docs: adjust function family (#58)

- Adjust family
- Created two templates: ch.R and spq-params.R
- General code formatting

NAMESPACE

@@ -72,6 +72,7 @@ export(flag_outlooktime)
 export(flex_index)
 export(g2g_network)
 export(generate_report)
+export(heat_colors)
 export(heat_colours)
 export(hr_trend)
 export(hrvar_count)

R/IV_by_Period.R

@@ -3,27 +3,52 @@
 # Licensed under the MIT License. See LICENSE.txt in the project root for license information.
 # --------------------------------------------------------------------------------------------
 
-#' @title Identify the WPA metrics that have the biggest change between two periods.
+#' @title Identify the WPA metrics that have the biggest change between two
+#'   periods.
 #'
 #' @description
-#' This function uses the Information Value algorithm to predict which WPA metrics are most explained by the change in dates.
+#' `r lifecycle::badge('experimental')`
+#'
+#' This function uses the Information Value algorithm to predict
+#' which Workplace Analytics metrics are most explained by the change in dates.
 #'
 #' @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 before_start Start date of "before" time period in YYYY-MM-DD. Defaults to earliest date in dataset.
-#' @param before_end End date of "before" time period in YYYY-MM-DD
-#' @param after_start Start date of "after" time period in YYYY-MM-DD. Defaults to day after before_end.
-#' @param after_end End date of "after" time period in YYYY-MM-DD. Defaults to latest date in dataset.
-#' @param mybins Number of bins to cut the data into for Information Value analysis. Defaults to 10.
-#' @param return  Specify whether to return a summary table or detailed Excel and PDF files
-#' Specify "table" or "detailed" for outputs. Defaults to table.
+#'   This function assumes the data format is `MM/DD/YYYY` as is standard in a
+#'   WpA query output.
+#' @param before_start Start date of "before" time period in `YYYY-MM-DD`.
+#'   Defaults to earliest date in dataset.
+#' @param before_end End date of "before" time period in `YYYY-MM-DD`
+#' @param after_start Start date of "after" time period in `YYYY-MM-DD`.
+#'   Defaults to day after before_end.
+#' @param after_end End date of "after" time period in `YYYY-MM-DD`. Defaults to
+#'   latest date in dataset.
+#' @param mybins Number of bins to cut the data into for Information Value
+#'   analysis. Defaults to 10.
+#' @param return  String specifying what to return. The current only valid
+#' option is `"table"`.
+#'
+#' @return
+#' data frame containing all the variables and the corresponding Information
+#' Value.
 #'
 #' @import dplyr
 #' @import Information
 #'
-#'
-#' @family Flexible Input
+#' @family Variable Association
 #' @family Information Value
+#' @family Time-series
+#'
+#' @examples
+#' # Returns a data frame
+#' \donttest{
+#'   sq_data %>%
+#'     IV_by_period(
+#'       before_start = "2019-11-03",
+#'       before_end = "2019-11-09",
+#'       after_start = "2019-11-10",
+#'       after_end = "2019-11-16"
+#'     )
+#' }
 #'
 #' @export
 
@@ -141,7 +166,7 @@ IV_by_period <-
       }
 
       # Return ranking table
-      return(create_dt(Tables$Summary, rounding = 2))
+      return(Tables$Summary)
       # print("Access individual metrics via Outputs[[metric_name]], e.g., Outputs[[Workweek_span]]")
 
       # # Store each variable's plot

R/IV_report.R

@@ -34,6 +34,7 @@
 #' }
 #'
 #' @family Reports
+#' @family Variable Association
 #' @family Information Value
 #'
 #' @inherit generate_report return

R/collaboration_dist.R

@@ -10,8 +10,7 @@
 #' Returns a stacked bar plot by default.
 #' Additional options available to return a table with distribution elements.
 #'
-#' @details
-#' Uses the metric `Collaboration_hours`.
+#' @template ch
 #'
 #' @inheritParams create_dist
 #' @inherit create_dist return

R/collaboration_fizz.R

@@ -10,8 +10,7 @@
 #' a 'fizzy' scatter plot by default.
 #' Additional options available to return a table with distribution elements.
 #'
-#' @details
-#' Uses the metric `Collaboration_hours`.
+#' @template ch
 #'
 #' @inheritParams create_fizz
 #' @inherit create_fizz return

R/collaboration_line.R

@@ -11,8 +11,7 @@
 #' with a separate panel per value in the HR attribute.
 #' Additional options available to return a summary table.
 #'
-#' @details
-#' Uses the metric `Collaboration_hours`.
+#' @template ch
 #'
 #' @inheritParams create_line
 #' @inherit create_line return

R/collaboration_trend.R

@@ -10,8 +10,7 @@
 #' By default returns a week by week heatmap, highlighting the points in time with most activity.
 #' Additional options available to return a summary table.
 #'
-#' @details
-#' Uses the metric `Collaboration_hours`.
+#' @template ch
 #'
 #' @inheritParams create_trend
 #'

R/create_bar.R

@@ -6,18 +6,17 @@
 #' @title Mean Bar Plot for any metric
 #'
 #' @description
-#' Provides an overview analysis of a selected metric by calculating a mean per metric.
+#' Provides an overview analysis of a selected metric by calculating a mean per
+#' metric.
 #' Returns a bar plot showing the average of a selected metric by default.
 #' Additional options available to return a summary table.
 #'
-#' @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"
-#' but accepts any character vector, e.g. "LevelDesignation"
-#' @param mingroup Numeric value setting the privacy threshold / minimum group size. Defaults to 5.
+#' @template spq-params
+#' @param mingroup Numeric value setting the privacy threshold / minimum group
+#'   size. Defaults to 5.
 #'
-#' @param return String specifying what to return. This must be one of the following strings:
+#' @param return String specifying what to return. This must be one of the
+#'   following strings:
 #'   - `"plot"`
 #'   - `"table"`
 #'

R/flag_ch_ratio.R

@@ -10,6 +10,8 @@
 #' of collaboration hours to after-hours collaboration hours.
 #' Returns a character string by default.
 #'
+#' @template ch
+#'
 #' @import dplyr
 #'
 #' @param data A data frame containing a Person Query.

R/heat_colours.R

@@ -8,12 +8,16 @@
 #' @param n the number of colors (>= 1) to be in the palette.
 #' @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.
+#' @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)
+#' barplot(rep(10, 50), col = heat_colours(n = 50, alpha = 0.5, rev = TRUE),
+#' border = NA)
+#'
+#' @family Support
 #'
 #' @return
 #' A character vector containing the HEX codes and the same length as `n` is
@@ -43,3 +47,8 @@ heat_colours <- function (n, alpha, rev = FALSE) {
     cols
   }
 }
+
+#' @rdname heat_colours
+#' @export
+heat_colors <- heat_colours
+

R/hr_trend.R

@@ -33,6 +33,7 @@
 #' # Return summary table
 #' hr_trend(dv_data, return = "table")
 #'
+#' @family Visualization
 #' @family Data Validation
 #'
 #' @export

R/hrvar_count.R

@@ -11,10 +11,10 @@
 #' bar chart as typically seen in 'Analysis Scope'.
 #'
 #' @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
-#' concatenated.
+#' @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 concatenated.
 #' @param return String specifying what to return. This must be one of the
 #'   following strings:
 #'   - `"plot"`

R/hrvar_count_all.R

@@ -3,20 +3,25 @@
 # Licensed under the MIT License. See LICENSE.txt in the project root for license information.
 # --------------------------------------------------------------------------------------------
 
-#' @title Create count of distinct fields and percentage of employees with NAs for all HR variables
+#' @title Create count of distinct fields and percentage of employees with
+#'   missing values for all HR variables
 #'
 #' @description
-#' This function enables you to create a summary table to validate organizational data. This table will provide a summary of the data found
-#' 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.
+#' This function enables you to create a summary table to validate
+#' organizational data. This table will provide a summary of the data found in
+#' the Workplace Analytics Sources page. This function will return a summary
+#' table with the count of distinct fields per HR attribute and the percentage
+#' of employees with missing values for that attribute. See `hrvar_count()`
+#' function for more detail on the specific HR attribute of interest.
 #'
 #' @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 n_var number of HR variables to include in report as rows. Default is
+#'   set to 50 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.
-#' @param maxna The max percentage of NAs allowable for any column. Default is 20.
+#' @param threshold The max number of unique values allowed for any attribute.
+#'   Default is 100.
+#' @param maxna The max percentage of NAs allowable for any column. Default is
+#'   20.
 #'
 #' @import dplyr
 #'
@@ -28,8 +33,11 @@
 #'
 #' @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.
-#' when 'message' is passed, outputs a message indicating which values are beyond the specified thresholds.
+#'   - `'table'`: data frame. A summary table listing the number of distinct
+#' fields and percentage of missing values for the specified number of HR
+#' attributes will be returned.
+#'   - `'message'`: outputs a message indicating which values are
+#' beyond the specified thresholds.
 #'
 #' @export
 hrvar_count_all <- function(data,

R/identify_churn.R

@@ -6,36 +6,56 @@
 #' @title Identify employees who have churned from the dataset
 #'
 #' @description
-#' This function identifies and counts the number of employees who have churned from
-#' the dataset by measuring whether an employee who is present in the first `n` (n1) weeks
-#' of the data is present in the last `n` (n2) weeks of the data.
+#' This function identifies and counts the number of employees who have churned
+#' from the dataset by measuring whether an employee who is present in the first
+#' `n` (n1) weeks of the data is present in the last `n` (n2) weeks of the data.
 #'
 #' @details
-#' An additional use case of this function is the ability to identify "new-joiners" by using
-#' the argument `flip`.
+#' An additional use case of this function is the ability to identify
+#' "new-joiners" by using the argument `flip`.
 #'
 #' @param data A Person Query as a data frame. Must contain a `PersonId`.
-#' @param n1 A numeric value specifying the number of weeks at the beginning of the period
-#' that defines the measured employee set. Defaults to 6.
-#' @param n2 A numeric value specifying the number of weeks at the end of the period
-#' to calculate whether employees have churned from the data. Defaults to 6.
-#' @param return String specifying what to return. Defaults to "message", with options to
-#' return a character string ("text") or the `PersonId` of employees who have been identified
-#' as churned ("data").
-#' @param flip Logical, defaults to FALSE. This determines whether to reverse the logic of identifying the
-#' non-overlapping set. If set to `TRUE`, this effectively identifies new-joiners, or those
-#' who were not present in the first n weeks of the data but were present in the final n weeks.
+#' @param n1 A numeric value specifying the number of weeks at the beginning of
+#'   the period that defines the measured employee set. Defaults to 6.
+#' @param n2 A numeric value specifying the number of weeks at the end of the
+#'   period to calculate whether employees have churned from the data. Defaults
+#'   to 6.
+#' @param return String specifying what to return. This must be one of the
+#'   following strings:
+#'   - `"message"` (default)
+#'   - `"text"`
+#'   - `"data"`
+#'
+#' See `Value` for more information.
+#'
+#' @param flip Logical, defaults to FALSE. This determines whether to reverse
+#'   the logic of identifying the non-overlapping set. If set to `TRUE`, this
+#'   effectively identifies new-joiners, or those who were not present in the
+#'   first n weeks of the data but were present in the final n weeks.
+#'
+#' @return
+#' A different output is returned depending on the value passed to the `return`
+#' argument:
+#'   - `"message"`: Message on console. A diagnostic message.
+#'   - `"text"`: String. A diagnostic message.
+#'   - `"data"`: Character vector containing the the `PersonId` of
+#'   employees who have been identified as churned.
 #'
 #' @details
-#' If an employee is present in the first `n` weeks of the data but not present in the last
-#' `n` weeks of the data, the function considers the employee as churned. As the measurement period
-#' is defined by the number of weeks from the start and the end of the passed data frame, you
-#' may consider filtering the dates accordingly before running this function.
+#' If an employee is present in the first `n` weeks of the data but not present
+#' in the last `n` weeks of the data, the function considers the employee as
+#' churned. As the measurement period is defined by the number of weeks from the
+#' start and the end of the passed data frame, you may consider filtering the
+#' dates accordingly before running this function.
+#'
+#' Another assumption that is in place is that any employee whose `PersonId` is
+#' not available in the data has churned. Note that there may be other reasons
+#' why an employee's `PersonId` may not be present, e.g. maternity/paternity
+#' leave, Workplace Analytics license has been removed, shift to a
+#' low-collaboration role (to the extent that he/she becomes inactive).
+#'
+#' @family Data Validation
 #'
-#' Another assumption that is in place is that any employee whose `PersonId` is not available in the
-#' data has churned. Note that there may be other reasons why an employee's `PersonId` may not
-#' be present, e.g. maternity/paternity leave, Workplace Analytics license has been removed,
-#' shift to a low-collaboration role (to the extent that he/she becomes inactive).
 #' @examples
 #' sq_data %>% identify_churn(n1 = 3, n2 = 3, return = "message")
 #'
@@ -100,18 +120,27 @@ identify_churn <- function(data,
              " (", n1, " weeks).")
 
