data$survey_response_1 == "1", 1, 0 ) ) ))) +``` + +### Convert answers into binary + +The `create_IV()` and `IV_report()` functions require the value of the outcome argument to be binary. A way to approach this can be to convert all agreeable answers into "1" and all disagreeable answers into "0". In this example, we are working with a scale from 1-5, which allows for a neutral answer with "3". As this response has limited value for predicting agreeableness or disagreeableness, you can filter this out. + +Create an outcome variable by turning survey answers into 1s and 0s (and neutral into 2): + +```{r message=FALSE, warning=FALSE, eval=FALSE} +survey_data$my_outcome <- + ifelse(survey_data$survey_response_1 == "5", 1, + ifelse(survey_data$survey_response_1 == "4", 1, + ifelse(survey_data$survey_response_1 == "3", 2, + ifelse(survey_data$survey_response_1 == "2", 0, + ifelse(survey_data$survey_response_1 == "1", 0, 2) ) ))) + +``` + +Then, you can easily filter out the neutral answers: + +```{r message=FALSE, warning=FALSE, eval=FALSE} +IV_data <- survey_data %>% filter(my_outcome<2) + +``` + +### Create IV and IV Report + +With all the groundwork being done, you can now create the IV and IV report for this specific question (survey_response_1) to see what WPA metrics might be a predictor for the responses. + +To create the IV: + +```{r message=FALSE, warning=FALSE, eval=FALSE} +IV_data %>% create_IV(outcome = "my_outcome", return="list") + +``` + +To run the IV report: + +```{r message=FALSE, warning=FALSE, eval=FALSE} +IV_data %>% IV_report( + outcome = "my_outcome", + path="survey_response_1", + predictors=c("Collaboration_hours", "Call_hours", "Meeting_hours", "Instant_Message_hours", "Email_hours", "Workweek_span", "After_hours_instant_messages" , "After_hours_in_unscheduled_calls", "After_hours_collaboration_hours")) + +``` + +This creates an IV report for the survey_response_1 of the whole population. To dive deeper and identify trends for a specific a population you can apply filters (e.g. `LevelDesignation`, `Organization`, etc.) before creating the report. + +For example, filtering by `LevelDesignation`: + +```{r message=FALSE, warning=FALSE, eval=FALSE} +IV_data %>% filter(LevelDesignation=="1") %>% IV_report() +``` + +--- + +## Feedback + +Hope you found this useful! If you have any suggestions or feedback, please visit https://github.com/microsoft/wpa/issues. diff --git a/vignettes/intro-to-wpa.Rmd b/vignettes/intro-to-wpa.Rmd index 3a61c122..ee172cba 100644 --- a/vignettes/intro-to-wpa.Rmd +++ b/vignettes/intro-to-wpa.Rmd @@ -1,6 +1,6 @@ --- title: "Introduction to {wpa}" -author: "Microsoft WpA" +author: "Martin Chan" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > From 662d218ad1b820596fe9ceb5a482f8b27bfb45dc Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Wed, 25 Nov 2020 18:52:19 +0000 Subject: [PATCH 038/192] fix: issue with article run --- vignettes/IV-report.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vignettes/IV-report.Rmd b/vignettes/IV-report.Rmd index b7408ea3..d7a59588 100644 --- a/vignettes/IV-report.Rmd +++ b/vignettes/IV-report.Rmd @@ -106,7 +106,7 @@ To get started, open a new session in R and load the following packages: ```{r message=FALSE, warning=FALSE} library(wpa) -library(tidyverse) # For data manipulation +library(dplyr) # For data manipulation ``` The general format looks like this: From f2832a6ddc9e7219d9857d5fd2cf87449f7cff90 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Wed, 25 Nov 2020 19:08:57 +0000 Subject: [PATCH 039/192] fix: typo with _pkgdown.yml --- _pkgdown.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_pkgdown.yml b/_pkgdown.yml index baba71fe..a75374b6 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -64,5 +64,5 @@ navbar: - text: Best Practices when using wpa href: articles/best-practices.html - text: Introduction to Information Value - - href: articles/IV-report.html + href: articles/IV-report.html From 5b5f81ee3ed195a25cea2250727b047c29fbca35 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Fri, 27 Nov 2020 10:40:51 +0000 Subject: [PATCH 040/192] feat: make combine_signals dynamic (#9) --- R/combine_signals.R | 18 +++++++++++++----- 1 file changed, 13 insertions(+), 5 deletions(-) diff --git a/R/combine_signals.R b/R/combine_signals.R index b1eec512..35c856e4 100644 --- a/R/combine_signals.R +++ b/R/combine_signals.R @@ -12,9 +12,13 @@ #' #' @param data Data containing Emails_sent and IMs_sent variables #' @param hr Numeric value between 0 to 23 to iterate through +#' @param signals Character vector for specifying which signal types to combine. +#' Defaults to c("Emails_sent", "IMs_sent"). #' #' @export -combine_signals <- function(data, hr){ +combine_signals <- function(data, + hr, + signals = c("Emails_sent", "IMs_sent")){ if(!is.numeric(hr) | hr < 0 | hr > 23){ @@ -22,15 +26,19 @@ combine_signals <- function(data, hr){ } + # End hour hr_two <- hr + 1 + # String pad to two digits hr1 <- ifelse(nchar(hr) == 1, paste0(0, hr), hr) hr2 <- ifelse(nchar(hr_two) == 1, paste0(0, hr_two), hr_two) + # Create string vectors full_string <- paste0("Signals_sent_", hr1, "_", hr2) - em_string <- paste0("Emails_sent_", hr1, "_", hr2) - im_string <- paste0("IMs_sent_", hr1, "_", hr2) + input_string <- paste0(signals, "_", hr1, "_", hr2) # Should be length > 1 - - dplyr::transmute(data, !!sym(full_string) := !!sym(em_string) + !!sym(im_string)) + # Sum columns and only return `Signals_sent_` prefixed column + data %>% + dplyr::transmute(!!sym(full_string) := select(., input_string) %>% + apply(1, sum, na.rm = TRUE)) } From 68d4a66826675a0d4a27c9b875b5ee867b6f0102 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Fri, 27 Nov 2020 11:02:41 +0000 Subject: [PATCH 041/192] docs: added CONTRIBUTORS --- DESCRIPTION | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 92ff4a36..e428b8af 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -9,8 +9,10 @@ Authors@R: c( person(given = "Ainize", family = "Cidoncha", role = "ctb", email = "ainize.cidoncha@microsoft.com"), person(given = "Rosamary", family = "Ochoa Vargas", role = "ctb", email = "rosamary.ochoa@microsoft.com"), person(given = "Tannaz", family = "Sattari", role = "ctb", email = "tannaz.sattari@microsoft.com"), - person(given = "Lucas", family = "Hogner", role = "ctb", email = "lucas.hogner@microsoft.com"), - person(given = "Ellen", family = "Trinklein", role = "ctb") + person(given = "Lucas", family = "Hogner", role = "ctb", email = "lucas.hogner@microsoft.com"), + person(given = "Jasminder", family = "Thind", role = "ctb", email = "jasminder.thind@microsoft.com"), + person(given = "Simone", family = "Liebal", role = "ctb", email = "simone.liebal@microsoft.com"), + person(given = "Ellen", family = "Trinklein", role = "ctb") ) Description: Opinionated functions that enable easier and faster analysis of Workplace Analytics data. There are three main types of functions in wpa: From 0f6c7885d035a8418b3efbfdc56b4353e7bee952 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Fri, 27 Nov 2020 13:41:59 +0000 Subject: [PATCH 042/192] feat: allow unscheduled calls workpatterns_hclust now can add unscheduled calls --- R/combine_signals.R | 12 +++- R/workpatterns_hclust.R | 132 +++++++++++++++++++++++++--------------- man/IV_by_period.Rd | 2 +- man/combine_signals.Rd | 5 +- 4 files changed, 98 insertions(+), 53 deletions(-) diff --git a/R/combine_signals.R b/R/combine_signals.R index 35c856e4..6bd3402e 100644 --- a/R/combine_signals.R +++ b/R/combine_signals.R @@ -34,7 +34,17 @@ combine_signals <- function(data, hr2 <- ifelse(nchar(hr_two) == 1, paste0(0, hr_two), hr_two) # Create string vectors - full_string <- paste0("Signals_sent_", hr1, "_", hr2) + # Use original supplied string if length of signals == 1 + if(length(signals) == 1){ + + full_string <- paste0(signals, "_", hr1, "_", hr2) + + } else { + + full_string <- paste0("Signals_sent_", hr1, "_", hr2) + + } + input_string <- paste0(signals, "_", hr1, "_", hr2) # Should be length > 1 # Sum columns and only return `Signals_sent_` prefixed column diff --git a/R/workpatterns_hclust.R b/R/workpatterns_hclust.R index 24d62d88..5bcbf3e6 100644 --- a/R/workpatterns_hclust.R +++ b/R/workpatterns_hclust.R @@ -28,7 +28,7 @@ #' and "abs". #' @param signals Character vector to specify which collaboration metrics to use: #' You may use "email" (default) for emails only, "IM" for Teams messages only, -#' or a combination of the two `c("email", "IM")`. +#' "unscheduled_calls" for Unscheduled Calls only, or a combination, such as `c("email", "IM")`. #' @param start_hour A character vector specifying starting hours, #' e.g. "0900" #' @param end_hour A character vector specifying starting hours, @@ -68,57 +68,13 @@ workpatterns_hclust <- function(data, start_hour = "0900", end_hour = "1700"){ - ## Select input variable names - if("email" %in% signals & "IM" %in% signals){ + # Text replacement only for allowed values - ## Create 24 summed `Signals_sent` columns - signal_cols <- - purrr::map(0:23, - ~combine_signals(data, - hr = .)) %>% - bind_cols() + if(any(signals %in% c("email", "IM", "unscheduled_calls"))){ - ## Use names for matching - input_var <- names(signal_cols) - - ## Average signals sent by Person - signals_df <- - data %>% - select(PersonId) %>% - cbind(signal_cols) %>% - group_by(PersonId) %>% - summarise_all(~mean(.)) - - ## Signal label - sig_label <- "Signals_sent" - - } else if(signals == "IM"){ - - match_index <- grepl(pattern = "^IMs_sent", x = names(data)) - input_var <- names(data)[match_index] - - ## Average signals sent by Person - signals_df <- - data %>% - select(PersonId, all_of(input_var)) %>% - group_by(PersonId) %>% - summarise_all(~mean(.)) - - sig_label <- "IMs_sent" - - } else if(signals == "email"){ - - match_index <- grepl(pattern = "^Emails_sent", x = names(data)) - input_var <-names(data)[match_index] - - ## Average signals sent by Person - signals_df <- - data %>% - select(PersonId, all_of(input_var)) %>% - group_by(PersonId) %>% - summarise_all(~mean(.)) - - sig_label <- "Emails_sent" + signal_set <- gsub(pattern = "email", replacement = "Emails_sent", x = signals) # case-sensitive + signal_set <- gsub(pattern = "IM", replacement = "IMs_sent", x = signal_set) + signal_set <- gsub(pattern = "unscheduled_calls", replacement = "Unscheduled_calls", x = signal_set) } else { @@ -126,6 +82,82 @@ workpatterns_hclust <- function(data, } + ## Create 24 summed `Signals_sent` columns + signal_cols <- purrr::map(0:23, ~combine_signals(data, hr = ., signals = signal_set)) + signal_cols <- bind_cols(signal_cols) + + ## Use names for matching + input_var <- names(signal_cols) + + ## Average signals sent by Person + signals_df <- + data %>% + select(PersonId) %>% + cbind(signal_cols) %>% + group_by(PersonId) %>% + summarise_all(~mean(.)) + + ## Signal label + sig_label <- ifelse(length(signal_set) > 1, "Signals_sent", signal_set) + + # ## Select input variable names + # if("email" %in% signals & "IM" %in% signals){ + # + # ## Create 24 summed `Signals_sent` columns + # signal_cols <- + # purrr::map(0:23, + # ~combine_signals(data, hr = ., + # signals = c("Emails_sent", "IMs_sent"))) %>% + # bind_cols() + # + # ## Use names for matching + # input_var <- names(signal_cols) + # + # ## Average signals sent by Person + # signals_df <- + # data %>% + # select(PersonId) %>% + # cbind(signal_cols) %>% + # group_by(PersonId) %>% + # summarise_all(~mean(.)) + # + # ## Signal label + # sig_label <- "Signals_sent" + # + # } else if(signals == "IM"){ + # + # match_index <- grepl(pattern = "^IMs_sent", x = names(data)) + # input_var <- names(data)[match_index] + # + # ## Average signals sent by Person + # signals_df <- + # data %>% + # select(PersonId, all_of(input_var)) %>% + # group_by(PersonId) %>% + # summarise_all(~mean(.)) + # + # sig_label <- "IMs_sent" + # + # } else if(signals == "email"){ + # + # match_index <- grepl(pattern = "^Emails_sent", x = names(data)) + # input_var <-names(data)[match_index] + # + # ## Average signals sent by Person + # signals_df <- + # data %>% + # select(PersonId, all_of(input_var)) %>% + # group_by(PersonId) %>% + # summarise_all(~mean(.)) + # + # sig_label <- "Emails_sent" + # + # } else { + # + # stop("Invalid input for `signals`.") + # + # } + ## Normalised pattern data ptn_data_norm <- signals_df %>% diff --git a/man/IV_by_period.Rd b/man/IV_by_period.Rd index 3ebc2b06..d1bd11f8 100644 --- a/man/IV_by_period.Rd +++ b/man/IV_by_period.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/IV_by_period.R +% Please edit documentation in R/IV_by_Period.R \name{IV_by_period} \alias{IV_by_period} \title{Identify the WPA metrics that have the biggest change between two periods.} diff --git a/man/combine_signals.Rd b/man/combine_signals.Rd index 94340806..324c0e39 100644 --- a/man/combine_signals.Rd +++ b/man/combine_signals.Rd @@ -4,12 +4,15 @@ \alias{combine_signals} \title{Combine signals from Emails and IMs} \usage{ -combine_signals(data, hr) +combine_signals(data, hr, signals = c("Emails_sent", "IMs_sent")) } \arguments{ \item{data}{Data containing Emails_sent and IMs_sent variables} \item{hr}{Numeric value between 0 to 23 to iterate through} + +\item{signals}{Character vector for specifying which signal types to combine. +Defaults to c("Emails_sent", "IMs_sent").} } \description{ Takes in an Hourly Collaboration Data, and for each hour sums and stores the From 66876b0ecdf6f9fb1b5548b1902050a01a58d114 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Fri, 27 Nov 2020 14:03:20 +0000 Subject: [PATCH 043/192] chore: unwrap dontrun (#12) --- R/track_hr_change.R | 4 ++-- man/track_HR_change.Rd | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/R/track_hr_change.R b/R/track_hr_change.R index c16cd4ae..4e64d1a4 100644 --- a/R/track_hr_change.R +++ b/R/track_hr_change.R @@ -32,9 +32,9 @@ #' When 'table' is passed, a summary table is returned as a data frame. #' #' @examples -#' \dontrun{ +#' #' dv_data %>% track_HR_change() -#'} +#' #' @export track_HR_change <- function(data, start_date = min(data$Date), diff --git a/man/track_HR_change.Rd b/man/track_HR_change.Rd index 20586e69..4ec09c60 100644 --- a/man/track_HR_change.Rd +++ b/man/track_HR_change.Rd @@ -46,9 +46,9 @@ Through this chart you can see: } } \examples{ -\dontrun{ + dv_data \%>\% track_HR_change() -} + } \seealso{ Other Data Validation: From 32c56430e06b5c72fa93ab2b8b7480b215829aa8 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Fri, 27 Nov 2020 14:04:00 +0000 Subject: [PATCH 044/192] feat: add pdf export (#11) options to export PDF and JPEG from export --- R/export.R | 16 ++++------------ man/export.Rd | 2 +- 2 files changed, 5 insertions(+), 13 deletions(-) diff --git a/R/export.R b/R/export.R index 4db7203c..f93f753d 100644 --- a/R/export.R +++ b/R/export.R @@ -13,7 +13,7 @@ #' #' @param x Data frame or ggplot object to be passed through. #' @param method Character string specifying the method of export. -#' Valid inputs include "clipboard" (default), "csv", "png", and "svg". +#' Valid inputs include "clipboard" (default), "csv", "png", "svg", "jpeg", and "pdf". #' @param path If exporting a file, enter the path and the desired file name, _excluding the file extension_. #' For example, "Analysis/SQ Overview". #' @param timestamp Logical vector specifying whether to include a timestamp in the file name. Defaults to TRUE. @@ -58,18 +58,10 @@ export <- function(x, write.csv(x = x, file = newpath) - ## Export option: PNG - } else if(method == "png"){ + ## Export option: any ggsave methods + } else if(method %in% c("png", "svg", "jpeg", "pdf")){ - newpath <- paste0(newpath, ".png") - - ggsave(filename = newpath, plot = x, width = width, height = height) - - - ## Export option: SVG - } else if(method == "svg"){ - - newpath <- paste0(newpath, ".svg") + newpath <- paste0(newpath, ".", method) ggsave(filename = newpath, plot = x, width = width, height = height) diff --git a/man/export.Rd b/man/export.Rd index 2b9e1254..e5e30159 100644 --- a/man/export.Rd +++ b/man/export.Rd @@ -17,7 +17,7 @@ export( \item{x}{Data frame or ggplot object to be passed through.} \item{method}{Character string specifying the method of export. -Valid inputs include "clipboard" (default), "csv", "png", and "svg".} +Valid inputs include "clipboard" (default), "csv", "png", "svg", "jpeg", and "pdf".} \item{path}{If exporting a file, enter the path and the desired file name, \emph{excluding the file extension}. For example, "Analysis/SQ Overview".} From 2b7e51ce727182bd5bc31023920d62604250d8f1 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Fri, 27 Nov 2020 14:04:40 +0000 Subject: [PATCH 045/192] docs: workpatterns_hclust Documentation for running unscheduled calls --- R/workpatterns_hclust.R | 8 +++++++- man/workpatterns_hclust.Rd | 10 ++++++++-- 2 files changed, 15 insertions(+), 3 deletions(-) diff --git a/R/workpatterns_hclust.R b/R/workpatterns_hclust.R index 5bcbf3e6..2646f455 100644 --- a/R/workpatterns_hclust.R +++ b/R/workpatterns_hclust.R @@ -49,10 +49,16 @@ #' #' @examples #' \dontrun{ -#' workpatterns_hclust(em_data, k = 4, return = "plot", signals = c("IM", "email")) +#' # Run clusters with all three signal types, return plot +#' workpatterns_hclust(em_data, +#' k = 4, +#' return = "plot", +#' signals = c("IM", "email", "unscheduled_calls")) #' +#' # Run clusters, return raw data #' workpatterns_hclust(em_data, k = 4, return = "data") #' +#' # Run clusters for instant messages only, return hclust object #' workpatterns_hclust(em_data, k = 4, return = "hclust", signals = c("IM")) #' } #' diff --git a/man/workpatterns_hclust.Rd b/man/workpatterns_hclust.Rd index 69920d21..6245e277 100644 --- a/man/workpatterns_hclust.Rd +++ b/man/workpatterns_hclust.Rd @@ -31,7 +31,7 @@ and "abs".} \item{signals}{Character vector to specify which collaboration metrics to use: You may use "email" (default) for emails only, "IM" for Teams messages only, -or a combination of the two \code{c("email", "IM")}.} +"unscheduled_calls" for Unscheduled Calls only, or a combination, such as \code{c("email", "IM")}.