Distribution of After-hours Collaboration Hours as a 100% stacked bar
Description
Analyse the distribution of weekly after-hours collaboration time. Returns a stacked bar plot by default. Additional options available to return a table with distribution elements.
Usage
afterhours_dist( data, hrvar = "Organization", mingroup = 5, return = "plot", cut = c(1, 2, 3) )afterhours_dist( data, hrvar = "Organization", mingroup = 5, return = "plot", cut = c(1, 2, 3) )
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
return |
String specifying what to return. This must be one of the following strings:
See |
cut |
A vector specifying the cuts to use for the data, accepting "default" or "range-cut" as character vector, or a numeric value of length three to specify the exact breaks to use. e.g. c(1, 3, 5) |
Details
Uses the metric After_hours_collaboration_hours.
See create_dist() for applying the same analysis to a different metric.
Value
A different output is returned depending on the value passed to the return argument:
-
"plot": 'ggplot' object. A stacked bar plot for the metric. -
"table": data frame. A summary table for the metric.
See Also
Other Visualization:
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other After-hours Collaboration:
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
external_rank()
Examples
# Return plot afterhours_dist(pq_data, hrvar = "Organization") # Return summary table afterhours_dist(pq_data, hrvar = "Organization", return = "table") # Return result with a custom specified breaks afterhours_dist(pq_data, hrvar = "LevelDesignation", cut = c(4, 7, 9))# Return plot afterhours_dist(pq_data, hrvar = "Organization") # Return summary table afterhours_dist(pq_data, hrvar = "Organization", return = "table") # Return result with a custom specified breaks afterhours_dist(pq_data, hrvar = "LevelDesignation", cut = c(4, 7, 9))
Distribution of After-hours Collaboration Hours (Fizzy Drink plot)
Description
Analyze weekly after-hours collaboration hours distribution, and returns a 'fizzy' scatter plot by default. Additional options available to return a table with distribution elements.
Usage
afterhours_fizz(data, hrvar = "Organization", mingroup = 5, return = "plot")afterhours_fizz(data, hrvar = "Organization", mingroup = 5, return = "plot")
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
return |
String specifying what to return. This must be one of the following strings:
See |
Details
Uses the metric After_hours_collaboration_hours.
See create_fizz() for applying the same analysis to a different metric.
Value
A different output is returned depending on the value passed to the return argument:
-
"plot": 'ggplot' object. A jittered scatter plot for the metric. -
"table": data frame. A summary table for the metric.
See Also
Other Visualization:
afterhours_dist(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other After-hours Collaboration:
afterhours_dist(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
external_rank()
Examples
# Return plot afterhours_fizz(pq_data, hrvar = "LevelDesignation", return = "plot") # Return summary table afterhours_fizz(pq_data, hrvar = "Organization", return = "table")# Return plot afterhours_fizz(pq_data, hrvar = "LevelDesignation", return = "plot") # Return summary table afterhours_fizz(pq_data, hrvar = "Organization", return = "table")
After-hours Collaboration Time Trend - Line Chart
Description
Provides a week by week view of after-hours collaboration time, visualized as line charts. By default returns a line chart for after-hours collaboration hours, with a separate panel per value in the HR attribute. Additional options available to return a summary table.
Usage
afterhours_line(data, hrvar = "Organization", mingroup = 5, return = "plot")afterhours_line(data, hrvar = "Organization", mingroup = 5, return = "plot")
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
return |
String specifying what to return. This must be one of the following strings:
See |
Details
Uses the metric After_hours_collaboration_hours.
Value
A different output is returned depending on the value passed to the return argument:
-
"plot": 'ggplot' object. A faceted line plot for the metric. -
"table": data frame. A summary table for the metric.
See Also
create_line() for applying the same analysis to a different metric.
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other After-hours Collaboration:
afterhours_dist(),
afterhours_fizz(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
external_rank()
Examples
# Return a line plot afterhours_line(pq_data, hrvar = "LevelDesignation") # Return summary table afterhours_line(pq_data, hrvar = "LevelDesignation", return = "table")# Return a line plot afterhours_line(pq_data, hrvar = "LevelDesignation") # Return summary table afterhours_line(pq_data, hrvar = "LevelDesignation", return = "table")
Rank groups with high After-Hours Collaboration Hours
Description
This function scans a Standard Person Query for groups with high levels of After-Hours Collaboration. Returns a plot by default, with an option to return a table with all groups (across multiple HR attributes) ranked by hours of After-Hours Collaboration Hours.
Usage
afterhours_rank( data, hrvar = extract_hr(data), mingroup = 5, mode = "simple", plot_mode = 1, return = "plot" )afterhours_rank( data, hrvar = extract_hr(data), mingroup = 5, mode = "simple", plot_mode = 1, return = "plot" )
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
mode |
String to specify calculation mode. Must be either:
|
plot_mode |
Numeric vector to determine which plot mode to return. Must
be either
|
return |
String specifying what to return. This must be one of the following strings:
See |
Details
Uses the metric After_hours_collaboration_hours.
See create_rank() for applying the same analysis to a different metric.
Value
When 'table' is passed in return, a summary table is returned as a data frame.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other After-hours Collaboration:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_summary(),
afterhours_trend(),
external_rank()
Examples
# Return plot afterhours_rank(pq_data, return = "plot") # Return summary table afterhours_rank(pq_data, return = "table")# Return plot afterhours_rank(pq_data, return = "plot") # Return summary table afterhours_rank(pq_data, return = "table")
Summary of After-Hours Collaboration Hours
Description
Provides an overview analysis of after-hours collaboration time. Returns a bar plot showing average weekly after-hours collaboration hours by default. Additional options available to return a summary table.
Usage
afterhours_summary(data, hrvar = "Organization", mingroup = 5, return = "plot") afterhours_sum(data, hrvar = "Organization", mingroup = 5, return = "plot")afterhours_summary(data, hrvar = "Organization", mingroup = 5, return = "plot") afterhours_sum(data, hrvar = "Organization", mingroup = 5, return = "plot")
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
return |
String specifying what to return. This must be one of the following strings:
See |
Details
Uses the metric After_hours_collaboration_hours.
Value
A different output is returned depending on the value passed to the return argument:
-
"plot": 'ggplot' object. A bar plot for the metric. -
"table": data frame. A summary table for the metric.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other After-hours Collaboration:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_trend(),
external_rank()
Examples
# Return a ggplot bar chart afterhours_summary(pq_data, hrvar = "LevelDesignation") # Return a summary table afterhours_summary(pq_data, hrvar = "LevelDesignation", return = "table")# Return a ggplot bar chart afterhours_summary(pq_data, hrvar = "LevelDesignation") # Return a summary table afterhours_summary(pq_data, hrvar = "LevelDesignation", return = "table")
After-Hours Time Trend
Description
Provides a week by week view of after-hours 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.
Usage
afterhours_trend(data, hrvar = "Organization", mingroup = 5, return = "plot")afterhours_trend(data, hrvar = "Organization", mingroup = 5, return = "plot")
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
return |
Character vector specifying what to return, defaults to
|
Details
Uses the metric After_hours_collaboration_hours.
Value
Returns a 'ggplot' object by default, where 'plot' is passed in return.
When 'table' is passed, a summary table is returned as a data frame.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other After-hours Collaboration:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
external_rank()
Examples
# Run plot afterhours_trend(pq_data) # Run table afterhours_trend(pq_data, hrvar = "LevelDesignation", return = "table")# Run plot afterhours_trend(pq_data) # Run table afterhours_trend(pq_data, hrvar = "LevelDesignation", return = "table")
Anonymise a categorical variable by replacing values
Description
Anonymize categorical variables such as HR variables by replacing values with dummy team names such as 'Team A'. The behaviour is to make 1 to 1 replacements by default, but there is an option to completely randomise values in the categorical variable.
Usage
anonymise(x, scramble = FALSE, replacement = NULL) anonymize(x, scramble = FALSE, replacement = NULL)anonymise(x, scramble = FALSE, replacement = NULL) anonymize(x, scramble = FALSE, replacement = NULL)
Arguments
x |
Character vector to be passed through. |
scramble |
Logical value determining whether to randomise values in the categorical variable. |
replacement |
Character vector containing the values to replace original
values in the categorical variable. The length of the vector must be at
least as great as the number of unique values in the original variable.
Defaults to |
Value
Character vector with the same length as input x, replaced with values
provided in replacement.
See Also
jitter
Examples
unique(anonymise(pq_data$Organization)) rep <- c("Manager+", "Manager", "IC") unique(anonymise(pq_data$Layer), replacement = rep)unique(anonymise(pq_data$Organization)) rep <- c("Manager+", "Manager", "IC") unique(anonymise(pq_data$Layer), replacement = rep)
Identify whether variable is an IDate class.
Description
This function checks whether the variable is an IDate class.
Usage
any_idate(x)any_idate(x)
Arguments
x |
Variable to test whether an IDate class. |
Value
logical value indicating whether the string is of an IDate class.
See Also
Other Support:
camel_clean(),
check_inputs(),
cut_hour(),
extract_date_range(),
extract_hr(),
heat_colours(),
is_date_format(),
maxmin(),
pairwise_count(),
read_preamble(),
rgb2hex(),
totals_bind(),
totals_col(),
tstamp(),
us_to_space(),
wrap()
Examples
any_idate("2023-12-15")any_idate("2023-12-15")
Convert "CamelCase" to "Camel Case"
Description
Convert a text string from the format "CamelCase" to "Camel Case". This is used for converting variable names such as "LevelDesignation" to "Level Designation" for the purpose of prettifying plot labels.
Usage
camel_clean(string)camel_clean(string)
Arguments
string |
A string vector in 'CamelCase' format to format |
Value
Returns a formatted string.
See Also
Other Support:
any_idate(),
check_inputs(),
cut_hour(),
extract_date_range(),
extract_hr(),
heat_colours(),
is_date_format(),
maxmin(),
pairwise_count(),
read_preamble(),
rgb2hex(),
totals_bind(),
totals_col(),
tstamp(),
us_to_space(),
wrap()
Examples
camel_clean("NoteHowTheStringIsFormatted")camel_clean("NoteHowTheStringIsFormatted")
Check whether a data frame contains all the required variable
Description
Checks whether a data frame contains all the required variables. Matching works via variable names, and used to support individual functions in the package. Not used directly.
Usage
check_inputs(input, requirements, return = "stop")check_inputs(input, requirements, return = "stop")
Arguments
input |
Pass a data frame for checking |
requirements |
A character vector specifying the required variable names |
return |
A character string specifying what to return. The default value is "stop". Also accepts "names" and "warning". |
Value
The default behaviour is to return an error message, informing the
user what variables are not included. When return is set to "names", a
character vector containing the unmatched variable names is returned.
See Also
Other Support:
any_idate(),
camel_clean(),
cut_hour(),
extract_date_range(),
extract_hr(),
heat_colours(),
is_date_format(),
maxmin(),
pairwise_count(),
read_preamble(),
rgb2hex(),
totals_bind(),
totals_col(),
tstamp(),
us_to_space(),
wrap()
Examples
# Return error message ## Not run: check_inputs(iris, c("Sepal.Length", "mpg")) ## End(Not run) #' # Return warning message check_inputs(iris, c("Sepal.Length", "mpg"), return = "warning") # Return variable names check_inputs(iris, c("Sepal.Length", "Sepal.Width", "RandomVariable"), return = "names")# Return error message ## Not run: check_inputs(iris, c("Sepal.Length", "mpg")) ## End(Not run) #' # Return warning message check_inputs(iris, c("Sepal.Length", "mpg"), return = "warning") # Return variable names check_inputs(iris, c("Sepal.Length", "Sepal.Width", "RandomVariable"), return = "names")
Check a query to ensure that it is suitable for analysis
Description
Prints diagnostic data about the data query to the R console, with information such as date range, number of employees, HR attributes identified, etc.
Usage
check_query(data, return = "message", validation = FALSE)check_query(data, return = "message", validation = FALSE)
Arguments
data |
A person-level query in the form of a data frame. This includes:
All person-level query have a |
return |
String specifying what to return. This must be one of the following strings:
See |
validation |
Logical value to specify whether to show summarized version. Defaults to |
Details
This can be used with any person-level query, such as the standard person query, Ways of Working assessment query, and the hourly collaboration query. When run, this prints diagnostic data to the R console.
Value
A different output is returned depending on the value passed to the return
argument:
-
"message": a message is returned to the console. -
"text": string containing the diagnostic message.
See Also
Other Data Validation:
extract_hr(),
flag_ch_ratio(),
flag_em_ratio(),
flag_extreme(),
flag_outlooktime(),
hr_trend(),
hrvar_count(),
hrvar_count_all(),
hrvar_trend(),
identify_churn(),
identify_holidayweeks(),
identify_inactiveweeks(),
identify_nkw(),
identify_outlier(),
identify_privacythreshold(),
identify_shifts(),
identify_tenure(),
track_HR_change(),
validation_report()
Examples
check_query(pq_data)check_query(pq_data)
Collaboration - Stacked Area Plot
Description
Provides an overview analysis of Weekly Digital Collaboration. Returns an stacked area plot of Email and Meeting Hours by default. Additional options available to return a summary table.
Usage
collaboration_area(data, hrvar = NULL, mingroup = 5, return = "plot") collab_area(data, hrvar = NULL, mingroup = 5, return = "plot")collaboration_area(data, hrvar = NULL, mingroup = 5, return = "plot") collab_area(data, hrvar = NULL, mingroup = 5, return = "plot")
Arguments
data |
A Standard Person Query dataset in the form of a data frame. A Ways of Working assessment dataset may also be provided, in which Unscheduled call hours would be included in the output. |
hrvar |
HR Variable by which to split metrics, defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
return |
String specifying what to return. This must be one of the following strings:
See |
Details
Uses the metrics Meeting_hours, Email_hours, Unscheduled_Call_hours,
and Instant_Message_hours.
Value
A different output is returned depending on the value passed to the return argument:
-
"plot": 'ggplot' object. A stacked area plot for the metric. -
"table": data frame. A summary table for the metric.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other Collaboration:
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend()
Examples
# Return plot with total (default) collaboration_area(pq_data) # Return plot with hrvar split collaboration_area(pq_data, hrvar = "Organization") # Return summary table collaboration_area(pq_data, return = "table")# Return plot with total (default) collaboration_area(pq_data) # Return plot with hrvar split collaboration_area(pq_data, hrvar = "Organization") # Return summary table collaboration_area(pq_data, return = "table")
Distribution of Collaboration Hours as a 100% stacked bar
Description
Analyze the distribution of Collaboration Hours. Returns a stacked bar plot by default. Additional options available to return a table with distribution elements.
Usage
collaboration_dist( data, hrvar = "Organization", mingroup = 5, return = "plot", cut = c(15, 20, 25) ) collab_dist( data, hrvar = "Organization", mingroup = 5, return = "plot", cut = c(15, 20, 25) )collaboration_dist( data, hrvar = "Organization", mingroup = 5, return = "plot", cut = c(15, 20, 25) ) collab_dist( data, hrvar = "Organization", mingroup = 5, return = "plot", cut = c(15, 20, 25) )
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
return |
String specifying what to return. This must be one of the following strings:
See |
cut |
A numeric vector of length three to specify the breaks for the distribution, e.g. c(10, 15, 20) |
Value
A different output is returned depending on the value passed to the return argument:
-
"plot": 'ggplot' object. A stacked bar plot for the metric. -
"table": data frame. A summary table for the metric.
Metrics used
The metric Collaboration_hours is used in the calculations. Please ensure
that your query contains a metric with the exact same name.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other Collaboration:
collaboration_area(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend()
Examples
# Return plot collaboration_dist(pq_data, hrvar = "Organization") # Return summary table collaboration_dist(pq_data, hrvar = "Organization", return = "table")# Return plot collaboration_dist(pq_data, hrvar = "Organization") # Return summary table collaboration_dist(pq_data, hrvar = "Organization", return = "table")
Distribution of Collaboration Hours (Fizzy Drink plot)
Description
Analyze weekly collaboration hours distribution, and returns a 'fizzy' scatter plot by default. Additional options available to return a table with distribution elements.
Usage
collaboration_fizz(data, hrvar = "Organization", mingroup = 5, return = "plot") collab_fizz(data, hrvar = "Organization", mingroup = 5, return = "plot")collaboration_fizz(data, hrvar = "Organization", mingroup = 5, return = "plot") collab_fizz(data, hrvar = "Organization", mingroup = 5, return = "plot")
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
return |
String specifying what to return. This must be one of the following strings:
See |
Value
A different output is returned depending on the value passed to the return argument:
-
"plot": 'ggplot' object. A jittered scatter plot for the metric. -
"table": data frame. A summary table for the metric.
Metrics used
The metric Collaboration_hours is used in the calculations. Please ensure
that your query contains a metric with the exact same name.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other Collaboration:
collaboration_area(),
collaboration_dist(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend()
Examples
# Return plot collaboration_fizz(pq_data, hrvar = "Organization", return = "plot") # Return summary table collaboration_fizz(pq_data, hrvar = "Organization", return = "table")# Return plot collaboration_fizz(pq_data, hrvar = "Organization", return = "plot") # Return summary table collaboration_fizz(pq_data, hrvar = "Organization", return = "table")
Collaboration Time Trend - Line Chart
Description
Provides a week by week view of collaboration time, visualised as line charts. 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.
