diff --git a/R/workpatterns_classify_bw.R b/R/workpatterns_classify_bw.R
index e7532db1..62b20bf4 100644
--- a/R/workpatterns_classify_bw.R
+++ b/R/workpatterns_classify_bw.R
@@ -2,6 +2,7 @@
#' using the binary week-based (bw) method.
#'
#' @description
+#' `r lifecycle::badge('experimental')`
#' Apply a rule based algorithm to emails sent by hour of day,
#' using the binary week-based (bw) method.
#'
@@ -97,60 +98,6 @@ workpatterns_classify_bw <- function(data,
## 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(data2, hr = .)) %>%
- # dplyr::bind_cols()
- #
- # ## Use names for matching
- # input_var <- names(signal_cols)
- #
- # ## Signals sent by Person and date
- # signals_df <-
- # data2 %>%
- # .[, c("PersonId", "Date")] %>%
- # cbind(signal_cols)
- #
- # ## Signal label
- # sig_label <- "Signals_sent"
- #
- # } else if(signals == "IM"){
- #
- # match_index <- grepl(pattern = "^IMs_sent", x = names(data2))
- # input_var <- names(data2)[match_index]
- # input_var2 <- c("PersonId", "Date", input_var)
- #
- # ## signals sent by Person and date
- # signals_df <-
- # data2 %>%
- # .[, ..input_var2]
- #
- # sig_label <- "IMs_sent"
- #
- #
- # } else if(signals == "email"){
- #
- # match_index <- grepl(pattern = "^Emails_sent", x = names(data2))
- # input_var <- names(data2)[match_index]
- # input_var2 <- c("PersonId", "Date", input_var)
- #
- # ## signals sent by Person and date
- # signals_df <-
- # data2 %>%
- # .[, ..input_var2]
- #
- # sig_label <- "Emails_sent"
- #
- # } else {
- #
- # stop("Invalid input for `signals`.")
- #
- # }
-
-
## Create binary variable 0 or 1
num_cols <- names(which(sapply(signals_df, is.numeric))) # Get numeric columns
diff --git a/R/workpatterns_classify_pav.R b/R/workpatterns_classify_pav.R
index 58aed2b3..20268a1d 100644
--- a/R/workpatterns_classify_pav.R
+++ b/R/workpatterns_classify_pav.R
@@ -2,22 +2,29 @@
#' the person-average volume-based (pav) method.
#'
#' @description
+#' `r lifecycle::badge('experimental')`
#' Apply a rule based algorithm to emails or instant messages sent by hour of day.
#' This uses a person-average volume-based (pav) method.
#'
#' @param data A data frame containing data from the Hourly Collaboration query.
#'
-#' @param return Character vector to specify what to return.
-#' Valid options include "plot" (default), "data", "table" and "plot-area".
-#' "plot" returns a bar plot, whilst "plot-area" returns an overlapping area plot.
+#' @param return Character vector to specify what to return. Valid options include:
+#' - "plot": returns a bar plot of signal distribution by hour and archetypes (default)
+#' - "data": returns the raw data with the classified archetypes
+#' - "table": returns a summary table of the archetypes
+#' - "plot-area": returns an overlapping area plot
#'
#' @param values Character vector to specify whether to return percentages
-#' or absolute values in "data" and "plot". Valid values are "percent" (default)
-#' and "abs".
+#' or absolute values in "data" and "plot". Valid values are:
+#' - "percent": percentage of signals divided by total signals (default)
+#' - "abs": absolute count of signals
#'
#' @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")`.
+#' - "email" (default) for emails only
+#' - "IM" for Teams messages only,
+#' - "unscheduled_calls" for Unscheduled Calls only
+#' - "meetings" for Meetings only
+#' - or a combination of signals, such as `c("email", "IM")`
#'
#' @param start_hour A character vector specifying starting hours,
#' e.g. "0900"
@@ -29,13 +36,14 @@
#' @import ggplot2
#'
#' @examples
-#' \dontrun{
-#' # Returns a plot by default
-#' workpatterns_classify(em_data)
+#' ## Returns a plot by default
+#' workpatterns_classify_pav(em_data)
#'
-#' # Returning a table
-#' workpatterns_classify(em_data, return = "table")
-#' }
+#' ## Return a table
+#' workpatterns_classify_pav(em_data, return = "table")
+#'
+#' ## Return an area plot
+#' workpatterns_classify_pav(em_data, return = "plot-area")
#'
#' @family Work Patterns
#'
@@ -82,65 +90,6 @@ workpatterns_classify_pav <- function(data,
## 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 = .)) %>%
- # 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/R/workpatterns_hclust.R b/R/workpatterns_hclust.R
index 5cb28c55..f8b694c3 100644
--- a/R/workpatterns_hclust.R
+++ b/R/workpatterns_hclust.R
@@ -18,15 +18,19 @@
#'
#' @param data A data frame containing data from the Hourly Collaboration query.
#' @param k Numeric vector to specify the `k` number of clusters to cut by.
-#' @param return Character vector to specify what to return.
-#' Valid options include "plot" (default), "data", "table", "plot-area", "hclust", and "dist".
-#' "plot" returns a bar plot, whilst "plot-area" returns an overlapping area plot.
-#' "hclust" returns the hierarchical model generated by the function.
-#' "dist" returns the distance matrix used to build the clustering model.