} \item{start_hour}{A character vector specifying starting hours, e.g. "0900"} @@ -55,10 +55,16 @@ are averaged by person and calculated as \% of total daily collaboration. } \examples{ \dontrun{ -workpatterns_hclust(em_data, k = 4, return = "plot", signals = c("IM", "email")) +# Run clusters with all three signal types, return plot +workpatterns_hclust(em_data, + k = 4, + return = "plot", + signals = c("IM", "email", "unscheduled_calls")) +# Run clusters, return raw data workpatterns_hclust(em_data, k = 4, return = "data") +# Run clusters for instant messages only, return hclust object workpatterns_hclust(em_data, k = 4, return = "hclust", signals = c("IM")) } From 3076f0f20487851c1c79c2d4982f6faa6f4399cf Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Fri, 27 Nov 2020 15:30:09 +0000 Subject: [PATCH 046/192] feat: add customization section (#15) Information on how to customize function outputs with ggplot in vignette --- vignettes/intro-to-wpa.Rmd | 67 +++++++++++++++++++++++++++----------- 1 file changed, 48 insertions(+), 19 deletions(-) diff --git a/vignettes/intro-to-wpa.Rmd b/vignettes/intro-to-wpa.Rmd index ee172cba..f8e31552 100644 --- a/vignettes/intro-to-wpa.Rmd +++ b/vignettes/intro-to-wpa.Rmd @@ -20,11 +20,11 @@ knitr::opts_chunk$set( ## Background -This document walks through the {wpa} package, and provides some examples on how to use some of the functions. The {wpa} package is still under early active development, and new functions and features are continuously being added. +This document walks through the **wpa** package, and provides some examples on how to use some of the functions. For our full online documentation for the package, please visit . For anything else related to Workplace Analytics, please visit . ### Setting up -To start off using the {wpa}, you'll have to load it by running `library(wpa)`. For the purpose of our examples, let's also load {tidyverse}, or its component packages: +To start off using **wpa**, you'll have to load it by running `library(wpa)`. For the purpose of our examples, let's also loada few of the component packages of **tidyverse** (alternatively, you can just run `library(tidyverse)`: ```{r message=FALSE, warning=FALSE} library(wpa) @@ -44,7 +44,7 @@ sq_data %>% glimpse() ``` ---- +------------------------------------------------------------------------ ## Example Analysis @@ -68,18 +68,18 @@ The `collaboration_summary()` function also comes with an option to return summa sq_data %>% collaboration_summary(hrvar = "LevelDesignation", return = "table") ``` ---- +------------------------------------------------------------------------ ### Summary of Key Metrics -The `keymetrics_scan()` function allows you to produce summary metrics from the Standard Query data. Similar to most of the functions in this package, you can specify what output to return with the `return` argument. In addition, you have to specify which HR attribute/variable to use as a grouping variable with the `hrvar` argument. +The `keymetrics_scan()` function allows you to produce summary metrics from the Standard Person Query data. Similar to most of the functions in this package, you can specify what output to return with the `return` argument. In addition, you have to specify which HR attribute/variable to use as a grouping variable with the `hrvar` argument. There are two valid return values for `keymetrics_scan()`: -- Heat map (`return = "plot"`) -- Summary table (`return = "table"`) +- Heat map (`return = "plot"`) +- Summary table (`return = "table"`) -And here are what the outputs look like. +And here are what the outputs look like. Heatmap: @@ -93,7 +93,7 @@ Summary table: sq_data %>% keymetrics_scan(hrvar = "Organization", return = "table") ``` ---- +------------------------------------------------------------------------ ## Meeting Habits @@ -101,13 +101,12 @@ The `meeting_summary()` provides a very similar output to the previous functions There are two valid return values for `meeting_summary()`: -- Heat map (`return = "plot"`) -- Summary table (`return = "table"`) +- Heat map (`return = "plot"`) +- Summary table (`return = "table"`) > The idea is that functions in this package will share a consistent design, and the required arguments and outputs will be what users 'expect' as they explore the package. The benefit of this is to improve ease of use and adoption. - -And here are what the outputs look like, for `meeting_summary()`. +And here are what the outputs look like, for `meeting_summary()`. Heatmap: @@ -121,7 +120,7 @@ Summary table: sq_data %>% meeting_summary(hrvar = "Organization", return = "table") ``` ---- +------------------------------------------------------------------------ ### Manager Relationship 2x2 Matrix @@ -138,6 +137,7 @@ sq_data %>% mgrrel_matrix(hrvar = "LevelDesignation", return = "table") ``` Alternatively, you can return the input data that is used to create the plot: + ```{r} sq_data %>% mgrrel_matrix(hrvar = "LevelDesignation", return = "chartdata") ``` @@ -156,21 +156,50 @@ A table can be generated: sq_data %>% mgrcoatt_dist(hrvar = "LevelDesignation", return = "table") ``` +------------------------------------------------------------------------ ---- +### Customizing plot outputs + +With a few rare exceptions such as `track_HR_change()`, the majority of plot outputs returned by **wpa** functions are ggplot outputs. What this means is that there is a lot of flexibility in adding or overriding visual elements in the plots. For instance, you can take the following 'fizzy drink' (jittered scatter) plot: + +```{r} +sq_data %>% + workloads_fizz(hrvar = "LevelDesignation", return = "plot") +``` + +... and add custom titles, subtitles, and flip the axes by adding ggplot layers: + +```{r} +library(ggplot2) # Requires ggplot2 for customizations + +sq_data %>% + workloads_fizz(hrvar = "LevelDesignation", return = "plot") + + labs(title = "This is a custom title", + subtitle = "This is a custom sub-title") + + coord_flip() # Flip coordinates +``` + +Note that the "pipe" syntax changes from `%>%` to `+` once you are manipulating a ggplot output, which will return an error if not used correctly. + +Adding customized elements may 'break' the visualization, so please exercise caution when doing so. + +For more information on ggplot, please visit . + + +------------------------------------------------------------------------ ### Getting a quick overview of Standard Person Query data The `collaboration_report()` function enables you to get a quick view of the Standard Query data by producing an automated HTML report. This can be done by passing the dataset as the argument: -```{r echo=TRUE, message=FALSE, warning=FALSE, results="hide"} -# sq_data %>% collaboration_report() +```R +sq_data %>% collaboration_report() ``` Running the function will open up a HTML report in your browser, summarising the key collaboration metrics and plots of the Standard Person Query. You can manually save this report, or only have it open as an ad-hoc reference document. ---- +------------------------------------------------------------------------ ## Feedback -Hope you found this useful! If you have any suggestions or feedback, please feel free to send them to mac@microsoft.com. +Hope you found this useful! If you have any suggestions or feedback, please log them at . From 2cd5bdce20be36ef81aba9842ff70ba9923821ea Mon Sep 17 00:00:00 2001 From: moralec Date: Fri, 27 Nov 2020 15:36:06 +0000 Subject: [PATCH 047/192] Create AnalystGuideOutline.txt --- .github/AnalystGuideOutline.txt | 26 ++++++++++++++++++++++++++ 1 file changed, 26 insertions(+) create mode 100644 .github/AnalystGuideOutline.txt diff --git a/.github/AnalystGuideOutline.txt b/.github/AnalystGuideOutline.txt new file mode 100644 index 00000000..9431fbdf --- /dev/null +++ b/.github/AnalystGuideOutline.txt @@ -0,0 +1,26 @@ +Analyst Guide + + +1. Introduction + + - Welcome Note + - Why use R for Workplace Analytics? + - Pre-Requisites + - Useful Resources + - Analysis Guide Contents + +2. Getting Stared + + - Intallation Instructions + - Importing Workplace Analytics Data + - Exploring the dataset + - Function Structure and additional parameters + - Exporting Plots and Tables + +3. Summary Functions + +4. Distribution Functions + +5. Trend Functions + +6. Network Functions \ No newline at end of file From 8370f599890bfdfbc55d4d48ec2df4bfdaf52389 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Fri, 27 Nov 2020 15:42:08 +0000 Subject: [PATCH 048/192] feat: add getting started - Add 'Getting Started' template to pkgdown site --- .github/analyst_guide_getting_started.md | 17 +++++++++++++++++ R/generate_report.R | 2 +- _pkgdown.yml | 4 +++- 3 files changed, 21 insertions(+), 2 deletions(-) create mode 100644 .github/analyst_guide_getting_started.md diff --git a/.github/analyst_guide_getting_started.md b/.github/analyst_guide_getting_started.md new file mode 100644 index 00000000..34c290d0 --- /dev/null +++ b/.github/analyst_guide_getting_started.md @@ -0,0 +1,17 @@ +# Getting Started + + +## Installation instructions +_Coming soon..._ + +## Importing Workplace Analytics Data +_Coming soon..._ + +## Exploring the dataset +_Coming soon..._ + +## Function Structure and additional parameters +_Coming soon..._ + +## Exporting Plots and Tables +_Coming soon..._ diff --git a/R/generate_report.R b/R/generate_report.R index 0f908ab6..33edb975 100644 --- a/R/generate_report.R +++ b/R/generate_report.R @@ -39,7 +39,7 @@ #' title_list <- c("Workloads Summary - Plot", "Workloads Summary - Table") #' n_title <- length(title_list) #' -#'# Step 3: Generate Report +#' # Step 3: Generate Report #' generate_report(title = "My First Report", #' filename = "My First Report", #' outputs = output_list, diff --git a/_pkgdown.yml b/_pkgdown.yml index a75374b6..73f44774 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -26,10 +26,12 @@ navbar: menu: - text: Introduction href: analyst_guide.html + - text: Getting Started + href: analyst_guide_getting_started.html - text: Summary analysis href: analyst_guide_summary.html - text: Distribution analysis - href: analyst_guide_distribution.html + href: analyst_guide_distribution.html - text: Trend analysis href: analyst_guide_trend.html - text: Network analysis From 0bc5b3b36b65e7d6a534a9e9e585a56d37b02c97 Mon Sep 17 00:00:00 2001 From: moralec Date: Fri, 27 Nov 2020 16:03:03 +0000 Subject: [PATCH 049/192] Update analyst_guide.md --- .github/analyst_guide.md | 24 +++++++++++++++++++++--- 1 file changed, 21 insertions(+), 3 deletions(-) diff --git a/.github/analyst_guide.md b/.github/analyst_guide.md index 9c315638..ae347b6d 100644 --- a/.github/analyst_guide.md +++ b/.github/analyst_guide.md @@ -1,6 +1,12 @@ -# Analyst Guide +# Introduction -This is an analyst guide for the **wpa** package. Please use the links in the navigation bar above to access the individual sections. +Welcome to the Analyst Guide for the **wpa** package. The objective of this document is to get you up an running with **wpa** and introduce you to the main functions that are included in this library. + +## Before we begin... + +Make sure you have: +1. Analyst access to Workplace Analytics +2. A local install of R or RStudio Desktop . ## Why use R for Workplace Analytics? @@ -12,8 +18,20 @@ There are multiple reasons: 4. **Integration**: If you already use R as part of your analysis toolkit, adopting the **wpa** package as part of the workflow will be seamless and easy 5. **Go beyond basic reporting**: One of the most appealing feature of R is the access it offers to a wide range of packages. For instance, clustering and text mining can be done very easily as part of a R workflow – which are both available from the **wpa** package +## Analyst Guide +This guide is organized in six sections. + +1. **Getting Stared**: This section contains the detailed installation instructions, and a general overview of how functions work in the **wpa** package. +2. **Summary Functions**: This section introduces functions that calculate averages and draw comparisons across groups. +3. **Distribution Functions**: This section describes functions that help you go beyond averages, exploring distributions across groups. +4. **Trend Functions**: This section explains functions that explore time dyanmics across a wide range of metrics. +5. **Network Functions**: This section explores functions that help you plot and analyse networks. + +## Additional Resources Also check out our package cheat sheet for more information: - +1. The official **wpa** cheat sheet. +2. Our growing list of articles for analysts. + From 329dc857f0039b8ced6c8d0255240503543e895d Mon Sep 17 00:00:00 2001 From: moralec Date: Fri, 27 Nov 2020 16:28:50 +0000 Subject: [PATCH 050/192] Updated analyst guide --- .github/analyst_guide.md | 18 ++++++------ .github/analyst_guide_getting_started.md | 35 ++++++++++++++++++++++-- 2 files changed, 41 insertions(+), 12 deletions(-) diff --git a/.github/analyst_guide.md b/.github/analyst_guide.md index ae347b6d..8f54644a 100644 --- a/.github/analyst_guide.md +++ b/.github/analyst_guide.md @@ -1,12 +1,12 @@ # Introduction -Welcome to the Analyst Guide for the **wpa** package. The objective of this document is to get you up an running with **wpa** and introduce you to the main functions that are included in this library. +Welcome to the Analyst Guide for the **wpa** package. The objective of this document is to get you up an running with this library, and introduce you to the main functions that are included. ## Before we begin... Make sure you have: -1. Analyst access to Workplace Analytics -2. A local install of R or RStudio Desktop . +1. Analyst access to +2. Either R or RStudio Desktop installed in your local device ## Why use R for Workplace Analytics? @@ -18,20 +18,20 @@ There are multiple reasons: 4. **Integration**: If you already use R as part of your analysis toolkit, adopting the **wpa** package as part of the workflow will be seamless and easy 5. **Go beyond basic reporting**: One of the most appealing feature of R is the access it offers to a wide range of packages. For instance, clustering and text mining can be done very easily as part of a R workflow – which are both available from the **wpa** package -## Analyst Guide +## Analyst Guide Contents This guide is organized in six sections. -1. **Getting Stared**: This section contains the detailed installation instructions, and a general overview of how functions work in the **wpa** package. +1. **Getting Stared**: This section contains the detailed installation instructions, and a general overview of how functions work. 2. **Summary Functions**: This section introduces functions that calculate averages and draw comparisons across groups. -3. **Distribution Functions**: This section describes functions that help you go beyond averages, exploring distributions across groups. +3. **Distribution Functions**: This section describes functions that help you explore distributions across groups. 4. **Trend Functions**: This section explains functions that explore time dyanmics across a wide range of metrics. 5. **Network Functions**: This section explores functions that help you plot and analyse networks. ## Additional Resources -Also check out our package cheat sheet for more information: +Also, make sure to check out these additional resources: -1. The official **wpa** cheat sheet. -2. Our growing list of articles for analysts. +1. Our official **wpa** cheat sheet. +2. A growing list of articles for analysts, written by multiple contributors. diff --git a/.github/analyst_guide_getting_started.md b/.github/analyst_guide_getting_started.md index 34c290d0..2fcc9829 100644 --- a/.github/analyst_guide_getting_started.md +++ b/.github/analyst_guide_getting_started.md @@ -1,11 +1,40 @@ # Getting Started +This section contains the detailed installation instructions, and a general overview of how functions work in the **wpa** package. +## Installation + +The latest stable version of *wpa* is available in Github. Use the following script in R to automatically download and install it in your local installation of R: + +```R +# Check if devtools is installed, if not then install it +if(!"devtools" %in% installed.packages()){ + install.packages("devtools") +} +devtools::install_git(url = "https://github.com/microsoft/wpa.git") + +``` + +The **wpa** is not yet released on CRAN, and therefore `install.packages()` will not work. If you prefer to do a local installation, you can download a .tar file from here. + +## Loading the wpa package +One the installation is complete, you can can load the package with: + +```R +library(wpa) +``` + +You will need to load the package everytime you start a new R session. -## Installation instructions -_Coming soon..._ ## Importing Workplace Analytics Data -_Coming soon..._ +All functions in *wpa* are designed to work with query data from Workpalce Analytics. To load in some data you can use wpa::import_wpa, loading any query file in .CSV format: + +```R + +person_data <- import_wpa("Desktop/myquery.csv") # Step 2 +``` + +The wpa::import_wpa function works with all workplace analytics query types. ## Exploring the dataset _Coming soon..._ From 378c9e4b019199354ae6d657605e0ac3c2e4a56c Mon Sep 17 00:00:00 2001 From: moralec Date: Fri, 27 Nov 2020 16:31:07 +0000 Subject: [PATCH 051/192] Update analyst_guide.md --- .github/analyst_guide.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/.github/analyst_guide.md b/.github/analyst_guide.md index 8f54644a..2afa5613 100644 --- a/.github/analyst_guide.md +++ b/.github/analyst_guide.md @@ -1,6 +1,6 @@ # Introduction -Welcome to the Analyst Guide for the **wpa** package. The objective of this document is to get you up an running with this library, and introduce you to the main functions that are included. +Welcome to the Analyst Guide of the **wpa** package. The objective of this document is to get you up an running with this library, and introduce you to the main functions that are included. ## Before we begin... @@ -12,11 +12,11 @@ Make sure you have: There are multiple reasons: -1. **Cutting edge data science**: R is an open-source language that is known for its **active user community** and a **wide range of packages** that together enable the quick and effective implementation of data science techniques -2. **Reproducibility**: Code-based workflows help facilitate **reproducible analysis**, which is the notion that analysis should be built in a way that is replicable by others. R as a tool promotes this good practice. +1. **Cutting edge data science**: R is an open-source language that is known for its active user community and a wide range of packages that together enable the quick and effective implementation of data science techniques +2. **Reproducibility**: Code-based workflows help facilitate reproducible analysis, which is the notion that analysis should be built in a way that is replicable by others. R as a tool promotes this good practice. 3. **Efficiency / scalability**: R scales relatively well in the context of large datasets. The application of functions and automated processes also help cut down routine analysis time -4. **Integration**: If you already use R as part of your analysis toolkit, adopting the **wpa** package as part of the workflow will be seamless and easy -5. **Go beyond basic reporting**: One of the most appealing feature of R is the access it offers to a wide range of packages. For instance, clustering and text mining can be done very easily as part of a R workflow – which are both available from the **wpa** package +4. **Integration**: If you already use R as part of your analysis toolkit, adopting this package as part of the workflow will be seamless and easy +5. **Go beyond basic reporting**: One of the most appealing feature of R is the access it offers to a wide range of packages. For instance, clustering and text mining can be done very easily as part of a R workflow – which are both available in this package ## Analyst Guide Contents From 5c27339fd050683cb2deb0467e917d52af4d20d0 Mon Sep 17 00:00:00 2001 From: moralec Date: Fri, 27 Nov 2020 16:36:10 +0000 Subject: [PATCH 052/192] Update analyst_guide.md --- .github/analyst_guide.