   } else {
+
     stop("Invalid argument for `flip`")
+
   }
 
 
 
   if(return == "message"){
+
     message(printMessage)
+
   } else if(return == "text"){
+
     printMessage
+
   } else if(return == "data"){
+
     churner_id
+
   } else {
+
     stop("Invalid `return`")
   }
 }

R/identify_holidayweeks.R

@@ -6,21 +6,45 @@
 #' @title Identify Holiday Weeks based on outliers
 #'
 #' @description
-#' This function scans a standard query output for weeks where collaboration hours is far outside the mean.
-#' Returns a list of weeks that appear to be holiday weeks and optionally an edited dataframe with outliers removed.
-#' By default, missing values are excluded.
+#' This function scans a standard query output for weeks where collaboration
+#' hours is far outside the mean. Returns a list of weeks that appear to be
+#' holiday weeks and optionally an edited dataframe with outliers removed. By
+#' default, missing values are excluded.
 #'
-#' As best practice, run this function prior to any analysis to remove atypical collaboration weeks from your dataset.
+#' As best practice, run this function prior to any analysis to remove atypical
+#' collaboration weeks from your dataset.
+#'
+#' @template ch
 #'
 #' @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. 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 plot with holiday weeks highlighted is returned as a dataframe.
+#' @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 specifying what to return. This must be one of the
+#'   following strings:
+#'   - `"message"` (default)
+#'   - `"data"`
+#'   - `"data_cleaned"`
+#'   - `"data_dirty"`
+#'   - `"plot"`
+#'
+#' See `Value` for more information.
+#'
+#' @return
+#' A different output is returned depending on the value passed to the `return`
+#' argument:
+#'   - `"message"`: message on console. a message is printed identifying holiday
+#'   weeks.
+#'   - `"data"`: data frame. A dataset with outlier weeks flagged in a new
+#'   column is returned as a dataframe.
+#'   - `"data_cleaned"`: data frame. A dataset with outlier weeks removed is
+#'   returned.
+#'   - `"data_dirty"`: data frame. A dataset with only outlier weeks is
+#'   returned.
+#'   - `"plot"`: ggplot object. A line plot of Collaboration Hours with holiday
+#'   weeks highlighted.
+#'
 #'
 #' @import dplyr
 #' @import ggplot2
@@ -35,7 +59,6 @@
 #' # Return plot
 #' identify_holidayweeks(sq_data, return = "plot")
 #'
-#'
 #' @export
 identify_holidayweeks <- function(data, sd = 1, return = "message"){
 

R/identify_inactiveweeks.R

@@ -6,23 +6,35 @@
 #' @title Identify Inactive Weeks
 #'
 #' @description
-#' This function scans a standard query output for weeks where collaboration hours is far outside the mean for any individual person in the dataset.
-#' Returns a list of weeks that appear to be inactive weeks and optionally an edited dataframe with outliers removed.
+#' This function scans a standard query output for weeks where collaboration
+#' hours is far outside the mean for any individual person in the dataset.
+#' Returns a list of weeks that appear to be inactive weeks and optionally an
+#' edited dataframe with outliers removed.
 #'
-#' As best practice, run this function prior to any analysis to remove atypical collaboration weeks from your dataset.
+#' As best practice, run this function prior to any analysis to remove atypical
+#' collaboration weeks from your dataset.
 #'
 #' @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 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 specifying what to return. This must be one of the
+#'   following strings:
+#'   - `"text"`
+#'   - `"data_cleaned"`
+#'   - `"data_dirty"`
+#'
+#' See `Value` for more information.
 #'
 #' @import dplyr
 #'
 #' @family Data Validation
 #'
 #' @return
-#' Returns an error message by default, where 'text' is returned.
-#' When 'data_cleaned' is passed, a dataset with outlier weeks removed is returned as a dataframe.
-#' When 'data_dirty' is passed, a dataset with outlier weeks is returned as a dataframe.
+#' Returns an error message by default, where `'text'` is returned. When
+#' `'data_cleaned'` is passed, a dataset with outlier weeks removed is returned
+#' as a dataframe. When `'data_dirty'` is passed, a dataset with outlier weeks
+#' is returned as a dataframe.
 #'
 #' @export
 identify_inactiveweeks <- function(data, sd = 2, return = "text"){

R/identify_nkw.R

@@ -3,26 +3,44 @@
 # Licensed under the MIT License. See LICENSE.txt in the project root for license information.
 # --------------------------------------------------------------------------------------------
 
-#' @title Identify Non-Knowledge workers in a Person Query using Collaboration Hours
+#' @title Identify Non-Knowledge workers in a Person Query using Collaboration
+#'   Hours
 #'
 #' @description
-#' 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.
+#' 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 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
+#' @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 specifying what to return. This must be one of the
+#'   following strings:
+#'   - `"text"`
+#'   - `"data_with_flag"`
+#'   - `"data_clean"`
+#'   - `"data_summary"`
+#'
+#' See `Value` for more information.
 #'
 #' @import dplyr
 #'
 #' @family Data Validation
 #'
 #' @return
-#' Returns an error message by default, where 'text' is passed in `return`.
-#' When 'data_with_flag' is passed, a copy of your original dataframe with an additional column containing the kw/nkw flag will be returned.
-#' When 'data_clean' is passed, a full dataframe excluding "nkw" employees will be returned.
-#' when 'data_summmary' is passed, a summary table by organization listing the number and % of non-knowledge workers will be returned.
+#' A different output is returned depending on the value passed to the `return`
+#' argument:
+#'   - `"text"`: string. Returns a diagnostic message.
+#'   - `"data_with_flag"`: data frame. Original input data with an additional
+#'   column containing the `kw`/`nkw` flag.
+#'   - `"data_clean"`: data frame. Data frame with non-knowledge workers
+#'   excluded.
+#'   - `"data_summary"`: data frame. A summary table by organization listing
+#'   the number and % of non-knowledge workers.
 #'
 #' @export
 identify_nkw <- function(data, collab_threshold = 5, return = "data_summary"){

R/identify_outlier.R

@@ -27,7 +27,6 @@
 #' Returns a data frame with `Date` (if grouping variable is not set),
 #' the metric, and the corresponding z-score.
 #'
-#' @family General
 #' @family Data Validation
 #'
 #' @export

R/identify_privacythreshold.R

@@ -6,16 +6,35 @@
 #' @title Identify groups under privacy threshold
 #'
 #' @description
-#' 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.
+#' 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 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.
-#' Defaults to 5.
-#' @param return A character vector specifying what to return.
-#' Valid values include "table" (default) and "text" (text)
+#' @param mingroup Numeric value setting the privacy threshold / minimum group
+#'   size. Defaults to 5.
+#' @param return String specifying what to return. This must be one of the
+#'   following strings:
+#'   - `"table"`
+#'   - `"text"`
+#'
+#' See `Value` for more information.
+#'
+#' @return
+#' A different output is returned depending on the value passed to the `return`
+#' argument:
+#'   - `"table"`: data frame. A summary table of groups that fall below the
+#'   privacy threshold.
+#'   - `"text"`: string. A diagnostic message.
+#'
+#' @examples
+#' # Return a summary table
+#' dv_data %>% identify_privacythreshold(return = "table")
+#'
+#' # Return a diagnostic message
+#' dv_data %>% identify_privacythreshold(return = "text")
 #'
 #' @import dplyr
 #' @import ggplot2

R/identify_query.R

@@ -13,11 +13,13 @@
 #' @param data A Workplace Analytics dataset in the form of a data frame.
 #' If the data is not identified as a Workplace Analytics dataset, the function
 #' will return an error.
-#' @param threshold Debugging use only. Increase to raise the 'strictness' of the
-#' guessing algorithm. Defaults to 2.
+#' @param threshold Debugging use only. Increase to raise the 'strictness' of
+#'   the guessing algorithm. Defaults to 2.
 #'
 #' @family Data Validation
 #'
+#' @return String. A diagnostic message is returned.
+#'
 #' @examples
 #' identify_query(sq_data) # Standard query
 #' identify_query(mt_data) # Meeting query
@@ -64,11 +66,17 @@ identify_query <- function(data, threshold = 2){
   f_guess <- qlabels[which.min(total_check_vec)]
 
   if(total_check_n == 0){
+
     stop("Error: please check if query data is properly formatted query data.")
+
   } else if(min_nm > threshold){
+
     stop("Column mismatches: please check if query data is properly formatted query data.")
+
   } else {
+
     f_guess
+
   }
 }
 

R/identify_shifts.R

@@ -28,11 +28,13 @@
 #' argument:
 #'   - `"plot"`: ggplot object. A bar plot for the weekly count of shifts.
 #'   - `"table"`: data frame. A summary table for the count of shifts.
-#'   - `"data`: data frame. Input data appended with the following columns:
-#'     - `Shifts`
+#'   - `"data`: data frame. Input data appended with the `Shifts` columns.
 #'
 #' @importFrom data.table ":=" "%like%" "%between%"
 #'
+#' @family Data Validation
+#' @family Working Patterns
+#'
 #' @examples
 #' # Return plot
 #' dv_data %>% identify_shifts()

R/identify_shifts_wp.R

@@ -37,6 +37,7 @@
 #'
 #' @importFrom data.table ":=" "%like%" "%between%"
 #'
+#' @family Data Validation
 #' @family Working Patterns
 #'
 #' @examples

R/identify_tenure.R

@@ -3,7 +3,8 @@
 # Licensed under the MIT License. See LICENSE.txt in the project root for license information.
 # --------------------------------------------------------------------------------------------
 
-#' @title Tenure calculation based on different input dates, returns data summary table or histogram
+#' @title Tenure calculation based on different input dates, returns data
+#'   summary table or histogram
 #'
 #' @description
 #' This function calculates employee tenure based on different input dates.
@@ -13,8 +14,10 @@
 #' @family Data Validation
 #'
 #' @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 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.
 #' If the tenure exceeds this threshold, it would be accounted for in the flag message.
 #' @param return String to specify what to return.
@@ -95,9 +98,11 @@ identify_tenure <- function(data,
     )
 
   } else if(return == "data_cleaned"){
+
     return(data %>% filter(!(PersonId %in% oddpeople$PersonId)) %>% data.frame())
 
   } else if(return == "data_dirty"){
+
     return(data %>% filter((PersonId %in% oddpeople$PersonId)) %>% data.frame())
 
   } else if(return == "data"){
@@ -108,7 +113,9 @@ identify_tenure <- function(data,
       select(PersonId, TenureYear)
 
   } else {
+
     stop("Error: please check inputs for `return`")
+
   }
 
 }

R/import_to_fst.R

@@ -8,7 +8,7 @@
 #'
 #' @description Uses `import_wpa()` to read a Workplace Analytics query in CSV
 #'   and convert this into the serialized FST format which is much faster to
-#'   read. The 'fst' package must be installed.
+#'   read. The 'fst' package must be installed, or an error message is returned.
 #'
 #' @details
 #' The [fst](https://www.fstpackage.org/) package provides a way to serialize
@@ -31,6 +31,8 @@
 #'   file will be saved in the same directory with a different file extension.
 #' @param ... Additional arguments to pass to `import_wpa()`.
 #'
+#' @family Import and Export
+#'
 #' @export
 import_to_fst <- function(path, ...){
 