Usage
collaboration_line(data, hrvar = "Organization", mingroup = 5, return = "plot") collab_line(data, hrvar = "Organization", mingroup = 5, return = "plot")collaboration_line(data, hrvar = "Organization", mingroup = 5, return = "plot") collab_line(data, hrvar = "Organization", mingroup = 5, return = "plot")
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
return |
String specifying what to return. This must be one of the following strings:
See |
Value
A different output is returned depending on the value passed to the return argument:
-
"plot": 'ggplot' object. A faceted line plot for the metric. -
"table": data frame. A summary table for the metric.
Metrics used
The metric Collaboration_hours is used in the calculations. Please ensure
that your query contains a metric with the exact same name.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other Collaboration:
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend()
Examples
# Return a line plot collaboration_line(pq_data, hrvar = "LevelDesignation") # Return summary table collaboration_line(pq_data, hrvar = "LevelDesignation", return = "table")# Return a line plot collaboration_line(pq_data, hrvar = "LevelDesignation") # Return summary table collaboration_line(pq_data, hrvar = "LevelDesignation", return = "table")
Collaboration Ranking
Description
This function scans a standard query output for groups with high levels of 'Weekly Digital Collaboration'. Returns a plot by default, with an option to return a table with a all of groups (across multiple HR attributes) ranked by hours of digital collaboration.
Usage
collaboration_rank( data, hrvar = extract_hr(data), mingroup = 5, mode = "simple", plot_mode = 1, return = "plot" ) collab_rank( data, hrvar = extract_hr(data), mingroup = 5, mode = "simple", plot_mode = 1, return = "plot" )collaboration_rank( data, hrvar = extract_hr(data), mingroup = 5, mode = "simple", plot_mode = 1, return = "plot" ) collab_rank( data, hrvar = extract_hr(data), mingroup = 5, mode = "simple", plot_mode = 1, return = "plot" )
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
mode |
String to specify calculation mode. Must be either:
|
plot_mode |
Numeric vector to determine which plot mode to return. Must
be either
|
return |
String specifying what to return. This must be one of the following strings:
See |
Details
Uses the metric Collaboration_hours.
See create_rank() for applying the same analysis to a different metric.
Value
A different output is returned depending on the value passed to the return
argument:
-
"plot": 'ggplot' object. A bubble plot where the x-axis represents the metric, the y-axis represents the HR attributes, and the size of the bubbles represent the size of the organizations. Note that there is no plot output ifmodeis set to"combine". -
"table": data frame. A summary table for the metric.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other Collaboration:
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_sum(),
collaboration_trend()
Examples
# Return rank table collaboration_rank( data = pq_data, return = "table" ) # Return plot collaboration_rank( data = pq_data, return = "plot" )# Return rank table collaboration_rank( data = pq_data, return = "table" ) # Return plot collaboration_rank( data = pq_data, return = "plot" )
Collaboration Summary
Description
Provides an overview analysis of 'Weekly Digital Collaboration'. Returns a stacked bar plot of Email and Meeting Hours by default. Additional options available to return a summary table.
Usage
collaboration_sum(data, hrvar = "Organization", mingroup = 5, return = "plot") collab_sum(data, hrvar = "Organization", mingroup = 5, return = "plot") collaboration_summary( data, hrvar = "Organization", mingroup = 5, return = "plot" ) collab_summary(data, hrvar = "Organization", mingroup = 5, return = "plot")collaboration_sum(data, hrvar = "Organization", mingroup = 5, return = "plot") collab_sum(data, hrvar = "Organization", mingroup = 5, return = "plot") collaboration_summary( data, hrvar = "Organization", mingroup = 5, return = "plot" ) collab_summary(data, hrvar = "Organization", mingroup = 5, return = "plot")
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
return |
Character vector specifying what to return, defaults to "plot". Valid inputs are "plot" and "table". |
Details
Uses the metrics Meeting_hours, Email_hours, Unscheduled_Call_hours,
and Instant_Message_hours.
Value
Returns a 'ggplot' object by default, where 'plot' is passed in return.
When 'table' is passed, a summary table is returned as a data frame.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other Collaboration:
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_trend()
Examples
# Return a ggplot bar chart collaboration_sum(pq_data, hrvar = "LevelDesignation") # Return a summary table collaboration_sum(pq_data, hrvar = "LevelDesignation", return = "table")# Return a ggplot bar chart collaboration_sum(pq_data, hrvar = "LevelDesignation") # Return a summary table collaboration_sum(pq_data, hrvar = "LevelDesignation", return = "table")
Collaboration Time Trend
Description
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.
Usage
collaboration_trend( data, hrvar = "Organization", mingroup = 5, return = "plot" )collaboration_trend( data, hrvar = "Organization", mingroup = 5, return = "plot" )
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
return |
Character vector specifying what to return, defaults to
|
Value
Returns a 'ggplot' object by default, where 'plot' is passed in return.
When 'table' is passed, a summary table is returned as a data frame.
Metrics used
The metric Collaboration_hours is used in the calculations. Please ensure
that your query contains a metric with the exact same name.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other Collaboration:
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum()
Examples
# Run plot collaboration_trend(pq_data) # Run table collaboration_trend(pq_data, hrvar = "LevelDesignation", return = "table")# Run plot collaboration_trend(pq_data) # Run table collaboration_trend(pq_data, hrvar = "LevelDesignation", return = "table")
Add comma separator for thousands
Description
Takes a numeric value and returns a character value
which is rounded to the whole number, and adds a comma
separator at the thousands. A convenient wrapper function
around round() and format().
Usage
comma(x)comma(x)
Arguments
x |
A numeric value |
Value
Returns a formatted string.
Copy a data frame to clipboard for pasting in Excel
Description
This is a pipe-optimised function, that feeds into vivainsights::export(),
but can be used as a stand-alone function.
Based on the original function from https://github.com/martinctc/surveytoolbox.
Usage
copy_df(x, row.names = FALSE, col.names = TRUE, quietly = FALSE, ...)copy_df(x, row.names = FALSE, col.names = TRUE, quietly = FALSE, ...)
Arguments
x |
Data frame to be passed through. Cannot contain list-columns or nested data frames. |
row.names |
A logical vector for specifying whether to allow row names.
Defaults to |
col.names |
A logical vector for specifying whether to allow column
names. Defaults to |
quietly |
Set this to TRUE to not print data frame on console |
... |
Additional arguments for write.table(). |
Value
Copies a data frame to the clipboard with no return value.
See Also
Other Import and Export:
create_dt(),
export(),
import_query()
Mean Bar Plot for any metric
Description
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.
Usage
create_bar( data, metric, hrvar = "Organization", mingroup = 5, return = "plot", bar_colour = "default", na.rm = FALSE, percent = FALSE, plot_title = us_to_space(metric), plot_subtitle = paste("Average by", tolower(camel_clean(hrvar))), legend_lab = NULL, rank = "descending", xlim = NULL, text_just = 0.5, text_colour = "#FFFFFF" )create_bar( data, metric, hrvar = "Organization", mingroup = 5, return = "plot", bar_colour = "default", na.rm = FALSE, percent = FALSE, plot_title = us_to_space(metric), plot_subtitle = paste("Average by", tolower(camel_clean(hrvar))), legend_lab = NULL, rank = "descending", xlim = NULL, text_just = 0.5, text_colour = "#FFFFFF" )
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
metric |
Character string containing the name of the metric, e.g. "Collaboration_hours" |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
return |
String specifying what to return. This must be one of the following strings:
See |
bar_colour |
String to specify colour to use for bars.
In-built accepted values include |
na.rm |
A logical value indicating whether |
percent |
Logical value to determine whether to show labels as
percentage signs. Defaults to |
plot_title |
An option to override plot title. |
plot_subtitle |
An option to override plot subtitle. |
legend_lab |
String. Option to override legend title/label. Defaults to
|
rank |
String specifying how to rank the bars. Valid inputs are:
|
xlim |
An option to set max value in x axis. |
text_just |
|
text_colour |
|
Value
A different output is returned depending on the value passed to the return argument:
-
"plot": 'ggplot' object. A bar plot for the metric. -
"table": data frame. A summary table for the metric.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other Flexible:
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_density(),
create_dist(),
create_fizz(),
create_hist(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend()
Examples
# Return a ggplot bar chart create_bar(pq_data, metric = "Collaboration_hours", hrvar = "LevelDesignation") # Change bar colour create_bar(pq_data, metric = "After_hours_collaboration_hours", bar_colour = "alert") # Custom data label positions and formatting pq_data %>% create_bar( metric = "Meetings", text_just = 1.1, text_colour = "black", xlim = 20) # Return a summary table create_bar(pq_data, metric = "Collaboration_hours", hrvar = "LevelDesignation", return = "table")# Return a ggplot bar chart create_bar(pq_data, metric = "Collaboration_hours", hrvar = "LevelDesignation") # Change bar colour create_bar(pq_data, metric = "After_hours_collaboration_hours", bar_colour = "alert") # Custom data label positions and formatting pq_data %>% create_bar( metric = "Meetings", text_just = 1.1, text_colour = "black", xlim = 20) # Return a summary table create_bar(pq_data, metric = "Collaboration_hours", hrvar = "LevelDesignation", return = "table")
Create a bar chart without aggregation for any metric
Description
This function creates a bar chart directly from the aggregated / summarised
data. Unlike create_bar() which performs a person-level aggregation, there
is no calculation for create_bar_asis() and the values are rendered as they
are passed into the function.
Usage
create_bar_asis( data, group_var, bar_var, title = NULL, subtitle = NULL, caption = NULL, ylab = group_var, xlab = bar_var, percent = FALSE, bar_colour = "default", rounding = 1 )create_bar_asis( data, group_var, bar_var, title = NULL, subtitle = NULL, caption = NULL, ylab = group_var, xlab = bar_var, percent = FALSE, bar_colour = "default", rounding = 1 )
Arguments
data |
Plotting data as a data frame. |
group_var |
String containing name of variable for the group. |
bar_var |
String containing name of variable representing the value of the bars. |
title |
Title of the plot. |
subtitle |
Subtitle of the plot. |
caption |
Caption of the plot. |
ylab |
Y-axis label for the plot (group axis) |
xlab |
X-axis label of the plot (bar axis). |
percent |
Logical value to determine whether to show labels as
percentage signs. Defaults to |
bar_colour |
String to specify colour to use for bars.
In-built accepted values include "default" (default), "alert" (red), and
"darkblue". Otherwise, hex codes are also accepted. You can also supply
RGB values via |
rounding |
Numeric value to specify number of digits to show in data labels |
Value
'ggplot' object. A horizontal bar plot.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other Flexible:
create_bar(),
create_boxplot(),
create_bubble(),
create_density(),
create_dist(),
create_fizz(),
create_hist(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend()
Examples
# Creating a custom bar plot without mean aggregation library(dplyr) pq_data %>% group_by(Organization) %>% summarise(across(.cols = Meeting_hours, .fns = ~sum(., na.rm = TRUE))) %>% create_bar_asis(group_var = "Organization", bar_var = "Meeting_hours", title = "Total Meeting Hours over period", subtitle = "By Organization", caption = extract_date_range(pq_data, return = "text"), bar_colour = "darkblue", rounding = 0) library(dplyr) # Summarise Non-person-average median `Emails_sent` med_df <- pq_data %>% group_by(Organization) %>% summarise(Emails_sent_median = median(Emails_sent)) med_df %>% create_bar_asis( group_var = "Organization", bar_var = "Emails_sent_median", title = "Emails sent by organization", subtitle = "Median values", bar_colour = "darkblue", caption = extract_date_range(pq_data, return = "text") )# Creating a custom bar plot without mean aggregation library(dplyr) pq_data %>% group_by(Organization) %>% summarise(across(.cols = Meeting_hours, .fns = ~sum(., na.rm = TRUE))) %>% create_bar_asis(group_var = "Organization", bar_var = "Meeting_hours", title = "Total Meeting Hours over period", subtitle = "By Organization", caption = extract_date_range(pq_data, return = "text"), bar_colour = "darkblue", rounding = 0) library(dplyr) # Summarise Non-person-average median `Emails_sent` med_df <- pq_data %>% group_by(Organization) %>% summarise(Emails_sent_median = median(Emails_sent)) med_df %>% create_bar_asis( group_var = "Organization", bar_var = "Emails_sent_median", title = "Emails sent by organization", subtitle = "Median values", bar_colour = "darkblue", caption = extract_date_range(pq_data, return = "text") )
Box Plot for any metric
Description
Analyzes a selected metric and returns a box plot by default. Additional options available to return a table with distribution elements.
Usage
create_boxplot( data, metric, hrvar = "Organization", mingroup = 5, return = "plot" )create_boxplot( data, metric, hrvar = "Organization", mingroup = 5, return = "plot" )
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
metric |
Character string containing the name of the metric, e.g. "Collaboration_hours" |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
return |
String specifying what to return. This must be one of the following strings:
See |
Details
This is a general purpose function that powers all the functions in the package that produce box plots.
Value
A different output is returned depending on the value passed to the return argument:
-
"plot": 'ggplot' object. A box plot for the metric. -
"table": data frame. A summary table for the metric.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other Flexible:
create_bar(),
create_bar_asis(),
create_bubble(),
create_density(),
create_dist(),
create_fizz(),
create_hist(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend()
Examples
# Create a box plot for Collaboration_hours by Level Designation create_boxplot(pq_data, metric = "Collaboration_hours", hrvar = "LevelDesignation", return = "plot") # Create a box plot for Collaboration_hours by Organization create_boxplot(pq_data, metric = "Collaboration_hours", hrvar = "Organization", return = "plot") # Create a summary statistics table for Collaboration_hoursby Organization create_boxplot(pq_data, metric = "Collaboration_hours", hrvar = "Organization", return = "table")# Create a box plot for Collaboration_hours by Level Designation create_boxplot(pq_data, metric = "Collaboration_hours", hrvar = "LevelDesignation", return = "plot") # Create a box plot for Collaboration_hours by Organization create_boxplot(pq_data, metric = "Collaboration_hours", hrvar = "Organization", return = "plot") # Create a summary statistics table for Collaboration_hoursby Organization create_boxplot(pq_data, metric = "Collaboration_hours", hrvar = "Organization", return = "table")
Create a bubble plot with two selected Viva Insights metrics (General Purpose), with size representing the number of employees in the group.
Description
Returns a bubble plot of two selected metrics, using size to map the number of employees.
Usage
create_bubble( data, metric_x, metric_y, hrvar = "Organization", mingroup = 5, return = "plot", bubble_size = c(1, 10) )create_bubble( data, metric_x, metric_y, hrvar = "Organization", mingroup = 5, return = "plot", bubble_size = c(1, 10) )
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
metric_x |
Character string containing the name of the metric, e.g. "Collaboration_hours" |
metric_y |
Character string containing the name of the metric, e.g. "Collaboration_hours" |
hrvar |
HR Variable by which to split metrics, defaults to "Organization" but accepts any character vector, e.g. "LevelDesignation" |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
return |
String specifying what to return. This must be one of the
following strings: - |
bubble_size |
A numeric vector of length two to specify the size range of the bubbles |
Details
This is a general purpose function that powers all the functions in the package that produce bubble plots.
Value
A different output is returned depending on the value passed to the
return argument:
-
"plot": 'ggplot' object. A bubble plot for the metric. -
"table": data frame. A summary table for the metric.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other Flexible:
create_bar(),
create_bar_asis(),
create_boxplot(),
create_density(),
create_dist(),
create_fizz(),
create_hist(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend()
Examples
create_bubble(pq_data, "Collaboration_hours", "Multitasking_hours", hrvar ="Organization")create_bubble(pq_data, "Collaboration_hours", "Multitasking_hours", hrvar ="Organization")
Create a density plot for any metric
Description
Provides an analysis of the distribution of a selected metric. Returns a faceted density plot by default. Additional options available to return the underlying frequency table.
Usage
create_density( data, metric, hrvar = "Organization", mingroup = 5, ncol = NULL, return = "plot" )create_density( data, metric, hrvar = "Organization", mingroup = 5, ncol = NULL, return = "plot" )
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
metric |
String containing the name of the metric, e.g. "Collaboration_hours" |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
ncol |
Numeric value setting the number of columns on the plot. Defaults
to |
return |
String specifying what to return. This must be one of the following strings:
See |
Value
A different output is returned depending on the value passed to the return
argument:
-
"plot": 'ggplot' object. A faceted density plot for the metric. -
"table": data frame. A summary table for the metric. -
"data": data frame. Data with calculated person averages. -
"frequency: list of data frames. Each data frame contains the frequencies used in each panel of the plotted histogram.
See Also
Other Flexible:
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_hist(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend()
Examples
# Return plot for whole organization create_density(pq_data, metric = "Collaboration_hours", hrvar = NULL) # Return plot create_density(pq_data, metric = "Collaboration_hours", hrvar = "Organization") # Return plot but coerce plot to three columns create_density(pq_data, metric = "Collaboration_hours", hrvar = "Organization", ncol = 3) # Return summary table create_density(pq_data, metric = "Collaboration_hours", hrvar = "Organization", return = "table")# Return plot for whole organization create_density(pq_data, metric = "Collaboration_hours", hrvar = NULL) # Return plot create_density(pq_data, metric = "Collaboration_hours", hrvar = "Organization") # Return plot but coerce plot to three columns create_density(pq_data, metric = "Collaboration_hours", hrvar = "Organization", ncol = 3) # Return summary table create_density(pq_data, metric = "Collaboration_hours", hrvar = "Organization", return = "table")
Horizontal 100 percent stacked bar plot for any metric
Description
Provides an analysis of the distribution of a selected metric. Returns a stacked bar plot by default. Additional options available to return a table with distribution elements.