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/analyst_guide.md b/.github/analyst_guide.md index 2afa5613..4bacb3ad 100644 --- a/.github/analyst_guide.md +++ b/.github/analyst_guide.md @@ -30,7 +30,7 @@ This guide is organized in six sections. ## Additional Resources -Also, make sure to check out these additional resources: +As you start actively using *wpa* for analysis, make sure to leaverage these additional resources: 1. Our official **wpa** cheat sheet. 2. A growing list of articles for analysts, written by multiple contributors. From af041950197013d1dbf7db0e34d0b38483ef69a9 Mon Sep 17 00:00:00 2001 From: moralec Date: Fri, 27 Nov 2020 16:44:53 +0000 Subject: [PATCH 053/192] Update analyst_guide.md --- .github/analyst_guide.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/analyst_guide.md b/.github/analyst_guide.md index 4bacb3ad..dc382b9e 100644 --- a/.github/analyst_guide.md +++ b/.github/analyst_guide.md @@ -5,7 +5,7 @@ Welcome to the Analyst Guide of the **wpa** package. The objective of this docum ## Before we begin... Make sure you have: -1. Analyst access to +1. Analyst access to Workplace Analytics 2. Either R or RStudio Desktop installed in your local device ## Why use R for Workplace Analytics? @@ -22,7 +22,7 @@ There are multiple reasons: This guide is organized in six sections. -1. **Getting Stared**: This section contains the detailed installation instructions, and a general overview of how functions work. +1. **Getting Stared**: This section contains the detailed installation instructions, and a general overview of how functions work. 2. **Summary Functions**: This section introduces functions that calculate averages and draw comparisons across groups. 3. **Distribution Functions**: This section describes functions that help you explore distributions across groups. 4. **Trend Functions**: This section explains functions that explore time dyanmics across a wide range of metrics. @@ -33,5 +33,5 @@ This guide is organized in six sections. As you start actively using *wpa* for analysis, make sure to leaverage these additional resources: 1. Our official **wpa** cheat sheet. -2. A growing list of articles for analysts, written by multiple contributors. +2. A growing list of articles with detailed walkthroughs, written by multiple contributors. From 60382279d5be20c85a56f03f301eacd5c2649427 Mon Sep 17 00:00:00 2001 From: moralec Date: Fri, 27 Nov 2020 16:45:51 +0000 Subject: [PATCH 054/192] Update analyst_guide.md --- .github/analyst_guide.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/analyst_guide.md b/.github/analyst_guide.md index dc382b9e..736c3094 100644 --- a/.github/analyst_guide.md +++ b/.github/analyst_guide.md @@ -32,6 +32,6 @@ This guide is organized in six sections. As you start actively using *wpa* for analysis, make sure to leaverage these additional resources: -1. Our official **wpa** cheat sheet. +1. Our official **wpa** cheat sheet. 2. A growing list of articles with detailed walkthroughs, written by multiple contributors. From 71078957623e41be2e36f6f421de1a4be1fe680f Mon Sep 17 00:00:00 2001 From: moralec Date: Fri, 27 Nov 2020 16:47:15 +0000 Subject: [PATCH 055/192] Updated guide sections 1 and 2 --- .github/analyst_guide.md | 4 ++-- .github/analyst_guide_getting_started.md | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/.github/analyst_guide.md b/.github/analyst_guide.md index 736c3094..5294f9d7 100644 --- a/.github/analyst_guide.md +++ b/.github/analyst_guide.md @@ -5,7 +5,7 @@ Welcome to the Analyst Guide of the **wpa** package. The objective of this docum ## Before we begin... Make sure you have: -1. Analyst access to Workplace Analytics +1. Analyst access to Workplace Analytics 2. Either R or RStudio Desktop installed in your local device ## Why use R for Workplace Analytics? @@ -30,7 +30,7 @@ This guide is organized in six sections. ## Additional Resources -As you start actively using *wpa* for analysis, make sure to leaverage these additional resources: +As you use **wpa** for analysis, make sure to leaverage these additional resources: 1. Our official **wpa** cheat sheet. 2. A growing list of articles with detailed walkthroughs, written by multiple contributors. diff --git a/.github/analyst_guide_getting_started.md b/.github/analyst_guide_getting_started.md index 2fcc9829..2db7557a 100644 --- a/.github/analyst_guide_getting_started.md +++ b/.github/analyst_guide_getting_started.md @@ -3,7 +3,7 @@ This section contains the detailed installation instructions, and a general over ## Installation -The latest stable version of *wpa* is available in Github. Use the following script in R to automatically download and install it in your local installation of R: +The latest stable version of **wpa** is available in Github. Use the following script in R to automatically download and install it in your local installation of R: ```R # Check if devtools is installed, if not then install it @@ -27,7 +27,7 @@ You will need to load the package everytime you start a new R session. ## Importing Workplace Analytics Data -All functions in *wpa* are designed to work with query data from Workpalce Analytics. To load in some data you can use wpa::import_wpa, loading any query file in .CSV format: +All functions in **wpa** are designed to work with query data from Workpalce Analytics. To load in some data you can use wpa::import_wpa, loading any query file in .CSV format: ```R From 35f3e915b9dfbe88a9c6eff32b092cb71b7db444 Mon Sep 17 00:00:00 2001 From: moralec Date: Fri, 27 Nov 2020 16:50:10 +0000 Subject: [PATCH 056/192] Update analyst_guide.md --- .github/analyst_guide.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/.github/analyst_guide.md b/.github/analyst_guide.md index 5294f9d7..db4cc5f6 100644 --- a/.github/analyst_guide.md +++ b/.github/analyst_guide.md @@ -5,8 +5,8 @@ Welcome to the Analyst Guide of the **wpa** package. The objective of this docum ## Before we begin... Make sure you have: -1. Analyst access to Workplace Analytics -2. Either R or RStudio Desktop installed in your local device +1. Analyst access to [Workplace Analytics](https://docs.microsoft.com/en-us/workplace-analytics/) +2. Either [R](https://www.r-project.org/) or [RStudio Desktop](https://rstudio.com/products/rstudio/download/#download) installed in your local device ## Why use R for Workplace Analytics? @@ -32,6 +32,6 @@ This guide is organized in six sections. As you use **wpa** for analysis, make sure to leaverage these additional resources: -1. Our official **wpa** cheat sheet. -2. A growing list of articles with detailed walkthroughs, written by multiple contributors. +1. Our official [**wpa** cheat sheet](https://github.com/microsoft/wpa/blob/main/man/figures/wpa%20cheatsheet_20201116.pdf). +2. A growing list of [articles]("https://microsoft.github.io/wpa/articles/) with detailed walkthroughs, written by multiple contributors. From 8fc5102771e6a5b29d6436003a08afa5a5f99798 Mon Sep 17 00:00:00 2001 From: moralec Date: Fri, 27 Nov 2020 16:50:45 +0000 Subject: [PATCH 057/192] Update analyst_guide_getting_started.md --- .github/analyst_guide_getting_started.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/analyst_guide_getting_started.md b/.github/analyst_guide_getting_started.md index 2db7557a..2824748d 100644 --- a/.github/analyst_guide_getting_started.md +++ b/.github/analyst_guide_getting_started.md @@ -31,7 +31,7 @@ All functions in **wpa** are designed to work with query data from Workpalce Ana ```R -person_data <- import_wpa("Desktop/myquery.csv") # Step 2 +person_data <- import_wpa("Desktop/myquery.csv") ``` The wpa::import_wpa function works with all workplace analytics query types. From 7402335ac19b93eab731d4b17f3eb0dbd0e551dd Mon Sep 17 00:00:00 2001 From: moralec Date: Fri, 27 Nov 2020 16:52:35 +0000 Subject: [PATCH 058/192] Update analyst_guide_getting_started.md --- .github/analyst_guide_getting_started.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/analyst_guide_getting_started.md b/.github/analyst_guide_getting_started.md index 2824748d..8c72b03e 100644 --- a/.github/analyst_guide_getting_started.md +++ b/.github/analyst_guide_getting_started.md @@ -27,14 +27,14 @@ You will need to load the package everytime you start a new R session. ## Importing Workplace Analytics Data -All functions in **wpa** are designed to work with query data from Workpalce Analytics. To load in some data you can use wpa::import_wpa, loading any query file in .CSV format: +All functions in **wpa** are designed to work with query data from Workpalce Analytics. To load in some data you can use the included [import_wpa()](https://microsoft.github.io/wpa/reference/import_wpa.html), loading any query file in .CSV format: ```R -person_data <- import_wpa("Desktop/myquery.csv") +person_data <- import_wpa("Desktop/myquery.csv") ``` -The wpa::import_wpa function works with all workplace analytics query types. +The [import_wpa()](https://microsoft.github.io/wpa/reference/import_wpa.html) function works with all workplace analytics query types. ## Exploring the dataset _Coming soon..._ From 2c4b2a781f76bc58bd9813ac2a1c87fac42cdd03 Mon Sep 17 00:00:00 2001 From: moralec Date: Fri, 27 Nov 2020 16:56:28 +0000 Subject: [PATCH 059/192] Update analyst_guide.md --- .github/analyst_guide.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/.github/analyst_guide.md b/.github/analyst_guide.md index db4cc5f6..c0570882 100644 --- a/.github/analyst_guide.md +++ b/.github/analyst_guide.md @@ -22,16 +22,16 @@ There are multiple reasons: This guide is organized in six sections. -1. **Getting Stared**: This section contains the detailed installation instructions, and a general overview of how functions work. -2. **Summary Functions**: This section introduces functions that calculate averages and draw comparisons across groups. -3. **Distribution Functions**: This section describes functions that help you explore distributions across groups. -4. **Trend Functions**: This section explains functions that explore time dyanmics across a wide range of metrics. -5. **Network Functions**: This section explores functions that help you plot and analyse networks. +1. [**Getting Stared**](analyst_guide_getting_started.html): This section contains the detailed installation instructions, and a general overview of how functions work. +2. [**Summary Functions**](analyst_guide_summary.html): This section introduces functions that calculate averages and draw comparisons across groups. +3. [**Distribution Functions**](analyst_guide_distribution.html): This section describes functions that help you explore distributions across groups. +4. [**Trend Functions**](analyst_guide_trend.html): This section explains functions that explore time dyanmics across a wide range of metrics. +5. [**Network Functions**](analyst_guide_network.html): This section explores functions that help you plot and analyse networks. ## Additional Resources As you use **wpa** for analysis, make sure to leaverage these additional resources: 1. Our official [**wpa** cheat sheet](https://github.com/microsoft/wpa/blob/main/man/figures/wpa%20cheatsheet_20201116.pdf). -2. A growing list of [articles]("https://microsoft.github.io/wpa/articles/) with detailed walkthroughs, written by multiple contributors. +2. A growing list of [articles](https://microsoft.github.io/wpa/articles/) with detailed walkthroughs, written by multiple contributors. From 6070d58ad2daa62c860a88ba2a5cb5417afe11dc Mon Sep 17 00:00:00 2001 From: moralec Date: Fri, 27 Nov 2020 16:57:49 +0000 Subject: [PATCH 060/192] Update analyst_guide_getting_started.md --- .github/analyst_guide_getting_started.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/analyst_guide_getting_started.md b/.github/analyst_guide_getting_started.md index 8c72b03e..e07de319 100644 --- a/.github/analyst_guide_getting_started.md +++ b/.github/analyst_guide_getting_started.md @@ -14,7 +14,7 @@ devtools::install_git(url = "https://github.com/microsoft/wpa.git") ``` -The **wpa** is not yet released on CRAN, and therefore `install.packages()` will not work. If you prefer to do a local installation, you can download a .tar file from here. +**wpa** is not yet released on CRAN, and therefore `install.packages()` will not work. If you prefer to do a local installation, you can download a .tar file from [here](https://github.com/microsoft/wpa/). ## Loading the wpa package One the installation is complete, you can can load the package with: From b68f43249beab3dac2763494e46f0f74ff0f12ec Mon Sep 17 00:00:00 2001 From: moralec Date: Fri, 27 Nov 2020 17:13:56 +0000 Subject: [PATCH 061/192] Update analyst_guide_getting_started.md --- .github/analyst_guide_getting_started.md | 34 +++++++++++++++++++++--- 1 file changed, 30 insertions(+), 4 deletions(-) diff --git a/.github/analyst_guide_getting_started.md b/.github/analyst_guide_getting_started.md index e07de319..605d7270 100644 --- a/.github/analyst_guide_getting_started.md +++ b/.github/analyst_guide_getting_started.md @@ -27,7 +27,7 @@ You will need to load the package everytime you start a new R session. ## Importing Workplace Analytics Data -All functions in **wpa** are designed to work with query data from Workpalce Analytics. To load in some data you can use the included [import_wpa()](https://microsoft.github.io/wpa/reference/import_wpa.html), loading any query file in .CSV format: +All functions in **wpa** are designed to work with query data from Workpalce Analytics. To load in some data you can use the [import_wpa()](https://microsoft.github.io/wpa/reference/import_wpa.html) funciton, to read any query file in .CSV format: ```R @@ -36,11 +36,37 @@ person_data <- import_wpa("Desktop/myquery.csv") The [import_wpa()](https://microsoft.github.io/wpa/reference/import_wpa.html) function works with all workplace analytics query types. -## Exploring the dataset -_Coming soon..._ +**wpa** includes a set of pre-loaded workplace analytics datasets that you we will use in this guide. These include: +1. *sq_data*: A Standard Person Query +2. *mt_data*: A Standard Meeting Query +3. *em_data*: An Hourly Collaboration Query +4. *g2g_data*: A Group-to-Group Query + +## Exploring the dataset +We can explore sq_data using some of the basic functions from **wpa**. [analysis_scope](https://microsoft.github.io/wpa/reference/hrvar_count.html) can help you to create a basic bar chart, with the count of the distinct individuals in a specified group / HR attribute. For example, if we want to know the number of individuals per organisation. + +```R +analysis_scope(sq_data, hrvar = "Organization") +``` + +If you prefer, you can also use pipes (%>%) a common notation in tidyverse. Lets use that notation to take a look at the distribution by both Organization and LevelDesignation. + +```R +sq_data %>% analysis_scope(hrvar = "Organization") +sq_data %>% analysis_scope(hrvar = "LevelDesignation") +``` + +The default behaviour of most functions in **wpa** is to create a plot. However, this behaviour can be changed by modifing the `return` argument. If you use `return`="table"', the function will now produce a table / data frame with the relevant summary statistics. + +```R +sq_data %>% analysis_scope(hrvar="LevelDesignation", return="table") +``` ## Function Structure and additional parameters -_Coming soon..._ + +The following illustrates the basic API of standard analysis functions: + + ## Exporting Plots and Tables _Coming soon..._ From 2545a675326207f11b7eeddc3af6f13a20bfc204 Mon Sep 17 00:00:00 2001 From: moralec Date: Fri, 27 Nov 2020 17:19:04 +0000 Subject: [PATCH 062/192] Update analyst_guide_getting_started.md --- .github/analyst_guide_getting_started.md | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git a/.github/analyst_guide_getting_started.md b/.github/analyst_guide_getting_started.md index 605d7270..0a50582a 100644 --- a/.github/analyst_guide_getting_started.md +++ b/.github/analyst_guide_getting_started.md @@ -27,24 +27,27 @@ You will need to load the package everytime you start a new R session. ## Importing Workplace Analytics Data -All functions in **wpa** are designed to work with query data from Workpalce Analytics. To load in some data you can use the [import_wpa()](https://microsoft.github.io/wpa/reference/import_wpa.html) funciton, to read any query file in .CSV format: +All functions in **wpa** are designed to work with query data from Workpalce Analytics. To load in some data you can use the [import_wpa()](https://microsoft.github.io/wpa/reference/import_wpa.html) funciton, to read any query file in .CSV format. + +Assuming you have a file called *myquery.csv* on your desktop, you can import that data into R using: ```R - person_data <- import_wpa("Desktop/myquery.csv") ``` The [import_wpa()](https://microsoft.github.io/wpa/reference/import_wpa.html) function works with all workplace analytics query types. -**wpa** includes a set of pre-loaded workplace analytics datasets that you we will use in this guide. These include: + +## Demo Data +**wpa** includes a set of pre-loaded workplace analytics datasets that you can use to explore the functionalty of this package. We will also use them extensively in this guide. The included dataframes are: 1. *sq_data*: A Standard Person Query 2. *mt_data*: A Standard Meeting Query 3. *em_data*: An Hourly Collaboration Query 4. *g2g_data*: A Group-to-Group Query -## Exploring the dataset -We can explore sq_data using some of the basic functions from **wpa**. [analysis_scope](https://microsoft.github.io/wpa/reference/hrvar_count.html) can help you to create a basic bar chart, with the count of the distinct individuals in a specified group / HR attribute. For example, if we want to know the number of individuals per organisation. +## Exploring a Person Query +We can explore a person query using some of the basic functions from **wpa**. For example, [analysis_scope](https://microsoft.github.io/wpa/reference/hrvar_count.html) can help you to create a basic bar plot, with the count of the distinct individuals by a specified group / HR attribute. For example, if we want to know the number of individuals per organisation. ```R analysis_scope(sq_data, hrvar = "Organization") @@ -57,14 +60,14 @@ sq_data %>% analysis_scope(hrvar = "Organization") sq_data %>% analysis_scope(hrvar = "LevelDesignation") ``` -The default behaviour of most functions in **wpa** is to create a plot. However, this behaviour can be changed by modifing the `return` argument. If you use `return`="table"', the function will now produce a table / data frame with the relevant summary statistics. +The default behaviour of most functions in **wpa** is to create a plot. However, this behaviour can be modified by changing the `return` argument. If you use `return`="table", the function will now produce a table / data frame with the relevant summary statistics. ```R sq_data %>% analysis_scope(hrvar="LevelDesignation", return="table") ``` ## Function Structure and additional parameters -The following illustrates the basic API of standard analysis functions: +All funcitons in **wpa** follow the same behaviour. The following illustrates the basic API of standard analysis functions: From 5d1d8baa2fe9bfc74d375409dc51b750f2761020 Mon Sep 17 00:00:00 2001 From: moralec Date: Fri, 27 Nov 2020 17:22:39 +0000 Subject: [PATCH 063/192] Update analyst_guide.md --- .github/analyst_guide.