R/import_wpa.R

@@ -6,8 +6,8 @@
 #' @title Import a Workplace Analytics Query
 #'
 #' @description
-#' Import a Workplace Analytics Query from a local CSV File, with variable classifications optimised
-#' for other 'wpa' functions.
+#' Import a Workplace Analytics Query from a local CSV File, with variable
+#' classifications optimised for other 'wpa' functions.
 #'
 #' @details
 #' `import_wpa()` uses `data.table::fread()` to import CSV files for speed,
@@ -28,6 +28,8 @@
 #'
 #' @return A `tibble` is returned.
 #'
+#' @family Import and Export
+#'
 #' @export
 import_wpa <- function(x,
                        standardise = FALSE,

R/internal_network_plot.R

@@ -6,9 +6,13 @@
 #' @title Plot Internal Network Breadth and Size as a scatter plot
 #'
 #' @description
-#' Plot the internal network metrics for a HR variable as a scatter plot, showing
-#' Internal Network Breadth as the vertical axis and Internal Network Size as the
-#' horizontal axis.
+#' Plot the internal network metrics for a HR variable as a scatter plot,
+#' showing Internal Network Breadth as the vertical axis and Internal Network
+#' Size as the horizontal axis.
+#'
+#' @details
+#' Uses the metrics `Internal_network_size` and
+#' `Networking_outside_organization`.
 #'
 #' @inheritParams create_bubble
 #'
@@ -16,7 +20,11 @@
 #' # Return plot
 #' sq_data %>% internal_network_plot(return = "plot")
 #'
-#' @family Connectivity
+#' # Return summary table
+#' sq_data %>% internal_network_plot(return = "table")
+#'
+#' @family Visualization
+#' @family Network
 #'
 #' @export
 

R/is_date_format.R

@@ -6,15 +6,19 @@
 #' @title Identify whether string is a date format
 #'
 #' @description
-#' This function uses regular expression to determine whether
-#' a string is of the format "mdy", separated by "-", "/",
-#' or ".", returning a logical vector.
+#' This function uses regular expression to determine whether a string is of the
+#' format `"mdy"`, separated by `"-"`, `"/"`, or `"."`, returning a logical
+#' vector.
 #'
 #' @param string Character string to test whether is a date format.
 #'
+#' @return logical value indicating whether the string is a date format.
+#'
 #' @examples
 #' is_date_format("1/5/2020")
 #'
+#' @family Support
+#'
 #' @export
 is_date_format <- function(string){
   grepl("^\\d{1,2}[- /.]\\d{1,2}[- /.]\\d{1,4}$",

R/keymetrics_scan.R

@@ -8,12 +8,11 @@
 #' @description
 #' Returns a heatmapped table by default, with options to return a table.
 #'
-#' @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.
+#' @template spq-params
+#' @param metrics A character vector containing the variable names to calculate
+#'   averages of.
 #' @param return Character vector specifying what to return, defaults to "plot".
-#' Valid inputs are "plot" and "table".
+#'   Valid inputs are "plot" and "table".
 #' @param textsize A numeric value specifying the text size to show in the plot.
 #'
 #' @import dplyr
@@ -58,6 +57,12 @@ keymetrics_scan <- function(data,
                             return = "plot",
                             textsize = 2){
 
+  ## Handling NULL values passed to hrvar
+  if(is.null(hrvar)){
+    data <- totals_col(data)
+    hrvar <- "Total"
+  }
+
   myTable <-
     data %>%
     rename(group = !!sym(hrvar)) %>% # Rename HRvar to `group`

man-roxygen/ch.R

@@ -0,0 +1,3 @@
+#' @section Metrics used:
+#'   The metric `Collaboration_hours` is used in the calculations. Please ensure
+#'   that your query contains a metric with the exact same name.

man-roxygen/spq-params.R

@@ -0,0 +1,6 @@
+#' @param data A Standard Person Query dataset in the form of a data frame.
+#' @param hrvar String containing the name of the HR Variable by which to split
+#'   metrics. Defaults to `"Organization"`. To run the analysis on the total
+#'   instead of splitting by an HR attribute, supply `NULL` (without quotes).
+#' @param mingroup Numeric value setting the privacy threshold / minimum group
+#'   size. Defaults to 5.

man/IV_by_period.Rd

@@ -2,7 +2,8 @@
 % 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.}
+\title{Identify the WPA metrics that have the biggest change between two
+periods.}
 \usage{
 IV_by_period(
   data,
@@ -16,31 +17,64 @@ IV_by_period(
 }
 \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.}
+This function assumes the data format is \code{MM/DD/YYYY} as is standard in a
+WpA query output.}
 
-\item{before_start}{Start date of "before" time period in YYYY-MM-DD. Defaults to earliest date in dataset.}
+\item{before_start}{Start date of "before" time period in \code{YYYY-MM-DD}.
+Defaults to earliest date in dataset.}
 
-\item{before_end}{End date of "before" time period in YYYY-MM-DD}
+\item{before_end}{End date of "before" time period in \code{YYYY-MM-DD}}
 
-\item{after_start}{Start date of "after" time period in YYYY-MM-DD. Defaults to day after before_end.}
+\item{after_start}{Start date of "after" time period in \code{YYYY-MM-DD}.
+Defaults to day after before_end.}
 
-\item{after_end}{End date of "after" time period in YYYY-MM-DD. Defaults to latest date in dataset.}
+\item{after_end}{End date of "after" time period in \code{YYYY-MM-DD}. Defaults to
+latest date in dataset.}
 
-\item{mybins}{Number of bins to cut the data into for Information Value analysis. Defaults to 10.}
+\item{mybins}{Number of bins to cut the data into for Information Value
+analysis. Defaults to 10.}
 
-\item{return}{Specify whether to return a summary table or detailed Excel and PDF files
-Specify "table" or "detailed" for outputs. Defaults to table.}
+\item{return}{String specifying what to return. The current only valid
+option is \code{"table"}.}
+}
+\value{
+data frame containing all the variables and the corresponding Information
+Value.
 }
 \description{
-This function uses the Information Value algorithm to predict which WPA metrics are most explained by the change in dates.
+\ifelse{html}{\out{<a href='https://www.tidyverse.org/lifecycle/#experimental'><img src='figures/lifecycle-experimental.svg' alt='Experimental lifecycle'></a>}}{\strong{Experimental}}
+
+This function uses the Information Value algorithm to predict
+which Workplace Analytics metrics are most explained by the change in dates.
+}
+\examples{
+# Returns a data frame
+\donttest{
+  sq_data \%>\%
+    IV_by_period(
+      before_start = "2019-11-03",
+      before_end = "2019-11-09",
+      after_start = "2019-11-10",
+      after_end = "2019-11-16"
+    )
+}
+
 }
 \seealso{
-Other Flexible Input: 
-\code{\link{period_change}()}
+Other Variable Association: 
+\code{\link{IV_report}()},
+\code{\link{create_IV}()}
 
 Other Information Value: 
 \code{\link{IV_report}()},
 \code{\link{create_IV}()}
+
+Other Time-series: 
+\code{\link{create_line_asis}()},
+\code{\link{create_line}()},
+\code{\link{create_period_scatter}()},
+\code{\link{create_trend}()}
 }
-\concept{Flexible Input}
 \concept{Information Value}
+\concept{Time-series}
+\concept{Variable Association}

man/IV_report.Rd

@@ -64,9 +64,14 @@ Other Reports:
 \code{\link{subject_validate_report}()},
 \code{\link{validation_report}()}
 
+Other Variable Association: 
+\code{\link{IV_by_period}()},
+\code{\link{create_IV}()}
+
 Other Information Value: 
 \code{\link{IV_by_period}()},
 \code{\link{create_IV}()}
 }
 \concept{Information Value}
 \concept{Reports}
+\concept{Variable Association}

man/afterhours_dist.Rd

@@ -95,7 +95,9 @@ Other Visualization:
 \code{\link{email_summary}()},
 \code{\link{email_trend}()},
 \code{\link{external_network_plot}()},
-\code{\link{hrvar_count}()}
+\code{\link{hr_trend}()},
+\code{\link{hrvar_count}()},
+\code{\link{internal_network_plot}()}
 
 Other After-hours Collaboration: 
 \code{\link{afterhours_fizz}()},

man/afterhours_fizz.Rd

@@ -79,7 +79,9 @@ Other Visualization:
 \code{\link{email_summary}()},
 \code{\link{email_trend}()},
 \code{\link{external_network_plot}()},
-\code{\link{hrvar_count}()}
+\code{\link{hr_trend}()},
+\code{\link{hrvar_count}()},
+\code{\link{internal_network_plot}()}
 
 Other After-hours Collaboration: 
 \code{\link{afterhours_dist}()},

man/afterhours_line.Rd

@@ -83,7 +83,9 @@ Other Visualization:
 \code{\link{email_summary}()},
 \code{\link{email_trend}()},
 \code{\link{external_network_plot}()},
-\code{\link{hrvar_count}()}
+\code{\link{hr_trend}()},
+\code{\link{hrvar_count}()},
+\code{\link{internal_network_plot}()}
 
 Other After-hours Collaboration: 
 \code{\link{afterhours_dist}()},

man/afterhours_rank.Rd

@@ -70,7 +70,9 @@ Other Visualization:
 \code{\link{email_summary}()},
 \code{\link{email_trend}()},
 \code{\link{external_network_plot}()},
-\code{\link{hrvar_count}()}
+\code{\link{hr_trend}()},
+\code{\link{hrvar_count}()},
+\code{\link{internal_network_plot}()}
 
 Other After-hours Collaboration: 
 \code{\link{afterhours_dist}()},

man/afterhours_summary.Rd

@@ -12,12 +12,15 @@ afterhours_sum(data, hrvar = "Organization", mingroup = 5, return = "plot")
 \arguments{
 \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"}
+\item{hrvar}{String containing the name of the HR Variable by which to split
+metrics. Defaults to \code{"Organization"}. To run the analysis on the total
+instead of splitting by an HR attribute, supply \code{NULL} (without quotes).}
 
-\item{mingroup}{Numeric value setting the privacy threshold / minimum group size. Defaults to 5.}
+\item{mingroup}{Numeric value setting the privacy threshold / minimum group
+size. Defaults to 5.}
 
-\item{return}{String specifying what to return. This must be one of the following strings:
+\item{return}{String specifying what to return. This must be one of the
+following strings:
 \itemize{
 \item \code{"plot"}
 \item \code{"table"}
@@ -83,7 +86,9 @@ Other Visualization:
 \code{\link{email_summary}()},
 \code{\link{email_trend}()},
 \code{\link{external_network_plot}()},
-\code{\link{hrvar_count}()}
+\code{\link{hr_trend}()},
+\code{\link{hrvar_count}()},
+\code{\link{internal_network_plot}()}
 
 Other After-hours Collaboration: 
 \code{\link{afterhours_dist}()},

man/afterhours_trend.Rd

@@ -72,7 +72,9 @@ Other Visualization:
 \code{\link{email_summary}()},
 \code{\link{email_trend}()},
 \code{\link{external_network_plot}()},
-\code{\link{hrvar_count}()}
+\code{\link{hr_trend}()},
+\code{\link{hrvar_count}()},
+\code{\link{internal_network_plot}()}
 