Usage
create_dist( data, metric, hrvar = "Organization", mingroup = 5, return = "plot", cut = c(15, 20, 25), dist_colours = c("#facebc", "#fcf0eb", "#b4d5dd", "#bfe5ee"), unit = "hours", lbound = 0, ubound = 200, sort_by = NULL, labels = NULL )create_dist( data, metric, hrvar = "Organization", mingroup = 5, return = "plot", cut = c(15, 20, 25), dist_colours = c("#facebc", "#fcf0eb", "#b4d5dd", "#bfe5ee"), unit = "hours", lbound = 0, ubound = 200, sort_by = NULL, labels = NULL )
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
metric |
String containing the name of the metric, e.g. "Collaboration_hours" |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
return |
String specifying what to return. This must be one of the following strings:
See |
cut |
A numeric vector of length three to specify the breaks for the distribution, e.g. c(10, 15, 20) |
dist_colours |
A character vector of length four to specify colour codes for the stacked bars. |
unit |
String to specify what unit to use. This defaults to |
lbound |
Numeric. Specifies the lower bound (inclusive) value for the minimum label. Defaults to 0. |
ubound |
Numeric. Specifies the upper bound (inclusive) value for the maximum label. Defaults to 100. |
sort_by |
String to specify the bucket label to sort by. Defaults to
|
labels |
Character vector to override labels for the created categorical variables. Must be a named vector - see examples. |
Value
A different output is returned depending on the value passed to the return argument:
-
"plot": 'ggplot' object. A stacked bar plot for the metric. -
"table": data frame. A summary table for the metric.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other Flexible:
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_density(),
create_fizz(),
create_hist(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend()
Examples
# Return plot create_dist(pq_data, metric = "Collaboration_hours", hrvar = "Organization") # Return summary table create_dist(pq_data, metric = "Collaboration_hours", hrvar = "Organization", return = "table") # Use custom labels by providing a label vector eh_labels <- c( "Fewer than fifteen" = "< 15 hours", "Between fifteen and twenty" = "15 - 20 hours", "Between twenty and twenty-five" = "20 - 25 hours", "More than twenty-five" = "25+ hours" ) pq_data %>% create_dist(metric = "Meeting_hours", labels = eh_labels, return = "plot") # Sort by a category pq_data %>% create_dist(metric = "Collaboration_hours", sort_by = "25+ hours")# Return plot create_dist(pq_data, metric = "Collaboration_hours", hrvar = "Organization") # Return summary table create_dist(pq_data, metric = "Collaboration_hours", hrvar = "Organization", return = "table") # Use custom labels by providing a label vector eh_labels <- c( "Fewer than fifteen" = "< 15 hours", "Between fifteen and twenty" = "15 - 20 hours", "Between twenty and twenty-five" = "20 - 25 hours", "More than twenty-five" = "25+ hours" ) pq_data %>% create_dist(metric = "Meeting_hours", labels = eh_labels, return = "plot") # Sort by a category pq_data %>% create_dist(metric = "Collaboration_hours", sort_by = "25+ hours")
Create interactive tables in HTML with 'download' buttons.
Description
See https://martinctc.github.io/blog/vignette-downloadable-tables-in-rmarkdown-with-the-dt-package/ for more.
Usage
create_dt(x, rounding = 1, freeze = 2, percent = FALSE)create_dt(x, rounding = 1, freeze = 2, percent = FALSE)
Arguments
x |
Data frame to be passed through. |
rounding |
Numeric vector to specify the number of decimal points to display |
freeze |
Number of columns from the left to 'freeze'. Defaults to 2, which includes the row number column. |
percent |
Logical value specifying whether to display numeric columns as percentages. |
Details
This is exported from wpa::create_dt().
Value
Returns an HTML widget displaying rectangular data.
See Also
Other Import and Export:
copy_df(),
export(),
import_query()
Examples
output <- hrvar_count(pq_data, return = "table") create_dt(output)output <- hrvar_count(pq_data, return = "table") create_dt(output)
Fizzy Drink / Jittered Scatter Plot for any metric
Description
Analyzes a selected metric and returns a 'fizzy' scatter plot by default. Additional options available to return a table with distribution elements.
Usage
create_fizz( data, metric, hrvar = "Organization", mingroup = 5, return = "plot" )create_fizz( data, metric, hrvar = "Organization", mingroup = 5, return = "plot" )
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
metric |
Character string containing the name of the metric,
e.g. |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
return |
String specifying what to return. This must be one of the following strings:
See |
Details
This is a general purpose function that powers all the functions in the package that produce 'fizzy drink' / jittered scatter plots.
Value
A different output is returned depending on the value passed to the return argument:
-
"plot": 'ggplot' object. A jittered scatter plot for the metric. -
"table": data frame. A summary table for the metric.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other Flexible:
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_density(),
create_dist(),
create_hist(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend()
Examples
# Create a fizzy plot for Collaboration hours by Level Designation create_fizz(pq_data, metric = "Collaboration_hours", hrvar = "LevelDesignation", return = "plot") # Create a summary statistics table for Collaboration hours by Organization create_fizz(pq_data, metric = "Collaboration_hours", hrvar = "Organization", return = "table")# Create a fizzy plot for Collaboration hours by Level Designation create_fizz(pq_data, metric = "Collaboration_hours", hrvar = "LevelDesignation", return = "plot") # Create a summary statistics table for Collaboration hours by Organization create_fizz(pq_data, metric = "Collaboration_hours", hrvar = "Organization", return = "table")
Create a histogram plot for any metric
Description
Provides an analysis of the distribution of a selected metric. Returns a faceted histogram by default. Additional options available to return the underlying frequency table.
Usage
create_hist( data, metric, hrvar = "Organization", mingroup = 5, binwidth = 1, ncol = NULL, return = "plot" )create_hist( data, metric, hrvar = "Organization", mingroup = 5, binwidth = 1, ncol = NULL, return = "plot" )
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
metric |
String containing the name of the metric, e.g. "Collaboration_hours" |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
binwidth |
Numeric value for setting |
ncol |
Numeric value setting the number of columns on the plot. Defaults
to |
return |
String specifying what to return. This must be one of the following strings:
See |
Value
A different output is returned depending on the value passed to the return
argument:
-
"plot": 'ggplot' object. A faceted histogram for the metric. -
"table": data frame. A summary table for the metric. -
"data": data frame. Data with calculated person averages. -
"frequency: list of data frames. Each data frame contains the frequencies used in each panel of the plotted histogram.
See Also
Other Flexible:
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_density(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend()
Examples
# Return plot for whole organization create_hist(pq_data, metric = "Collaboration_hours", hrvar = NULL) # Return plot create_hist(pq_data, metric = "Collaboration_hours", hrvar = "Organization") # Return plot but coerce plot to 3 columns create_hist(pq_data, metric = "Collaboration_hours", hrvar = "Organization", ncol = 3) # Return summary table create_hist(pq_data, metric = "Collaboration_hours", hrvar = "Organization", return = "table")# Return plot for whole organization create_hist(pq_data, metric = "Collaboration_hours", hrvar = NULL) # Return plot create_hist(pq_data, metric = "Collaboration_hours", hrvar = "Organization") # Return plot but coerce plot to 3 columns create_hist(pq_data, metric = "Collaboration_hours", hrvar = "Organization", ncol = 3) # Return summary table create_hist(pq_data, metric = "Collaboration_hours", hrvar = "Organization", return = "table")
Create an incidence analysis reflecting proportion of population scoring above or below a threshold for a metric
Description
An incidence analysis is generated, with each value in the table reflecting
the proportion of the population that is above or below a threshold for a
specified metric. There is an option to only provide a single hrvar in
which a bar plot is generated, or two hrvar values where an incidence table
(heatmap) is generated.
Usage
create_inc( data, metric, hrvar, mingroup = 5, threshold, position, return = "plot" ) create_incidence( data, metric, hrvar, mingroup = 5, threshold, position, return = "plot" )create_inc( data, metric, hrvar, mingroup = 5, threshold, position, return = "plot" ) create_incidence( data, metric, hrvar, mingroup = 5, threshold, position, return = "plot" )
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
metric |
Character string containing the name of the metric, e.g. "Collaboration_hours" |
hrvar |
Character vector of at most length 2 containing the name of the HR Variable by which to split metrics. |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
threshold |
Numeric value specifying the threshold. |
position |
String containing the below valid values:
|
return |
String specifying what to return. This must be one of the following strings:
See |
Value
A different output is returned depending on the value passed to the return argument:
-
"plot": 'ggplot' object. A heat map. -
"table": data frame. A summary table.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other Flexible:
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_density(),
create_dist(),
create_fizz(),
create_hist(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend()
Examples
# Only a single HR attribute create_inc( data = pq_data, metric = "After_hours_collaboration_hours", hrvar = "Organization", threshold = 4, position = "above" ) # Two HR attributes create_inc( data = pq_data, metric = "Collaboration_hours", hrvar = c("LevelDesignation", "Organization"), threshold = 20, position = "below" )# Only a single HR attribute create_inc( data = pq_data, metric = "After_hours_collaboration_hours", hrvar = "Organization", threshold = 4, position = "above" ) # Two HR attributes create_inc( data = pq_data, metric = "Collaboration_hours", hrvar = c("LevelDesignation", "Organization"), threshold = 20, position = "below" )
Compute Information Value for Predictive Variables
Description
This function calculates the Information Value (IV) for the selected numeric predictor variables in the dataset, given a specified outcome variable. The Information Value provides a measure of the predictive power of each variable in relation to the outcome variable, which can be useful in feature selection for predictive modeling.
Usage
create_IV( data, predictors = NULL, outcome, bins = 5, siglevel = 0.05, exc_sig = FALSE, return = "plot" )create_IV( data, predictors = NULL, outcome, bins = 5, siglevel = 0.05, exc_sig = FALSE, return = "plot" )
Arguments
data |
A Person Query dataset in the form of a data frame. |
predictors |
A character vector specifying the columns to be used as predictors. Defaults to NULL, where all numeric vectors in the data will be used as predictors. |
outcome |
String specifying the column name for a binary variable, containing only the values 1 or 0. |
bins |
Number of bins to use, defaults to 5. |
siglevel |
Significance level to use in comparing populations for the outcomes, defaults to 0.05 |
exc_sig |
Logical value determining whether to exclude values where the
p-value lies below what is set at |
return |
String specifying what to return. This must be one of the following strings:
See |
Details
This is a wrapper around wpa::create_IV().
Value
A different output is returned depending on the value passed to the return
argument:
-
"plot": 'ggplot' object. A bar plot showing the IV value of the top (maximum 12) variables. -
"summary": data frame. A summary table for the metric. -
"list": list. A list of outputs for all the input variables. -
"plot-WOE": A list of 'ggplot' objects that show the WOE for each predictor used in the model. -
"IV"returns a list object which mirrors the return inInformation::create_infotables().
See Also
Other Variable Association:
IV_report()
Other Information Value:
IV_report()
Examples
# Return a summary table of IV pq_data %>% dplyr::mutate(X = ifelse(Internal_network_size > 40, 1, 0)) %>% create_IV(outcome = "X", predictors = c("Email_hours", "Meeting_hours", "Chat_hours"), return = "plot") # Return summary pq_data %>% dplyr::mutate(X = ifelse(Internal_network_size > 40, 1, 0)) %>% create_IV(outcome = "X", predictors = c("Email_hours", "Meeting_hours"), return = "summary")# Return a summary table of IV pq_data %>% dplyr::mutate(X = ifelse(Internal_network_size > 40, 1, 0)) %>% create_IV(outcome = "X", predictors = c("Email_hours", "Meeting_hours", "Chat_hours"), return = "plot") # Return summary pq_data %>% dplyr::mutate(X = ifelse(Internal_network_size > 40, 1, 0)) %>% create_IV(outcome = "X", predictors = c("Email_hours", "Meeting_hours"), return = "summary")
Time Trend - Line Chart for any metric
Description
Provides a week by week view of a selected metric, visualised as line charts. By default returns a line chart for the defined metric, with a separate panel per value in the HR attribute. Additional options available to return a summary table.
Usage
create_line( data, metric, hrvar = "Organization", mingroup = 5, ncol = NULL, return = "plot" )create_line( data, metric, hrvar = "Organization", mingroup = 5, ncol = NULL, return = "plot" )
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
metric |
Character string containing the name of the metric, e.g. "Collaboration_hours" |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
ncol |
Numeric value setting the number of columns on the plot. Defaults
to |
return |
String specifying what to return. This must be one of the following strings:
See |
Details
This is a general purpose function that powers all the functions in the package that produce faceted line plots.
Value
A different output is returned depending on the value passed to the return argument:
-
"plot": 'ggplot' object. A faceted line plot for the metric. -
"table": data frame. A summary table for the metric.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other Flexible:
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_density(),
create_dist(),
create_fizz(),
create_hist(),
create_inc(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend()
Other Time-series:
create_line_asis(),
create_period_scatter(),
create_trend()
Examples
# Return plot of Email Hours pq_data %>% create_line(metric = "Email_hours", return = "plot") # Return plot of Collaboration Hours pq_data %>% create_line(metric = "Collaboration_hours", return = "plot") # Return plot but coerce plot to two columns pq_data %>% create_line( metric = "Collaboration_hours", hrvar = "Organization", ncol = 2 ) # Return plot of email hours and cut by `LevelDesignation` pq_data %>% create_line(metric = "Email_hours", hrvar = "LevelDesignation")# Return plot of Email Hours pq_data %>% create_line(metric = "Email_hours", return = "plot") # Return plot of Collaboration Hours pq_data %>% create_line(metric = "Collaboration_hours", return = "plot") # Return plot but coerce plot to two columns pq_data %>% create_line( metric = "Collaboration_hours", hrvar = "Organization", ncol = 2 ) # Return plot of email hours and cut by `LevelDesignation` pq_data %>% create_line(metric = "Email_hours", hrvar = "LevelDesignation")
Create a line chart without aggregation for any metric
Description
This function creates a line chart directly from the aggregated / summarised data.
Unlike create_line() which performs a person-level aggregation, there is no
calculation for create_line_asis() and the values are rendered as they are passed
into the function. The only requirement is that a date_var is provided for the x-axis.
Usage
create_line_asis( data, date_var = "MetricDate", metric, title = NULL, subtitle = NULL, caption = NULL, ylab = date_var, xlab = metric, line_colour = rgb2hex(0, 120, 212) )create_line_asis( data, date_var = "MetricDate", metric, title = NULL, subtitle = NULL, caption = NULL, ylab = date_var, xlab = metric, line_colour = rgb2hex(0, 120, 212) )
Arguments
data |
Plotting data as a data frame. |
date_var |
String containing name of variable for the horizontal axis. |
metric |
String containing name of variable representing the line. |
title |
Title of the plot. |
subtitle |
Subtitle of the plot. |
caption |
Caption of the plot. |
ylab |
Y-axis label for the plot (group axis) |
xlab |
X-axis label of the plot (bar axis). |
line_colour |
String to specify colour to use for the line.
Hex codes are accepted. You can also supply
RGB values via |
Value
Returns a 'ggplot' object representing a line plot.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other Flexible:
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_density(),
create_dist(),
create_fizz(),
create_hist(),
create_inc(),
create_line(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend()
Other Time-series:
create_line(),
create_period_scatter(),
create_trend()
Examples
library(dplyr) # Median `Emails_sent` grouped by `MetricDate` # Without Person Averaging med_df <- pq_data %>% group_by(MetricDate) %>% summarise(Emails_sent_median = median(Emails_sent)) med_df %>% create_line_asis( date_var = "MetricDate", metric = "Emails_sent_median", title = "Median Emails Sent", subtitle = "Person Averaging Not Applied", caption = extract_date_range(pq_data, return = "text") )library(dplyr) # Median `Emails_sent` grouped by `MetricDate` # Without Person Averaging med_df <- pq_data %>% group_by(MetricDate) %>% summarise(Emails_sent_median = median(Emails_sent)) med_df %>% create_line_asis( date_var = "MetricDate", metric = "Emails_sent_median", title = "Median Emails Sent", subtitle = "Person Averaging Not Applied", caption = extract_date_range(pq_data, return = "text") )
Calculate the Lorenz Curve and Gini Coefficient in a Person Query
Description
This function computes the Gini coefficient and plots the Lorenz curve based on a selected metric from a Person Query data frame. It provides a way to measure inequality in the distribution of the selected metric.This function can be integrated into a larger analysis pipeline to assess inequality in metric distribution.
Usage
create_lorenz(data, metric, return = "plot")create_lorenz(data, metric, return = "plot")
Arguments
data |
Data frame containing a Person Query. |
metric |
Character string identifying the metric to be used for the Lorenz curve and Gini coefficient calculation. |
return |
Character string identifying the return type. Options are:
|
Gini coefficient
The Gini coefficient is a measure of statistical dispersion most commonly used to represent income inequality within a population. It is calculated as the ratio of the area between the Lorenz curve and the line of perfect equality (the 45-degree line) to the total area under the line of perfect equality. It has a range of 0 to 1, where 0 represents perfect equality and 1 represents perfect inequality. It can be applied to any Viva Insights metric where inequality is of interest.