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/analyst_guide.md b/.github/analyst_guide.md index c0570882..371e24ea 100644 --- a/.github/analyst_guide.md +++ b/.github/analyst_guide.md @@ -20,7 +20,7 @@ There are multiple reasons: ## Analyst Guide Contents -This guide is organized in six sections. +This guide is organized in five key sections: 1. [**Getting Stared**](analyst_guide_getting_started.html): This section contains the detailed installation instructions, and a general overview of how functions work. 2. [**Summary Functions**](analyst_guide_summary.html): This section introduces functions that calculate averages and draw comparisons across groups. @@ -30,7 +30,7 @@ This guide is organized in six sections. ## Additional Resources -As you use **wpa** for analysis, make sure to leaverage these additional resources: +To get the most out of **wpa**, make sure to leaverage these additional resources: 1. Our official [**wpa** cheat sheet](https://github.com/microsoft/wpa/blob/main/man/figures/wpa%20cheatsheet_20201116.pdf). 2. A growing list of [articles](https://microsoft.github.io/wpa/articles/) with detailed walkthroughs, written by multiple contributors. From ef6d961519f46c35aeedf96f5b87ff252ca8956d Mon Sep 17 00:00:00 2001 From: moralec Date: Fri, 27 Nov 2020 17:24:13 +0000 Subject: [PATCH 064/192] Update analyst_guide_getting_started.md --- .github/analyst_guide_getting_started.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/analyst_guide_getting_started.md b/.github/analyst_guide_getting_started.md index 0a50582a..1c4cbd9e 100644 --- a/.github/analyst_guide_getting_started.md +++ b/.github/analyst_guide_getting_started.md @@ -69,7 +69,7 @@ sq_data %>% analysis_scope(hrvar="LevelDesignation", return="table") All funcitons in **wpa** follow the same behaviour. The following illustrates the basic API of standard analysis functions: - + ## Exporting Plots and Tables _Coming soon..._ From 18678468536eaba60a8b5d76323a9d674ab270c2 Mon Sep 17 00:00:00 2001 From: moralec Date: Fri, 27 Nov 2020 17:28:08 +0000 Subject: [PATCH 065/192] Update analyst_guide_getting_started.md --- .github/analyst_guide_getting_started.md | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/.github/analyst_guide_getting_started.md b/.github/analyst_guide_getting_started.md index 1c4cbd9e..9ab9878b 100644 --- a/.github/analyst_guide_getting_started.md +++ b/.github/analyst_guide_getting_started.md @@ -3,7 +3,7 @@ This section contains the detailed installation instructions, and a general over ## Installation -The latest stable version of **wpa** is available in Github. Use the following script in R to automatically download and install it in your local installation of R: +The latest stable version of **wpa** is available in Github. Use the following script in R to automatically download and install it in your local device: ```R # Check if devtools is installed, if not then install it @@ -14,22 +14,21 @@ devtools::install_git(url = "https://github.com/microsoft/wpa.git") ``` -**wpa** is not yet released on CRAN, and therefore `install.packages()` will not work. If you prefer to do a local installation, you can download a .tar file from [here](https://github.com/microsoft/wpa/). +This package is not yet released on CRAN, and therefore `install.packages()` will not work. If you prefer to do a local installation, you can download a .tar file [here](https://github.com/microsoft/wpa/). ## Loading the wpa package -One the installation is complete, you can can load the package with: +One the installation is complete, you can load the package with: ```R library(wpa) ``` -You will need to load the package everytime you start a new R session. - +You only need to install the package once, but will need to load it everytime you start a new R session. ## Importing Workplace Analytics Data -All functions in **wpa** are designed to work with query data from Workpalce Analytics. To load in some data you can use the [import_wpa()](https://microsoft.github.io/wpa/reference/import_wpa.html) funciton, to read any query file in .CSV format. +All functions in **wpa** are designed to work with query data from Workpalce Analytics. To read data into R, you can use the [import_wpa()](https://microsoft.github.io/wpa/reference/import_wpa.html) function, that accepts any query file in .CSV format. -Assuming you have a file called *myquery.csv* on your desktop, you can import that data into R using: +Assuming you have a file called *myquery.csv* on your desktop, you can import it into R using: ```R person_data <- import_wpa("Desktop/myquery.csv") From 764bdd0cc6532fb4059fba785db370a6a64a2dc3 Mon Sep 17 00:00:00 2001 From: moralec Date: Fri, 27 Nov 2020 17:29:45 +0000 Subject: [PATCH 066/192] Update analyst_guide.md --- .github/analyst_guide.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/analyst_guide.md b/.github/analyst_guide.md index 371e24ea..bf74d61a 100644 --- a/.github/analyst_guide.md +++ b/.github/analyst_guide.md @@ -22,7 +22,7 @@ There are multiple reasons: This guide is organized in five key sections: -1. [**Getting Stared**](analyst_guide_getting_started.html): This section contains the detailed installation instructions, and a general overview of how functions work. +1. [**Getting Started**](analyst_guide_getting_started.html): This section contains the detailed installation instructions, and a general overview of how functions work. 2. [**Summary Functions**](analyst_guide_summary.html): This section introduces functions that calculate averages and draw comparisons across groups. 3. [**Distribution Functions**](analyst_guide_distribution.html): This section describes functions that help you explore distributions across groups. 4. [**Trend Functions**](analyst_guide_trend.html): This section explains functions that explore time dyanmics across a wide range of metrics. From 9d0ada1c999d9695aa142fb95fa75c223b2675a0 Mon Sep 17 00:00:00 2001 From: moralec Date: Fri, 27 Nov 2020 17:41:57 +0000 Subject: [PATCH 067/192] Update analyst_guide_getting_started.md --- .github/analyst_guide_getting_started.md | 17 +++++++++++------ 1 file changed, 11 insertions(+), 6 deletions(-) diff --git a/.github/analyst_guide_getting_started.md b/.github/analyst_guide_getting_started.md index 9ab9878b..ae4e7bf8 100644 --- a/.github/analyst_guide_getting_started.md +++ b/.github/analyst_guide_getting_started.md @@ -46,27 +46,32 @@ The [import_wpa()](https://microsoft.github.io/wpa/reference/import_wpa.html) fu 4. *g2g_data*: A Group-to-Group Query ## Exploring a Person Query -We can explore a person query using some of the basic functions from **wpa**. For example, [analysis_scope](https://microsoft.github.io/wpa/reference/hrvar_count.html) can help you to create a basic bar plot, with the count of the distinct individuals by a specified group / HR attribute. For example, if we want to know the number of individuals per organisation. +We can explore a person query using some of the basic functions from **wpa**. The function [analysis_scope](https://microsoft.github.io/wpa/reference/hrvar_count.html) can help you to create a basic bar plot, with the count of the distinct individuals by a specified group / HR attribute. + +For example, if we want to know the number of individuals per organization, you can use: ```R -analysis_scope(sq_data, hrvar = "Organization") +analysis_scope(sq_data, hrvar = "Organization") # Option 1 +sq_data %>% analysis_scope(hrvar = "Organization") # Option 2 + ``` -If you prefer, you can also use pipes (%>%) a common notation in tidyverse. Lets use that notation to take a look at the distribution by both Organization and LevelDesignation. +The syntaxis in option 2, illustrates how you can use pipes (%>%). This is the common notation in tidyverse, and the one we will use in this guide moving forward. + +This function can be use to explore of other groups. For example, for LevelDesignation: ```R -sq_data %>% analysis_scope(hrvar = "Organization") sq_data %>% analysis_scope(hrvar = "LevelDesignation") ``` -The default behaviour of most functions in **wpa** is to create a plot. However, this behaviour can be modified by changing the `return` argument. If you use `return`="table", the function will now produce a table / data frame with the relevant summary statistics. +The default behaviour of most functions in **wpa** is to create a plot. However, this can be altered by adding a `return` argument. If you use `return`="table", the function will now produce a table / data frame with the relevant summary statistics. ```R sq_data %>% analysis_scope(hrvar="LevelDesignation", return="table") ``` ## Function Structure and additional parameters -All funcitons in **wpa** follow the same behaviour. The following illustrates the basic API of standard analysis functions: +All funcitons in **wpa** follow a similar behaviou, and include many common arguments. The following illustrates the basic API of standard analysis functions: From fed3f56ecc95ac5a3d0fb6ea38d8fda4d65bf98d Mon Sep 17 00:00:00 2001 From: C Morales <62895857+moralec@users.noreply.github.com> Date: Fri, 27 Nov 2020 17:44:42 +0000 Subject: [PATCH 068/192] Update analyst_guide.md --- .github/analyst_guide.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/analyst_guide.md b/.github/analyst_guide.md index bf74d61a..824be197 100644 --- a/.github/analyst_guide.md +++ b/.github/analyst_guide.md @@ -1,6 +1,6 @@ # Introduction -Welcome to the Analyst Guide of the **wpa** package. The objective of this document is to get you up an running with this library, and introduce you to the main functions that are included. +Welcome to the Analyst Guide of the **wpa** package. The objective of this document is to get you up an running with this library, introduce you to the broad functionality, and make you aware of some of the functions that are included. ## Before we begin... From 1a50fe1ccb1b0e667790333b28da3371ac9a000e Mon Sep 17 00:00:00 2001 From: moralec Date: Mon, 30 Nov 2020 09:16:31 +0000 Subject: [PATCH 069/192] Update analyst_guide.md --- .github/analyst_guide.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/analyst_guide.md b/.github/analyst_guide.md index bf74d61a..edee5525 100644 --- a/.github/analyst_guide.md +++ b/.github/analyst_guide.md @@ -1,6 +1,6 @@ # Introduction -Welcome to the Analyst Guide of the **wpa** package. The objective of this document is to get you up an running with this library, and introduce you to the main functions that are included. +Welcome to the Analyst Guide of the **wpa** package. This document will guide you through the installation process, explain the package functionality and structure, and get you comfortable with some common functions for analysis. ## Before we begin... From 79bf1484021d8d987871ba6e8647b03b4a597a45 Mon Sep 17 00:00:00 2001 From: moralec Date: Mon, 30 Nov 2020 09:17:18 +0000 Subject: [PATCH 070/192] Update analyst_guide.md --- .github/analyst_guide.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/analyst_guide.md b/.github/analyst_guide.md index edee5525..5e51fe8b 100644 --- a/.github/analyst_guide.md +++ b/.github/analyst_guide.md @@ -35,3 +35,5 @@ To get the most out of **wpa**, make sure to leaverage these additional resource 1. Our official [**wpa** cheat sheet](https://github.com/microsoft/wpa/blob/main/man/figures/wpa%20cheatsheet_20201116.pdf). 2. A growing list of [articles](https://microsoft.github.io/wpa/articles/) with detailed walkthroughs, written by multiple contributors. + +Continue to the [**Getting Started**](analyst_guide_getting_started.html) section. \ No newline at end of file From a74a5b231735e29f0d06140ca272f1297c500b36 Mon Sep 17 00:00:00 2001 From: moralec Date: Mon, 30 Nov 2020 09:18:44 +0000 Subject: [PATCH 071/192] Update analyst_guide.md --- .github/analyst_guide.md | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/.github/analyst_guide.md b/.github/analyst_guide.md index 824be197..6b878735 100644 --- a/.github/analyst_guide.md +++ b/.github/analyst_guide.md @@ -1,6 +1,6 @@ # Introduction -Welcome to the Analyst Guide of the **wpa** package. The objective of this document is to get you up an running with this library, introduce you to the broad functionality, and make you aware of some of the functions that are included. +Welcome to the Analyst Guide of the **wpa** package. This document will guide you through the installation process, explain the package functionality and structure, and get you comfortable with some common functions for analysis. ## Before we begin... @@ -35,3 +35,6 @@ To get the most out of **wpa**, make sure to leaverage these additional resource 1. Our official [**wpa** cheat sheet](https://github.com/microsoft/wpa/blob/main/man/figures/wpa%20cheatsheet_20201116.pdf). 2. A growing list of [articles](https://microsoft.github.io/wpa/articles/) with detailed walkthroughs, written by multiple contributors. + +Continue to the [**Getting Started**](analyst_guide_getting_started.html) section. + From f1f17369cbd60e4495d80cd2e0e9bed95e4bf85e Mon Sep 17 00:00:00 2001 From: moralec Date: Mon, 30 Nov 2020 09:29:37 +0000 Subject: [PATCH 072/192] Update analyst_guide_getting_started.md --- .github/analyst_guide_getting_started.md | 27 +++++++++++++++--------- 1 file changed, 17 insertions(+), 10 deletions(-) diff --git a/.github/analyst_guide_getting_started.md b/.github/analyst_guide_getting_started.md index ae4e7bf8..69cdc51b 100644 --- a/.github/analyst_guide_getting_started.md +++ b/.github/analyst_guide_getting_started.md @@ -1,9 +1,9 @@ # Getting Started -This section contains the detailed installation instructions, and a general overview of how functions work in the **wpa** package. +This section contains the detailed installation instructions, and a first introduction on how functions work in the **wpa** package. ## Installation -The latest stable version of **wpa** is available in Github. Use the following script in R to automatically download and install it in your local device: +The latest stable version of **wpa** is available in Github. You can automatically download and install the latest version it in your local device, by using the following script in R to: ```R # Check if devtools is installed, if not then install it @@ -23,22 +23,29 @@ One the installation is complete, you can load the package with: library(wpa) ``` -You only need to install the package once, but will need to load it everytime you start a new R session. +You only need to install the package once; however, you will need to load it every time you start a new R session. -## Importing Workplace Analytics Data -All functions in **wpa** are designed to work with query data from Workpalce Analytics. To read data into R, you can use the [import_wpa()](https://microsoft.github.io/wpa/reference/import_wpa.html) function, that accepts any query file in .CSV format. - -Assuming you have a file called *myquery.csv* on your desktop, you can import it into R using: +**wpa** is designed to work side by side with other Data Science R packages from [tidyverse](https://www.tidyverse.org/). We generally recommend to load that set of packages too. ```R -person_data <- import_wpa("Desktop/myquery.csv") +library(tidyverse) +``` + +## Importing Workplace Analytics Data +All functions in **wpa** are designed to work with query data from Workplace Analytics. To read data into R, you can use the [import_wpa()](https://microsoft.github.io/wpa/reference/import_wpa.html) function, that accepts any query file in .CSV format. + +Assuming you have a file called *mystandardquery.csv* on your desktop, you can import it into R using: + +```R +setwd("C:/Users/myuser/Desktop/") +person_data <- import_wpa("myquery.csv") ``` The [import_wpa()](https://microsoft.github.io/wpa/reference/import_wpa.html) function works with all workplace analytics query types. ## Demo Data -**wpa** includes a set of pre-loaded workplace analytics datasets that you can use to explore the functionalty of this package. We will also use them extensively in this guide. The included dataframes are: +**wpa** includes a set of pre-loaded Workplace Analytics datasets that you can use to explore the functionality of this package. We will also use them extensively in this guide. The included data frames are: 1. *sq_data*: A Standard Person Query 2. *mt_data*: A Standard Meeting Query @@ -71,7 +78,7 @@ sq_data %>% analysis_scope(hrvar="LevelDesignation", return="table") ``` ## Function Structure and additional parameters -All funcitons in **wpa** follow a similar behaviou, and include many common arguments. The following illustrates the basic API of standard analysis functions: +All functions in **wpa** follow a similar behaviour, including many common arguments. The following illustrates the basic API of standard analysis functions: From 70b30355ba54eb9f7561b8af2b07abcea147a202 Mon Sep 17 00:00:00 2001 From: moralec Date: Mon, 30 Nov 2020 09:36:16 +0000 Subject: [PATCH 073/192] Update analyst_guide_getting_started.md --- .github/analyst_guide_getting_started.