 Other After-hours Collaboration: 
 \code{\link{afterhours_dist}()},

man/camel_clean.Rd

@@ -26,6 +26,8 @@ Other Support:
 \code{\link{cut_hour}()},
 \code{\link{extract_date_range}()},
 \code{\link{extract_hr}()},
+\code{\link{heat_colours}()},
+\code{\link{is_date_format}()},
 \code{\link{rgb2hex}()}
 }
 \concept{Support}

man/check_inputs.Rd

@@ -45,6 +45,8 @@ Other Support:
 \code{\link{cut_hour}()},
 \code{\link{extract_date_range}()},
 \code{\link{extract_hr}()},
+\code{\link{heat_colours}()},
+\code{\link{is_date_format}()},
 \code{\link{rgb2hex}()}
 }
 \concept{Support}

man/check_query.Rd

@@ -61,12 +61,15 @@ Other Data Validation:
 \code{\link{hr_trend}()},
 \code{\link{hrvar_count_all}()},
 \code{\link{hrvar_count}()},
+\code{\link{identify_churn}()},
 \code{\link{identify_holidayweeks}()},
 \code{\link{identify_inactiveweeks}()},
 \code{\link{identify_nkw}()},
 \code{\link{identify_outlier}()},
 \code{\link{identify_privacythreshold}()},
 \code{\link{identify_query}()},
+\code{\link{identify_shifts_wp}()},
+\code{\link{identify_shifts}()},
 \code{\link{identify_tenure}()},
 \code{\link{remove_outliers}()},
 \code{\link{subject_validate_report}()},

man/collaboration_area.Rd

@@ -92,7 +92,9 @@ Other Visualization:
 \code{\link{email_summary}()},
 \code{\link{email_trend}()},
 \code{\link{external_network_plot}()},
-\code{\link{hrvar_count}()}
+\code{\link{hr_trend}()},
+\code{\link{hrvar_count}()},
+\code{\link{internal_network_plot}()}
 
 Other Collaboration: 
 \code{\link{collaboration_dist}()},

man/collaboration_dist.Rd

@@ -55,9 +55,12 @@ Analyze the distribution of Collaboration Hours.
 Returns a stacked bar plot by default.
 Additional options available to return a table with distribution elements.
 }
-\details{
-Uses the metric \code{Collaboration_hours}.
+\section{Metrics used}{
+
+The metric \code{Collaboration_hours} is used in the calculations. Please ensure
+that your query contains a metric with the exact same name.
 }
+
 \examples{
 # Return plot
 collaboration_dist(sq_data, hrvar = "Organization")
@@ -100,7 +103,9 @@ Other Visualization:
 \code{\link{email_summary}()},
 \code{\link{email_trend}()},
 \code{\link{external_network_plot}()},
-\code{\link{hrvar_count}()}
+\code{\link{hr_trend}()},
+\code{\link{hrvar_count}()},
+\code{\link{internal_network_plot}()}
 
 Other Collaboration: 
 \code{\link{collaboration_area}()},

man/collaboration_fizz.Rd

@@ -36,9 +36,12 @@ Analyze weekly collaboration hours distribution, and returns
 a 'fizzy' scatter plot by default.
 Additional options available to return a table with distribution elements.
 }
-\details{
-Uses the metric \code{Collaboration_hours}.
+\section{Metrics used}{
+
+The metric \code{Collaboration_hours} is used in the calculations. Please ensure
+that your query contains a metric with the exact same name.
 }
+
 \examples{
 # Return plot
 collaboration_fizz(sq_data, hrvar = "Organization", return = "plot")
@@ -82,7 +85,9 @@ Other Visualization:
 \code{\link{email_summary}()},
 \code{\link{email_trend}()},
 \code{\link{external_network_plot}()},
-\code{\link{hrvar_count}()}
+\code{\link{hr_trend}()},
+\code{\link{hrvar_count}()},
+\code{\link{internal_network_plot}()}
 
 Other Collaboration: 
 \code{\link{collaboration_area}()},

man/collaboration_line.Rd

@@ -38,9 +38,12 @@ By default returns a line chart for collaboration hours,
 with a separate panel per value in the HR attribute.
 Additional options available to return a summary table.
 }
-\details{
-Uses the metric \code{Collaboration_hours}.
+\section{Metrics used}{
+
+The metric \code{Collaboration_hours} is used in the calculations. Please ensure
+that your query contains a metric with the exact same name.
 }
+
 \examples{
 # Return a line plot
 collaboration_line(sq_data, hrvar = "LevelDesignation")
@@ -84,7 +87,9 @@ Other Visualization:
 \code{\link{email_summary}()},
 \code{\link{email_trend}()},
 \code{\link{external_network_plot}()},
-\code{\link{hrvar_count}()}
+\code{\link{hr_trend}()},
+\code{\link{hrvar_count}()},
+\code{\link{internal_network_plot}()}
 
 Other Collaboration: 
 \code{\link{collaboration_area}()},

man/collaboration_rank.Rd

@@ -78,7 +78,9 @@ Other Visualization:
 \code{\link{email_summary}()},
 \code{\link{email_trend}()},
 \code{\link{external_network_plot}()},
-\code{\link{hrvar_count}()}
+\code{\link{hr_trend}()},
+\code{\link{hrvar_count}()},
+\code{\link{internal_network_plot}()}
 
 Other Collaboration: 
 \code{\link{collaboration_area}()},

man/collaboration_sum.Rd

@@ -78,7 +78,9 @@ Other Visualization:
 \code{\link{email_summary}()},
 \code{\link{email_trend}()},
 \code{\link{external_network_plot}()},
-\code{\link{hrvar_count}()}
+\code{\link{hr_trend}()},
+\code{\link{hrvar_count}()},
+\code{\link{internal_network_plot}()}
 
 Other Collaboration: 
 \code{\link{collaboration_area}()},

man/collaboration_trend.Rd

@@ -31,9 +31,12 @@ Provides a week by week view of collaboration time.
 By default returns a week by week heatmap, highlighting the points in time with most activity.
 Additional options available to return a summary table.
 }
-\details{
-Uses the metric \code{Collaboration_hours}.
+\section{Metrics used}{
+
+The metric \code{Collaboration_hours} is used in the calculations. Please ensure
+that your query contains a metric with the exact same name.
 }
+
 \seealso{
 Other Visualization: 
 \code{\link{afterhours_dist}()},
@@ -69,7 +72,9 @@ Other Visualization:
 \code{\link{email_summary}()},
 \code{\link{email_trend}()},
 \code{\link{external_network_plot}()},
-\code{\link{hrvar_count}()}
+\code{\link{hr_trend}()},
+\code{\link{hrvar_count}()},
+\code{\link{internal_network_plot}()}
 
 Other Collaboration: 
 \code{\link{collaboration_area}()},

man/combine_signals.Rd

@@ -40,6 +40,8 @@ Other Support:
 \code{\link{cut_hour}()},
 \code{\link{extract_date_range}()},
 \code{\link{extract_hr}()},
+\code{\link{heat_colours}()},
+\code{\link{is_date_format}()},
 \code{\link{rgb2hex}()}
 }
 \concept{Support}

man/copy_df.Rd

@@ -30,6 +30,8 @@ https://www.github.com/martinctc/surveytoolbox.
 \seealso{
 Other Import and Export: 
 \code{\link{create_dt}()},
-\code{\link{export}()}
+\code{\link{export}()},
+\code{\link{import_to_fst}()},
+\code{\link{import_wpa}()}
 }
 \concept{Import and Export}

man/create_IV.Rd

@@ -77,6 +77,10 @@ sq_data \%>\%
 }
 }
 \seealso{
+Other Variable Association: 
+\code{\link{IV_by_period}()},
+\code{\link{IV_report}()}
+
 Other Information Value: 
 \code{\link{IV_by_period}()},
 \code{\link{IV_report}()}

man/create_bar.Rd

@@ -17,15 +17,15 @@ create_bar(
 \arguments{
 \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"}
+\item{hrvar}{String containing the name of the HR Variable by which to split
+metrics. Defaults to \code{"Organization"}. To run the analysis on the total
+instead of splitting by an HR attribute, supply \code{NULL} (without quotes).}
 
-\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{mingroup}{Numeric value setting the privacy threshold / minimum group size. Defaults to 5.}
-
-\item{return}{String specifying what to return. This must be one of the following strings:
+\item{return}{String specifying what to return. This must be one of the
+following strings:
 \itemize{
 \item \code{"plot"}
 \item \code{"table"}
@@ -49,7 +49,8 @@ A different output is returned depending on the value passed to the \code{return
 }
 }
 \description{
-Provides an overview analysis of a selected metric by calculating a mean per metric.
+Provides an overview analysis of a selected metric by calculating a mean per
+metric.
 Returns a bar plot showing the average of a selected metric by default.
 Additional options available to return a summary table.
 }
@@ -103,7 +104,9 @@ Other Visualization:
 \code{\link{email_summary}()},
 \code{\link{email_trend}()},
 \code{\link{external_network_plot}()},
-\code{\link{hrvar_count}()}
+\code{\link{hr_trend}()},
+\code{\link{hrvar_count}()},
+\code{\link{internal_network_plot}()}
 
 Other Flexible: 
 \code{\link{create_bar_asis}()},

man/create_bar_asis.Rd

@@ -127,7 +127,9 @@ Other Visualization:
 \code{\link{email_summary}()},
 \code{\link{email_trend}()},
 \code{\link{external_network_plot}()},
-\code{\link{hrvar_count}()}
+\code{\link{hr_trend}()},
+\code{\link{hrvar_count}()},
+\code{\link{internal_network_plot}()}
 
 Other Flexible: 
 \code{\link{create_bar}()},

man/create_boxplot.Rd

@@ -103,7 +103,9 @@ Other Visualization:
 \code{\link{email_summary}()},
 \code{\link{email_trend}()},
 \code{\link{external_network_plot}()},
-\code{\link{hrvar_count}()}
+\code{\link{hr_trend}()},
+\code{\link{hrvar_count}()},
+\code{\link{internal_network_plot}()}
 
 Other Flexible: 
 \code{\link{create_bar_asis}()},

man/create_bubble.Rd

@@ -100,7 +100,9 @@ Other Visualization:
 \code{\link{email_summary}()},
 \code{\link{email_trend}()},
 \code{\link{external_network_plot}()},
-\code{\link{hrvar_count}()}
+\code{\link{hr_trend}()},
+\code{\link{hrvar_count}()},
+\code{\link{internal_network_plot}()}
 
 Other Flexible: 
 \code{\link{create_bar_asis}()},

man/create_dist.Rd

@@ -100,7 +100,9 @@ Other Visualization:
 \code{\link{email_summary}()},
 \code{\link{email_trend}()},
 \code{\link{external_network_plot}()},
-\code{\link{hrvar_count}()}
+\code{\link{hr_trend}()},
+\code{\link{hrvar_count}()},
+\code{\link{internal_network_plot}()}
 
 Other Flexible: 
 \code{\link{create_bar_asis}()},

man/create_dt.Rd

@@ -20,6 +20,8 @@ See https://martinctc.github.io/blog/vignette-downloadable-tables-in-rmarkdown-w
 \seealso{
 Other Import and Export: 
 \code{\link{copy_df}()},
-\code{\link{export}()}
+\code{\link{export}()},
+\code{\link{import_to_fst}()},
+\code{\link{import_wpa}()}
 }
 \concept{Import and Export}

man/create_fizz.Rd

@@ -90,7 +90,9 @@ Other Visualization:
 \code{\link{email_summary}()},
 \code{\link{email_trend}()},
 \code{\link{external_network_plot}()},
-\code{\link{hrvar_count}()}
+\code{\link{hr_trend}()},
+\code{\link{hrvar_count}()},
+\code{\link{internal_network_plot}()}
 
 Other Flexible: 
 \code{\link{create_bar_asis}()},

man/create_line.Rd

@@ -94,7 +94,9 @@ Other Visualization:
 \code{\link{email_summary}()},
 \code{\link{email_trend}()},
 \code{\link{external_network_plot}()},
-\code{\link{hrvar_count}()}
+\code{\link{hr_trend}()},
+\code{\link{hrvar_count}()},
+\code{\link{internal_network_plot}()}
 
 Other Flexible: 
 \code{\link{create_bar_asis}()},
@@ -113,6 +115,7 @@ Other Flexible:
 \code{\link{create_trend}()}
 
 Other Time-series: 
+\code{\link{IV_by_period}()},
 \code{\link{create_line_asis}()},
 \code{\link{create_period_scatter}()},
 \code{\link{create_trend}()}

man/create_line_asis.Rd

@@ -98,7 +98,9 @@ Other Visualization:
 \code{\link{email_summary}()},
 \code{\link{email_trend}()},
 \code{\link{external_network_plot}()},
-\code{\link{hrvar_count}()}
+\code{\link{hr_trend}()},
+\code{\link{hrvar_count}()},
+\code{\link{internal_network_plot}()}
 