+#' @param return Character vector to specify what to return. Valid options include:
+#' - "plot": returns a bar plot (default)
+#' - "data": returns raw data with the clusters
+#' - "table": returns a summary table
+#' - "plot-area": returns an overlapping area plot
+#' - "hclust": returns the hierarchical model generated by the function
+#' - "dist": returns the distance matrix used to build the clustering model
#'
#' @param values Character vector to specify whether to return percentages
-#' or absolute values in "data" and "plot". Valid values are "percent" (default)
-#' and "abs".
+#' or absolute values in "data" and "plot". Valid values are:
+#' - "percent": percentage of signals divided by total signals (default)
+#' - "abs": absolute count of signals
+#'
#' @param signals Character vector to specify which collaboration metrics to use:
#' - "email" (default) for emails only
#' - "IM" for Teams messages only
diff --git a/man/workpatterns_classify_bw.Rd b/man/workpatterns_classify_bw.Rd
index 9c0e36bf..44602926 100644
--- a/man/workpatterns_classify_bw.Rd
+++ b/man/workpatterns_classify_bw.Rd
@@ -39,6 +39,7 @@ Valid options include "plot" (default), "data", "table" and "plot-area".
"plot" returns a bar plot, whilst "plot-area" returns an overlapping area plot.}
}
\description{
+\ifelse{html}{\out{
}}{\strong{Experimental}}
Apply a rule based algorithm to emails sent by hour of day,
using the binary week-based (bw) method.
}
diff --git a/man/workpatterns_classify_pav.Rd b/man/workpatterns_classify_pav.Rd
index 1951b67f..99288fb8 100644
--- a/man/workpatterns_classify_pav.Rd
+++ b/man/workpatterns_classify_pav.Rd
@@ -18,12 +18,20 @@ workpatterns_classify_pav(
\item{data}{A data frame containing data from the Hourly Collaboration query.}
\item{values}{Character vector to specify whether to return percentages
-or absolute values in "data" and "plot". Valid values are "percent" (default)
-and "abs".}
+or absolute values in "data" and "plot". Valid values are:
+\itemize{
+\item "percent": percentage of signals divided by total signals (default)
+\item "abs": absolute count of signals
+}}
\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")}.}
+\itemize{
+\item "email" (default) for emails only
+\item "IM" for Teams messages only,
+\item "unscheduled_calls" for Unscheduled Calls only
+\item "meetings" for Meetings only
+\item or a combination of signals, such as \code{c("email", "IM")}
+}}
\item{start_hour}{A character vector specifying starting hours,
e.g. "0900"}
@@ -31,22 +39,28 @@ e.g. "0900"}
\item{end_hour}{A character vector specifying starting hours,
e.g. "1700"}
-\item{return}{Character vector to specify what to return.
-Valid options include "plot" (default), "data", "table" and "plot-area".
-"plot" returns a bar plot, whilst "plot-area" returns an overlapping area plot.}
+\item{return}{Character vector to specify what to return. Valid options include:
+\itemize{
+\item "plot": returns a bar plot of signal distribution by hour and archetypes (default)
+\item "data": returns the raw data with the classified archetypes
+\item "table": returns a summary table of the archetypes
+\item "plot-area": returns an overlapping area plot
+}}
}
\description{
+\ifelse{html}{\out{}}{\strong{Experimental}}
Apply a rule based algorithm to emails or instant messages sent by hour of day.
This uses a person-average volume-based (pav) method.
}
\examples{
-\dontrun{
-# Returns a plot by default
-workpatterns_classify(em_data)
+## Returns a plot by default
+workpatterns_classify_pav(em_data)
-# Returning a table
-workpatterns_classify(em_data, return = "table")
-}
+## Return a table
+workpatterns_classify_pav(em_data, return = "table")
+
+## Return an area plot
+workpatterns_classify_pav(em_data, return = "plot-area")
}
\seealso{
diff --git a/man/workpatterns_hclust.Rd b/man/workpatterns_hclust.Rd
index 7f198b88..a9f858b5 100644
--- a/man/workpatterns_hclust.Rd
+++ b/man/workpatterns_hclust.Rd
@@ -19,15 +19,22 @@ workpatterns_hclust(
\item{k}{Numeric vector to specify the \code{k} number of clusters to cut by.}
-\item{return}{Character vector to specify what to return.
-Valid options include "plot" (default), "data", "table", "plot-area", "hclust", and "dist".
-"plot" returns a bar plot, whilst "plot-area" returns an overlapping area plot.
-"hclust" returns the hierarchical model generated by the function.
-"dist" returns the distance matrix used to build the clustering model.}
+\item{return}{Character vector to specify what to return. Valid options include:
+\itemize{
+\item "plot": returns a bar plot (default)
+\item "data": returns raw data with the clusters
+\item "table": returns a summary table
+\item "plot-area": returns an overlapping area plot
+\item "hclust": returns the hierarchical model generated by the function
+\item "dist": returns the distance matrix used to build the clustering model
+}}
\item{values}{Character vector to specify whether to return percentages
-or absolute values in "data" and "plot". Valid values are "percent" (default)
-and "abs".}
+or absolute values in "data" and "plot". Valid values are:
+\itemize{
+\item "percent": percentage of signals divided by total signals (default)
+\item "abs": absolute count of signals
+}}
\item{signals}{Character vector to specify which collaboration metrics to use:
\itemize{