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/analyst_guide_getting_started.md b/.github/analyst_guide_getting_started.md index 69cdc51b..4877f794 100644 --- a/.github/analyst_guide_getting_started.md +++ b/.github/analyst_guide_getting_started.md @@ -80,7 +80,7 @@ sq_data %>% analysis_scope(hrvar="LevelDesignation", return="table") All functions in **wpa** follow a similar behaviour, including many common arguments. The following illustrates the basic API of standard analysis functions: - + ## Exporting Plots and Tables _Coming soon..._ From 201edc3d45fba9f5e6dcc83bd07c824eb119ccda Mon Sep 17 00:00:00 2001 From: moralec Date: Mon, 30 Nov 2020 09:41:34 +0000 Subject: [PATCH 074/192] Update analyst_guide_getting_started.md --- .github/analyst_guide_getting_started.md | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/.github/analyst_guide_getting_started.md b/.github/analyst_guide_getting_started.md index 4877f794..fb19da41 100644 --- a/.github/analyst_guide_getting_started.md +++ b/.github/analyst_guide_getting_started.md @@ -63,7 +63,7 @@ sq_data %>% analysis_scope(hrvar = "Organization") # Option 2 ``` -The syntaxis in option 2, illustrates how you can use pipes (%>%). This is the common notation in tidyverse, and the one we will use in this guide moving forward. +Both options above should get you the same result. Option 2 illustrates how you can use pipes (%>%) to feed a speciifc data frame into a funciton. This is the common notation in tidyverse, and the one we will use in this guide moving forward. This function can be use to explore of other groups. For example, for LevelDesignation: @@ -71,6 +71,12 @@ This function can be use to explore of other groups. For example, for LevelDesig sq_data %>% analysis_scope(hrvar = "LevelDesignation") ``` +Combining this function with the [filter()](https://dplyr.tidyverse.org/reference/filter.html) function from tiyverse, allows you to drill into a specific subset of the data: + +```R +sq_data %>% filter(LevelDesignation=="Support") %>% analysis_scope(hrvar = "Organization") +``` + The default behaviour of most functions in **wpa** is to create a plot. However, this can be altered by adding a `return` argument. If you use `return`="table", the function will now produce a table / data frame with the relevant summary statistics. ```R From 1a0dbf5b4682f8aea65717b3d067ce9bbf802ad6 Mon Sep 17 00:00:00 2001 From: moralec Date: Mon, 30 Nov 2020 09:43:26 +0000 Subject: [PATCH 075/192] Update analyst_guide_getting_started.md --- .github/analyst_guide_getting_started.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/analyst_guide_getting_started.md b/.github/analyst_guide_getting_started.md index fb19da41..8f167dd3 100644 --- a/.github/analyst_guide_getting_started.md +++ b/.github/analyst_guide_getting_started.md @@ -25,7 +25,7 @@ library(wpa) You only need to install the package once; however, you will need to load it every time you start a new R session. -**wpa** is designed to work side by side with other Data Science R packages from [tidyverse](https://www.tidyverse.org/). We generally recommend to load that set of packages too. +**wpa** is designed to work side by side with other Data Science R packages from [tidyverse](https://www.tidyverse.org/). We generally recommend to load that package too. ```R library(tidyverse) @@ -53,7 +53,7 @@ The [import_wpa()](https://microsoft.github.io/wpa/reference/import_wpa.html) fu 4. *g2g_data*: A Group-to-Group Query ## Exploring a Person Query -We can explore a person query using some of the basic functions from **wpa**. The function [analysis_scope](https://microsoft.github.io/wpa/reference/hrvar_count.html) can help you to create a basic bar plot, with the count of the distinct individuals by a specified group / HR attribute. +We can explore a person query using some of the basic functions from **wpa**. The function [analysis_scope()](https://microsoft.github.io/wpa/reference/hrvar_count.html) can help you to create a basic bar plot, with the count of the distinct individuals by a specified group / HR attribute. For example, if we want to know the number of individuals per organization, you can use: @@ -63,7 +63,7 @@ sq_data %>% analysis_scope(hrvar = "Organization") # Option 2 ``` -Both options above should get you the same result. Option 2 illustrates how you can use pipes (%>%) to feed a speciifc data frame into a funciton. This is the common notation in tidyverse, and the one we will use in this guide moving forward. +Both options above should get you the same result (a bar plot). Option 2 illustrates how you can use pipes (%>%) to feed a speciifc data frame into a funciton. This is the common notation in tidyverse, and the one we will use in this guide moving forward. This function can be use to explore of other groups. For example, for LevelDesignation: From dd54a5219b723a31d333adfe5bf6b20154301089 Mon Sep 17 00:00:00 2001 From: moralec Date: Mon, 30 Nov 2020 10:58:59 +0000 Subject: [PATCH 076/192] Update analyst_guide_getting_started.md --- .github/analyst_guide_getting_started.md | 26 +++++++++++++++++++++++- 1 file changed, 25 insertions(+), 1 deletion(-) diff --git a/.github/analyst_guide_getting_started.md b/.github/analyst_guide_getting_started.md index 8f167dd3..22e9ca92 100644 --- a/.github/analyst_guide_getting_started.md +++ b/.github/analyst_guide_getting_started.md @@ -89,4 +89,28 @@ All functions in **wpa** follow a similar behaviour, including many common argum ## Exporting Plots and Tables -_Coming soon..._ +Tables and plots can be saved with the [export()](https://microsoft.github.io/wpa/reference/export.html) function. This functions allows you to save plots and tables into your local drive: + +```R +person_data %>% analysis_scope(hrvar = "Organization") %>% export() + +``` + +## Summary: 4 simple steps from data to output + +The usage of **wpa** can be summarized in 4 simple steps: Load the package, read-in query data, run functions and export results: + + +```R +library(wpa) # Step 1 + +person_data <- import_wpa("myquery.csv") # Step 2 + +person_data %>% analysis_scope() # Step 3 + +person_data %>% analysis_scope() %>% export() # Step 4 + +``` + + +Continue to the [**Summary Functions**](analyst_guide_summary.html) section. From 6ddf7bd3f51c24c242e9ce3935a16bc8e49bc772 Mon Sep 17 00:00:00 2001 From: moralec Date: Mon, 30 Nov 2020 11:00:35 +0000 Subject: [PATCH 077/192] Update analyst_guide_getting_started.md --- .github/analyst_guide_getting_started.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/analyst_guide_getting_started.md b/.github/analyst_guide_getting_started.md index 22e9ca92..aac40ceb 100644 --- a/.github/analyst_guide_getting_started.md +++ b/.github/analyst_guide_getting_started.md @@ -14,7 +14,7 @@ devtools::install_git(url = "https://github.com/microsoft/wpa.git") ``` -This package is not yet released on CRAN, and therefore `install.packages()` will not work. If you prefer to do a local installation, you can download a .tar file [here](https://github.com/microsoft/wpa/). +This package is not yet released on CRAN, and therefore `install.packages()` will not work. If you prefer to do a local installation, you can download a installation file [here](https://github.com/microsoft/wpa/releases). ## Loading the wpa package One the installation is complete, you can load the package with: From 33ac3fbd08cee963fa2628f29516425e866399d9 Mon Sep 17 00:00:00 2001 From: moralec Date: Mon, 30 Nov 2020 11:34:37 +0000 Subject: [PATCH 078/192] Update analyst_guide.md --- .github/analyst_guide.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/analyst_guide.md b/.github/analyst_guide.md index 6b878735..a23c6215 100644 --- a/.github/analyst_guide.md +++ b/.github/analyst_guide.md @@ -5,7 +5,7 @@ Welcome to the Analyst Guide of the **wpa** package. This document will guide yo ## Before we begin... Make sure you have: -1. Analyst access to [Workplace Analytics](https://docs.microsoft.com/en-us/workplace-analytics/) +1. Analyst access to [Workplace Analytics](https://www.microsoft.com/en-us/microsoft-365/business/workplace-analytics) 2. Either [R](https://www.r-project.org/) or [RStudio Desktop](https://rstudio.com/products/rstudio/download/#download) installed in your local device ## Why use R for Workplace Analytics? From 0472f0208713cb365aa08e50881a337fe8e2b420 Mon Sep 17 00:00:00 2001 From: moralec Date: Mon, 30 Nov 2020 11:36:49 +0000 Subject: [PATCH 079/192] Update analyst_guide_getting_started.md --- .github/analyst_guide_getting_started.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/analyst_guide_getting_started.md b/.github/analyst_guide_getting_started.md index aac40ceb..dc68839c 100644 --- a/.github/analyst_guide_getting_started.md +++ b/.github/analyst_guide_getting_started.md @@ -34,7 +34,7 @@ library(tidyverse) ## Importing Workplace Analytics Data All functions in **wpa** are designed to work with query data from Workplace Analytics. To read data into R, you can use the [import_wpa()](https://microsoft.github.io/wpa/reference/import_wpa.html) function, that accepts any query file in .CSV format. -Assuming you have a file called *mystandardquery.csv* on your desktop, you can import it into R using: +Assuming you have a file called *myquery.csv* on your desktop, you can import it into R using: ```R setwd("C:/Users/myuser/Desktop/") From 6ebe95004e89a445ffc0c1b120e94fd0533680f3 Mon Sep 17 00:00:00 2001 From: moralec Date: Mon, 30 Nov 2020 11:47:17 +0000 Subject: [PATCH 080/192] Update analyst_guide_getting_started.md --- .github/analyst_guide_getting_started.md | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/.github/analyst_guide_getting_started.md b/.github/analyst_guide_getting_started.md index dc68839c..dd40606d 100644 --- a/.github/analyst_guide_getting_started.md +++ b/.github/analyst_guide_getting_started.md @@ -41,16 +41,18 @@ setwd("C:/Users/myuser/Desktop/") person_data <- import_wpa("myquery.csv") ``` -The [import_wpa()](https://microsoft.github.io/wpa/reference/import_wpa.html) function works with all workplace analytics query types. +In the code above, [setwd()](https://stat.ethz.ch/R-manual/R-patched/library/base/html/getwd.html) will set the working directory to the Desktop, then [import_wpa()](https://microsoft.github.io/wpa/reference/import_wpa.html) will read the source CSV that is there, and the contents will be saved to the object person_data (using <- an [Assignment Operator](https://stat.ethz.ch/R-manual/R-devel/library/base/html/assignOps.html)). +The [import_wpa()](https://microsoft.github.io/wpa/reference/import_wpa.html) function works with all workplace analytics query types. ## Demo Data **wpa** includes a set of pre-loaded Workplace Analytics datasets that you can use to explore the functionality of this package. We will also use them extensively in this guide. The included data frames are: 1. *sq_data*: A Standard Person Query -2. *mt_data*: A Standard Meeting Query -3. *em_data*: An Hourly Collaboration Query -4. *g2g_data*: A Group-to-Group Query +2. *dv_data*: A Standard Person Query with outliers +3. *mt_data*: A Standard Meeting Query +4. *em_data*: An Hourly Collaboration Query +5. *g2g_data*: A Group-to-Group Query ## Exploring a Person Query We can explore a person query using some of the basic functions from **wpa**. The function [analysis_scope()](https://microsoft.github.io/wpa/reference/hrvar_count.html) can help you to create a basic bar plot, with the count of the distinct individuals by a specified group / HR attribute. From 469826ce37519507d685e7791b2e9bd9577b2198 Mon Sep 17 00:00:00 2001 From: moralec Date: Mon, 30 Nov 2020 11:54:59 +0000 Subject: [PATCH 081/192] Update analyst_guide_getting_started.md --- .github/analyst_guide_getting_started.md | 29 +++++++++++++++--------- 1 file changed, 18 insertions(+), 11 deletions(-) diff --git a/.github/analyst_guide_getting_started.md b/.github/analyst_guide_getting_started.md index dd40606d..6fc34d59 100644 --- a/.github/analyst_guide_getting_started.md +++ b/.github/analyst_guide_getting_started.md @@ -60,20 +60,26 @@ We can explore a person query using some of the basic functions from **wpa**. Th For example, if we want to know the number of individuals per organization, you can use: ```R -analysis_scope(sq_data, hrvar = "Organization") # Option 1 -sq_data %>% analysis_scope(hrvar = "Organization") # Option 2 - +analysis_scope(sq_data, hrvar = "Organization") ``` -Both options above should get you the same result (a bar plot). Option 2 illustrates how you can use pipes (%>%) to feed a speciifc data frame into a funciton. This is the common notation in tidyverse, and the one we will use in this guide moving forward. +The [analysis_scope()](https://microsoft.github.io/wpa/reference/hrvar_count.html) function requires you to provide the person query to be used (sq_data) and the HR variable that you will like to cut the data by (hrvar). As we decided to cut the data by organisation, the resulting bar chart will show the number of individuals per organization. -This function can be use to explore of other groups. For example, for LevelDesignation: +The same comman can also be written using pipes (%>%) to feed a speciifc data frame into a funciton. + +```R +sq_data %>% analysis_scope(hrvar = "Organization") +``` + +The notation above is common in tidyverse, and is the one we will use moving forward. + +Let's now use the [analysis_scope()](https://microsoft.github.io/wpa/reference/hrvar_count.html) function to explore of other groups. For example, for LevelDesignation: ```R sq_data %>% analysis_scope(hrvar = "LevelDesignation") ``` -Combining this function with the [filter()](https://dplyr.tidyverse.org/reference/filter.html) function from tiyverse, allows you to drill into a specific subset of the data: +Combining this function with the [filter()](https://dplyr.tidyverse.org/reference/filter.html) function from tiyverse, allows us to drill into a specific subset of the data: ```R sq_data %>% filter(LevelDesignation=="Support") %>% analysis_scope(hrvar = "Organization") @@ -91,17 +97,18 @@ All functions in **wpa** follow a similar behaviour, including many common argum ## Exporting Plots and Tables -Tables and plots can be saved with the [export()](https://microsoft.github.io/wpa/reference/export.html) function. This functions allows you to save plots and tables into your local drive: +Tables and plots can be saved with the [export()](https://microsoft.github.io/wpa/reference/export.html) function. This functions allows you to save plots and tables into your local drive. + +This is where pipes become powerful, as we can write a single line that takes the original data, creates the plot, and exports it to your current directory: ```R -person_data %>% analysis_scope(hrvar = "Organization") %>% export() +sq_data %>% analysis_scope(hrvar = "Organization") %>% export() ``` ## Summary: 4 simple steps from data to output -The usage of **wpa** can be summarized in 4 simple steps: Load the package, read-in query data, run functions and export results: - +The examples above illustrate how the use of **wpa** can be summarized in 4 simple steps: Load the package, read-in query data, run functions and export results: ```R library(wpa) # Step 1 @@ -115,4 +122,4 @@ person_data %>% analysis_scope() %>% export() # Step 4 ``` -Continue to the [**Summary Functions**](analyst_guide_summary.html) section. +Continue to the [**Summary Functions**](analyst_guide_summary.html) section, to explore how we can analyse different Workplace Analytics Metrics in the same way. From 956f4a3ce19d778efb6caea73833d705e9fa0750 Mon Sep 17 00:00:00 2001 From: moralec Date: Mon, 30 Nov 2020 11:59:29 +0000 Subject: [PATCH 082/192] Update analyst_guide_getting_started.md --- .github/analyst_guide_getting_started.md | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/.github/analyst_guide_getting_started.md b/.github/analyst_guide_getting_started.md index 6fc34d59..33cf4eed 100644 --- a/.github/analyst_guide_getting_started.md +++ b/.github/analyst_guide_getting_started.md @@ -79,7 +79,8 @@ Let's now use the [analysis_scope()](https://microsoft.github.io/wpa/reference/ sq_data %>% analysis_scope(hrvar = "LevelDesignation") ``` -Combining this function with the [filter()](https://dplyr.tidyverse.org/reference/filter.html) function from tiyverse, allows us to drill into a specific subset of the data: +Combining this function with the [filter()](https://dplyr.tidyverse.org/reference/filter.html) function from tiyverse, allows us to drill into a specific subset of the data. This is where pipes become powerful, as we can write a single line that takes the original data, applies a filter, and then creates the plot: + ```R sq_data %>% filter(LevelDesignation=="Support") %>% analysis_scope(hrvar = "Organization") @@ -97,9 +98,7 @@ All functions in **wpa** follow a similar behaviour, including many common argum ## Exporting Plots and Tables -Tables and plots can be saved with the [export()](https://microsoft.github.io/wpa/reference/export.html) function. This functions allows you to save plots and tables into your local drive. - -This is where pipes become powerful, as we can write a single line that takes the original data, creates the plot, and exports it to your current directory: +Tables and plots can be saved with the [export()](https://microsoft.github.io/wpa/reference/export.html) function. This functions allows you to save plots and tables into your local drive: ```R sq_data %>% analysis_scope(hrvar = "Organization") %>% export() From d8884fc7c8627cefb3c7ae933c632d20a596f9f4 Mon Sep 17 00:00:00 2001 From: moralec Date: Mon, 30 Nov 2020 13:53:57 +0000 Subject: [PATCH 083/192] Update analyst_guide.md --- .github/analyst_guide.md | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/.github/analyst_guide.md b/.github/analyst_guide.md index a23c6215..59a27452 100644 --- a/.github/analyst_guide.md +++ b/.github/analyst_guide.md @@ -1,6 +1,6 @@ # Introduction -Welcome to the Analyst Guide of the **wpa** package. This document will guide you through the installation process, explain the package functionality and structure, and get you comfortable with some common functions for analysis. +Welcome to the Analyst Guide of the **wpa** R package. This document will guide you through the installation process, explain the package functionality and structure, and get you comfortable with some common functions for analysis. ## Before we begin... @@ -12,11 +12,11 @@ Make sure you have: There are multiple reasons: -1. **Cutting edge data science**: R is an open-source language that is known for its active user community and a wide range of packages that together enable the quick and effective implementation of data science techniques +1. **Cutting edge data science**: R is an open-source language that is known for its active user community and a wide range of packages that together enable the quick and effective implementation of data science techniques. 2. **Reproducibility**: Code-based workflows help facilitate reproducible analysis, which is the notion that analysis should be built in a way that is replicable by others. R as a tool promotes this good practice. 3. **Efficiency / scalability**: R scales relatively well in the context of large datasets. The application of functions and automated processes also help cut down routine analysis time 4. **Integration**: If you already use R as part of your analysis toolkit, adopting this package as part of the workflow will be seamless and easy -5. **Go beyond basic reporting**: One of the most appealing feature of R is the access it offers to a wide range of packages. For instance, clustering and text mining can be done very easily as part of a R workflow – which are both available in this package +5. **Extensibility**: One of the most appealing feature of R is the access it offers to a wide range of packages. For instance, clustering and text mining can be done very easily as part of a R workflow – which are both available in this package ## Analyst Guide Contents @@ -35,6 +35,7 @@ To get the most out of **wpa**, make sure to leaverage these additional resource 1. Our official [**wpa** cheat sheet](https://github.com/microsoft/wpa/blob/main/man/figures/wpa%20cheatsheet_20201116.pdf). 