Examples
create_lorenz(data = pq_data, metric = "Emails_sent", return = "gini") create_lorenz(data = pq_data, metric = "Emails_sent", return = "plot") create_lorenz(data = pq_data, metric = "Emails_sent", return = "table")create_lorenz(data = pq_data, metric = "Emails_sent", return = "gini") create_lorenz(data = pq_data, metric = "Emails_sent", return = "plot") create_lorenz(data = pq_data, metric = "Emails_sent", return = "table")
Period comparison scatter plot for any two metrics
Description
Returns two side-by-side scatter plots representing two selected metrics, using colour to map an HR attribute and size to represent number of employees. Returns a faceted scatter plot by default, with additional options to return a summary table.
Usage
create_period_scatter( data, hrvar = "Organization", metric_x = "Large_and_long_meeting_hours", metric_y = "Meeting_hours", before_start = min(as.Date(data$MetricDate, "%m/%d/%Y")), before_end, after_start = as.Date(before_end) + 1, after_end = max(as.Date(data$MetricDate, "%m/%d/%Y")), before_label = "Period 1", after_label = "Period 2", mingroup = 5, return = "plot" )create_period_scatter( data, hrvar = "Organization", metric_x = "Large_and_long_meeting_hours", metric_y = "Meeting_hours", before_start = min(as.Date(data$MetricDate, "%m/%d/%Y")), before_end, after_start = as.Date(before_end) + 1, after_end = max(as.Date(data$MetricDate, "%m/%d/%Y")), before_label = "Period 1", after_label = "Period 2", mingroup = 5, return = "plot" )
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
hrvar |
HR Variable by which to split metrics. Accepts a character vector, defaults to "Organization" but accepts any character vector, e.g. "LevelDesignation" |
metric_x |
Character string containing the name of the metric, e.g. "Collaboration_hours" |
metric_y |
Character string containing the name of the metric, e.g. "Collaboration_hours" |
before_start |
Start date of "before" time period in YYYY-MM-DD |
before_end |
End date of "before" time period in YYYY-MM-DD |
after_start |
Start date of "after" time period in YYYY-MM-DD |
after_end |
End date of "after" time period in YYYY-MM-DD |
before_label |
String to specify a label for the "before" period. Defaults to "Period 1". |
after_label |
String to specify a label for the "after" period. Defaults to "Period 2". |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
return |
Character vector specifying what to return, defaults to "plot". Valid inputs are "plot" and "table". |
Details
This is a general purpose function that powers all the functions in the package that produce faceted scatter plots.
Value
Returns a 'ggplot' object showing two scatter plots side by side representing the two periods.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other Flexible:
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_density(),
create_dist(),
create_fizz(),
create_hist(),
create_inc(),
create_line(),
create_line_asis(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend()
Other Time-series:
create_line(),
create_line_asis(),
create_trend()
Examples
# Return plot create_period_scatter(pq_data, hrvar = "LevelDesignation", before_start = "2022-05-01", before_end = "2022-05-31", after_start = "2022-06-01", after_end = "2022-07-03") # Return a summary table create_period_scatter(pq_data, before_end = "2022-05-31", return = "table")# Return plot create_period_scatter(pq_data, hrvar = "LevelDesignation", before_start = "2022-05-01", before_end = "2022-05-31", after_start = "2022-06-01", after_end = "2022-07-03") # Return a summary table create_period_scatter(pq_data, before_end = "2022-05-31", return = "table")
Rank all groups across HR attributes on a selected Viva Insights metric
Description
This function scans a standard Person query output for groups with high levels of a given Viva Insights Metric. Returns a plot by default, with an option to return a table with all groups (across multiple HR attributes) ranked by the specified metric.
Usage
create_rank( data, metric, hrvar = extract_hr(data, exclude_constants = TRUE), mingroup = 5, return = "table", mode = "simple", plot_mode = 1 )create_rank( data, metric, hrvar = extract_hr(data, exclude_constants = TRUE), mingroup = 5, return = "table", mode = "simple", plot_mode = 1 )
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
metric |
Character string containing the name of the metric, e.g. "Collaboration_hours" |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
return |
String specifying what to return. This must be one of the following strings:
See |
mode |
String to specify calculation mode. Must be either:
|
plot_mode |
Numeric vector to determine which plot mode to return. Must
be either
|
Value
A different output is returned depending on the value passed to the return
argument:
-
"plot": 'ggplot' object. A bubble plot where the x-axis represents the metric, the y-axis represents the HR attributes, and the size of the bubbles represent the size of the organizations. Note that there is no plot output ifmodeis set to"combine". -
"table": data frame. A summary table for the metric.
Author(s)
Carlos Morales Torrado [email protected]
Martin Chan [email protected]
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other Flexible:
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_density(),
create_dist(),
create_fizz(),
create_hist(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend()
Examples
pq_data_small <- dplyr::slice_sample(pq_data, prop = 0.1) # Plot mode 1 - show top and bottom five groups create_rank( data = pq_data_small, hrvar = c("FunctionType", "LevelDesignation"), metric = "Emails_sent", return = "plot", plot_mode = 1 ) # Plot mode 2 - show top and bottom groups per HR variable create_rank( data = pq_data_small, hrvar = c("FunctionType", "LevelDesignation"), metric = "Emails_sent", return = "plot", plot_mode = 2 ) # Return a table create_rank( data = pq_data_small, metric = "Emails_sent", return = "table" ) # Return a table - combination mode create_rank( data = pq_data_small, metric = "Emails_sent", mode = "combine", return = "table" )pq_data_small <- dplyr::slice_sample(pq_data, prop = 0.1) # Plot mode 1 - show top and bottom five groups create_rank( data = pq_data_small, hrvar = c("FunctionType", "LevelDesignation"), metric = "Emails_sent", return = "plot", plot_mode = 1 ) # Plot mode 2 - show top and bottom groups per HR variable create_rank( data = pq_data_small, hrvar = c("FunctionType", "LevelDesignation"), metric = "Emails_sent", return = "plot", plot_mode = 2 ) # Return a table create_rank( data = pq_data_small, metric = "Emails_sent", return = "table" ) # Return a table - combination mode create_rank( data = pq_data_small, metric = "Emails_sent", mode = "combine", return = "table" )
Create combination pairs of HR variables and run 'create_rank()'
Description
Create pairwise combinations of HR variables and compute an average of a specified advanced insights metric.
Usage
create_rank_combine(data, hrvar = extract_hr(data), metric, mingroup = 5)create_rank_combine(data, hrvar = extract_hr(data), metric, mingroup = 5)
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
metric |
Character string containing the name of the metric, e.g. "Collaboration_hours" |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
Details
This function is called when the mode argument in create_rank() is
specified as "combine".
Value
Data frame containing the following variables:
-
hrvar: placeholder column that denotes the output as"Combined". -
group: pairwise combinations of HR attributes with the HR attribute in square brackets followed by the value of the HR attribute. -
Name of the metric (as passed to
metric) -
n
Examples
# Use a small sample for faster runtime pq_data_small <- dplyr::slice_sample(pq_data, prop = 0.1) create_rank_combine( data = pq_data_small, metric = "Email_hours", hrvar = c("Organization", "FunctionType", "LevelDesignation") )# Use a small sample for faster runtime pq_data_small <- dplyr::slice_sample(pq_data, prop = 0.1) create_rank_combine( data = pq_data_small, metric = "Email_hours", hrvar = c("Organization", "FunctionType", "LevelDesignation") )
Create a sankey chart from a two-column count table
Description
Create a 'networkD3' style sankey chart based on a long count table with two variables. The input data should have three columns, where each row is a unique group:
-
Variable 1
-
Variable 2
-
Count
Usage
create_sankey(data, var1, var2, count = "n")create_sankey(data, var1, var2, count = "n")
Arguments
data |
Data frame of the long count table. |
var1 |
String containing the name of the variable to be shown on the left. |
var2 |
String containing the name of the variable to be shown on the right. |
count |
String containing the name of the count variable. |
Value
A 'sankeyNetwork' and 'htmlwidget' object containing a two-tier
sankey plot. The output can be saved locally with
htmlwidgets::saveWidget().
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other Flexible:
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_density(),
create_dist(),
create_fizz(),
create_hist(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend()
Examples
pq_data %>% dplyr::count(Organization, FunctionType) %>% create_sankey(var1 = "Organization", var2 = "FunctionType")pq_data %>% dplyr::count(Organization, FunctionType) %>% create_sankey(var1 = "Organization", var2 = "FunctionType")
Create a Scatter plot with two selected Viva Insights metrics (General Purpose)
Description
Returns a scatter plot of two selected metrics, using colour to map an HR attribute. Returns a scatter plot by default, with additional options to return a summary table.
Usage
create_scatter( data, metric_x, metric_y, hrvar = "Organization", mingroup = 5, return = "plot" )create_scatter( data, metric_x, metric_y, hrvar = "Organization", mingroup = 5, return = "plot" )
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
metric_x |
Character string containing the name of the metric, e.g. "Collaboration_hours" |
metric_y |
Character string containing the name of the metric, e.g. "Collaboration_hours" |
hrvar |
HR Variable by which to split metrics, defaults to "Organization" but accepts any character vector, e.g. "LevelDesignation" |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
return |
Character vector specifying what to return, defaults to "plot". Valid inputs are "plot" and "table". |
Details
This is a general purpose function that powers all the functions in the package that produce scatter plots.
Value
Returns a 'ggplot' object by default, where 'plot' is passed in return.
When 'table' is passed, a summary table is returned as a data frame.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other Flexible:
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_density(),
create_dist(),
create_fizz(),
create_hist(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_stacked(),
create_tracking(),
create_trend()
Examples
create_scatter( pq_data, metric_x = "Collaboration_hours", metric_y = "Multitasking_hours", hrvar = "Organization" ) create_scatter( pq_data, metric_x = "Collaboration_hours", metric_y = "Multitasking_hours", hrvar = "Organization", mingroup = 100, return = "plot" )create_scatter( pq_data, metric_x = "Collaboration_hours", metric_y = "Multitasking_hours", hrvar = "Organization" ) create_scatter( pq_data, metric_x = "Collaboration_hours", metric_y = "Multitasking_hours", hrvar = "Organization", mingroup = 100, return = "plot" )
Horizontal stacked bar plot for any metric
Description
Creates either a single bar plot, of a stacked bar using selected metrics (where the typical use case is to create different definitions of collaboration hours). Returns a plot by default. Additional options available to return a summary table.
Usage
create_stacked( data, hrvar = "Organization", metrics = c("Meeting_hours", "Email_hours"), mingroup = 5, return = "plot", stack_colours = c("#1d627e", "#34b1e2", "#b4d5dd", "#adc0cb"), percent = FALSE, plot_title = "Collaboration Hours", plot_subtitle = paste("Average by", tolower(camel_clean(hrvar))), legend_lab = NULL, rank = "descending", xlim = NULL, text_just = 0.5, text_colour = "#FFFFFF" )create_stacked( data, hrvar = "Organization", metrics = c("Meeting_hours", "Email_hours"), mingroup = 5, return = "plot", stack_colours = c("#1d627e", "#34b1e2", "#b4d5dd", "#adc0cb"), percent = FALSE, plot_title = "Collaboration Hours", plot_subtitle = paste("Average by", tolower(camel_clean(hrvar))), legend_lab = NULL, rank = "descending", xlim = NULL, text_just = 0.5, text_colour = "#FFFFFF" )
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
metrics |
A character vector to specify variables to be used in calculating the "Total" value, e.g. c("Meeting_hours", "Email_hours"). The order of the variable names supplied determine the order in which they appear on the stacked plot. |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
return |
Character vector specifying what to return, defaults to "plot". Valid inputs are "plot" and "table". |
stack_colours |
A character vector to specify the colour codes for the stacked bar charts. |
percent |
Logical value to determine whether to show labels as
percentage signs. Defaults to |
plot_title |
String. Option to override plot title. |
plot_subtitle |
String. Option to override plot subtitle. |
legend_lab |
String. Option to override legend title/label. Defaults to
|
rank |
String specifying how to rank the bars. Valid inputs are:
|
xlim |
An option to set max value in x axis. |
text_just |
|
text_colour |
|
Value
Returns a 'ggplot' object by default, where 'plot' is passed in return.
When 'table' is passed, a summary table is returned as a data frame.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other Flexible:
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_density(),
create_dist(),
create_fizz(),
create_hist(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_tracking(),
create_trend()
Examples
pq_data %>% create_stacked(hrvar = "LevelDesignation", metrics = c("Meeting_hours", "Email_hours"), return = "plot") pq_data %>% create_stacked(hrvar = "FunctionType", metrics = c("Meeting_hours", "Email_hours", "Call_hours", "Chat_hours"), return = "plot", rank = "ascending") pq_data %>% create_stacked(hrvar = "FunctionType", metrics = c("Meeting_hours", "Email_hours", "Call_hours", "Chat_hours"), return = "table")pq_data %>% create_stacked(hrvar = "LevelDesignation", metrics = c("Meeting_hours", "Email_hours"), return = "plot") pq_data %>% create_stacked(hrvar = "FunctionType", metrics = c("Meeting_hours", "Email_hours", "Call_hours", "Chat_hours"), return = "plot", rank = "ascending") pq_data %>% create_stacked(hrvar = "FunctionType", metrics = c("Meeting_hours", "Email_hours", "Call_hours", "Chat_hours"), return = "table")
Create a line chart that tracks metrics over time with a 4-week rolling average
Description
![[Experimental]](data:image/svg+xml;base64,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)
Create a two-series line chart that visualizes a set of metric over time for the selected population, with one of the series being a four-week rolling average.
Usage
create_tracking( data, metric, plot_title = us_to_space(metric), plot_subtitle = "Measure over time", percent = FALSE )create_tracking( data, metric, plot_title = us_to_space(metric), plot_subtitle = "Measure over time", percent = FALSE )
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
metric |
Character string containing the name of the metric,
e.g. "Collaboration_hours"
percentage signs. Defaults to |
plot_title |
An option to override plot title. |
plot_subtitle |
An option to override plot subtitle. |
percent |
Logical value to determine whether to show labels as
percentage signs. Defaults to |
Value
A different output is returned depending on the value passed to the return argument:
-
"plot": 'ggplot' object. A time-series plot for the metric. -
"table": data frame. A summary table for the metric.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other Flexible:
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_density(),
create_dist(),
create_fizz(),
create_hist(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_trend()
Examples
pq_data %>% create_tracking( metric = "Collaboration_hours", percent = FALSE )pq_data %>% create_tracking( metric = "Collaboration_hours", percent = FALSE )
Heat mapped horizontal bar plot over time for any metric
Description
Provides a week by week view of a selected Viva Insights metric. By default returns a week by week heatmap bar plot, highlighting the points in time with most activity. Additional options available to return a summary table.
Usage
create_trend( data, metric, hrvar = "Organization", mingroup = 5, palette = c("steelblue4", "aliceblue", "white", "mistyrose1", "tomato1"), return = "plot", legend_title = "Hours" )create_trend( data, metric, hrvar = "Organization", mingroup = 5, palette = c("steelblue4", "aliceblue", "white", "mistyrose1", "tomato1"), return = "plot", legend_title = "Hours" )
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
metric |
Character string containing the name of the metric, e.g. "Collaboration_hours" |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
palette |
Character vector containing colour codes, ranked from the
lowest value to the highest value. This is passed directly to
|
return |
Character vector specifying what to return, defaults to
|
legend_title |
String to be used as the title of the legend. Defaults to
|
Value
Returns a 'ggplot' object by default, where 'plot' is passed in return.
When 'table' is passed, a summary table is returned as a data frame.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other Flexible:
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_density(),
create_dist(),
create_fizz(),
create_hist(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking()
Other Time-series:
create_line(),
create_line_asis(),
create_period_scatter()
Examples
create_trend(pq_data, metric = "Collaboration_hours", hrvar = "LevelDesignation") # custom colours create_trend( pq_data, metric = "Collaboration_hours", hrvar = "LevelDesignation", palette = c( "#FB6107", "#F3DE2C", "#7CB518", "#5C8001" ) )create_trend(pq_data, metric = "Collaboration_hours", hrvar = "LevelDesignation") # custom colours create_trend( pq_data, metric = "Collaboration_hours", hrvar = "LevelDesignation", palette = c( "#FB6107", "#F3DE2C", "#7CB518", "#5C8001" ) )
Convert a numeric variable for hours into categorical
Description
Supply a numeric variable, e.g. Collaboration_hours, and return a character
vector.
Usage
cut_hour(metric, cuts, unit = "hours", lbound = 0, ubound = 100)cut_hour(metric, cuts, unit = "hours", lbound = 0, ubound = 100)
Arguments
metric |
A numeric variable representing hours. |
cuts |
A numeric vector of minimum length 3 to represent the cut points required. The minimum and maximum values provided in the vector are inclusive. |
unit |
String to specify the unit of the labels. Defaults to "hours". |
lbound |
Numeric. Specifies the lower bound (inclusive) value for the minimum label. Defaults to 0. |
ubound |
Numeric. Specifies the upper bound (inclusive) value for the maximum label. Defaults to 100. |
Details
This is used within create_dist() for numeric to categorical conversion.
Value
Character vector representing a converted categorical variable, appended
with the label of the unit. See examples for more information.
See Also
Other Support:
any_idate(),
camel_clean(),
check_inputs(),
extract_date_range(),
extract_hr(),
heat_colours(),
is_date_format(),
maxmin(),
pairwise_count(),
read_preamble(),
rgb2hex(),
totals_bind(),
totals_col(),
tstamp(),
us_to_space(),
wrap()
Examples
# Direct use cut_hour(1:30, cuts = c(15, 20, 25)) # Use on a query cut_hour(pq_data$Collaboration_hours, cuts = c(10, 15, 20), ubound = 150)# Direct use cut_hour(1:30, cuts = c(15, 20, 25)) # Use on a query cut_hour(pq_data$Collaboration_hours, cuts = c(10, 15, 20), ubound = 150)
Distribution of Email Hours as a 100% stacked bar
Description
Analyze Email Hours distribution. Returns a stacked bar plot by default. Additional options available to return a table with distribution elements.