 Other Flexible: 
 \code{\link{create_bar_asis}()},
@@ -117,6 +119,7 @@ Other Flexible:
 \code{\link{create_trend}()}
 
 Other Time-series: 
+\code{\link{IV_by_period}()},
 \code{\link{create_line}()},
 \code{\link{create_period_scatter}()},
 \code{\link{create_trend}()}

man/create_period_scatter.Rd

@@ -108,7 +108,9 @@ Other Visualization:
 \code{\link{email_summary}()},
 \code{\link{email_trend}()},
 \code{\link{external_network_plot}()},
-\code{\link{hrvar_count}()}
+\code{\link{hr_trend}()},
+\code{\link{hrvar_count}()},
+\code{\link{internal_network_plot}()}
 
 Other Flexible: 
 \code{\link{create_bar_asis}()},
@@ -127,6 +129,7 @@ Other Flexible:
 \code{\link{create_trend}()}
 
 Other Time-series: 
+\code{\link{IV_by_period}()},
 \code{\link{create_line_asis}()},
 \code{\link{create_line}()},
 \code{\link{create_trend}()}

man/create_rank.Rd

@@ -118,7 +118,9 @@ Other Visualization:
 \code{\link{email_summary}()},
 \code{\link{email_trend}()},
 \code{\link{external_network_plot}()},
-\code{\link{hrvar_count}()}
+\code{\link{hr_trend}()},
+\code{\link{hrvar_count}()},
+\code{\link{internal_network_plot}()}
 
 Other Flexible: 
 \code{\link{create_bar_asis}()},

man/create_sankey.Rd

@@ -75,7 +75,9 @@ Other Visualization:
 \code{\link{email_summary}()},
 \code{\link{email_trend}()},
 \code{\link{external_network_plot}()},
-\code{\link{hrvar_count}()}
+\code{\link{hr_trend}()},
+\code{\link{hrvar_count}()},
+\code{\link{internal_network_plot}()}
 
 Other Flexible: 
 \code{\link{create_bar_asis}()},

man/create_scatter.Rd

@@ -91,7 +91,9 @@ Other Visualization:
 \code{\link{email_summary}()},
 \code{\link{email_trend}()},
 \code{\link{external_network_plot}()},
-\code{\link{hrvar_count}()}
+\code{\link{hr_trend}()},
+\code{\link{hrvar_count}()},
+\code{\link{internal_network_plot}()}
 
 Other Flexible: 
 \code{\link{create_bar_asis}()},

man/create_stacked.Rd

@@ -104,7 +104,9 @@ Other Visualization:
 \code{\link{email_summary}()},
 \code{\link{email_trend}()},
 \code{\link{external_network_plot}()},
-\code{\link{hrvar_count}()}
+\code{\link{hr_trend}()},
+\code{\link{hrvar_count}()},
+\code{\link{internal_network_plot}()}
 
 Other Flexible: 
 \code{\link{create_bar_asis}()},

man/create_trend.Rd

@@ -75,7 +75,9 @@ Other Visualization:
 \code{\link{email_summary}()},
 \code{\link{email_trend}()},
 \code{\link{external_network_plot}()},
-\code{\link{hrvar_count}()}
+\code{\link{hr_trend}()},
+\code{\link{hrvar_count}()},
+\code{\link{internal_network_plot}()}
 
 Other Flexible: 
 \code{\link{create_bar_asis}()},
@@ -94,6 +96,7 @@ Other Flexible:
 \code{\link{create_stacked}()}
 
 Other Time-series: 
+\code{\link{IV_by_period}()},
 \code{\link{create_line_asis}()},
 \code{\link{create_line}()},
 \code{\link{create_period_scatter}()}

man/cut_hour.Rd

@@ -36,6 +36,8 @@ Other Support:
 \code{\link{combine_signals}()},
 \code{\link{extract_date_range}()},
 \code{\link{extract_hr}()},
+\code{\link{heat_colours}()},
+\code{\link{is_date_format}()},
 \code{\link{rgb2hex}()}
 }
 \concept{Support}

man/email_dist.Rd

@@ -92,7 +92,9 @@ Other Visualization:
 \code{\link{email_summary}()},
 \code{\link{email_trend}()},
 \code{\link{external_network_plot}()},
-\code{\link{hrvar_count}()}
+\code{\link{hr_trend}()},
+\code{\link{hrvar_count}()},
+\code{\link{internal_network_plot}()}
 
 Other Emails: 
 \code{\link{email_fizz}()},

man/email_fizz.Rd

@@ -77,7 +77,9 @@ Other Visualization:
 \code{\link{email_summary}()},
 \code{\link{email_trend}()},
 \code{\link{external_network_plot}()},
-\code{\link{hrvar_count}()}
+\code{\link{hr_trend}()},
+\code{\link{hrvar_count}()},
+\code{\link{internal_network_plot}()}
 
 Other Emails: 
 \code{\link{email_dist}()},

man/email_line.Rd

@@ -78,7 +78,9 @@ Other Visualization:
 \code{\link{email_summary}()},
 \code{\link{email_trend}()},
 \code{\link{external_network_plot}()},
-\code{\link{hrvar_count}()}
+\code{\link{hr_trend}()},
+\code{\link{hrvar_count}()},
+\code{\link{internal_network_plot}()}
 
 Other Emails: 
 \code{\link{email_dist}()},

man/email_rank.Rd

@@ -78,7 +78,9 @@ Other Visualization:
 \code{\link{email_summary}()},
 \code{\link{email_trend}()},
 \code{\link{external_network_plot}()},
-\code{\link{hrvar_count}()}
+\code{\link{hr_trend}()},
+\code{\link{hrvar_count}()},
+\code{\link{internal_network_plot}()}
 
 Other Emails: 
 \code{\link{email_dist}()},

man/email_summary.Rd

@@ -12,12 +12,15 @@ email_sum(data, hrvar = "Organization", mingroup = 5, return = "plot")
 \arguments{
 \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"}
+\item{hrvar}{String containing the name of the HR Variable by which to split
+metrics. Defaults to \code{"Organization"}. To run the analysis on the total
+instead of splitting by an HR attribute, supply \code{NULL} (without quotes).}
 
-\item{mingroup}{Numeric value setting the privacy threshold / minimum group size. Defaults to 5.}
+\item{mingroup}{Numeric value setting the privacy threshold / minimum group
+size. Defaults to 5.}
 
-\item{return}{String specifying what to return. This must be one of the following strings:
+\item{return}{String specifying what to return. This must be one of the
+following strings:
 \itemize{
 \item \code{"plot"}
 \item \code{"table"}
@@ -80,7 +83,9 @@ Other Visualization:
 \code{\link{email_rank}()},
 \code{\link{email_trend}()},
 \code{\link{external_network_plot}()},
-\code{\link{hrvar_count}()}
+\code{\link{hr_trend}()},
+\code{\link{hrvar_count}()},
+\code{\link{internal_network_plot}()}
 
 Other Emails: 
 \code{\link{email_dist}()},

man/email_trend.Rd

@@ -64,7 +64,9 @@ Other Visualization:
 \code{\link{email_rank}()},
 \code{\link{email_summary}()},
 \code{\link{external_network_plot}()},
-\code{\link{hrvar_count}()}
+\code{\link{hr_trend}()},
+\code{\link{hrvar_count}()},
+\code{\link{internal_network_plot}()}
 
 Other Emails: 
 \code{\link{email_dist}()},

man/export.Rd

@@ -58,6 +58,8 @@ the default behaviour is to export a PNG.
 \seealso{
 Other Import and Export: 
 \code{\link{copy_df}()},
-\code{\link{create_dt}()}
+\code{\link{create_dt}()},
+\code{\link{import_to_fst}()},
+\code{\link{import_wpa}()}
 }
 \concept{Import and Export}

man/external_network_plot.Rd

@@ -75,10 +75,13 @@ Other Visualization:
 \code{\link{email_rank}()},
 \code{\link{email_summary}()},
 \code{\link{email_trend}()},
-\code{\link{hrvar_count}()}
+\code{\link{hr_trend}()},
+\code{\link{hrvar_count}()},
+\code{\link{internal_network_plot}()}
 
 Other Network: 
 \code{\link{g2g_data}},
+\code{\link{internal_network_plot}()},
 \code{\link{network_g2g}()},
 \code{\link{network_leiden}()},
 \code{\link{network_louvain}()},

man/extract_date_range.Rd

@@ -27,6 +27,8 @@ Other Support:
 \code{\link{combine_signals}()},
 \code{\link{cut_hour}()},
 \code{\link{extract_hr}()},
+\code{\link{heat_colours}()},
+\code{\link{is_date_format}()},
 \code{\link{rgb2hex}()}
 }
 \concept{Support}

man/extract_hr.Rd

@@ -52,6 +52,8 @@ Other Support:
 \code{\link{combine_signals}()},
 \code{\link{cut_hour}()},
 \code{\link{extract_date_range}()},
+\code{\link{heat_colours}()},
+\code{\link{is_date_format}()},
 \code{\link{rgb2hex}()}
 
 Other Data Validation: 
@@ -63,12 +65,15 @@ Other Data Validation:
 \code{\link{hr_trend}()},
 \code{\link{hrvar_count_all}()},
 \code{\link{hrvar_count}()},
+\code{\link{identify_churn}()},
 \code{\link{identify_holidayweeks}()},
 \code{\link{identify_inactiveweeks}()},
 \code{\link{identify_nkw}()},
 \code{\link{identify_outlier}()},
 \code{\link{identify_privacythreshold}()},
 \code{\link{identify_query}()},
+\code{\link{identify_shifts_wp}()},
+\code{\link{identify_shifts}()},
 \code{\link{identify_tenure}()},
 \code{\link{remove_outliers}()},
 \code{\link{subject_validate_report}()},

man/flag_ch_ratio.Rd

@@ -35,6 +35,12 @@ This function flags persons who have an unusual ratio
 of collaboration hours to after-hours collaboration hours.
 Returns a character string by default.
 }
+\section{Metrics used}{
+
+The metric \code{Collaboration_hours} is used in the calculations. Please ensure
+that your query contains a metric with the exact same name.
+}
+
 \examples{
 flag_ch_ratio(sq_data)
 
@@ -55,12 +61,15 @@ Other Data Validation:
 \code{\link{hr_trend}()},
 \code{\link{hrvar_count_all}()},
 \code{\link{hrvar_count}()},
+\code{\link{identify_churn}()},
 \code{\link{identify_holidayweeks}()},
 \code{\link{identify_inactiveweeks}()},
 \code{\link{identify_nkw}()},
 \code{\link{identify_outlier}()},
 \code{\link{identify_privacythreshold}()},
 \code{\link{identify_query}()},
+\code{\link{identify_shifts_wp}()},
+\code{\link{identify_shifts}()},
 \code{\link{identify_tenure}()},
 \code{\link{remove_outliers}()},
 \code{\link{subject_validate_report}()},

man/flag_em_ratio.Rd

@@ -50,12 +50,15 @@ Other Data Validation:
 \code{\link{hr_trend}()},
 \code{\link{hrvar_count_all}()},
 \code{\link{hrvar_count}()},
+\code{\link{identify_churn}()},
 \code{\link{identify_holidayweeks}()},
 \code{\link{identify_inactiveweeks}()},
 \code{\link{identify_nkw}()},
 \code{\link{identify_outlier}()},
 \code{\link{identify_privacythreshold}()},
 \code{\link{identify_query}()},
+\code{\link{identify_shifts_wp}()},
+\code{\link{identify_shifts}()},
 \code{\link{identify_tenure}()},
 \code{\link{remove_outliers}()},
 \code{\link{subject_validate_report}()},

man/flag_extreme.Rd

@@ -62,12 +62,15 @@ Other Data Validation:
 \code{\link{hr_trend}()},
 \code{\link{hrvar_count_all}()},
 \code{\link{hrvar_count}()},
+\code{\link{identify_churn}()},
 \code{\link{identify_holidayweeks}()},
 \code{\link{identify_inactiveweeks}()},
 \code{\link{identify_nkw}()},
 \code{\link{identify_outlier}()},
 \code{\link{identify_privacythreshold}()},
 \code{\link{identify_query}()},
+\code{\link{identify_shifts_wp}()},
+\code{\link{identify_shifts}()},
 \code{\link{identify_tenure}()},
 \code{\link{remove_outliers}()},
 \code{\link{subject_validate_report}()},

man/flag_outlooktime.Rd

@@ -63,12 +63,15 @@ Other Data Validation:
 \code{\link{hr_trend}()},
 \code{\link{hrvar_count_all}()},
 \code{\link{hrvar_count}()},
+\code{\link{identify_churn}()},
 \code{\link{identify_holidayweeks}()},
 \code{\link{identify_inactiveweeks}()},
 \code{\link{identify_nkw}()},
 \code{\link{identify_outlier}()},
 \code{\link{identify_privacythreshold}()},
 \code{\link{identify_query}()},
+\code{\link{identify_shifts_wp}()},
+\code{\link{identify_shifts}()},
 \code{\link{identify_tenure}()},
 \code{\link{remove_outliers}()},
 \code{\link{subject_validate_report}()},

man/flex_index.Rd

@@ -130,6 +130,7 @@ em_data \%>\%
 \seealso{
 Other Working Patterns: 
 \code{\link{identify_shifts_wp}()},
+\code{\link{identify_shifts}()},
 \code{\link{plot_flex_index}()},
 \code{\link{workpatterns_area}()},
 \code{\link{workpatterns_classify_bw}()},

man/g2g_data.Rd

@@ -35,6 +35,7 @@ Other Data:
 