2. A growing list of [articles](https://microsoft.github.io/wpa/articles/) with detailed walkthroughs, written by multiple contributors. +## Ready to Go? -Continue to the [**Getting Started**](analyst_guide_getting_started.html) section. +Let's get going, with the [**Getting Started**](analyst_guide_getting_started.html) section. From 03f62745ea924ccf4b49a00d0cbe724b022903c2 Mon Sep 17 00:00:00 2001 From: moralec Date: Mon, 30 Nov 2020 14:44:54 +0000 Subject: [PATCH 084/192] Update analyst_guide_getting_started.md --- .github/analyst_guide_getting_started.md | 44 +++++++++++++----------- 1 file changed, 23 insertions(+), 21 deletions(-) diff --git a/.github/analyst_guide_getting_started.md b/.github/analyst_guide_getting_started.md index 33cf4eed..57552edc 100644 --- a/.github/analyst_guide_getting_started.md +++ b/.github/analyst_guide_getting_started.md @@ -1,9 +1,9 @@ # Getting Started -This section contains the detailed installation instructions, and a first introduction on how functions work in the **wpa** package. +This section contains the detailed installation instructions, and a first overview on how functions work in the **wpa** package. ## Installation -The latest stable version of **wpa** is available in Github. You can automatically download and install the latest version it in your local device, by using the following script in R to: +The latest stable version of **wpa** is available in Github. You can automatically download and install it in your local device, by running the following code in R: ```R # Check if devtools is installed, if not then install it @@ -14,10 +14,10 @@ devtools::install_git(url = "https://github.com/microsoft/wpa.git") ``` -This package is not yet released on CRAN, and therefore `install.packages()` will not work. If you prefer to do a local installation, you can download a installation file [here](https://github.com/microsoft/wpa/releases). +This package is not yet released on CRAN, and therefore `install.packages()` will not work. If you prefer to proceed with a local installation, you can download a installation file [here](https://github.com/microsoft/wpa/releases). ## Loading the wpa package -One the installation is complete, you can load the package with: +Once the installation is complete, you can load the package with: ```R library(wpa) @@ -25,14 +25,14 @@ library(wpa) You only need to install the package once; however, you will need to load it every time you start a new R session. -**wpa** is designed to work side by side with other Data Science R packages from [tidyverse](https://www.tidyverse.org/). We generally recommend to load that package too. +**wpa** is designed to work side by side with other Data Science R packages from [tidyverse](https://www.tidyverse.org/). We generally recommend to load that package too: ```R library(tidyverse) ``` ## Importing Workplace Analytics Data -All functions in **wpa** are designed to work with query data from Workplace Analytics. To read data into R, you can use the [import_wpa()](https://microsoft.github.io/wpa/reference/import_wpa.html) function, that accepts any query file in .CSV format. +All functions in **wpa** exectute operations on the top of query data from Workplace Analytics. To read data into R, you can use the [import_wpa()](https://microsoft.github.io/wpa/reference/import_wpa.html) function. This funciton accepts any query file in .CSV format. Assuming you have a file called *myquery.csv* on your desktop, you can import it into R using: @@ -41,12 +41,12 @@ setwd("C:/Users/myuser/Desktop/") person_data <- import_wpa("myquery.csv") ``` -In the code above, [setwd()](https://stat.ethz.ch/R-manual/R-patched/library/base/html/getwd.html) will set the working directory to the Desktop, then [import_wpa()](https://microsoft.github.io/wpa/reference/import_wpa.html) will read the source CSV that is there, and the contents will be saved to the object person_data (using <- an [Assignment Operator](https://stat.ethz.ch/R-manual/R-devel/library/base/html/assignOps.html)). +In the code above, [setwd()](https://stat.ethz.ch/R-manual/R-patched/library/base/html/getwd.html) will set the working directory to the Desktop, then [import_wpa()](https://microsoft.github.io/wpa/reference/import_wpa.html) will read the source CSV. The contents will be saved to the object person_data (using "<-" as an [Assignment Operator](https://stat.ethz.ch/R-manual/R-devel/library/base/html/assignOps.html)). -The [import_wpa()](https://microsoft.github.io/wpa/reference/import_wpa.html) function works with all workplace analytics query types. +[import_wpa()](https://microsoft.github.io/wpa/reference/import_wpa.html) works with all workplace analytics query types. ## Demo Data -**wpa** includes a set of pre-loaded Workplace Analytics datasets that you can use to explore the functionality of this package. We will also use them extensively in this guide. The included data frames are: +The **wpa** package includes a set of demo Workplace Analytics datasets that you can use to explore the functionality of this package. We will also use them extensively in this guide. The included datasets are: 1. *sq_data*: A Standard Person Query 2. *dv_data*: A Standard Person Query with outliers @@ -55,38 +55,37 @@ The [import_wpa()](https://microsoft.github.io/wpa/reference/import_wpa.html) fu 5. *g2g_data*: A Group-to-Group Query ## Exploring a Person Query -We can explore a person query using some of the basic functions from **wpa**. The function [analysis_scope()](https://microsoft.github.io/wpa/reference/hrvar_count.html) can help you to create a basic bar plot, with the count of the distinct individuals by a specified group / HR attribute. +We can explore the sq_data person query using the [analysis_scope()](https://microsoft.github.io/wpa/reference/hrvar_count.html) function from **wpa**. This function create a basic bar plot, with the count of the distinct individuals within a specified group (an HR attribute in your query). -For example, if we want to know the number of individuals per organization, you can use: +For example, if we want to know the number of individuals in sq_data per organization, we can use: ```R analysis_scope(sq_data, hrvar = "Organization") ``` -The [analysis_scope()](https://microsoft.github.io/wpa/reference/hrvar_count.html) function requires you to provide the person query to be used (sq_data) and the HR variable that you will like to cut the data by (hrvar). As we decided to cut the data by organisation, the resulting bar chart will show the number of individuals per organization. +The [analysis_scope()](https://microsoft.github.io/wpa/reference/hrvar_count.html) function requires you to provide a person query (sq_data) and the HR variable that you will be used to group the data (hrvar). As we have specifified the Organization attribute, the resulting bar chart will show the number of individuals per organization. + +The same R code can be written using a Forward-Pipe Operator (%>%) to feed our query into a funciton. The notation is common in R data science applications, and is the one we will use moving forward. -The same comman can also be written using pipes (%>%) to feed a speciifc data frame into a funciton. ```R sq_data %>% analysis_scope(hrvar = "Organization") ``` -The notation above is common in tidyverse, and is the one we will use moving forward. - -Let's now use the [analysis_scope()](https://microsoft.github.io/wpa/reference/hrvar_count.html) function to explore of other groups. For example, for LevelDesignation: +Let's now use the [analysis_scope()](https://microsoft.github.io/wpa/reference/hrvar_count.html) function to explore of other groups. For example: ```R sq_data %>% analysis_scope(hrvar = "LevelDesignation") +sq_data %>% analysis_scope(hrvar = "TimeZone") ``` -Combining this function with the [filter()](https://dplyr.tidyverse.org/reference/filter.html) function from tiyverse, allows us to drill into a specific subset of the data. This is where pipes become powerful, as we can write a single line that takes the original data, applies a filter, and then creates the plot: - +We can combine the [analysis_scope()](https://microsoft.github.io/wpa/reference/hrvar_count.html) function with the [filter()](https://dplyr.tidyverse.org/reference/filter.html) function from tiyverse. This will allows us to drill into a specific subset of the data. This is where the Forward-Pipe Operators (%>%) become very useful, as we can write a single line that takes the original data, applies a filter, and then creates the plot: ```R sq_data %>% filter(LevelDesignation=="Support") %>% analysis_scope(hrvar = "Organization") ``` -The default behaviour of most functions in **wpa** is to create a plot. However, this can be altered by adding a `return` argument. If you use `return`="table", the function will now produce a table / data frame with the relevant summary statistics. +Most functions in **wpa** create plot by default, but can also return tables by adding a `return` argument. If you add `return`="table" to the [analysis_scope()](https://microsoft.github.io/wpa/reference/hrvar_count.html) function it will now produce a table with the count of the distinct individuals by group. ```R sq_data %>% analysis_scope(hrvar="LevelDesignation", return="table") @@ -97,6 +96,8 @@ All functions in **wpa** follow a similar behaviour, including many common argum +So far we have explored the `hrvar` and `return` arguments. We will use the `mingroup` in the next section. + ## Exporting Plots and Tables Tables and plots can be saved with the [export()](https://microsoft.github.io/wpa/reference/export.html) function. This functions allows you to save plots and tables into your local drive: @@ -107,7 +108,7 @@ sq_data %>% analysis_scope(hrvar = "Organization") %>% export() ## Summary: 4 simple steps from data to output -The examples above illustrate how the use of **wpa** can be summarized in 4 simple steps: Load the package, read-in query data, run functions and export results: +The examples above illustrate how the use of **wpa** can be summarized in 4 simple steps: Load the package, read-in query data, run functions and export results. The script below illustrates this funcitonality: ```R library(wpa) # Step 1 @@ -120,5 +121,6 @@ person_data %>% analysis_scope() %>% export() # Step 4 ``` +## Ready to Learn More? -Continue to the [**Summary Functions**](analyst_guide_summary.html) section, to explore how we can analyse different Workplace Analytics Metrics in the same way. +Let's go to the [**Summary Functions**](analyst_guide_summary.html) section, to see how we can analyse different Workplace Analytics Metrics in a similar way. From 27d3bfefa36cf66b3fbf2b0e961d5312d3b72cdc Mon Sep 17 00:00:00 2001 From: moralec Date: Mon, 30 Nov 2020 14:51:59 +0000 Subject: [PATCH 085/192] Update analyst_guide_getting_started.md --- .github/analyst_guide_getting_started.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/.github/analyst_guide_getting_started.md b/.github/analyst_guide_getting_started.md index 57552edc..5c577a33 100644 --- a/.github/analyst_guide_getting_started.md +++ b/.github/analyst_guide_getting_started.md @@ -32,7 +32,7 @@ library(tidyverse) ``` ## Importing Workplace Analytics Data -All functions in **wpa** exectute operations on the top of query data from Workplace Analytics. To read data into R, you can use the [import_wpa()](https://microsoft.github.io/wpa/reference/import_wpa.html) function. This funciton accepts any query file in .CSV format. +To read data into R, you can use the [import_wpa()](https://microsoft.github.io/wpa/reference/import_wpa.html) function. This funciton accepts any query file in .CSV format. Assuming you have a file called *myquery.csv* on your desktop, you can import it into R using: @@ -43,7 +43,6 @@ person_data <- import_wpa("myquery.csv") In the code above, [setwd()](https://stat.ethz.ch/R-manual/R-patched/library/base/html/getwd.html) will set the working directory to the Desktop, then [import_wpa()](https://microsoft.github.io/wpa/reference/import_wpa.html) will read the source CSV. The contents will be saved to the object person_data (using "<-" as an [Assignment Operator](https://stat.ethz.ch/R-manual/R-devel/library/base/html/assignOps.html)). -[import_wpa()](https://microsoft.github.io/wpa/reference/import_wpa.html) works with all workplace analytics query types. ## Demo Data The **wpa** package includes a set of demo Workplace Analytics datasets that you can use to explore the functionality of this package. We will also use them extensively in this guide. The included datasets are: From 475a6533849d6383ea2b14c3aebb95eef6e586f5 Mon Sep 17 00:00:00 2001 From: moralec Date: Mon, 30 Nov 2020 14:55:14 +0000 Subject: [PATCH 086/192] Update analyst_guide_getting_started.md --- .github/analyst_guide_getting_started.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/.github/analyst_guide_getting_started.md b/.github/analyst_guide_getting_started.md index 5c577a33..fb55a4d7 100644 --- a/.github/analyst_guide_getting_started.md +++ b/.github/analyst_guide_getting_started.md @@ -62,7 +62,7 @@ For example, if we want to know the number of individuals in sq_data per organiz analysis_scope(sq_data, hrvar = "Organization") ``` -The [analysis_scope()](https://microsoft.github.io/wpa/reference/hrvar_count.html) function requires you to provide a person query (sq_data) and the HR variable that you will be used to group the data (hrvar). As we have specifified the Organization attribute, the resulting bar chart will show the number of individuals per organization. +This function requires you to provide a person query (sq_data) and the HR variable that you will be used to group the data (hrvar). As we have specifified the Organization attribute, the resulting bar chart will show the number of individuals per organization. The same R code can be written using a Forward-Pipe Operator (%>%) to feed our query into a funciton. The notation is common in R data science applications, and is the one we will use moving forward. @@ -71,20 +71,20 @@ The same R code can be written using a Forward-Pipe Operator (%>%) to feed our q sq_data %>% analysis_scope(hrvar = "Organization") ``` -Let's now use the [analysis_scope()](https://microsoft.github.io/wpa/reference/hrvar_count.html) function to explore of other groups. For example: +Let's now use this function to explore of other groups. For example: ```R sq_data %>% analysis_scope(hrvar = "LevelDesignation") sq_data %>% analysis_scope(hrvar = "TimeZone") ``` -We can combine the [analysis_scope()](https://microsoft.github.io/wpa/reference/hrvar_count.html) function with the [filter()](https://dplyr.tidyverse.org/reference/filter.html) function from tiyverse. This will allows us to drill into a specific subset of the data. This is where the Forward-Pipe Operators (%>%) become very useful, as we can write a single line that takes the original data, applies a filter, and then creates the plot: +We can combine analysis_scope() with the [filter()](https://dplyr.tidyverse.org/reference/filter.html) function from tiyverse. This will allows us to drill into a specific subset of the data. This is where the Forward-Pipe Operators (%>%) become very useful, as we can write a single line that takes the original data, applies a filter, and then creates the plot: ```R sq_data %>% filter(LevelDesignation=="Support") %>% analysis_scope(hrvar = "Organization") ``` -Most functions in **wpa** create plot by default, but can also return tables by adding a `return` argument. If you add `return`="table" to the [analysis_scope()](https://microsoft.github.io/wpa/reference/hrvar_count.html) function it will now produce a table with the count of the distinct individuals by group. +Most functions in **wpa** create plot by default, but can change their behaviour by adding a `return` argument. If you add `return`="table" to this function it will now produce a table with the count of the distinct individuals by group. ```R sq_data %>% analysis_scope(hrvar="LevelDesignation", return="table") From bd24e3a2d948ce1257a647007c1eb322bd3e8d16 Mon Sep 17 00:00:00 2001 From: moralec Date: Mon, 30 Nov 2020 14:57:17 +0000 Subject: [PATCH 087/192] Update analyst_guide_getting_started.md --- .github/analyst_guide_getting_started.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/.github/analyst_guide_getting_started.md b/.github/analyst_guide_getting_started.md index fb55a4d7..5b59cc96 100644 --- a/.github/analyst_guide_getting_started.md +++ b/.github/analyst_guide_getting_started.md @@ -98,14 +98,16 @@ All functions in **wpa** follow a similar behaviour, including many common argum So far we have explored the `hrvar` and `return` arguments. We will use the `mingroup` in the next section. ## Exporting Plots and Tables -Tables and plots can be saved with the [export()](https://microsoft.github.io/wpa/reference/export.html) function. This functions allows you to save plots and tables into your local drive: +Tables and plots can be saved with the [export()](https://microsoft.github.io/wpa/reference/export.html) function. This functions allows you to save plots and tables into your local drive. + +One again, adding an additional forward-Pipe operator we can write: ```R sq_data %>% analysis_scope(hrvar = "Organization") %>% export() ``` -## Summary: 4 simple steps from data to output +## Summary: Four simple steps from data to output The examples above illustrate how the use of **wpa** can be summarized in 4 simple steps: Load the package, read-in query data, run functions and export results. The script below illustrates this funcitonality: From 4700d6321c4ede85424b0ff5c814746b11322564 Mon Sep 17 00:00:00 2001 From: moralec Date: Mon, 30 Nov 2020 15:00:16 +0000 Subject: [PATCH 088/192] Update analyst_guide_getting_started.md --- .github/analyst_guide_getting_started.