Usage
email_dist( data, hrvar = "Organization", mingroup = 5, return = "plot", cut = c(0.5, 1, 1.5) )email_dist( data, hrvar = "Organization", mingroup = 5, return = "plot", cut = c(0.5, 1, 1.5) )
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
return |
String specifying what to return. This must be one of the following strings:
See |
cut |
A numeric vector of length three to specify the breaks for the distribution, e.g. c(10, 15, 20) |
Value
A different output is returned depending on the value passed to the return argument:
-
"plot": 'ggplot' object. A stacked bar plot for the metric. -
"table": data frame. A summary table for the metric.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other Emails:
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend()
Examples
# Return plot email_dist(pq_data, hrvar = "Organization") # Return summary table email_dist(pq_data, hrvar = "Organization", return = "table") # Return result with a custom specified breaks email_dist(pq_data, hrvar = "LevelDesignation", cut = c(1, 2, 3))# Return plot email_dist(pq_data, hrvar = "Organization") # Return summary table email_dist(pq_data, hrvar = "Organization", return = "table") # Return result with a custom specified breaks email_dist(pq_data, hrvar = "LevelDesignation", cut = c(1, 2, 3))
Distribution of Email Hours (Fizzy Drink plot)
Description
Analyze weekly email hours distribution, and returns a 'fizzy' scatter plot by default. Additional options available to return a table with distribution elements.
Usage
email_fizz(data, hrvar = "Organization", mingroup = 5, return = "plot")email_fizz(data, hrvar = "Organization", mingroup = 5, return = "plot")
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
return |
String specifying what to return. This must be one of the following strings:
See |
Value
A different output is returned depending on the value passed to the return argument:
-
"plot": 'ggplot' object. A jittered scatter plot for the metric. -
"table": data frame. A summary table for the metric.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other Emails:
email_dist(),
email_line(),
email_rank(),
email_summary(),
email_trend()
Examples
# Return plot email_fizz(pq_data, hrvar = "Organization", return = "plot") # Return summary table email_fizz(pq_data, hrvar = "Organization", return = "table")# Return plot email_fizz(pq_data, hrvar = "Organization", return = "plot") # Return summary table email_fizz(pq_data, hrvar = "Organization", return = "table")
Email Time Trend - Line Chart
Description
Provides a week by week view of email time, visualised as line charts. By default returns a line chart for email hours, with a separate panel per value in the HR attribute. Additional options available to return a summary table.
Usage
email_line(data, hrvar = "Organization", mingroup = 5, return = "plot")email_line(data, hrvar = "Organization", mingroup = 5, return = "plot")
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
return |
String specifying what to return. This must be one of the following strings:
See |
Value
A different output is returned depending on the value passed to the return argument:
-
"plot": 'ggplot' object. A faceted line plot for the metric. -
"table": data frame. A summary table for the metric.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other Emails:
email_dist(),
email_fizz(),
email_rank(),
email_summary(),
email_trend()
Examples
# Return a line plot email_line(pq_data, hrvar = "LevelDesignation") # Return summary table email_line(pq_data, hrvar = "LevelDesignation", return = "table")# Return a line plot email_line(pq_data, hrvar = "LevelDesignation") # Return summary table email_line(pq_data, hrvar = "LevelDesignation", return = "table")
Email Hours Ranking
Description
This function scans a standard query output for groups with high levels of 'Weekly Email Collaboration'. Returns a plot by default, with an option to return a table with a all of groups (across multiple HR attributes) ranked by hours of digital collaboration.
Usage
email_rank( data, hrvar = extract_hr(data), mingroup = 5, mode = "simple", plot_mode = 1, return = "plot" )email_rank( data, hrvar = extract_hr(data), mingroup = 5, mode = "simple", plot_mode = 1, return = "plot" )
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
mode |
String to specify calculation mode. Must be either:
|
plot_mode |
Numeric vector to determine which plot mode to return. Must
be either
|
return |
String specifying what to return. This must be one of the following strings:
See |
Details
Uses the metric Email_hours.
See create_rank() for applying the same analysis to a different metric.
Value
A different output is returned depending on the value passed to the return
argument:
-
"plot": 'ggplot' object. A bubble plot where the x-axis represents the metric, the y-axis represents the HR attributes, and the size of the bubbles represent the size of the organizations. Note that there is no plot output ifmodeis set to"combine". -
"table": data frame. A summary table for the metric.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other Emails:
email_dist(),
email_fizz(),
email_line(),
email_summary(),
email_trend()
Examples
# Return rank table email_rank( data = pq_data, return = "table" ) # Return plot email_rank( data = pq_data, return = "plot" )# Return rank table email_rank( data = pq_data, return = "table" ) # Return plot email_rank( data = pq_data, return = "plot" )
Email Summary
Description
Provides an overview analysis of weekly email hours. Returns a bar plot showing average weekly email hours by default. Additional options available to return a summary table.
Usage
email_summary(data, hrvar = "Organization", mingroup = 5, return = "plot") email_sum(data, hrvar = "Organization", mingroup = 5, return = "plot")email_summary(data, hrvar = "Organization", mingroup = 5, return = "plot") email_sum(data, hrvar = "Organization", mingroup = 5, return = "plot")
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
return |
String specifying what to return. This must be one of the following strings:
See |
Value
A different output is returned depending on the value passed to the return argument:
-
"plot": 'ggplot' object. A bar plot for the metric. -
"table": data frame. A summary table for the metric.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other Emails:
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_trend()
Examples
# Return a ggplot bar chart email_summary(pq_data, hrvar = "LevelDesignation") # Return a summary table email_summary(pq_data, hrvar = "LevelDesignation", return = "table")# Return a ggplot bar chart email_summary(pq_data, hrvar = "LevelDesignation") # Return a summary table email_summary(pq_data, hrvar = "LevelDesignation", return = "table")
Email Hours Time Trend
Description
Provides a week by week view of email 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.
Usage
email_trend(data, hrvar = "Organization", mingroup = 5, return = "plot")email_trend(data, hrvar = "Organization", mingroup = 5, return = "plot")
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
return |
Character vector specifying what to return, defaults to
|
Details
Uses the metric Email_hours.
Value
Returns a 'ggplot' object by default, where 'plot' is passed in return.
When 'table' is passed, a summary table is returned as a data frame.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other Emails:
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary()
Examples
# Run plot email_trend(pq_data) # Run table email_trend(pq_data, hrvar = "LevelDesignation", return = "table")# Run plot email_trend(pq_data) # Run table email_trend(pq_data, hrvar = "LevelDesignation", return = "table")
Export 'vivainsights' outputs to CSV, clipboard, or save as images
Description
A general use function to export 'vivainsights' outputs to CSV, clipboard, or save as
images. By default, export() copies a data frame to the clipboard. If the
input is a 'ggplot' object, the default behaviour is to export a PNG.
Usage
export( x, method = "clipboard", path = "insights export", timestamp = TRUE, width = 12, height = 9 )export( x, method = "clipboard", path = "insights export", timestamp = TRUE, width = 12, height = 9 )
Arguments
x |
Data frame or 'ggplot' object to be passed through. |
method |
Character string specifying the method of export. Valid inputs include:
|
path |
If exporting a file, enter the path and the desired file name,
excluding the file extension. For example, |
timestamp |
Logical vector specifying whether to include a timestamp in
the file name. Defaults to |
width |
Width of the plot |
height |
Height of the plot |
Value
A different output is returned depending on the value passed to the method
argument:
-
"clipboard": no return - data frame is saved to clipboard. -
"csv": CSV file containing data frame is saved to specified path. -
"png": PNG file containing 'ggplot' object is saved to specified path. -
"svg": SVG file containing 'ggplot' object is saved to specified path. -
"jpeg": JPEG file containing 'ggplot' object is saved to specified path. -
"pdf": PDF file containing 'ggplot' object is saved to specified path.
Author(s)
Martin Chan [email protected]
See Also
Other Import and Export:
copy_df(),
create_dt(),
import_query()
Distribution of External Collaboration Hours as a 100% stacked bar
Description
Analyze the distribution of External Collaboration Hours. Returns a stacked bar plot by default. Additional options available to return a table with distribution elements.
Usage
external_dist( data, hrvar = "Organization", mingroup = 5, return = "plot", cut = c(5, 10, 15) )external_dist( data, hrvar = "Organization", mingroup = 5, return = "plot", cut = c(5, 10, 15) )
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
return |
String specifying what to return. This must be one of the following strings:
See |
cut |
A numeric vector of length three to specify the breaks for the distribution, e.g. c(10, 15, 20) |
Details
Uses the metric External_collaboration_hours.
See create_dist() for applying the same analysis to a different metric.
Value
A different output is returned depending on the value passed to the return argument:
-
"plot": 'ggplot' object. A stacked bar plot for the metric. -
"table": data frame. A summary table for the metric.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other External Collaboration:
external_fizz(),
external_line(),
external_sum()
Examples
# Return plot external_dist(pq_data, hrvar = "Organization") # Return summary table external_dist(pq_data, hrvar = "Organization", return = "table") # Return result with a custom specified breaks external_dist(pq_data, hrvar = "LevelDesignation", cut = c(2, 4, 6))# Return plot external_dist(pq_data, hrvar = "Organization") # Return summary table external_dist(pq_data, hrvar = "Organization", return = "table") # Return result with a custom specified breaks external_dist(pq_data, hrvar = "LevelDesignation", cut = c(2, 4, 6))
Distribution of External Collaboration Hours (Fizzy Drink plot)
Description
Analyze weekly External Collaboration hours distribution, and returns a 'fizzy' scatter plot by default. Additional options available to return a table with distribution elements.
Usage
external_fizz(data, hrvar = "Organization", mingroup = 5, return = "plot")external_fizz(data, hrvar = "Organization", mingroup = 5, return = "plot")
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
return |
String specifying what to return. This must be one of the following strings:
See |
Details
Uses the metric Collaboration_hours_external.
See create_fizz() for applying the same analysis to a different metric.
Value
A different output is returned depending on the value passed to the return argument:
-
"plot": 'ggplot' object. A jittered scatter plot for the metric. -
"table": data frame. A summary table for the metric.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other External Collaboration:
external_dist(),
external_line(),
external_sum()
Examples
# Return plot external_fizz(pq_data, hrvar = "LevelDesignation", return = "plot") # Return summary table external_fizz(pq_data, hrvar = "Organization", return = "table")# Return plot external_fizz(pq_data, hrvar = "LevelDesignation", return = "plot") # Return summary table external_fizz(pq_data, hrvar = "Organization", return = "table")
External Collaboration Hours Time Trend - Line Chart
Description
Provides a week by week view of External collaboration time, visualized as line chart. By default returns a separate panel per value in the HR attribute. Additional options available to return a summary table.
Usage
external_line(data, hrvar = "Organization", mingroup = 5, return = "plot")external_line(data, hrvar = "Organization", mingroup = 5, return = "plot")
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
return |
String specifying what to return. This must be one of the following strings:
See |
Details
Uses the metric Collaboration_hours_external.
Value
A different output is returned depending on the value passed to the return argument:
-
"plot": 'ggplot' object. A faceted line plot for the metric. -
"table": data frame. A summary table for the metric.
See Also
create_line() for applying the same analysis to a different metric.
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other External Collaboration:
external_dist(),
external_fizz(),
external_sum()
Examples
# Return a line plot external_line(pq_data, hrvar = "LevelDesignation") # Return summary table external_line(pq_data, hrvar = "LevelDesignation", return = "table")# Return a line plot external_line(pq_data, hrvar = "LevelDesignation") # Return summary table external_line(pq_data, hrvar = "LevelDesignation", return = "table")
Rank groups with high External Collaboration Hours
Description
This function scans a Standard Person Query for groups with high levels of External Collaboration. Returns a plot by default, with an option to return a table with all groups (across multiple HR attributes) ranked by hours of External Collaboration.
Usage
external_rank( data, hrvar = extract_hr(data), mingroup = 5, mode = "simple", plot_mode = 1, return = "plot" )external_rank( data, hrvar = extract_hr(data), mingroup = 5, mode = "simple", plot_mode = 1, return = "plot" )
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
mode |
String to specify calculation mode. Must be either:
|
plot_mode |
Numeric vector to determine which plot mode to return. Must
be either
|
return |
String specifying what to return. This must be one of the following strings:
See |
Details
Uses the metric Collaboration_hours_external.
See create_rank() for applying the same analysis to a different metric.
Value
When 'table' is passed in return, a summary table is returned as a data frame.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other After-hours Collaboration:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend()
Examples
# Return rank table external_rank(data = pq_data, return = "table") # Return plot external_rank(data = pq_data, return = "plot")# Return rank table external_rank(data = pq_data, return = "table") # Return plot external_rank(data = pq_data, return = "plot")
External Collaboration Summary
Description
Provides an overview analysis of 'External Collaboration'. Returns a stacked bar plot of internal and external collaboration. Additional options available to return a summary table.
Usage
external_sum( data, hrvar = "Organization", mingroup = 5, stack_colours = c("#1d327e", "#1d7e6a"), return = "plot" ) external_summary( data, hrvar = "Organization", mingroup = 5, stack_colours = c("#1d327e", "#1d7e6a"), return = "plot" )external_sum( data, hrvar = "Organization", mingroup = 5, stack_colours = c("#1d327e", "#1d7e6a"), return = "plot" ) external_summary( data, hrvar = "Organization", mingroup = 5, stack_colours = c("#1d327e", "#1d7e6a"), return = "plot" )
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
stack_colours |
A character vector to specify the colour codes for the stacked bar charts. |
return |
Character vector specifying what to return, defaults to "plot". Valid inputs are "plot" and "table". |
Value
Returns a 'ggplot' object by default, where 'plot' is passed in return.
When 'table' is passed, a summary table is returned as a data frame.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other External Collaboration:
external_dist(),
external_fizz(),
external_line()
Examples
# Return a plot external_sum(pq_data, hrvar = "LevelDesignation") # Return summary table external_sum(pq_data, hrvar = "LevelDesignation", return = "table")# Return a plot external_sum(pq_data, hrvar = "LevelDesignation") # Return summary table external_sum(pq_data, hrvar = "LevelDesignation", return = "table")
Extract date period
Description
Return a data frame with the start and end date of the query data by default. There are options to return a descriptive string, which is used in the caption of plots in this package.
Usage
extract_date_range(data, return = "table")extract_date_range(data, return = "table")
Arguments
data |
Data frame containing a query to pass through.
The data frame must contain a |
return |
String specifying what output to return. Returns a table by default ("table"), but allows returning a descriptive string ("text"). |
Value
A different output is returned depending on the value passed to the return
argument:
-
"table": data frame. A summary table containing the start and end date for the dataset. -
"text": string. Contains a descriptive string on the start and end date for the dataset.
See Also
Other Support:
any_idate(),
camel_clean(),
check_inputs(),
cut_hour(),
extract_hr(),
heat_colours(),
is_date_format(),
maxmin(),
pairwise_count(),
read_preamble(),
rgb2hex(),
totals_bind(),
totals_col(),
tstamp(),
us_to_space(),
wrap()
Extract HR attribute variables
Description
This function uses a combination of variable class, number of unique values, and regular expression matching to extract HR / organisational attributes from a data frame.
Usage
extract_hr(data, max_unique = 50, exclude_constants = TRUE, return = "names")extract_hr(data, max_unique = 50, exclude_constants = TRUE, return = "names")
Arguments
data |
A data frame to be passed through. |
max_unique |
A numeric value representing the maximum number of unique values to accept for an HR attribute. Defaults to 50. |
exclude_constants |
Logical value to specify whether single-value HR
attributes are to be excluded. Defaults to |
return |
String specifying what to return. This must be one of the following strings:
See |
Value
A different output is returned depending on the value passed to the return
argument:
-
"names": character vector identifying all the names of HR variables present in the data. -
"vars": data frame containing all the columns of HR variables present in the data.
See Also
Other Support:
any_idate(),
camel_clean(),
check_inputs(),
cut_hour(),
extract_date_range(),
heat_colours(),
is_date_format(),
maxmin(),
pairwise_count(),
read_preamble(),
rgb2hex(),
totals_bind(),
totals_col(),
tstamp(),
us_to_space(),
wrap()
Other Data Validation:
check_query(),
flag_ch_ratio(),
flag_em_ratio(),
flag_extreme(),
flag_outlooktime(),
hr_trend(),
hrvar_count(),
hrvar_count_all(),
hrvar_trend(),
identify_churn(),
identify_holidayweeks(),
identify_inactiveweeks(),
identify_nkw(),
identify_outlier(),
identify_privacythreshold(),
identify_shifts(),
identify_tenure(),
track_HR_change(),
validation_report()
Examples
pq_data %>% extract_hr(return = "names") pq_data %>% extract_hr(return = "vars")pq_data %>% extract_hr(return = "names") pq_data %>% extract_hr(return = "vars")
Flag unusual high collaboration hours to after-hours collaboration hours ratio
Description
This function flags persons who have an unusual ratio of collaboration hours to after-hours collaboration hours. Returns a character string by default.