 Other Network: 
 \code{\link{external_network_plot}()},
+\code{\link{internal_network_plot}()},
 \code{\link{network_g2g}()},
 \code{\link{network_leiden}()},
 \code{\link{network_louvain}()},

man/heat_colours.Rd

@@ -2,9 +2,12 @@
 % Please edit documentation in R/heat_colours.R
 \name{heat_colours}
 \alias{heat_colours}
+\alias{heat_colors}
 \title{Generate a vector of \code{n} contiguous colours, as a red-yellow-green palette.}
 \usage{
 heat_colours(n, alpha, rev = FALSE)
+
+heat_colors(n, alpha, rev = FALSE)
 }
 \arguments{
 \item{n}{the number of colors (>= 1) to be in the palette.}
@@ -12,7 +15,8 @@ heat_colours(n, alpha, rev = FALSE)
 \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.}
+\item{rev}{logical indicating whether the ordering of the colors should be
+reversed.}
 }
 \value{
 A character vector containing the HEX codes and the same length as \code{n} is
@@ -25,6 +29,19 @@ corresponding to the heat map 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)
+barplot(rep(10, 50), col = heat_colours(n = 50, alpha = 0.5, rev = TRUE),
+border = NA)
 
 }
+\seealso{
+Other Support: 
+\code{\link{camel_clean}()},
+\code{\link{check_inputs}()},
+\code{\link{combine_signals}()},
+\code{\link{cut_hour}()},
+\code{\link{extract_date_range}()},
+\code{\link{extract_hr}()},
+\code{\link{is_date_format}()},
+\code{\link{rgb2hex}()}
+}
+\concept{Support}

man/hr_trend.Rd

@@ -40,6 +40,44 @@ hr_trend(dv_data, return = "table")
 
 }
 \seealso{
+Other Visualization: 
+\code{\link{afterhours_dist}()},
+\code{\link{afterhours_fizz}()},
+\code{\link{afterhours_line}()},
+\code{\link{afterhours_rank}()},
+\code{\link{afterhours_summary}()},
+\code{\link{afterhours_trend}()},
+\code{\link{collaboration_area}()},
+\code{\link{collaboration_dist}()},
+\code{\link{collaboration_fizz}()},
+\code{\link{collaboration_line}()},
+\code{\link{collaboration_rank}()},
+\code{\link{collaboration_sum}()},
+\code{\link{collaboration_trend}()},
+\code{\link{create_bar_asis}()},
+\code{\link{create_bar}()},
+\code{\link{create_boxplot}()},
+\code{\link{create_bubble}()},
+\code{\link{create_dist}()},
+\code{\link{create_fizz}()},
+\code{\link{create_line_asis}()},
+\code{\link{create_line}()},
+\code{\link{create_period_scatter}()},
+\code{\link{create_rank}()},
+\code{\link{create_sankey}()},
+\code{\link{create_scatter}()},
+\code{\link{create_stacked}()},
+\code{\link{create_trend}()},
+\code{\link{email_dist}()},
+\code{\link{email_fizz}()},
+\code{\link{email_line}()},
+\code{\link{email_rank}()},
+\code{\link{email_summary}()},
+\code{\link{email_trend}()},
+\code{\link{external_network_plot}()},
+\code{\link{hrvar_count}()},
+\code{\link{internal_network_plot}()}
+
 Other Data Validation: 
 \code{\link{check_query}()},
 \code{\link{extract_hr}()},
@@ -49,12 +87,15 @@ Other Data Validation:
 \code{\link{flag_outlooktime}()},
 \code{\link{hrvar_count_all}()},
 \code{\link{hrvar_count}()},
+\code{\link{identify_churn}()},
 \code{\link{identify_holidayweeks}()},
 \code{\link{identify_inactiveweeks}()},
 \code{\link{identify_nkw}()},
 \code{\link{identify_outlier}()},
 \code{\link{identify_privacythreshold}()},
 \code{\link{identify_query}()},
+\code{\link{identify_shifts_wp}()},
+\code{\link{identify_shifts}()},
 \code{\link{identify_tenure}()},
 \code{\link{remove_outliers}()},
 \code{\link{subject_validate_report}()},
@@ -62,3 +103,4 @@ Other Data Validation:
 \code{\link{track_HR_change}()}
 }
 \concept{Data Validation}
+\concept{Visualization}

man/hrvar_count.Rd

@@ -12,10 +12,10 @@ analysis_scope(data, hrvar = "Organization", return = "plot")
 \arguments{
 \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".
-If a vector with more than one value is provided, the HR attributes are automatically
-concatenated.}
+\item{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 concatenated.}
 
 \item{return}{String specifying what to return. This must be one of the
 following strings:
@@ -82,7 +82,9 @@ Other Visualization:
 \code{\link{email_rank}()},
 \code{\link{email_summary}()},
 \code{\link{email_trend}()},
-\code{\link{external_network_plot}()}
+\code{\link{external_network_plot}()},
+\code{\link{hr_trend}()},
+\code{\link{internal_network_plot}()}
 
 Other Data Validation: 
 \code{\link{check_query}()},
@@ -93,12 +95,15 @@ Other Data Validation:
 \code{\link{flag_outlooktime}()},
 \code{\link{hr_trend}()},
 \code{\link{hrvar_count_all}()},
+\code{\link{identify_churn}()},
 \code{\link{identify_holidayweeks}()},
 \code{\link{identify_inactiveweeks}()},
 \code{\link{identify_nkw}()},
 \code{\link{identify_outlier}()},
 \code{\link{identify_privacythreshold}()},
 \code{\link{identify_query}()},
+\code{\link{identify_shifts_wp}()},
+\code{\link{identify_shifts}()},
 \code{\link{identify_tenure}()},
 \code{\link{remove_outliers}()},
 \code{\link{subject_validate_report}()},

man/hrvar_count_all.Rd

@@ -2,7 +2,8 @@
 % Please edit documentation in R/hrvar_count_all.R
 \name{hrvar_count_all}
 \alias{hrvar_count_all}
-\title{Create count of distinct fields and percentage of employees with NAs for all HR variables}
+\title{Create count of distinct fields and percentage of employees with
+missing values for all HR variables}
 \usage{
 hrvar_count_all(
   data,
@@ -15,25 +16,34 @@ hrvar_count_all(
 \arguments{
 \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.}
+\item{n_var}{number of HR variables to include in report as rows. Default is
+set to 50 HR variables.}
 
 \item{return}{String to specify what to return}
 
-\item{threshold}{The max number of unique values allowed for any attribute. Default is 100.}
+\item{threshold}{The max number of unique values allowed for any attribute.
+Default is 100.}
 
-\item{maxna}{The max percentage of NAs allowable for any column. Default is 20.}
+\item{maxna}{The max percentage of NAs allowable for any column. Default is
+20.}
 }
 \value{
 Returns an error message by default, where 'text' is passed in \code{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.
-when 'message' is passed, outputs a message indicating which values are beyond the specified thresholds.
+\itemize{
+\item \code{'table'}: data frame. A summary table listing the number of distinct
+fields and percentage of missing values for the specified number of HR
+attributes will be returned.
+\item \code{'message'}: outputs a message indicating which values are
+beyond the specified thresholds.
+}
 }
 \description{
-This function enables you to create a summary table to validate organizational data. This table will provide a summary of the data found
-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.
+This function enables you to create a summary table to validate
+organizational data. This table will provide a summary of the data found in
+the Workplace Analytics Sources page. This function will return a summary
+table with the count of distinct fields per HR attribute and the percentage
+of employees with missing values for that attribute. See \code{hrvar_count()}
+function for more detail on the specific HR attribute of interest.
 }
 \examples{
 # Return a summary table of all HR attributes
@@ -50,12 +60,15 @@ Other Data Validation:
 \code{\link{flag_outlooktime}()},
 \code{\link{hr_trend}()},
 \code{\link{hrvar_count}()},
+\code{\link{identify_churn}()},
 \code{\link{identify_holidayweeks}()},
 \code{\link{identify_inactiveweeks}()},
 \code{\link{identify_nkw}()},
 \code{\link{identify_outlier}()},
 \code{\link{identify_privacythreshold}()},
 \code{\link{identify_query}()},
+\code{\link{identify_shifts_wp}()},
+\code{\link{identify_shifts}()},
 \code{\link{identify_tenure}()},
 \code{\link{remove_outliers}()},
 \code{\link{subject_validate_report}()},

man/identify_churn.Rd

@@ -9,40 +9,86 @@ identify_churn(data, n1 = 6, n2 = 6, return = "message", flip = FALSE)
 \arguments{
 \item{data}{A Person Query as a data frame. Must contain a \code{PersonId}.}
 
-\item{n1}{A numeric value specifying the number of weeks at the beginning of the period
-that defines the measured employee set. Defaults to 6.}
+\item{n1}{A numeric value specifying the number of weeks at the beginning of
+the period that defines the measured employee set. Defaults to 6.}
 
-\item{n2}{A numeric value specifying the number of weeks at the end of the period
-to calculate whether employees have churned from the data. Defaults to 6.}
+\item{n2}{A numeric value specifying the number of weeks at the end of the
+period to calculate whether employees have churned from the data. Defaults
+to 6.}
 
-\item{return}{String specifying what to return. Defaults to "message", with options to
-return a character string ("text") or the \code{PersonId} of employees who have been identified
-as churned ("data").}
+\item{return}{String specifying what to return. This must be one of the
+following strings:
+\itemize{
+\item \code{"message"} (default)
+\item \code{"text"}
+\item \code{"data"}
+}
 
-\item{flip}{Logical, defaults to FALSE. This determines whether to reverse the logic of identifying the
-non-overlapping set. If set to \code{TRUE}, this effectively identifies new-joiners, or those
-who were not present in the first n weeks of the data but were present in the final n weeks.}
+See \code{Value} for more information.}
+
+\item{flip}{Logical, defaults to FALSE. This determines whether to reverse
+the logic of identifying the non-overlapping set. If set to \code{TRUE}, this
+effectively identifies new-joiners, or those who were not present in the
+first n weeks of the data but were present in the final n weeks.}
+}
+\value{
+A different output is returned depending on the value passed to the \code{return}
+argument:
+\itemize{
+\item \code{"message"}: Message on console. A diagnostic message.
+\item \code{"text"}: String. A diagnostic message.
+\item \code{"data"}: Character vector containing the the \code{PersonId} of
+employees who have been identified as churned.
+}
 }
 \description{
-This function identifies and counts the number of employees who have churned from
-the dataset by measuring whether an employee who is present in the first \code{n} (n1) weeks
-of the data is present in the last \code{n} (n2) weeks of the data.
+This function identifies and counts the number of employees who have churned
+from the dataset by measuring whether an employee who is present in the first
+\code{n} (n1) weeks of the data is present in the last \code{n} (n2) weeks of the data.
 }
 \details{
-An additional use case of this function is the ability to identify "new-joiners" by using
-the argument \code{flip}.
+An additional use case of this function is the ability to identify
+"new-joiners" by using the argument \code{flip}.
 