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/.github/analyst_guide_getting_started.md b/.github/analyst_guide_getting_started.md index 5b59cc96..ce72c216 100644 --- a/.github/analyst_guide_getting_started.md +++ b/.github/analyst_guide_getting_started.md @@ -1,4 +1,4 @@ -# Getting Started +# Getting started This section contains the detailed installation instructions, and a first overview on how functions work in the **wpa** package. ## Installation @@ -31,7 +31,7 @@ You only need to install the package once; however, you will need to load it eve library(tidyverse) ``` -## Importing Workplace Analytics Data +## Importing Workplace Analytics data To read data into R, you can use the [import_wpa()](https://microsoft.github.io/wpa/reference/import_wpa.html) function. This funciton accepts any query file in .CSV format. Assuming you have a file called *myquery.csv* on your desktop, you can import it into R using: @@ -44,7 +44,7 @@ person_data <- import_wpa("myquery.csv") In the code above, [setwd()](https://stat.ethz.ch/R-manual/R-patched/library/base/html/getwd.html) will set the working directory to the Desktop, then [import_wpa()](https://microsoft.github.io/wpa/reference/import_wpa.html) will read the source CSV. The contents will be saved to the object person_data (using "<-" as an [Assignment Operator](https://stat.ethz.ch/R-manual/R-devel/library/base/html/assignOps.html)). -## Demo Data +## Demo data The **wpa** package includes a set of demo Workplace Analytics datasets that you can use to explore the functionality of this package. We will also use them extensively in this guide. The included datasets are: 1. *sq_data*: A Standard Person Query @@ -53,7 +53,7 @@ The **wpa** package includes a set of demo Workplace Analytics datasets that you 4. *em_data*: An Hourly Collaboration Query 5. *g2g_data*: A Group-to-Group Query -## Exploring a Person Query +## Exploring a person Query We can explore the sq_data person query using the [analysis_scope()](https://microsoft.github.io/wpa/reference/hrvar_count.html) function from **wpa**. This function create a basic bar plot, with the count of the distinct individuals within a specified group (an HR attribute in your query). For example, if we want to know the number of individuals in sq_data per organization, we can use: @@ -89,7 +89,7 @@ Most functions in **wpa** create plot by default, but can change their behaviour ```R sq_data %>% analysis_scope(hrvar="LevelDesignation", return="table") ``` -## Function Structure and additional parameters +## Function structure and additional parameters All functions in **wpa** follow a similar behaviour, including many common arguments. The following illustrates the basic API of standard analysis functions: @@ -107,7 +107,7 @@ sq_data %>% analysis_scope(hrvar = "Organization") %>% export() ``` -## Summary: Four simple steps from data to output +## Summary: Four steps from data to output The examples above illustrate how the use of **wpa** can be summarized in 4 simple steps: Load the package, read-in query data, run functions and export results. The script below illustrates this funcitonality: @@ -122,6 +122,6 @@ person_data %>% analysis_scope() %>% export() # Step 4 ``` -## Ready to Learn More? +## Ready to learn more? Let's go to the [**Summary Functions**](analyst_guide_summary.html) section, to see how we can analyse different Workplace Analytics Metrics in a similar way. From a23a9cb30fe6e9351e52c0ae1351b06c549b6232 Mon Sep 17 00:00:00 2001 From: moralec Date: Mon, 30 Nov 2020 15:01:25 +0000 Subject: [PATCH 089/192] Update analyst_guide.md --- .github/analyst_guide.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/.github/analyst_guide.md b/.github/analyst_guide.md index 59a27452..c16ff539 100644 --- a/.github/analyst_guide.md +++ b/.github/analyst_guide.md @@ -18,7 +18,7 @@ There are multiple reasons: 4. **Integration**: If you already use R as part of your analysis toolkit, adopting this package as part of the workflow will be seamless and easy 5. **Extensibility**: One of the most appealing feature of R is the access it offers to a wide range of packages. For instance, clustering and text mining can be done very easily as part of a R workflow – which are both available in this package -## Analyst Guide Contents +## Guide contents This guide is organized in five key sections: @@ -28,14 +28,14 @@ This guide is organized in five key sections: 4. [**Trend Functions**](analyst_guide_trend.html): This section explains functions that explore time dyanmics across a wide range of metrics. 5. [**Network Functions**](analyst_guide_network.html): This section explores functions that help you plot and analyse networks. -## Additional Resources +## Additional resources To get the most out of **wpa**, make sure to leaverage these additional resources: 1. Our official [**wpa** cheat sheet](https://github.com/microsoft/wpa/blob/main/man/figures/wpa%20cheatsheet_20201116.pdf). 2. A growing list of [articles](https://microsoft.github.io/wpa/articles/) with detailed walkthroughs, written by multiple contributors. -## Ready to Go? +## Ready to go? -Let's get going, with the [**Getting Started**](analyst_guide_getting_started.html) section. +Let's being, with the [**Getting Started**](analyst_guide_getting_started.html) section. From c2f10d87d17cac1d21447c5fa47f6112e6ea2bfa Mon Sep 17 00:00:00 2001 From: moralec Date: Mon, 30 Nov 2020 15:04:27 +0000 Subject: [PATCH 090/192] Update analyst_guide_getting_started.md --- .github/analyst_guide_getting_started.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/analyst_guide_getting_started.md b/.github/analyst_guide_getting_started.md index ce72c216..929fe036 100644 --- a/.github/analyst_guide_getting_started.md +++ b/.github/analyst_guide_getting_started.md @@ -89,7 +89,7 @@ Most functions in **wpa** create plot by default, but can change their behaviour ```R sq_data %>% analysis_scope(hrvar="LevelDesignation", return="table") ``` -## Function structure and additional parameters +## Function structure All functions in **wpa** follow a similar behaviour, including many common arguments. The following illustrates the basic API of standard analysis functions: From bc16da4b72c655e16c80174f62a398126c04ec52 Mon Sep 17 00:00:00 2001 From: moralec Date: Mon, 30 Nov 2020 15:16:22 +0000 Subject: [PATCH 091/192] Update analyst_guide_getting_started.md --- .github/analyst_guide_getting_started.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/analyst_guide_getting_started.md b/.github/analyst_guide_getting_started.md index 929fe036..6111fda8 100644 --- a/.github/analyst_guide_getting_started.md +++ b/.github/analyst_guide_getting_started.md @@ -97,7 +97,7 @@ All functions in **wpa** follow a similar behaviour, including many common argum So far we have explored the `hrvar` and `return` arguments. We will use the `mingroup` in the next section. -## Exporting Plots and Tables +## Exporting plots and tables Tables and plots can be saved with the [export()](https://microsoft.github.io/wpa/reference/export.html) function. This functions allows you to save plots and tables into your local drive. One again, adding an additional forward-Pipe operator we can write: From f90cf1393ab00c0baa36606de654cab18ce5a9cd Mon Sep 17 00:00:00 2001 From: moralec Date: Mon, 30 Nov 2020 15:16:55 +0000 Subject: [PATCH 092/192] Update analyst_guide_getting_started.md --- .github/analyst_guide_getting_started.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/analyst_guide_getting_started.md b/.github/analyst_guide_getting_started.md index 6111fda8..dcefa572 100644 --- a/.github/analyst_guide_getting_started.md +++ b/.github/analyst_guide_getting_started.md @@ -53,7 +53,7 @@ The **wpa** package includes a set of demo Workplace Analytics datasets that you 4. *em_data*: An Hourly Collaboration Query 5. *g2g_data*: A Group-to-Group Query -## Exploring a person Query +## Exploring a person query We can explore the sq_data person query using the [analysis_scope()](https://microsoft.github.io/wpa/reference/hrvar_count.html) function from **wpa**. This function create a basic bar plot, with the count of the distinct individuals within a specified group (an HR attribute in your query). For example, if we want to know the number of individuals in sq_data per organization, we can use: From 9aa6125bfd042a648d7991be8dced3cf4292e8a1 Mon Sep 17 00:00:00 2001 From: moralec Date: Mon, 30 Nov 2020 15:20:21 +0000 Subject: [PATCH 093/192] Update analyst_guide_getting_started.md --- .github/analyst_guide_getting_started.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/analyst_guide_getting_started.md b/.github/analyst_guide_getting_started.md index dcefa572..96aaa79e 100644 --- a/.github/analyst_guide_getting_started.md +++ b/.github/analyst_guide_getting_started.md @@ -54,7 +54,7 @@ The **wpa** package includes a set of demo Workplace Analytics datasets that you 5. *g2g_data*: A Group-to-Group Query ## Exploring a person query -We can explore the sq_data person query using the [analysis_scope()](https://microsoft.github.io/wpa/reference/hrvar_count.html) function from **wpa**. This function create a basic bar plot, with the count of the distinct individuals within a specified group (an HR attribute in your query). +We can explore the sq_data person query using the [analysis_scope()](https://microsoft.github.io/wpa/reference/hrvar_count.html) function. This function create a basic bar plot, with the count of the distinct individuals for different group (groups defined by an HR attribute in your query). For example, if we want to know the number of individuals in sq_data per organization, we can use: @@ -62,9 +62,9 @@ For example, if we want to know the number of individuals in sq_data per organiz analysis_scope(sq_data, hrvar = "Organization") ``` -This function requires you to provide a person query (sq_data) and the HR variable that you will be used to group the data (hrvar). As we have specifified the Organization attribute, the resulting bar chart will show the number of individuals per organization. +This function requires you to provide a person query (sq_data) and specify which HR variable will be used to slice the data (hrvar). As we have indicated that the Organization attribute should be used, the resulting bar chart will show the number of individuals for each organization in the database. -The same R code can be written using a Forward-Pipe Operator (%>%) to feed our query into a funciton. The notation is common in R data science applications, and is the one we will use moving forward. +The same R code can be written using a Forward-Pipe Operator (%>%) to feed our query into the funciton. The notation is common in R data science applications, and is the one we will use moving forward. ```R From 7550587af2372fc9982c26d10e97b49fa1bb872a Mon Sep 17 00:00:00 2001 From: moralec Date: Mon, 30 Nov 2020 15:21:30 +0000 Subject: [PATCH 094/192] Update analyst_guide_getting_started.md --- .github/analyst_guide_getting_started.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/analyst_guide_getting_started.md b/.github/analyst_guide_getting_started.md index 96aaa79e..3c9feb18 100644 --- a/.github/analyst_guide_getting_started.md +++ b/.github/analyst_guide_getting_started.md @@ -78,7 +78,7 @@ sq_data %>% analysis_scope(hrvar = "LevelDesignation") sq_data %>% analysis_scope(hrvar = "TimeZone") ``` -We can combine analysis_scope() with the [filter()](https://dplyr.tidyverse.org/reference/filter.html) function from tiyverse. This will allows us to drill into a specific subset of the data. This is where the Forward-Pipe Operators (%>%) become very useful, as we can write a single line that takes the original data, applies a filter, and then creates the plot: +We can expand this analysis by using the [filter()](https://dplyr.tidyverse.org/reference/filter.html) function from tiyverse. This will allows us to drill into a specific subset of the data. This is where the Forward-Pipe Operators (%>%) become very useful, as we can write a single line that takes the original data, applies a filter, and then creates the plot: ```R sq_data %>% filter(LevelDesignation=="Support") %>% analysis_scope(hrvar = "Organization") From 59f4d6f5094a42f1476a3064e5b0cfa41c60b2eb Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Mon, 30 Nov 2020 15:43:44 +0000 Subject: [PATCH 095/192] docs: change to analyst_guide.md Proof-reading and make IDE optional --- .github/analyst_guide.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/.github/analyst_guide.md b/.github/analyst_guide.md index c16ff539..c5d567d5 100644 --- a/.github/analyst_guide.md +++ b/.github/analyst_guide.md @@ -6,7 +6,9 @@ Welcome to the Analyst Guide of the **wpa** R package. This document will guide Make sure you have: 1. Analyst access to [Workplace Analytics](https://www.microsoft.com/en-us/microsoft-365/business/workplace-analytics) -2. Either [R](https://www.r-project.org/) or [RStudio Desktop](https://rstudio.com/products/rstudio/download/#download) installed in your local device +2. [R](https://www.r-project.org/) installed in your local device. + +An IDE is optional, but we recommend either [RStudio Desktop](https://rstudio.com/products/rstudio/download/#download) or [VS Code](https://code.visualstudio.com/). ## Why use R for Workplace Analytics? @@ -37,5 +39,5 @@ To get the most out of **wpa**, make sure to leaverage these additional resource ## Ready to go? -Let's being, with the [**Getting Started**](analyst_guide_getting_started.html) section. +Let's begin with the [**Getting Started**](analyst_guide_getting_started.html) section. From 293c330a183feced70260277aab8853a9f05b9d6 Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Mon, 30 Nov 2020 15:50:16 +0000 Subject: [PATCH 096/192] docs: update Getting Started guide - Use auto-linking by pkgdown instead of explicit links - Proof-reading --- .github/analyst_guide_getting_started.md | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/.github/analyst_guide_getting_started.md b/.github/analyst_guide_getting_started.md index 3c9feb18..4df7a629 100644 --- a/.github/analyst_guide_getting_started.md +++ b/.github/analyst_guide_getting_started.md @@ -32,7 +32,7 @@ library(tidyverse) ``` ## Importing Workplace Analytics data -To read data into R, you can use the [import_wpa()](https://microsoft.github.io/wpa/reference/import_wpa.html) function. This funciton accepts any query file in .CSV format. +To read data into R, you can use the `import_wpa()` function. This function accepts any query file in CSV format and performs variable type conversions optimized for Workplace Analytics. Assuming you have a file called *myquery.csv* on your desktop, you can import it into R using: @@ -41,7 +41,7 @@ setwd("C:/Users/myuser/Desktop/") person_data <- import_wpa("myquery.csv") ``` -In the code above, [setwd()](https://stat.ethz.ch/R-manual/R-patched/library/base/html/getwd.html) will set the working directory to the Desktop, then [import_wpa()](https://microsoft.github.io/wpa/reference/import_wpa.html) will read the source CSV. The contents will be saved to the object person_data (using "<-" as an [Assignment Operator](https://stat.ethz.ch/R-manual/R-devel/library/base/html/assignOps.html)). +In the code above, `set_wd()` will set the working directory to the Desktop, then `import_wpa()` will read the source CSV. The contents will be saved to the object person_data (using `<-` as an [Assignment Operator](https://stat.ethz.ch/R-manual/R-devel/library/base/html/assignOps.html)). ## Demo data @@ -54,17 +54,17 @@ The **wpa** package includes a set of demo Workplace Analytics datasets that you 5. *g2g_data*: A Group-to-Group Query ## Exploring a person query -We can explore the sq_data person query using the [analysis_scope()](https://microsoft.github.io/wpa/reference/hrvar_count.html) function. This function create a basic bar plot, with the count of the distinct individuals for different group (groups defined by an HR attribute in your query). +We can explore the sq_data person query using the `analysis_scope()` function. This function create a basic bar plot, with the count of the distinct individuals for different group (groups defined by an HR attribute in your query). -For example, if we want to know the number of individuals in sq_data per organization, we can use: +For example, if we want to know the number of individuals in `sq_data` per organization, we can use: ```R analysis_scope(sq_data, hrvar = "Organization") ``` -This function requires you to provide a person query (sq_data) and specify which HR variable will be used to slice the data (hrvar). As we have indicated that the Organization attribute should be used, the resulting bar chart will show the number of individuals for each organization in the database. +This function requires you to provide a person query (`sq_data`) and specify which HR variable will be used to slice the data (`hrvar`). As we have indicated that the `Organization` attribute should be used, the resulting bar chart will show the number of individuals for each organization in the database. -The same R code can be written using a Forward-Pipe Operator (%>%) to feed our query into the funciton. The notation is common in R data science applications, and is the one we will use moving forward. +The same R code can be written using a Forward-Pipe Operator (`%>%`) to feed our query into the funciton. The notation is common in R data science applications, and is the one we will use moving forward. ```R @@ -78,13 +78,13 @@ sq_data %>% analysis_scope(hrvar = "LevelDesignation") sq_data %>% analysis_scope(hrvar = "TimeZone") ``` -We can expand this analysis by using the [filter()](https://dplyr.tidyverse.org/reference/filter.html) function from tiyverse. This will allows us to drill into a specific subset of the data. This is where the Forward-Pipe Operators (%>%) become very useful, as we can write a single line that takes the original data, applies a filter, and then creates the plot: +We can expand this analysis by using the `dplyr::filter()` function from **dplyr**. This will allows us to drill into a specific subset of the data. This is where the Forward-Pipe Operators (`%>%`) become very useful, as we can write a single line that takes the original data, applies a filter, and then creates the plot: ```R -sq_data %>% filter(LevelDesignation=="Support") %>% analysis_scope(hrvar = "Organization") +sq_data %>% filter(LevelDesignation=="Support") %>% analysis_scope(hrvar = "Organization") ``` -Most functions in **wpa** create plot by default, but can change their behaviour by adding a `return` argument. If you add `return`="table" to this function it will now produce a table with the count of the distinct individuals by group. +Most functions in **wpa** create plot by default, but can change their behaviour by adding a `return` argument. If you add `return="table"` to this function it will now produce a table with the count of the distinct individuals by group. ```R sq_data %>% analysis_scope(hrvar="LevelDesignation", return="table") @@ -98,7 +98,7 @@ All functions in **wpa** follow a similar behaviour, including many common argum So far we have explored the `hrvar` and `return` arguments. We will use the `mingroup` in the next section. ## Exporting plots and tables -Tables and plots can be