Usage
flag_ch_ratio(data, threshold = c(1, 30), return = "message")flag_ch_ratio(data, threshold = c(1, 30), return = "message")
Arguments
data |
A data frame containing a Person Query. |
threshold |
Numeric value specifying the threshold for flagging. Defaults to 30. |
return |
String to specify what to return. Options include:
|
Value
A different output is returned depending on the value passed to the return
argument:
-
"message": message in the console containing diagnostic summary -
"text": string containing diagnostic summary -
"data": data frame. Person-level data with flags on unusually high or low ratios
Metrics used
The metric Collaboration_hours is used in the calculations. Please ensure
that your query contains a metric with the exact same name.
See Also
Other Data Validation:
check_query(),
extract_hr(),
flag_em_ratio(),
flag_extreme(),
flag_outlooktime(),
hr_trend(),
hrvar_count(),
hrvar_count_all(),
hrvar_trend(),
identify_churn(),
identify_holidayweeks(),
identify_inactiveweeks(),
identify_nkw(),
identify_outlier(),
identify_privacythreshold(),
identify_shifts(),
identify_tenure(),
track_HR_change(),
validation_report()
Examples
flag_ch_ratio(pq_data) data.frame(PersonId = c("Alice", "Bob"), Collaboration_hours = c(30, 0.5), After_hours_collaboration_hours = c(0.5, 30)) %>% flag_ch_ratio()flag_ch_ratio(pq_data) data.frame(PersonId = c("Alice", "Bob"), Collaboration_hours = c(30, 0.5), After_hours_collaboration_hours = c(0.5, 30)) %>% flag_ch_ratio()
Flag Persons with unusually high Email Hours to Emails Sent ratio
Description
This function flags persons who have an unusual ratio
of email hours to emails sent. If the ratio between Email Hours and
Emails Sent is greater than the threshold, then observations tied to
a PersonId is flagged as unusual.
Usage
flag_em_ratio(data, threshold = 1, return = "text")flag_em_ratio(data, threshold = 1, return = "text")
Arguments
data |
A data frame containing a Person Query. |
threshold |
Numeric value specifying the threshold for flagging. Defaults to 1. |
return |
String specifying what to return. This must be one of the following strings:
See |
Value
A different output is returned depending on the value passed to the return
argument:
-
"text": string. A diagnostic message. -
"data": data frame. Person-level data with those flagged with unusual ratios.
See Also
Other Data Validation:
check_query(),
extract_hr(),
flag_ch_ratio(),
flag_extreme(),
flag_outlooktime(),
hr_trend(),
hrvar_count(),
hrvar_count_all(),
hrvar_trend(),
identify_churn(),
identify_holidayweeks(),
identify_inactiveweeks(),
identify_nkw(),
identify_outlier(),
identify_privacythreshold(),
identify_shifts(),
identify_tenure(),
track_HR_change(),
validation_report()
Examples
flag_em_ratio(pq_data)flag_em_ratio(pq_data)
Warn for extreme values by checking against a threshold
Description
This is used as part of data validation to check if there are extreme values in the dataset.
Usage
flag_extreme( data, metric, person = TRUE, threshold, mode = "above", return = "message" )flag_extreme( data, metric, person = TRUE, threshold, mode = "above", return = "message" )
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
metric |
A character string specifying the metric to test. |
person |
A logical value to specify whether to calculate
person-averages. Defaults to |
threshold |
Numeric value specifying the threshold for flagging. |
mode |
String determining mode to use for identifying extreme values.
|
return |
String specifying what to return. This must be one of the following strings:
See |
Value
A different output is returned depending on the value passed to the return
argument:
-
"text": string. A diagnostic message. -
"message": message on console. A diagnostic message. -
"table": data frame. A person-level table withPersonIdand the extreme values of the selected metric.
See Also
Other Data Validation:
check_query(),
extract_hr(),
flag_ch_ratio(),
flag_em_ratio(),
flag_outlooktime(),
hr_trend(),
hrvar_count(),
hrvar_count_all(),
hrvar_trend(),
identify_churn(),
identify_holidayweeks(),
identify_inactiveweeks(),
identify_nkw(),
identify_outlier(),
identify_privacythreshold(),
identify_shifts(),
identify_tenure(),
track_HR_change(),
validation_report()
Examples
# The threshold values are intentionally set low to trigger messages. flag_extreme(pq_data, "Email_hours", threshold = 15) # Return a summary table flag_extreme(pq_data, "Email_hours", threshold = 15, return = "table") # Person-week level flag_extreme(pq_data, "Email_hours", person = FALSE, threshold = 15) # Check for values equal to threshold flag_extreme(pq_data, "Email_hours", person = TRUE, mode = "equal", threshold = 0) # Check for values below threshold flag_extreme(pq_data, "Email_hours", person = TRUE, mode = "below", threshold = 5)# The threshold values are intentionally set low to trigger messages. flag_extreme(pq_data, "Email_hours", threshold = 15) # Return a summary table flag_extreme(pq_data, "Email_hours", threshold = 15, return = "table") # Person-week level flag_extreme(pq_data, "Email_hours", person = FALSE, threshold = 15) # Check for values equal to threshold flag_extreme(pq_data, "Email_hours", person = TRUE, mode = "equal", threshold = 0) # Check for values below threshold flag_extreme(pq_data, "Email_hours", person = TRUE, mode = "below", threshold = 5)
Flag unusual outlook time settings for work day start and end time
Description
This function flags unusual outlook calendar settings for start and end time of work day.
Usage
flag_outlooktime(data, threshold = c(4, 15), return = "message")flag_outlooktime(data, threshold = c(4, 15), return = "message")
Arguments
data |
A data frame containing a Person Query. |
threshold |
A numeric vector of length two, specifying the hour threshold for flagging. Defaults to c(4, 15). |
return |
String specifying what to return. This must be one of the following strings:
|
Value
A different output is returned depending on the value passed to the return
argument:
-
"text": string. A diagnostic message. -
"message": message on console. A diagnostic message. -
"data": data frame. Data where flag is present.
See Value for more information.
See Also
Other Data Validation:
check_query(),
extract_hr(),
flag_ch_ratio(),
flag_em_ratio(),
flag_extreme(),
hr_trend(),
hrvar_count(),
hrvar_count_all(),
hrvar_trend(),
identify_churn(),
identify_holidayweeks(),
identify_inactiveweeks(),
identify_nkw(),
identify_outlier(),
identify_privacythreshold(),
identify_shifts(),
identify_tenure(),
track_HR_change(),
validation_report()
Examples
# Demo with `pq_data` example where Outlook Start and End times are imputed spq_df <- pq_data spq_df$WorkingStartTimeSetInOutlook <- "6:30" spq_df$WorkingEndTimeSetInOutlook <- "23:30" # Return a message flag_outlooktime(spq_df, threshold = c(5, 13)) # Return data flag_outlooktime(spq_df, threshold = c(5, 13), return = "data")# Demo with `pq_data` example where Outlook Start and End times are imputed spq_df <- pq_data spq_df$WorkingStartTimeSetInOutlook <- "6:30" spq_df$WorkingEndTimeSetInOutlook <- "23:30" # Return a message flag_outlooktime(spq_df, threshold = c(5, 13)) # Return data flag_outlooktime(spq_df, threshold = c(5, 13), return = "data")
Sample Group-to-Group dataset
Description
A demo dataset representing a Group-to-Group Query. The grouping
organizational attribute used here is Organization, where the variable have
been prefixed with PrimaryCollaborator_ and SecondaryCollaborator_ to represent the
direction of collaboration.
Usage
g2g_datag2g_data
Format
A data frame with 150 rows and 11 variables:
- PrimaryCollaborator_Organization
- PrimaryCollaborator_GroupSize
- SecondaryCollaborator_Organization
- SecondaryCollaborator_GroupSize
- MetricDate
- Percent_Group_collaboration_time_invested
- Group_collaboration_time_invested
- Group_email_sent_count
- Group_email_time_invested
- Group_meeting_count
- Group_meeting_time_invested
...
Value
data frame.
Source
https://analysis.insights.viva.office.com/analyst/analysis/
See Also
Other Data:
mt_data,
p2p_data,
p2p_data_sim(),
pq_data
Other Network:
network_g2g(),
network_p2p(),
network_summary(),
p2p_data,
p2p_data_sim()
Generate HTML report with list inputs
Description
This is a support function using a list-pmap workflow to create a HTML document, using RMarkdown as the engine.
Usage
generate_report( title = "My minimal HTML generator", filename = "minimal_html", outputs = output_list, titles, subheaders, echos, levels, theme = "united", preamble = "" )generate_report( title = "My minimal HTML generator", filename = "minimal_html", outputs = output_list, titles, subheaders, echos, levels, theme = "united", preamble = "" )
Arguments
title |
Character string to specify the title of the chunk. |
filename |
File name to be used in the exported HTML. |
outputs |
A list of outputs to be added to the HTML report.
Note that |
titles |
A list/vector of character strings to specify the title of the chunks. |
subheaders |
A list/vector of character strings to specify the subheaders for each chunk. |
echos |
A list/vector of logical values to specify whether to display code. |
levels |
A list/vector of numeric value to specify the header level of the chunk. |
theme |
Character vector to specify theme to be used for the report.
E.g. |
preamble |
A preamble to appear at the beginning of the report, passed as a text string. |
Value
An HTML report with the same file name as specified in the arguments is generated in the working directory. No outputs are directly returned by the function.
Creating a custom report
Below is an example on how to set up a custom report.
The first step is to define the content that will go into a report and assign the outputs to a list.
# Step 1: Define Content
output_list <-
list(pq_data %>% workloads_summary(return = "plot"),
pq_data %>% workloads_summary(return = "table")) %>%
purrr::map_if(is.data.frame, create_dt)
The next step is to add a list of titles for each of the objects on the list:
# Step 2: Add Corresponding Titles
title_list <- c("Workloads Summary - Plot", "Workloads Summary - Table")
n_title <- length(title_list)
The final step is to run generate_report(). This can all be wrapped within
a function such that the function can be used to generate a HTML report.
# Step 3: Generate Report
generate_report(title = "My First Report",
filename = "My First Report",
outputs = output_list,
titles = title_list,
subheaders = rep("", n_title),
echos = rep(FALSE, n_title
Author(s)
Martin Chan [email protected]
See Also
Other Reports:
IV_report(),
meeting_tm_report(),
read_preamble(),
validation_report()
Generate HTML report based on existing RMarkdown documents
Description
This is a support function that accepts parameters and creates a HTML
document based on an RMarkdown template. This is an alternative to
generate_report() which instead creates an RMarkdown document from scratch
using individual code chunks.
Usage
generate_report2( output_format = rmarkdown::html_document(toc = TRUE, toc_depth = 6, theme = "cosmo"), output_file = "report.html", output_dir = getwd(), report_title = "Report", rmd_dir = system.file("rmd_template/minimal.rmd", package = "vivainsights"), ... )generate_report2( output_format = rmarkdown::html_document(toc = TRUE, toc_depth = 6, theme = "cosmo"), output_file = "report.html", output_dir = getwd(), report_title = "Report", rmd_dir = system.file("rmd_template/minimal.rmd", package = "vivainsights"), ... )
Arguments
output_format |
output format in |
output_file |
output file name in |
output_dir |
output directory for report in |
report_title |
report title. Default is |
rmd_dir |
string specifying the path to the directory containing the RMarkdown template files. |
... |
other arguments to be passed to |
Note
The implementation of this function was inspired by the 'DataExplorer' package by boxuancui, with credits due to the original author.
Generate a vector of n contiguous colours, as a red-yellow-green palette.
Description
Takes a numeric value n and returns a character vector of colour HEX codes
corresponding to the heat map palette.
Usage
heat_colours(n, alpha, rev = FALSE) heat_colors(n, alpha, rev = FALSE)heat_colours(n, alpha, rev = FALSE) heat_colors(n, alpha, rev = FALSE)
Arguments
n |
the number of colors (>= 1) to be in the palette. |
alpha |
an alpha-transparency level in the range of 0 to 1 (0 means transparent and 1 means opaque) |
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 n is
returned.
See Also
Other Support:
any_idate(),
camel_clean(),
check_inputs(),
cut_hour(),
extract_date_range(),
extract_hr(),
is_date_format(),
maxmin(),
pairwise_count(),
read_preamble(),
rgb2hex(),
totals_bind(),
totals_col(),
tstamp(),
us_to_space(),
wrap()
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), border = NA) barplot(rep(10, 50), col = heat_colours(n = 50, alpha = 0.5, rev = TRUE), border = NA)
Employee count over time
Description
Returns a line chart showing the change in employee count over time. Part of a data validation process to check for unusual license growth / declines over time.
Usage
hr_trend(data, return = "plot")hr_trend(data, return = "plot")
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
return |
String specifying what to return. This must be one of the following strings:
See |
Value
A different output is returned depending on the value passed to the return
argument:
-
"plot": ggplot object. A line plot showing employee count over time. -
"table": data frame containing a summary table.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other Data Validation:
check_query(),
extract_hr(),
flag_ch_ratio(),
flag_em_ratio(),
flag_extreme(),
flag_outlooktime(),
hrvar_count(),
hrvar_count_all(),
hrvar_trend(),
identify_churn(),
identify_holidayweeks(),
identify_inactiveweeks(),
identify_nkw(),
identify_outlier(),
identify_privacythreshold(),
identify_shifts(),
identify_tenure(),
track_HR_change(),
validation_report()
Examples
# Return plot hr_trend(pq_data) # Return summary table hr_trend(pq_data, return = "table")# Return plot hr_trend(pq_data) # Return summary table hr_trend(pq_data, return = "table")
Create a count of distinct people in a specified HR variable
Description
This function enables you to create a count of the distinct people by the specified HR attribute.The default behaviour is to return a bar chart as typically seen in 'Analysis Scope'.
Usage
hrvar_count(data, hrvar = "Organization", return = "plot") analysis_scope(data, hrvar = "Organization", return = "plot")hrvar_count(data, hrvar = "Organization", return = "plot") analysis_scope(data, hrvar = "Organization", return = "plot")
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
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. |
return |
String specifying what to return. This must be one of the following strings:
See |
Value
A different output is returned depending on the value passed to the return
argument:
-
"plot": 'ggplot' object containing a bar plot. -
"table": data frame containing a count table.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other Data Validation:
check_query(),
extract_hr(),
flag_ch_ratio(),
flag_em_ratio(),
flag_extreme(),
flag_outlooktime(),
hr_trend(),
hrvar_count_all(),
hrvar_trend(),
identify_churn(),
identify_holidayweeks(),
identify_inactiveweeks(),
identify_nkw(),
identify_outlier(),
identify_privacythreshold(),
identify_shifts(),
identify_tenure(),
track_HR_change(),
validation_report()
Examples
# Return a bar plot hrvar_count(pq_data, hrvar = "LevelDesignation") # Return a summary table hrvar_count(pq_data, hrvar = "LevelDesignation", return = "table")# Return a bar plot hrvar_count(pq_data, hrvar = "LevelDesignation") # Return a summary table hrvar_count(pq_data, hrvar = "LevelDesignation", return = "table")
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 Viva Insights Data 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.
Usage
hrvar_count_all( data, n_var = 50, return = "message", threshold = 100, maxna = 20 )hrvar_count_all( data, n_var = 50, return = "message", threshold = 100, maxna = 20 )
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
n_var |
number of HR variables to include in report as rows. Default is set to 50 HR variables. |
return |
String to specify what to return |
threshold |
The max number of unique values allowed for any attribute. Default is 100. |
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 return.
-
'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.
See Also
Other Data Validation:
check_query(),
extract_hr(),
flag_ch_ratio(),
flag_em_ratio(),
flag_extreme(),
flag_outlooktime(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
identify_churn(),
identify_holidayweeks(),
identify_inactiveweeks(),
identify_nkw(),
identify_outlier(),
identify_privacythreshold(),
identify_shifts(),
identify_tenure(),
track_HR_change(),
validation_report()
Examples
# Return a summary table of all HR attributes hrvar_count_all(pq_data, return = "table")# Return a summary table of all HR attributes hrvar_count_all(pq_data, return = "table")
Track count of distinct people over time in a specified HR variable
Description
This function provides a week by week view of the count of the distinct people by the specified HR attribute.The default behaviour is to return a week by week heatmap bar plot.
Usage
hrvar_trend(data, hrvar = "Organization", return = "plot")hrvar_trend(data, hrvar = "Organization", return = "plot")
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
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. |
return |
String specifying what to return. This must be one of the following strings:
See |
Value
A different output is returned depending on the value passed to the return
argument:
-
"plot": 'ggplot' object containing a bar plot. -
"table": data frame containing a count table.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other Data Validation:
check_query(),
extract_hr(),
flag_ch_ratio(),
flag_em_ratio(),
flag_extreme(),
flag_outlooktime(),
hr_trend(),
hrvar_count(),
hrvar_count_all(),
identify_churn(),
identify_holidayweeks(),
identify_inactiveweeks(),
identify_nkw(),
identify_outlier(),
identify_privacythreshold(),
identify_shifts(),
identify_tenure(),
track_HR_change(),
validation_report()
Examples
# Return a bar plot hrvar_trend(pq_data, hrvar = "LevelDesignation") # Return a summary table hrvar_trend(pq_data, hrvar = "LevelDesignation", return = "table")# Return a bar plot hrvar_trend(pq_data, hrvar = "LevelDesignation") # Return a summary table hrvar_trend(pq_data, hrvar = "LevelDesignation", return = "table")
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.