-If an employee is present in the first \code{n} weeks of the data but not present in the last
-\code{n} weeks of the data, the function considers the employee as churned. As the measurement period
-is defined by the number of weeks from the start and the end of the passed data frame, you
-may consider filtering the dates accordingly before running this function.
+If an employee is present in the first \code{n} weeks of the data but not present
+in the last \code{n} weeks of the data, the function considers the employee as
+churned. As the measurement period is defined by the number of weeks from the
+start and the end of the passed data frame, you may consider filtering the
+dates accordingly before running this function.
 
-Another assumption that is in place is that any employee whose \code{PersonId} is not available in the
-data has churned. Note that there may be other reasons why an employee's \code{PersonId} may not
-be present, e.g. maternity/paternity leave, Workplace Analytics license has been removed,
-shift to a low-collaboration role (to the extent that he/she becomes inactive).
+Another assumption that is in place is that any employee whose \code{PersonId} is
+not available in the data has churned. Note that there may be other reasons
+why an employee's \code{PersonId} may not be present, e.g. maternity/paternity
+leave, Workplace Analytics license has been removed, shift to a
+low-collaboration role (to the extent that he/she becomes inactive).
 }
 \examples{
 sq_data \%>\% identify_churn(n1 = 3, n2 = 3, return = "message")
 
 }
+\seealso{
+Other Data Validation: 
+\code{\link{check_query}()},
+\code{\link{extract_hr}()},
+\code{\link{flag_ch_ratio}()},
+\code{\link{flag_em_ratio}()},
+\code{\link{flag_extreme}()},
+\code{\link{flag_outlooktime}()},
+\code{\link{hr_trend}()},
+\code{\link{hrvar_count_all}()},
+\code{\link{hrvar_count}()},
+\code{\link{identify_holidayweeks}()},
+\code{\link{identify_inactiveweeks}()},
+\code{\link{identify_nkw}()},
+\code{\link{identify_outlier}()},
+\code{\link{identify_privacythreshold}()},
+\code{\link{identify_query}()},
+\code{\link{identify_shifts_wp}()},
+\code{\link{identify_shifts}()},
+\code{\link{identify_tenure}()},
+\code{\link{remove_outliers}()},
+\code{\link{subject_validate_report}()},
+\code{\link{subject_validate}()},
+\code{\link{track_HR_change}()}
+}
+\concept{Data Validation}

man/identify_holidayweeks.Rd

@@ -9,25 +9,53 @@ identify_holidayweeks(data, sd = 1, return = "message")
 \arguments{
 \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.}
+\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. Returns a message by default (\code{return} = "message").
-Valid options are:
+#' @param return String specifying what to return. This must be one of the
+following strings:
 \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 plot with holiday weeks highlighted is returned as a dataframe.
-}}
+\item \code{"message"} (default)
+\item \code{"data"}
+\item \code{"data_cleaned"}
+\item \code{"data_dirty"}
+\item \code{"plot"}
+}
+
+See \code{Value} for more information.}
+}
+\value{
+A different output is returned depending on the value passed to the \code{return}
+argument:
+\itemize{
+\item \code{"message"}: message on console. a message is printed identifying holiday
+weeks.
+\item \code{"data"}: data frame. A dataset with outlier weeks flagged in a new
+column is returned as a dataframe.
+\item \code{"data_cleaned"}: data frame. A dataset with outlier weeks removed is
+returned.
+\item \code{"data_dirty"}: data frame. A dataset with only outlier weeks is
+returned.
+\item \code{"plot"}: ggplot object. A line plot of Collaboration Hours with holiday
+weeks highlighted.
+}
 }
 \description{
-This function scans a standard query output for weeks where collaboration hours is far outside the mean.
-Returns a list of weeks that appear to be holiday weeks and optionally an edited dataframe with outliers removed.
-By default, missing values are excluded.
+This function scans a standard query output for weeks where collaboration
+hours is far outside the mean. Returns a list of weeks that appear to be
+holiday weeks and optionally an edited dataframe with outliers removed. By
+default, missing values are excluded.
 
-As best practice, run this function prior to any analysis to remove atypical collaboration weeks from your dataset.
+As best practice, run this function prior to any analysis to remove atypical
+collaboration weeks from your dataset.
 }
+\section{Metrics used}{
+
+The metric \code{Collaboration_hours} is used in the calculations. Please ensure
+that your query contains a metric with the exact same name.
+}
+
 \examples{
 # Return a message by default
 identify_holidayweeks(sq_data)
@@ -35,7 +63,6 @@ identify_holidayweeks(sq_data)
 # Return plot
 identify_holidayweeks(sq_data, return = "plot")
 
-
 }
 \seealso{
 Other Data Validation: 
@@ -48,11 +75,14 @@ Other Data Validation:
 \code{\link{hr_trend}()},
 \code{\link{hrvar_count_all}()},
 \code{\link{hrvar_count}()},
+\code{\link{identify_churn}()},
 \code{\link{identify_inactiveweeks}()},
 \code{\link{identify_nkw}()},
 \code{\link{identify_outlier}()},
 \code{\link{identify_privacythreshold}()},
 \code{\link{identify_query}()},
+\code{\link{identify_shifts_wp}()},
+\code{\link{identify_shifts}()},
 \code{\link{identify_tenure}()},
 \code{\link{remove_outliers}()},
 \code{\link{subject_validate_report}()},

man/identify_inactiveweeks.Rd

@@ -9,20 +9,34 @@ identify_inactiveweeks(data, sd = 2, return = "text")
 \arguments{
 \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.}
+\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 specifying what to return. This must be one of the
+following strings:
+\itemize{
+\item \code{"text"}
+\item \code{"data_cleaned"}
+\item \code{"data_dirty"}
+}
+
+See \code{Value} for more information.}
 }
 \value{
-Returns an error message by default, where 'text' is returned.
-When 'data_cleaned' is passed, a dataset with outlier weeks removed is returned as a dataframe.
-When 'data_dirty' is passed, a dataset with outlier weeks is returned as a dataframe.
+Returns an error message by default, where \code{'text'} is returned. When
+\code{'data_cleaned'} is passed, a dataset with outlier weeks removed is returned
+as a dataframe. When \code{'data_dirty'} is passed, a dataset with outlier weeks
+is returned as a dataframe.
 }
 \description{
-This function scans a standard query output for weeks where collaboration hours is far outside the mean for any individual person in the dataset.
-Returns a list of weeks that appear to be inactive weeks and optionally an edited dataframe with outliers removed.
+This function scans a standard query output for weeks where collaboration
+hours is far outside the mean for any individual person in the dataset.
+Returns a list of weeks that appear to be inactive weeks and optionally an
+edited dataframe with outliers removed.
 
-As best practice, run this function prior to any analysis to remove atypical collaboration weeks from your dataset.
+As best practice, run this function prior to any analysis to remove atypical
+collaboration weeks from your dataset.
 }
 \seealso{
 Other Data Validation: 
@@ -35,11 +49,14 @@ Other Data Validation:
 \code{\link{hr_trend}()},
 \code{\link{hrvar_count_all}()},
 \code{\link{hrvar_count}()},
+\code{\link{identify_churn}()},
 \code{\link{identify_holidayweeks}()},
 \code{\link{identify_nkw}()},
 \code{\link{identify_outlier}()},
 \code{\link{identify_privacythreshold}()},
 \code{\link{identify_query}()},
+\code{\link{identify_shifts_wp}()},
+\code{\link{identify_shifts}()},
 \code{\link{identify_tenure}()},
 \code{\link{remove_outliers}()},
 \code{\link{subject_validate_report}()},

man/identify_nkw.Rd

@@ -2,27 +2,49 @@
 % Please edit documentation in R/identify_nkw.R
 \name{identify_nkw}
 \alias{identify_nkw}
-\title{Identify Non-Knowledge workers in a Person Query using Collaboration Hours}
+\title{Identify Non-Knowledge workers in a Person Query using Collaboration
+Hours}
 \usage{
 identify_nkw(data, collab_threshold = 5, return = "data_summary")
 }
 \arguments{
 \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.}
+\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.}
 
-\item{return}{String to specify what to return}
+\item{return}{String specifying what to return. This must be one of the
+following strings:
+\itemize{
+\item \code{"text"}
+\item \code{"data_with_flag"}
+\item \code{"data_clean"}
+\item \code{"data_summary"}
+}
+
+See \code{Value} for more information.}
 }
 \value{
-Returns an error message by default, where 'text' is passed in \code{return}.
-When 'data_with_flag' is passed, a copy of your original dataframe with an additional column containing the kw/nkw flag will be returned.
-When 'data_clean' is passed, a full dataframe excluding "nkw" employees will be returned.
-when 'data_summmary' is passed, a summary table by organization listing the number and \% of non-knowledge workers will be returned.
+A different output is returned depending on the value passed to the \code{return}
+argument:
+\itemize{
+\item \code{"text"}: string. Returns a diagnostic message.
+\item \code{"data_with_flag"}: data frame. Original input data with an additional
+column containing the \code{kw}/\code{nkw} flag.
+\item \code{"data_clean"}: data frame. Data frame with non-knowledge workers
+excluded.
+\item \code{"data_summary"}: data frame. A summary table by organization listing
+the number and \% of non-knowledge workers.
+}
 }
 \description{
-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.
+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.
 }
 \seealso{
 Other Data Validation: 
@@ -35,11 +57,14 @@ Other Data Validation:
 \code{\link{hr_trend}()},
 \code{\link{hrvar_count_all}()},
 \code{\link{hrvar_count}()},
+\code{\link{identify_churn}()},
 \code{\link{identify_holidayweeks}()},
 \code{\link{identify_inactiveweeks}()},
 \code{\link{identify_outlier}()},
 \code{\link{identify_privacythreshold}()},
 \code{\link{identify_query}()},
+\code{\link{identify_shifts_wp}()},
+\code{\link{identify_shifts}()},
 \code{\link{identify_tenure}()},
 \code{\link{remove_outliers}()},
 \code{\link{subject_validate_report}()},

man/identify_outlier.Rd

@@ -42,11 +42,14 @@ Other Data Validation:
 \code{\link{hr_trend}()},
 \code{\link{hrvar_count_all}()},
 \code{\link{hrvar_count}()},
+\code{\link{identify_churn}()},
 \code{\link{identify_holidayweeks}()},
 \code{\link{identify_inactiveweeks}()},
 \code{\link{identify_nkw}()},
 \code{\link{identify_privacythreshold}()},
 \code{\link{identify_query}()},
+\code{\link{identify_shifts_wp}()},
+\code{\link{identify_shifts}()},
 \code{\link{identify_tenure}()},
 \code{\link{remove_outliers}()},
 \code{\link{subject_validate_report}()},
@@ -54,4 +57,3 @@ Other Data Validation:
 \code{\link{track_HR_change}()}
 }
 \concept{Data Validation}
-\concept{General}

man/identify_privacythreshold.Rd

@@ -17,19 +17,42 @@ identify_privacythreshold(
 \item{hrvar}{A list of HR Variables to consider in the scan.
 Defaults to all HR attributes identified.}
 
-\item{mingroup}{Numeric value setting the privacy threshold / minimum group size.
-Defaults to 5.}
+\item{mingroup}{Numeric value setting the privacy threshold / minimum group
+size. Defaults to 5.}
 