saved with the [export()](https://microsoft.github.io/wpa/reference/export.html) function. This functions allows you to save plots and tables into your local drive. +Tables and plots can be saved with the `export()` function. This functions allows you to save plots and tables into your local drive. One again, adding an additional forward-Pipe operator we can write: From 253b273ae7ca558831b5d159a1373fa2fa4488bc Mon Sep 17 00:00:00 2001 From: Martin Chan Date: Mon, 30 Nov 2020 18:28:30 +0000 Subject: [PATCH 097/192] chore: change style for contributors.yaml --- .github/workflows/contributors.yaml | 12 +----------- 1 file changed, 1 insertion(+), 11 deletions(-) diff --git a/.github/workflows/contributors.yaml b/.github/workflows/contributors.yaml index 2d231b94..66925406 100644 --- a/.github/workflows/contributors.yaml +++ b/.github/workflows/contributors.yaml @@ -12,14 +12,4 @@ jobs: - uses: bubkoo/contributors-list@v1 with: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} - svgTemplate: > - - - {{{ collaborators }}} - + round: true From b74a462d6c538a26764eb7da50eca90370e40b6f Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Mon, 30 Nov 2020 18:29:25 +0000 Subject: [PATCH 098/192] chore: update contributors [skip ci] --- CONTRIBUTORS.svg | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/CONTRIBUTORS.svg b/CONTRIBUTORS.svg index fb5827f4..b9eb5207 100644 --- a/CONTRIBUTORS.svg +++ b/CONTRIBUTORS.svg @@ -1,4 +1,9 @@ - + - + + + + + + \ No newline at end of file From 8f2fd1be648f002ea48b455eecd9c3837c9c0d3b Mon Sep 17 00:00:00 2001 From: moralec Date: Tue, 1 Dec 2020 14:29:04 +0000 Subject: [PATCH 099/192] Update analyst_guide_summary.md --- .github/analyst_guide_summary.md | 73 ++++++++++++++++++++------------ 1 file changed, 47 insertions(+), 26 deletions(-) diff --git a/.github/analyst_guide_summary.md b/.github/analyst_guide_summary.md index 585e863e..21a20c85 100644 --- a/.github/analyst_guide_summary.md +++ b/.github/analyst_guide_summary.md @@ -1,46 +1,52 @@ -# Summary +# Summary functions -**Summary functions** allow you to compare averages across organizational attributes. +This section describes the use of summary functions, that allow you to compare averages across organizational attributes. + + +## Summary of Key Metrics + +The `keymetrics_scan()` function allows you to produce a summary table with a wide range of metrics from the Standard Person Query data. Similar to most of the functions in this package, you can specify what output to obtain (either "plot" or "table") with the `return` argument . In addition, you have to specify which HR attribute/variable to use as a grouping variable with the `hrvar` argument. -An instance of a summary function in action would be: ```R +sq_data %>% keymetrics_scan(hrvar = "Organization", return = "plot") +sq_data %>% keymetrics_scan(hrvar = "Organization", return = "table") +``` + +## Collaboration Summary + +The `collaboration_summary()` function allows you to generate a stacked bar plot summarising the email and meeting hours by an HR attribute you specify (if none is specified, organization will be used by default) + +```{r} sq_data %>% collaboration_summary() ``` -This returns a plot that you can either as a SVG (vector) or PNG (scalar) image by passing the output to `export()`. Since the plot output is a ggplot object, you can also make further customizations to it by appending ggplot layers, for instance: -```R -sq_data %>% - collaboration_summary() + - ggtitle("This is a custom title") +By changing the `hrvar()` argument, you can change the data being shown easily: + +```{r} +sq_data %>% collaboration_summary(hrvar = "LevelDesignation") ``` -You can use return a summary table rather than a plot: - -```R -sq_data %>% collaboration_summary(return = "table") -``` - -To export the results to a clipboard, you can pass the outputs to `export()` again: - -```R -sq_data %>% - collaboration_summary(return = "table") %>% - export() -``` - -There is also an option to change the threshold for excluding group size: +By default, all summary functions exclude groups with less that five individuals. This is also something that can be adjusted, using the `mingroup()` argument: ``` sq_data %>% collaboration_sum(hrvar = "LevelDesignation", - mingroup = 10, - return = "table") + mingroup = 10) ``` +Finally, you can also use "table" in the `return` argument, to obtain summary table instead of a plot. The `export()` function will copy all contents to the clipboard. + +```{r} +sq_data %>% collaboration_summary(hrvar = "LevelDesignation", return = "table") +``` + + +## Other summary functions + Other similar functions include: - `email_summary()` @@ -49,5 +55,20 @@ Other similar functions include: - `workloads_summary()` - `afterhours_summary()` -See `create_bar()` for creating summary functions with other Workplace Analytics metrics. +All of these functions use Standard Person Query data, and accept `hrvar`, `return` and `mingroup` arguments. + +## Customizing plots + +All plots in *wpa* are [ggplot objects](https://rafalab.github.io/dsbook/ggplot2.html). This means you can also make further customizations to it by appending ggplot parameters and layers. For instance, you can customize the title of a `collaboration_summary()` plot: + +```R +sq_data %>% + collaboration_summary() + + ggtitle("This is a custom title") +``` + +## Going Beyond Averages? + +In the next section, we will explore how we can analyse distributions from different Workplace Analytics Metrics in a similar way. Let's continue to [**Distribution Functions**](analyst_guide_distribution.html). + From a3cab175d1fc53588462c8cc4f2754430ce165ad Mon Sep 17 00:00:00 2001 From: moralec Date: Tue, 1 Dec 2020 15:03:28 +0000 Subject: [PATCH 100/192] Update analyst_guide_summary.md --- .github/analyst_guide_summary.md | 11 ++++------- 1 file changed, 4 insertions(+), 7 deletions(-) diff --git a/.github/analyst_guide_summary.md b/.github/analyst_guide_summary.md index 21a20c85..cae269d3 100644 --- a/.github/analyst_guide_summary.md +++ b/.github/analyst_guide_summary.md @@ -3,7 +3,7 @@ This section describes the use of summary functions, that allow you to compare averages across organizational attributes. -## Summary of Key Metrics +## Averages for key metrics The `keymetrics_scan()` function allows you to produce a summary table with a wide range of metrics from the Standard Person Query data. Similar to most of the functions in this package, you can specify what output to obtain (either "plot" or "table") with the `return` argument . In addition, you have to specify which HR attribute/variable to use as a grouping variable with the `hrvar` argument. @@ -13,7 +13,7 @@ sq_data %>% keymetrics_scan(hrvar = "Organization", return = "plot") sq_data %>% keymetrics_scan(hrvar = "Organization", return = "table") ``` -## Collaboration Summary +## Email and meeting hours The `collaboration_summary()` function allows you to generate a stacked bar plot summarising the email and meeting hours by an HR attribute you specify (if none is specified, organization will be used by default) @@ -34,8 +34,7 @@ By default, all summary functions exclude groups with less that five individuals ``` sq_data %>% - collaboration_sum(hrvar = "LevelDesignation", - mingroup = 10) + collaboration_sum(hrvar = "LevelDesignation", mingroup = 10) ``` Finally, you can also use "table" in the `return` argument, to obtain summary table instead of a plot. The `export()` function will copy all contents to the clipboard. @@ -62,9 +61,7 @@ All of these functions use Standard Person Query data, and accept `hrvar`, `retu All plots in *wpa* are [ggplot objects](https://rafalab.github.io/dsbook/ggplot2.html). This means you can also make further customizations to it by appending ggplot parameters and layers. For instance, you can customize the title of a `collaboration_summary()` plot: ```R -sq_data %>% - collaboration_summary() + - ggtitle("This is a custom title") +sq_data %>% collaboration_summary() + ggtitle("This is a custom title") ``` ## Going Beyond Averages? From 429ae07c62eccf9eb19f9e00d618858f65575918 Mon Sep 17 00:00:00 2001 From: moralec Date: Tue, 1 Dec 2020 15:17:58 +0000 Subject: [PATCH 101/192] Update analyst_guide_summary.md --- .github/analyst_guide_summary.md | 44 ++++++++++++++++++++------------ 1 file changed, 27 insertions(+), 17 deletions(-) diff --git a/.github/analyst_guide_summary.md b/.github/analyst_guide_summary.md index cae269d3..9b7095da 100644 --- a/.github/analyst_guide_summary.md +++ b/.github/analyst_guide_summary.md @@ -1,21 +1,28 @@ # Summary functions -This section describes the use of summary functions, that allow you to compare averages across organizational attributes. +This section describes the use of summary functions. These functions allow you to compare averages for many Workplace Analytics metrics, across the groups defined by an organizational attribute. ## Averages for key metrics -The `keymetrics_scan()` function allows you to produce a summary table with a wide range of metrics from the Standard Person Query data. Similar to most of the functions in this package, you can specify what output to obtain (either "plot" or "table") with the `return` argument . In addition, you have to specify which HR attribute/variable to use as a grouping variable with the `hrvar` argument. - +The `keymetrics_scan()` function allows you to produce a summary table with a wide range of metrics from an Standard Person Query. Just like in the function we studied in the previous section, you can specify which HR attribute/variable to use as a grouping variable with the `hrvar` argument, and what output to obtain (either "plot" or "table") with the `return` argument. ```R sq_data %>% keymetrics_scan(hrvar = "Organization", return = "plot") sq_data %>% keymetrics_scan(hrvar = "Organization", return = "table") ``` -## Email and meeting hours +The resulting table, will provide a averages for 18 key Workplace Analytics Metrics. You can customised what specific indicators to include, with the `metrics` argument: -The `collaboration_summary()` function allows you to generate a stacked bar plot summarising the email and meeting hours by an HR attribute you specify (if none is specified, organization will be used by default) +```R +sq_data %>% keymetrics_scan(hrvar = "Organization", return = "plot", metrics= c("Workweek_span", "Collaboration_hours", "After_hours_collaboration_hours") +``` + +The `keymetrics_scan()` function is a great starting point for exploratory data analysis, before you dive deeper into particular metrics. + +## Average Email and meeting hours + +The `collaboration_summary()` function generates a stacked bar plot summarising the email and meeting hours by an HR attribute you specify. If no HR attribute is specified, "organization" will be used by default: ```{r} sq_data %>% collaboration_summary() @@ -23,8 +30,7 @@ sq_data %>% collaboration_summary() - -By changing the `hrvar()` argument, you can change the data being shown easily: +By changing the `hrvar()` argument, you can change the groups being shown easily: ```{r} sq_data %>% collaboration_summary(hrvar = "LevelDesignation") @@ -43,28 +49,32 @@ Finally, you can also use "table" in the `return` argument, to obtain summary ta sq_data %>% collaboration_summary(hrvar = "LevelDesignation", return = "table") ``` - ## Other summary functions -Other similar functions include: +The package includes a wide range of summary functions, that create bar plots for specific metrics. These include: -- `email_summary()` -- `meeting_summary()` -- `one2one_summary()` -- `workloads_summary()` -- `afterhours_summary()` +- `email_summary()`: Bar plot summarising email hours by an HR attribute. +- `meeting_summary()`: Bar plot summarising meeting hours by an HR attribute. +- `one2one_summary()`: Bar plot summarising manager one-to-one meeting hours, by an HR attribute. +- `workloads_summary()`: Bar plot summarising workweek span by an HR attribute. +- `afterhours_summary()`: Bar plot summarising after-hours collaboration hours by an HR attribute. -All of these functions use Standard Person Query data, and accept `hrvar`, `return` and `mingroup` arguments. +All of these functions work equivalently to the `collaboration_summary()` function: they use Standard Person Query data as an input, and accept `hrvar`, `return` and `mingroup` arguments. + +```{r} +sq_data %>% email_summary(hrvar = "LevelDesignation", return = "table") +sq_data %>% meeting_summary(hrvar = "Organization", return = "Plot", mingroup=10) +``` ## Customizing plots -All plots in *wpa* are [ggplot objects](https://rafalab.github.io/dsbook/ggplot2.html). This means you can also make further customizations to it by appending ggplot parameters and layers. For instance, you can customize the title of a `collaboration_summary()` plot: +All plots in *wpa* are [ggplot objects](https://rafalab.github.io/dsbook/ggplot2.html). This means you can customize them by adding ggplot arguments and layers. For instance, you can change the title of a `collaboration_summary()` plot: ```R sq_data %>% collaboration_summary() + ggtitle("This is a custom title") ``` -## Going Beyond Averages? +## Going beyond averages In the next section, we will explore how we can analyse distributions from different Workplace Analytics Metrics in a similar way. Let's continue to [**Distribution Functions**](analyst_guide_distribution.html). From b2c356f04c1189f381238bfeee9368bd04193ec5 Mon Sep 17 00:00:00 2001 From: moralec Date: Tue, 1 Dec 2020 15:29:26 +0000 Subject: [PATCH 102/192] Update analyst_guide_summary.md --- .github/analyst_guide_summary.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.github/analyst_guide_summary.md b/.github/analyst_guide_summary.md index 9b7095da..d4eb11c2 100644 --- a/.github/analyst_guide_summary.md +++ b/.github/analyst_guide_summary.md @@ -15,7 +15,8 @@ sq_data %>% keymetrics_scan(hrvar = "Organization", return = "table") The resulting table, will provide a averages for 18 key Workplace Analytics Metrics. You can customised what specific indicators to include, with the `metrics` argument: ```R -sq_data %>% keymetrics_scan(hrvar = "Organization", return = "plot", metrics= c("Workweek_span", "Collaboration_hours", "After_hours_collaboration_hours") +sq_data %>% keymetrics_scan(hrvar = "Organization", return = "plot", + metrics= c("Workweek_span", "Collaboration_hours", "After_hours_collaboration_hours") ``` The `keymetrics_scan()` function is a great starting point for exploratory data analysis, before you dive deeper into particular metrics. From 6d7bceec9ef70120b2c5e8c517bf31c1d1202438 Mon Sep 17 00:00:00 2001 From: moralec Date: Tue, 1 Dec 2020 15:39:22 +0000 Subject: [PATCH 103/192] Update analyst_guide_summary.md --- .github/analyst_guide_summary.md | 24 ++++++++++++++++++------ 1 file changed, 18 insertions(+), 6 deletions(-) diff --git a/.github/analyst_guide_summary.md b/.github/analyst_guide_summary.md index d4eb11c2..66b35ae4 100644 --- a/.github/analyst_guide_summary.md +++ b/.github/analyst_guide_summary.md @@ -21,7 +21,7 @@ sq_data %>% keymetrics_scan(hrvar = "Organization", return = "plot", The `keymetrics_scan()` function is a great starting point for exploratory data analysis, before you dive deeper into particular metrics. -## Average Email and meeting hours +## Average email and meeting hours The `collaboration_summary()` function generates a stacked bar plot summarising the email and meeting hours by an HR attribute you specify. If no HR attribute is specified, "organization" will be used by default: @@ -40,8 +40,7 @@ sq_data %>% collaboration_summary(hrvar = "LevelDesignation") By default, all summary functions exclude groups with less that five individuals. This is also something that can be adjusted, using the `mingroup()` argument: ``` -sq_data %>% - collaboration_sum(hrvar = "LevelDesignation", mingroup = 10) +sq_data %>% collaboration_sum(hrvar = "LevelDesignation", mingroup = 10) ``` Finally, you can also use "table" in the `return` argument, to obtain summary table instead of a plot. The `export()` function will copy all contents to the clipboard. @@ -62,10 +61,23 @@ The package includes a wide range of summary functions, that create bar plots fo All of these functions work equivalently to the `collaboration_summary()` function: they use Standard Person Query data as an input, and accept `hrvar`, `return` and `mingroup` arguments. -```{r} -sq_data %>% email_summary(hrvar = "LevelDesignation", return = "table") -sq_data %>% meeting_summary(hrvar = "Organization", return = "Plot", mingroup=10) +For example, + ``` +sq_data %>% afterhours_summary() +``` + + +## Flexible Function + +For other metrics (including custom ones) the `create_bar()` function is a good way to obtain a summary bar chart for any metric. This function requires you to include a character string containing the name of the metric you want to analyze, e.g. "Generated_workload_email_hours": + +``` +sq_data %>% create_bar(metric = "Generated_workload_email_hours") +``` + +The `create_bar()` function also accepts `hrvar`, `return` and `mingroup` arguments. + ## Customizing plots From da7b6455d7bc4bf62a9e7a78f3e2f89c58e6f772 Mon Sep 17 00:00:00 2001 From: moralec Date: Tue, 1 Dec 2020 15:41:20 +0000 Subject: [PATCH 104/192] Update analyst_guide_summary.md --- .github/analyst_guide_summary.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/analyst_guide_summary.md b/.github/analyst_guide_summary.md index 66b35ae4..7504257d 100644 --- a/.github/analyst_guide_summary.md +++ b/.github/analyst_guide_summary.md @@ -89,6 +89,6 @@ sq_data %>% collaboration_summary() + ggtitle("This is a custom title") ## Going beyond averages -In the next section, we will explore how we can analyse distributions from different Workplace Analytics Metrics in a similar way. Let's continue to [**Distribution Functions**](analyst_guide_distribution.html). + Let's continue to [**Distribution Functions**](analyst_guide_distribution.html) to explore how we can analyse distributions from different Workplace Analytics Metrics with unique functions. From 5fd730950c277131ec78e3fdd023073592ec7879 Mon Sep 17 00:00:00 2001 From: moralec Date: Tue, 1 Dec 2020 15:56:59 +0000 Subject: [PATCH 105/192] Update analyst_guide_summary.md --- .github/analyst_guide_summary.md | 63 ++++++++++++++++++++++++++++++++ 1 file changed, 63 insertions(+) diff --git a/.github/analyst_guide_summary.md b/.github/analyst_guide_summary.md index 7504257d..f5f5eab0 100644 --- a/.github/analyst_guide_summary.md +++ b/.github/analyst_guide_summary.md @@ -87,6 +87,69 @@ All plots in *wpa* are [ggplot objects](https://rafalab.github.io/dsbook/ggplot2 sq_data %>% collaboration_summary() + ggtitle("This is a custom title") ``` + +## Summary Functions: Gallery + + + + + + + +

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