Usage
identify_churn(data, n1 = 6, n2 = 6, return = "message", flip = FALSE)identify_churn(data, n1 = 6, n2 = 6, return = "message", flip = FALSE)
Arguments
data |
A Person Query as a data frame. Must contain a |
n1 |
A numeric value specifying the number of weeks at the beginning of the period that defines the measured employee set. Defaults to 6. |
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. |
return |
String specifying what to return. This must be one of the following strings:
See |
flip |
Logical, defaults to FALSE. This determines whether to reverse
the logic of identifying the non-overlapping set. If set to |
Details
An additional use case of this function is the ability to identify
"new-joiners" by using the argument flip.
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, Viva Insights license has been removed, shift to a
low-collaboration role (to the extent that he/she becomes inactive).
Value
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 thePersonIdof employees who have been identified as churned.
See Also
Other Data Validation:
check_query(),
extract_hr(),
flag_ch_ratio(),
flag_em_ratio(),
flag_extreme(),
flag_outlooktime(),
hr_trend(),
hrvar_count(),
hrvar_count_all(),
hrvar_trend(),
identify_holidayweeks(),
identify_inactiveweeks(),
identify_nkw(),
identify_outlier(),
identify_privacythreshold(),
identify_shifts(),
identify_tenure(),
track_HR_change(),
validation_report()
Examples
pq_data %>% identify_churn(n1 = 3, n2 = 3, return = "message")pq_data %>% identify_churn(n1 = 3, n2 = 3, return = "message")
Identify date frequency based on a series of dates
Description
Takes a vector of dates and identify whether the frequency is 'daily', 'weekly', or 'monthly'. The primary use case for this function is to provide an accurate description of the query type used and for raising errors should a wrong date grouping be used in the data input.
Usage
identify_datefreq(x)identify_datefreq(x)
Arguments
x |
Vector containing a series of dates. |
Details
Date frequency detection works as follows:
-
If at least three days of the week are present (e.g., Monday, Wednesday, Thursday) in the series, then the series is classified as 'daily'
-
If the total number of months in the series is equal to the length, then the series is classified as 'monthly'
-
If the total number of sundays in the series is equal to the length of the series, then the series is classified as 'weekly
Value
String describing the detected date frequency, i.e.:
-
'daily' -
'weekly' -
'monthly'
Limitations
One of the assumptions made behind the classification is that weeks are denoted with Sundays, hence the count of sundays to measure the number of weeks. In this case, weeks where a Sunday is missing would result in an 'unable to classify' error.
Another assumption made is that dates are evenly distributed, i.e. that the gap between dates are equal. If dates are unevenly distributed, e.g. only two days of the week are available for a given week, then the algorithm will fail to identify the frequency as 'daily'.
Examples
start_date <- as.Date("2022/06/26") end_date <- as.Date("2022/11/27") # Daily day_seq <- seq.Date( from = start_date, to = end_date, by = "day" ) identify_datefreq(day_seq) # Weekly week_seq <- seq.Date( from = start_date, to = end_date, by = "week" ) identify_datefreq(week_seq) # Monthly month_seq <- seq.Date( from = start_date, to = end_date, by = "month" ) identify_datefreq(month_seq)start_date <- as.Date("2022/06/26") end_date <- as.Date("2022/11/27") # Daily day_seq <- seq.Date( from = start_date, to = end_date, by = "day" ) identify_datefreq(day_seq) # Weekly week_seq <- seq.Date( from = start_date, to = end_date, by = "week" ) identify_datefreq(week_seq) # Monthly month_seq <- seq.Date( from = start_date, to = end_date, by = "month" ) identify_datefreq(month_seq)
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.
As best practice, run this function prior to any analysis to remove atypical collaboration weeks from your dataset.
Usage
identify_holidayweeks(data, sd = 1, return = "message")identify_holidayweeks(data, sd = 1, return = "message")
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
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. |
return |
String specifying what to return. This must be one of the following strings:
See |
Value
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.
Metrics used
The metric Collaboration_hours is used in the calculations. Please ensure
that your query contains a metric with the exact same name.
See Also
Other Data Validation:
check_query(),
extract_hr(),
flag_ch_ratio(),
flag_em_ratio(),
flag_extreme(),
flag_outlooktime(),
hr_trend(),
hrvar_count(),
hrvar_count_all(),
hrvar_trend(),
identify_churn(),
identify_inactiveweeks(),
identify_nkw(),
identify_outlier(),
identify_privacythreshold(),
identify_shifts(),
identify_tenure(),
track_HR_change(),
validation_report()
Examples
# Return a message by default identify_holidayweeks(pq_data) # Return plot identify_holidayweeks(pq_data, return = "plot")# Return a message by default identify_holidayweeks(pq_data) # Return plot identify_holidayweeks(pq_data, return = "plot")
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.
As best practice, run this function prior to any analysis to remove atypical collaboration weeks from your dataset.
Usage
identify_inactiveweeks(data, sd = 2, return = "text")identify_inactiveweeks(data, sd = 2, return = "text")
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
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. |
return |
String specifying what to return. This must be one of the following strings:
See |
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.
See Also
Other Data Validation:
check_query(),
extract_hr(),
flag_ch_ratio(),
flag_em_ratio(),
flag_extreme(),
flag_outlooktime(),
hr_trend(),
hrvar_count(),
hrvar_count_all(),
hrvar_trend(),
identify_churn(),
identify_holidayweeks(),
identify_nkw(),
identify_outlier(),
identify_privacythreshold(),
identify_shifts(),
identify_tenure(),
track_HR_change(),
validation_report()
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 data frame with non-knowledge workers removed, or the full data frame with the kw/nkw flag added.
Usage
identify_nkw(data, collab_threshold = 5, return = "data_summary")identify_nkw(data, collab_threshold = 5, return = "data_summary")
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
collab_threshold |
Positive numeric value representing 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"). Default is set to 5 collaboration hours. Any versions after v1.4.3,
this uses a "greater than or equal to" logic ( |
return |
String specifying what to return. This must be one of the following strings:
See |
Value
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 thekw/nkwflag. -
"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.
See Also
Other Data Validation:
check_query(),
extract_hr(),
flag_ch_ratio(),
flag_em_ratio(),
flag_extreme(),
flag_outlooktime(),
hr_trend(),
hrvar_count(),
hrvar_count_all(),
hrvar_trend(),
identify_churn(),
identify_holidayweeks(),
identify_inactiveweeks(),
identify_outlier(),
identify_privacythreshold(),
identify_shifts(),
identify_tenure(),
track_HR_change(),
validation_report()
Identify metric outliers over a date interval
Description
This function takes in a selected metric and uses
z-score (number of standard deviations) to identify outliers
across time. There are applications in this for identifying
weeks with abnormally low collaboration activity, e.g. holidays.
Time as a grouping variable can be overridden with the group_var
argument.
Usage
identify_outlier( data, group_var = "MetricDate", metric = "Collaboration_hours" )identify_outlier( data, group_var = "MetricDate", metric = "Collaboration_hours" )
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
group_var |
A string with the name of the grouping variable.
Defaults to |
metric |
Character string containing the name of the metric, e.g. "Collaboration_hours" |
Value
Returns a data frame with MetricDate (if grouping variable is not set),
the metric, and the corresponding z-score.
See Also
Other Data Validation:
check_query(),
extract_hr(),
flag_ch_ratio(),
flag_em_ratio(),
flag_extreme(),
flag_outlooktime(),
hr_trend(),
hrvar_count(),
hrvar_count_all(),
hrvar_trend(),
identify_churn(),
identify_holidayweeks(),
identify_inactiveweeks(),
identify_nkw(),
identify_privacythreshold(),
identify_shifts(),
identify_tenure(),
track_HR_change(),
validation_report()
Examples
identify_outlier(pq_data, metric = "Collaboration_hours")identify_outlier(pq_data, metric = "Collaboration_hours")
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.
Usage
identify_privacythreshold( data, hrvar = extract_hr(data), mingroup = 5, return = "table" )identify_privacythreshold( data, hrvar = extract_hr(data), mingroup = 5, return = "table" )
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
hrvar |
A list of HR Variables to consider in the scan. Defaults to all HR attributes identified. |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
return |
String specifying what to return. This must be one of the following strings:
See |
Value
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.
Returns a ggplot object by default, where 'plot' is passed in return.
When 'table' is passed, a summary table is returned as a data frame.
See Also
Other Data Validation:
check_query(),
extract_hr(),
flag_ch_ratio(),
flag_em_ratio(),
flag_extreme(),
flag_outlooktime(),
hr_trend(),
hrvar_count(),
hrvar_count_all(),
hrvar_trend(),
identify_churn(),
identify_holidayweeks(),
identify_inactiveweeks(),
identify_nkw(),
identify_outlier(),
identify_shifts(),
identify_tenure(),
track_HR_change(),
validation_report()
Examples
# Return a summary table pq_data %>% identify_privacythreshold(return = "table") # Return a diagnostic message pq_data %>% identify_privacythreshold(return = "text")# Return a summary table pq_data %>% identify_privacythreshold(return = "table") # Return a diagnostic message pq_data %>% identify_privacythreshold(return = "text")
Identify shifts based on outlook time settings for work day start and end time
Description
This function uses outlook calendar settings for start and end time of work
day to identify work shifts. The relevant variables are
WorkingStartTimeSetInOutlook and WorkingEndTimeSetInOutlook.
Usage
identify_shifts(data, return = "plot")identify_shifts(data, return = "plot")
Arguments
data |
A data frame containing data from the Hourly Collaboration query. |
return |
String specifying what to return. This must be one of the following strings:
See |
Value
A different output is returned depending on the value passed to the return
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 theShiftscolumns.
See Also
Other Data Validation:
check_query(),
extract_hr(),
flag_ch_ratio(),
flag_em_ratio(),
flag_extreme(),
flag_outlooktime(),
hr_trend(),
hrvar_count(),
hrvar_count_all(),
hrvar_trend(),
identify_churn(),
identify_holidayweeks(),
identify_inactiveweeks(),
identify_nkw(),
identify_outlier(),
identify_privacythreshold(),
identify_tenure(),
track_HR_change(),
validation_report()
Examples
# Demo with `pq_data` example where Outlook Start and End times are imputed spq_df <- pq_data spq_df$WorkingStartTimeSetInOutlook <- "6:30" spq_df$WorkingEndTimeSetInOutlook <- "23:30" # Return plot spq_df %>% identify_shifts() # Return summary table spq_df %>% identify_shifts(return = "table")# Demo with `pq_data` example where Outlook Start and End times are imputed spq_df <- pq_data spq_df$WorkingStartTimeSetInOutlook <- "6:30" spq_df$WorkingEndTimeSetInOutlook <- "23:30" # Return plot spq_df %>% identify_shifts() # Return summary table spq_df %>% identify_shifts(return = "table")
Tenure calculation based on different input dates, returns data summary table or histogram
Description
This function calculates employee tenure based on different input dates.
identify_tenure uses the latest Date available if user selects "MetricDate",
but also have flexibility to select a specific date, e.g. "1/1/2020".
Usage
identify_tenure( data, end_date = "MetricDate", beg_date = "HireDate", maxten = 40, return = "message" )identify_tenure( data, end_date = "MetricDate", beg_date = "HireDate", maxten = 40, return = "message" )
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
end_date |
A string specifying the name of the date variable representing the latest date. Defaults to "MetricDate". |
beg_date |
A string specifying the name of the date variable representing the hire date. Defaults to "HireDate". |
maxten |
A numeric value representing the maximum tenure. If the tenure exceeds this threshold, it would be accounted for in the flag message. |
return |
String specifying what to return. This must be one of the following strings:
See |
Value
A different output is returned depending on the value passed to the return
argument:
-
"message": message on console with a diagnostic message. -
"text": string containing a diagnostic message. -
"plot": 'ggplot' object. A line plot showing tenure. -
"data_cleaned": data frame filtered only by rows with tenure values lying within the threshold. -
"data_dirty": data frame filtered only by rows with tenure values lying outside the threshold. -
"data": data frame with thePersonIdand a calculated variable calledTenureYearis returned.
See Also
Other Data Validation:
check_query(),
extract_hr(),
flag_ch_ratio(),
flag_em_ratio(),
flag_extreme(),
flag_outlooktime(),
hr_trend(),
hrvar_count(),
hrvar_count_all(),
hrvar_trend(),
identify_churn(),
identify_holidayweeks(),
identify_inactiveweeks(),
identify_nkw(),
identify_outlier(),
identify_privacythreshold(),
identify_shifts(),
track_HR_change(),
validation_report()
Examples
library(dplyr) # Add HireDate to `pq_data` pq_data2 <- pq_data %>% mutate(HireDate = as.Date("1/1/2015", format = "%m/%d/%Y")) identify_tenure(pq_data2)library(dplyr) # Add HireDate to `pq_data` pq_data2 <- pq_data %>% mutate(HireDate = as.Date("1/1/2015", format = "%m/%d/%Y")) identify_tenure(pq_data2)
Import a query from Viva Insights Analyst Experience
Description
Import a Viva Insights Query from a .csv file, with variable classifications optimised for other functions in the package.
Usage
import_query( x, pid = NULL, dateid = NULL, date_format = "%m/%d/%Y", convert_date = TRUE, encoding = "UTF-8" )import_query( x, pid = NULL, dateid = NULL, date_format = "%m/%d/%Y", convert_date = TRUE, encoding = "UTF-8" )
Arguments
x |
String containing the path to the Viva Insights query to be
imported. The input file must be a .csv file, and the file extension must
be explicitly entered, e.g. |
pid |
String specifying the unique person or individual identifier
variable. |
dateid |
String specifying the date variable. |
date_format |
String specifying the date format for converting any
variable that may be a date to a Date variable. Defaults to |
convert_date |
Logical. Defaults to |
encoding |
String to specify encoding to be used within
|
Details
import_query() uses data.table::fread() to import .csv files for
speed, and by default stringsAsFactors is set to FALSE. A data frame is
returned by the function (not a data.table). Column names are automatically
cleaned, replacing spaces and special characters with underscores.
Value
A tibble is returned.
See Also
Other Import and Export:
copy_df(),
create_dt(),
export()
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.
Usage
is_date_format(string)is_date_format(string)
Arguments
string |
Character string to test whether is a date format. |
Value
logical value indicating whether the string is a date format.
See Also
Other Support:
any_idate(),
camel_clean(),
check_inputs(),
cut_hour(),
extract_date_range(),
extract_hr(),
heat_colours(),
maxmin(),
pairwise_count(),
read_preamble(),
rgb2hex(),
totals_bind(),
totals_col(),
tstamp(),
us_to_space(),
wrap()
Examples
is_date_format("1/5/2020")is_date_format("1/5/2020")
Generate a Information Value HTML Report
Description
The function generates an interactive HTML report using Standard Person Query data as an input. The report contains a full Information Value analysis, a data exploration technique that helps determine which columns in a data set have predictive power or influence on the value of a specified dependent variable.
Usage
IV_report( data, predictors = NULL, outcome, bins = 5, max_var = 9, path = "IV report", timestamp = TRUE )IV_report( data, predictors = NULL, outcome, bins = 5, max_var = 9, path = "IV report", timestamp = TRUE )
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
predictors |
A character vector specifying the columns to be used as predictors. Defaults to NULL, where all numeric vectors in the data will be used as predictors. |
outcome |
A string specifying a binary variable, i.e. can only contain the values 1 or 0. |
bins |
Number of bins to use in |
max_var |
Numeric value to represent the maximum number of variables to show on plots. |
path |
Pass the file path and the desired file name, excluding the file
extension. For example, |
timestamp |
Logical vector specifying whether to include a timestamp in the file name. Defaults to TRUE. |
Value
An HTML report with the same file name as specified in the arguments is generated in the working directory. No outputs are directly returned by the function.
Creating a report
Below is an example on how to run the report.
library(dplyr)
pq_data %>%
mutate(CH_binary = ifelse(Collaboration_hours > 12, 1, 0)) %>% # Simulate binary variable
IV_report(outcome = "CH_binary",
predictors = c("Email_hours", "Meeting_hours"))
See Also
Other Reports:
generate_report(),
meeting_tm_report(),
read_preamble(),
validation_report()
Other Variable Association:
create_IV()
Other Information Value:
create_IV()
Jitter metrics in a data frame
Description
Convenience wrapper around jitter() to add a layer of
anonymity to a query. This can be used in combination with anonymise() to
produce a demo dataset from real data.
Usage
jitter_metrics(data, cols = NULL, ...)jitter_metrics(data, cols = NULL, ...)
Arguments
data |
Data frame containing a query. |
cols |
Character vector containing the metrics to jitter. When set to
|
... |
Additional arguments to pass to |
Value
data frame where numeric columns specified by cols are jittered using the
function jitter().
See Also
anonymise
Examples
jittered <- jitter_metrics(pq_data, cols = "Collaboration_hours") # compare jittered vs original results of top rows head( data.frame( original = pq_data$Collaboration_hours, jittered = jittered$Collaboration_hours ) )jittered <- jitter_metrics(pq_data, cols = "Collaboration_hours") # compare jittered vs original results of top rows head( data.frame( original = pq_data$Collaboration_hours, jittered = jittered$Collaboration_hours ) )
Run a summary of Key Metrics from the Standard Person Query data
Description
Returns a heatmapped table by default, with options to return a table.