-\item{return}{A character vector specifying what to return.
-Valid values include "table" (default) and "text" (text)}
+\item{return}{String specifying what to return. This must be one of the
+following strings:
+\itemize{
+\item \code{"table"}
+\item \code{"text"}
+}
+
+See \code{Value} for more information.}
 }
 \value{
+A different output is returned depending on the value passed to the \code{return}
+argument:
+\itemize{
+\item \code{"table"}: data frame. A summary table of groups that fall below the
+privacy threshold.
+\item \code{"text"}: string. A diagnostic message.
+}
+
 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{
-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.
+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.
+}
+\examples{
+# Return a summary table
+dv_data \%>\% identify_privacythreshold(return = "table")
+
+# Return a diagnostic message
+dv_data \%>\% identify_privacythreshold(return = "text")
+
 }
 \seealso{
 Other Data Validation: 
@@ -42,11 +65,14 @@ Other Data Validation:
 \code{\link{hr_trend}()},
 \code{\link{hrvar_count_all}()},
 \code{\link{hrvar_count}()},
+\code{\link{identify_churn}()},
 \code{\link{identify_holidayweeks}()},
 \code{\link{identify_inactiveweeks}()},
 \code{\link{identify_nkw}()},
 \code{\link{identify_outlier}()},
 \code{\link{identify_query}()},
+\code{\link{identify_shifts_wp}()},
+\code{\link{identify_shifts}()},
 \code{\link{identify_tenure}()},
 \code{\link{remove_outliers}()},
 \code{\link{subject_validate_report}()},

man/identify_query.Rd

@@ -11,8 +11,11 @@ identify_query(data, threshold = 2)
 If the data is not identified as a Workplace Analytics dataset, the function
 will return an error.}
 
-\item{threshold}{Debugging use only. Increase to raise the 'strictness' of the
-guessing algorithm. Defaults to 2.}
+\item{threshold}{Debugging use only. Increase to raise the 'strictness' of
+the guessing algorithm. Defaults to 2.}
+}
+\value{
+String. A diagnostic message is returned.
 }
 \description{
 Pass a Workplace Analytics dataset and return the identified
@@ -39,11 +42,14 @@ Other Data Validation:
 \code{\link{hr_trend}()},
 \code{\link{hrvar_count_all}()},
 \code{\link{hrvar_count}()},
+\code{\link{identify_churn}()},
 \code{\link{identify_holidayweeks}()},
 \code{\link{identify_inactiveweeks}()},
 \code{\link{identify_nkw}()},
 \code{\link{identify_outlier}()},
 \code{\link{identify_privacythreshold}()},
+\code{\link{identify_shifts_wp}()},
+\code{\link{identify_shifts}()},
 \code{\link{identify_tenure}()},
 \code{\link{remove_outliers}()},
 \code{\link{subject_validate_report}()},

man/identify_shifts.Rd

@@ -26,10 +26,7 @@ argument:
 \itemize{
 \item \code{"plot"}: ggplot object. A bar plot for the weekly count of shifts.
 \item \code{"table"}: data frame. A summary table for the count of shifts.
-\item \verb{"data}: data frame. Input data appended with the following columns:
-\itemize{
-\item \code{Shifts}
-}
+\item \verb{"data}: data frame. Input data appended with the \code{Shifts} columns.
 }
 }
 \description{
@@ -45,3 +42,40 @@ dv_data \%>\% identify_shifts()
 dv_data \%>\% identify_shifts(return = "table")
 
 }
+\seealso{
+Other Data Validation: 
+\code{\link{check_query}()},
+\code{\link{extract_hr}()},
+\code{\link{flag_ch_ratio}()},
+\code{\link{flag_em_ratio}()},
+\code{\link{flag_extreme}()},
+\code{\link{flag_outlooktime}()},
+\code{\link{hr_trend}()},
+\code{\link{hrvar_count_all}()},
+\code{\link{hrvar_count}()},
+\code{\link{identify_churn}()},
+\code{\link{identify_holidayweeks}()},
+\code{\link{identify_inactiveweeks}()},
+\code{\link{identify_nkw}()},
+\code{\link{identify_outlier}()},
+\code{\link{identify_privacythreshold}()},
+\code{\link{identify_query}()},
+\code{\link{identify_shifts_wp}()},
+\code{\link{identify_tenure}()},
+\code{\link{remove_outliers}()},
+\code{\link{subject_validate_report}()},
+\code{\link{subject_validate}()},
+\code{\link{track_HR_change}()}
+
+Other Working Patterns: 
+\code{\link{flex_index}()},
+\code{\link{identify_shifts_wp}()},
+\code{\link{plot_flex_index}()},
+\code{\link{workpatterns_area}()},
+\code{\link{workpatterns_classify_bw}()},
+\code{\link{workpatterns_classify_pav}()},
+\code{\link{workpatterns_classify}()},
+\code{\link{workpatterns_hclust}()}
+}
+\concept{Data Validation}
+\concept{Working Patterns}

man/identify_shifts_wp.Rd

@@ -76,8 +76,33 @@ em_data \%>\% identify_shifts_wp(return = "table")
 
 }
 \seealso{
+Other Data Validation: 
+\code{\link{check_query}()},
+\code{\link{extract_hr}()},
+\code{\link{flag_ch_ratio}()},
+\code{\link{flag_em_ratio}()},
+\code{\link{flag_extreme}()},
+\code{\link{flag_outlooktime}()},
+\code{\link{hr_trend}()},
+\code{\link{hrvar_count_all}()},
+\code{\link{hrvar_count}()},
+\code{\link{identify_churn}()},
+\code{\link{identify_holidayweeks}()},
+\code{\link{identify_inactiveweeks}()},
+\code{\link{identify_nkw}()},
+\code{\link{identify_outlier}()},
+\code{\link{identify_privacythreshold}()},
+\code{\link{identify_query}()},
+\code{\link{identify_shifts}()},
+\code{\link{identify_tenure}()},
+\code{\link{remove_outliers}()},
+\code{\link{subject_validate_report}()},
+\code{\link{subject_validate}()},
+\code{\link{track_HR_change}()}
+
 Other Working Patterns: 
 \code{\link{flex_index}()},
+\code{\link{identify_shifts}()},
 \code{\link{plot_flex_index}()},
 \code{\link{workpatterns_area}()},
 \code{\link{workpatterns_classify_bw}()},
@@ -85,4 +110,5 @@ Other Working Patterns:
 \code{\link{workpatterns_classify}()},
 \code{\link{workpatterns_hclust}()}
 }
+\concept{Data Validation}
 \concept{Working Patterns}

man/identify_tenure.Rd

@@ -2,7 +2,8 @@
 % Please edit documentation in R/identify_tenure.R
 \name{identify_tenure}
 \alias{identify_tenure}
-\title{Tenure calculation based on different input dates, returns data summary table or histogram}
+\title{Tenure calculation based on different input dates, returns data
+summary table or histogram}
 \usage{
 identify_tenure(
   data,
@@ -15,9 +16,11 @@ identify_tenure(
 \arguments{
 \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".}
+\item{end_date}{A string specifying the name of the date variable
+representing the latest date. Defaults to "Date".}
 
-\item{beg_date}{A string specifying the name of the date variable representing the hire date. Defaults to "HireDate".}
+\item{beg_date}{A string specifying the name of the date variable
+representing the hire date. Defaults to "HireDate".}
 
 \item{maxten}{A numeric value representing the maximum tenure.
 If the tenure exceeds this threshold, it would be accounted for in the flag message.}
@@ -53,12 +56,15 @@ Other Data Validation:
 \code{\link{hr_trend}()},
 \code{\link{hrvar_count_all}()},
 \code{\link{hrvar_count}()},
+\code{\link{identify_churn}()},
 \code{\link{identify_holidayweeks}()},
 \code{\link{identify_inactiveweeks}()},
 \code{\link{identify_nkw}()},
 \code{\link{identify_outlier}()},
 \code{\link{identify_privacythreshold}()},
 \code{\link{identify_query}()},
+\code{\link{identify_shifts_wp}()},
+\code{\link{identify_shifts}()},
 \code{\link{remove_outliers}()},
 \code{\link{subject_validate_report}()},
 \code{\link{subject_validate}()},

man/import_to_fst.Rd

@@ -18,7 +18,7 @@ file will be saved in the same directory with a different file extension.}
 \description{
 Uses \code{import_wpa()} to read a Workplace Analytics query in CSV
 and convert this into the serialized FST format which is much faster to
-read. The 'fst' package must be installed.
+read. The 'fst' package must be installed, or an error message is returned.
 }
 \details{
 The \href{https://www.fstpackage.org/}{fst} package provides a way to serialize
@@ -35,3 +35,11 @@ at the start of your analysis script.
 Internally, \code{import_to_fst()} uses \code{import_wpa()}, and additional arguments
 to \code{import_wpa()} can be passed with \code{...}.
 }
+\seealso{
+Other Import and Export: 
+\code{\link{copy_df}()},
+\code{\link{create_dt}()},
+\code{\link{export}()},
+\code{\link{import_wpa}()}
+}
+\concept{Import and Export}

man/import_wpa.Rd

@@ -25,11 +25,19 @@ information. Defaults to \code{'UTF-8'}.}
 A \code{tibble} is returned.
 }
 \description{
-Import a Workplace Analytics Query from a local CSV File, with variable classifications optimised
-for other 'wpa' functions.
+Import a Workplace Analytics Query from a local CSV File, with variable
+classifications optimised for other 'wpa' functions.
 }
 \details{
 \code{import_wpa()} uses \code{data.table::fread()} to import CSV files for speed,
 and by default \code{stringsAsFactors} is set to FALSE.
 A data frame is returned by the function (not a \code{data.table}).
 }
+\seealso{
+Other Import and Export: 
+\code{\link{copy_df}()},
+\code{\link{create_dt}()},
+\code{\link{export}()},
+\code{\link{import_to_fst}()}
+}
+\concept{Import and Export}

man/internal_network_plot.Rd

@@ -28,13 +28,69 @@ following strings: - \code{"plot"} - \code{"table"}}
 of the bubbles}
 }
 \description{
-Plot the internal network metrics for a HR variable as a scatter plot, showing
-Internal Network Breadth as the vertical axis and Internal Network Size as the
-horizontal axis.
+Plot the internal network metrics for a HR variable as a scatter plot,
+showing Internal Network Breadth as the vertical axis and Internal Network
+Size as the horizontal axis.
+}
+\details{
+Uses the metrics \code{Internal_network_size} and
+\code{Networking_outside_organization}.
 }
 \examples{
 # Return plot
 sq_data \%>\% internal_network_plot(return = "plot")
 
+# Return summary table
+sq_data \%>\% internal_network_plot(return = "table")
+
 }
-\concept{Connectivity}
+\seealso{
+Other Visualization: 
+\code{\link{afterhours_dist}()},
+\code{\link{afterhours_fizz}()},
+\code{\link{afterhours_line}()},
+\code{\link{afterhours_rank}()},
+\code{\link{afterhours_summary}()},
+\code{\link{afterhours_trend}()},
+\code{\link{collaboration_area}()},
+\code{\link{collaboration_dist}()},
+\code{\link{collaboration_fizz}()},
+\code{\link{collaboration_line}()},
+\code{\link{collaboration_rank}()},
+\code{\link{collaboration_sum}()},
+\code{\link{collaboration_trend}()},
+\code{\link{create_bar_asis}()},
+\code{\link{create_bar}()},
+\code{\link{create_boxplot}()},
+\code{\link{create_bubble}()},
+\code{\link{create_dist}()},
+\code{\link{create_fizz}()},
+\code{\link{create_line_asis}()},
+\code{\link{create_line}()},
+\code{\link{create_period_scatter}()},
+\code{\link{create_rank}()},
+\code{\link{create_sankey}()},
+\code{\link{create_scatter}()},
+\code{\link{create_stacked}()},
+\code{\link{create_trend}()},
+\code{\link{email_dist}()},
+\code{\link{email_fizz}()},
+\code{\link{email_line}()},
+\code{\link{email_rank}()},
+\code{\link{email_summary}()},
+\code{\link{email_trend}()},
+\code{\link{external_network_plot}()},
+\code{\link{hr_trend}()},
+\code{\link{hrvar_count}()}
+
+Other Network: 
+\code{\link{external_network_plot}()},
+\code{\link{g2g_data}},
+\code{\link{network_g2g}()},
+\code{\link{network_leiden}()},
+\code{\link{network_louvain}()},
+\code{\link{network_p2p}()},
+\code{\link{p2p_data_sim}()}
+}
+\concept{Network}
+\concept{Visualization}