Usage
keymetrics_scan( data, hrvar = "Organization", mingroup = 5, metrics = c("Workweek_span", "Collaboration_hours", "After_hours_collaboration_hours", "Meetings", "Meeting_hours", "After_hours_meeting_hours", "Low_quality_meeting_hours", "Meeting_hours_with_manager_1_on_1", "Meeting_hours_with_manager", "Emails_sent", "Email_hours", "After_hours_email_hours", "Generated_workload_email_hours", "Total_focus_hours", "Internal_network_size", "Networking_outside_organization", "External_network_size", "Networking_outside_company"), return = "plot", low = rgb2hex(7, 111, 161), mid = rgb2hex(241, 204, 158), high = rgb2hex(216, 24, 42), textsize = 2 )keymetrics_scan( data, hrvar = "Organization", mingroup = 5, metrics = c("Workweek_span", "Collaboration_hours", "After_hours_collaboration_hours", "Meetings", "Meeting_hours", "After_hours_meeting_hours", "Low_quality_meeting_hours", "Meeting_hours_with_manager_1_on_1", "Meeting_hours_with_manager", "Emails_sent", "Email_hours", "After_hours_email_hours", "Generated_workload_email_hours", "Total_focus_hours", "Internal_network_size", "Networking_outside_organization", "External_network_size", "Networking_outside_company"), return = "plot", low = rgb2hex(7, 111, 161), mid = rgb2hex(241, 204, 158), high = rgb2hex(216, 24, 42), textsize = 2 )
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
metrics |
A character vector containing the variable names to calculate averages of. |
return |
Character vector specifying what to return, defaults to "plot". Valid inputs are "plot" and "table". |
low |
String specifying colour code to use for low-value metrics.
Arguments are passed directly to |
mid |
String specifying colour code to use for mid-value metrics.
Arguments are passed directly to |
high |
String specifying colour code to use for high-value metrics.
Arguments are passed directly to |
textsize |
A numeric value specifying the text size to show in the plot. |
Value
Returns a ggplot object by default, when 'plot' is passed in return.
When 'table' is passed, a summary table is returned as a data frame.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Examples
# Heatmap plot is returned by default keymetrics_scan(pq_data) # Heatmap plot with custom colours keymetrics_scan(pq_data, low = "purple", high = "yellow") # Return summary table keymetrics_scan(pq_data, hrvar = "LevelDesignation", return = "table")# Heatmap plot is returned by default keymetrics_scan(pq_data) # Heatmap plot with custom colours keymetrics_scan(pq_data, low = "purple", high = "yellow") # Return summary table keymetrics_scan(pq_data, hrvar = "LevelDesignation", return = "table")
Run a summary of Key Metrics without aggregation
Description
Return a heatmapped table directly from the aggregated / summarised data.
Unlike keymetrics_scan() which performs a person-level aggregation, there
is no calculation for keymetrics_scan_asis() and the values are rendered as
they are passed into the function.
Usage
keymetrics_scan_asis( data, row_var, col_var, group_var = col_var, value_var = "value", title = NULL, subtitle = NULL, caption = NULL, ylab = row_var, xlab = "Metrics", rounding = 1, low = rgb2hex(7, 111, 161), mid = rgb2hex(241, 204, 158), high = rgb2hex(216, 24, 42), textsize = 2 )keymetrics_scan_asis( data, row_var, col_var, group_var = col_var, value_var = "value", title = NULL, subtitle = NULL, caption = NULL, ylab = row_var, xlab = "Metrics", rounding = 1, low = rgb2hex(7, 111, 161), mid = rgb2hex(241, 204, 158), high = rgb2hex(216, 24, 42), textsize = 2 )
Arguments
data |
data frame containing data to plot. It is recommended to provide data in a 'long' table format where one grouping column forms the rows, a second column forms the columns, and a third numeric columns forms the |
row_var |
String containing name of the grouping variable that will form the rows of the heatmapped table. |
col_var |
String containing name of the grouping variable that will form the columns of the heatmapped table. |
group_var |
String containing name of the grouping variable by which
heatmapping would apply. Defaults to |
value_var |
String containing name of the value variable that will form
the values of the heatmapped table. Defaults to |
title |
Title of the plot. |
subtitle |
Subtitle of the plot. |
caption |
Caption of the plot. |
ylab |
Y-axis label for the plot (group axis) |
xlab |
X-axis label of the plot (bar axis). |
rounding |
Numeric value to specify number of digits to show in data labels |
low |
String specifying colour code to use for low-value metrics.
Arguments are passed directly to |
mid |
String specifying colour code to use for mid-value metrics.
Arguments are passed directly to |
high |
String specifying colour code to use for high-value metrics.
Arguments are passed directly to |
textsize |
A numeric value specifying the text size to show in the plot. |
Value
ggplot object for a heatmap table.
Examples
library(dplyr) # Compute summary table out_df <- pq_data %>% group_by(Organization) %>% summarise( across( .cols = c( Email_hours, Collaboration_hours ), .fns = ~median(., na.rm = TRUE) ), .groups = "drop" ) %>% tidyr::pivot_longer( cols = c("Email_hours", "Collaboration_hours"), names_to = "metrics" ) keymetrics_scan_asis( data = out_df, col_var = "metrics", row_var = "Organization" ) # Show data the other way round keymetrics_scan_asis( data = out_df, col_var = "Organization", row_var = "metrics", group_var = "metrics" )library(dplyr) # Compute summary table out_df <- pq_data %>% group_by(Organization) %>% summarise( across( .cols = c( Email_hours, Collaboration_hours ), .fns = ~median(., na.rm = TRUE) ), .groups = "drop" ) %>% tidyr::pivot_longer( cols = c("Email_hours", "Collaboration_hours"), names_to = "metrics" ) keymetrics_scan_asis( data = out_df, col_var = "metrics", row_var = "Organization" ) # Show data the other way round keymetrics_scan_asis( data = out_df, col_var = "Organization", row_var = "metrics", group_var = "metrics" )
Max-Min Scaling Function
Description
This function allows you to scale vectors or an entire data frame using the max-min scaling method A numeric vector is always returned.
Usage
maxmin(x)maxmin(x)
Arguments
x |
Pass a vector or the required columns of a data frame through this argument. |
Details
This is used within keymetrics_scan() to enable row-wise
heatmapping. Originally implemented in
https://github.com/martinctc/surveytoolbox.
Value
Returns a numeric vector with the input rescaled.
See Also
Other Support:
any_idate(),
camel_clean(),
check_inputs(),
cut_hour(),
extract_date_range(),
extract_hr(),
heat_colours(),
is_date_format(),
pairwise_count(),
read_preamble(),
rgb2hex(),
totals_bind(),
totals_col(),
tstamp(),
us_to_space(),
wrap()
Examples
numbers <- c(15, 40, 10, 2) maxmin(numbers)numbers <- c(15, 40, 10, 2) maxmin(numbers)
Distribution of Meeting Hours as a 100% stacked bar
Description
Analyze Meeting Hours distribution. Returns a stacked bar plot by default. Additional options available to return a table with distribution elements.
Usage
meeting_dist( data, hrvar = "Organization", mingroup = 5, return = "plot", cut = c(5, 10, 15) )meeting_dist( data, hrvar = "Organization", mingroup = 5, return = "plot", cut = c(5, 10, 15) )
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
return |
String specifying what to return. This must be one of the following strings:
See |
cut |
A numeric vector of length three to specify the breaks for the distribution, e.g. c(10, 15, 20) |
Value
A different output is returned depending on the value passed to the return argument:
-
"plot": 'ggplot' object. A stacked bar plot for the metric. -
"table": data frame. A summary table for the metric.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other Meetings:
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_tm_report(),
meeting_trend()
Examples
# Return plot meeting_dist(pq_data, hrvar = "Organization") # Return summary table meeting_dist(pq_data, hrvar = "Organization", return = "table") # Return result with a custom specified breaks meeting_dist(pq_data, hrvar = "LevelDesignation", cut = c(4, 7, 9))# Return plot meeting_dist(pq_data, hrvar = "Organization") # Return summary table meeting_dist(pq_data, hrvar = "Organization", return = "table") # Return result with a custom specified breaks meeting_dist(pq_data, hrvar = "LevelDesignation", cut = c(4, 7, 9))
Distribution of Meeting Hours (Fizzy Drink plot)
Description
Analyze weekly meeting hours distribution, and returns a 'fizzy' scatter plot by default. Additional options available to return a table with distribution elements.
Usage
meeting_fizz(data, hrvar = "Organization", mingroup = 5, return = "plot")meeting_fizz(data, hrvar = "Organization", mingroup = 5, return = "plot")
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
return |
String specifying what to return. This must be one of the following strings:
See |
Details
Uses the metric Meeting_hours.
Value
A different output is returned depending on the value passed to the return argument:
-
"plot": 'ggplot' object. A jittered scatter plot for the metric. -
"table": data frame. A summary table for the metric.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other Meetings:
meeting_dist(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_tm_report(),
meeting_trend()
Examples
# Return plot meeting_fizz(pq_data, hrvar = "Organization", return = "plot") # Return summary table meeting_fizz(pq_data, hrvar = "Organization", return = "table")# Return plot meeting_fizz(pq_data, hrvar = "Organization", return = "plot") # Return summary table meeting_fizz(pq_data, hrvar = "Organization", return = "table")
Meeting Time Trend - Line Chart
Description
Provides a week by week view of meeting time, visualised as line charts. By default returns a line chart for meeting hours, with a separate panel per value in the HR attribute. Additional options available to return a summary table.
Usage
meeting_line(data, hrvar = "Organization", mingroup = 5, return = "plot")meeting_line(data, hrvar = "Organization", mingroup = 5, return = "plot")
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
return |
String specifying what to return. This must be one of the following strings:
See |
Value
A different output is returned depending on the value passed to the return argument:
-
"plot": 'ggplot' object. A faceted line plot for the metric. -
"table": data frame. A summary table for the metric.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other Meetings:
meeting_dist(),
meeting_fizz(),
meeting_rank(),
meeting_summary(),
meeting_tm_report(),
meeting_trend()
Examples
# Return a line plot meeting_line(pq_data, hrvar = "LevelDesignation") # Return summary table meeting_line(pq_data, hrvar = "LevelDesignation", return = "table")# Return a line plot meeting_line(pq_data, hrvar = "LevelDesignation") # Return summary table meeting_line(pq_data, hrvar = "LevelDesignation", return = "table")
Meeting Hours Ranking
Description
This function scans a standard query output for groups with high levels of Weekly Meeting Collaboration. Returns a plot by default, with an option to return a table with a all of groups (across multiple HR attributes) ranked by hours of digital collaboration.
Usage
meeting_rank( data, hrvar = extract_hr(data), mingroup = 5, mode = "simple", plot_mode = 1, return = "plot" )meeting_rank( data, hrvar = extract_hr(data), mingroup = 5, mode = "simple", plot_mode = 1, return = "plot" )
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
mode |
String to specify calculation mode. Must be either:
|
plot_mode |
Numeric vector to determine which plot mode to return. Must
be either
|
return |
String specifying what to return. This must be one of the following strings:
See |
Details
Uses the metric Meeting_hours.
See create_rank() for applying the same analysis to a different metric.
Value
A different output is returned depending on the value passed to the return
argument:
-
"plot": 'ggplot' object. A bubble plot where the x-axis represents the metric, the y-axis represents the HR attributes, and the size of the bubbles represent the size of the organizations. Note that there is no plot output ifmodeis set to"combine". -
"table": data frame. A summary table for the metric.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other Meetings:
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_summary(),
meeting_tm_report(),
meeting_trend()
Examples
# Return rank table meeting_rank(data = pq_data, return = "table") # Return plot meeting_rank(data = pq_data, return = "plot")# Return rank table meeting_rank(data = pq_data, return = "table") # Return plot meeting_rank(data = pq_data, return = "plot")
Meeting Summary
Description
Provides an overview analysis of weekly meeting hours. Returns a bar plot showing average weekly meeting hours by default. Additional options available to return a summary table.
Usage
meeting_summary(data, hrvar = "Organization", mingroup = 5, return = "plot") meeting_sum(data, hrvar = "Organization", mingroup = 5, return = "plot")meeting_summary(data, hrvar = "Organization", mingroup = 5, return = "plot") meeting_sum(data, hrvar = "Organization", mingroup = 5, return = "plot")
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
return |
String specifying what to return. This must be one of the following strings:
See |
Value
A different output is returned depending on the value passed to the return argument:
-
"plot": 'ggplot' object. A bar plot for the metric. -
"table": data frame. A summary table for the metric.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other Meetings:
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_tm_report(),
meeting_trend()
Examples
# Return a ggplot bar chart meeting_summary(pq_data, hrvar = "LevelDesignation") # Return a summary table meeting_summary(pq_data, hrvar = "LevelDesignation", return = "table")# Return a ggplot bar chart meeting_summary(pq_data, hrvar = "LevelDesignation") # Return a summary table meeting_summary(pq_data, hrvar = "LevelDesignation", return = "table")
Generate a Meeting Text Mining report in HTML
Description
Create a text mining report in HTML based on Meeting Subject Lines
Usage
meeting_tm_report( data, path = "meeting text mining report", stopwords = NULL, timestamp = TRUE, keep = 100, seed = 100 )meeting_tm_report( data, path = "meeting text mining report", stopwords = NULL, timestamp = TRUE, keep = 100, seed = 100 )
Arguments
data |
A Meeting Query dataset in the form of a data frame. |
path |
Pass the file path and the desired file name, excluding the file
extension. For example, |
stopwords |
A character vector OR a single-column data frame labelled
|
timestamp |
Logical vector specifying whether to include a timestamp in the file name. Defaults to TRUE. |
keep |
A numeric vector specifying maximum number of words to keep. |
seed |
A numeric vector to set seed for random generation. |
Details
Note that the column Subject must be available within the input data frame
in order to run.d
Value
An HTML report with the same file name as specified in the arguments is generated in the working directory. No outputs are directly returned by the function.
How to run
meeting_tm_report(mt_data)
This will generate a HTML report as specified in path.
See Also
Other Reports:
IV_report(),
generate_report(),
read_preamble(),
validation_report()
Other Meetings:
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend()
Other Text-mining:
pairwise_count(),
tm_clean(),
tm_cooc(),
tm_freq(),
tm_wordcloud()
Meeting Hours Time Trend
Description
Provides a week by week view of meeting 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.
Usage
meeting_trend(data, hrvar = "Organization", mingroup = 5, return = "plot")meeting_trend(data, hrvar = "Organization", mingroup = 5, return = "plot")
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
return |
Character vector specifying what to return, defaults to
|
Details
Uses the metric Meeting_hours.
Value
Returns a 'ggplot' object by default, where 'plot' is passed in return.
When 'table' is passed, a summary table is returned as a data frame.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other Meetings:
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_tm_report()
Examples
# Run plot meeting_trend(pq_data) # Run table meeting_trend(pq_data, hrvar = "LevelDesignation", return = "table")# Run plot meeting_trend(pq_data) # Run table meeting_trend(pq_data, hrvar = "LevelDesignation", return = "table")
Sample Meeting Query dataset
Description
A dataset generated from a Meeting Query from Viva Insights.
Usage
mt_datamt_data
Format
A data frame with 612 rows and 41 variables:
- MeetingId
- Attendee_meeting_hours
- Number_of_attendees
- Number_of_attendees_multitasking
- Number_of_attendees_who_didn_t_end_the_meeting_on_time
- Number_of_attendees_who_didn_t_join_the_meeting_on_time
- Number_of_attendees_who_ended_the_meeting_on_time
- Number_of_attendees_who_joined_the_meeting_on_time
- Number_of_chats_sent_during_the_meeting
- Number_of_emails_sent_during_the_meeting
- Number_of_redundant_attendees
- Subject
- All_Day_Meeting
- Cancelled
- Recurring
- Accept_count
- No_response_count
- Decline_count
- Tentatively_accepted_count
- Intended_participant_count
- Collaboration_start_time
- Organizer
- zId
- attainment
- TimeZone
- SupervisorIndicator
- Region
- Population_Type
- Organization
- OnsiteDays
- Number_of_directs
- LevelDesignation
- Layer
- HireDate
- GroupNum
- GroupName
- FunctionType
- Domain
- ADO_PersonSK
- ADO_PersonIndicator
- Duration
Value
data frame.
Source
https://learn.microsoft.com/en-us/viva/insights/advanced/analyst/meeting-query/
See Also
Other Data:
g2g_data,
p2p_data,
p2p_data_sim(),
pq_data
Create a network plot with the group-to-group query
Description
Pass a data frame containing a group-to-group query and return a network
plot. Automatically handles "Within Group" and "Other_collaborators"
values within query data.
Usage
network_g2g( data, primary = NULL, secondary = NULL, metric = "Group_collaboration_time_invested", algorithm = "fr", node_colour = "lightblue", exc_threshold = 0.1, org_count = NULL, subtitle = "Collaboration Across Organizations", return = "plot" )network_g2g( data, primary = NULL, secondary = NULL, metric = "Group_collaboration_time_invested", algorithm = "fr", node_colour = "lightblue", exc_threshold = 0.1, org_count = NULL, subtitle = "Collaboration Across Organizations", return = "plot" )
Arguments
data |
Data frame containing a group-to-group query. |
primary |
String containing the variable name for the Primary Collaborator column. |
secondary |
String containing the variable name for the Secondary Collaborator column. |
metric |
String containing the variable name for metric. Defaults to
|
algorithm |
String to specify the node placement algorithm to be used.
Defaults to |
node_colour |
String or named vector to specify the colour to be used
for displaying nodes. Defaults to
|
exc_threshold |
Numeric value between 0 and 1 specifying the exclusion
threshold to apply. Defaults to 0.1, which means that the plot will only
display collaboration above 10% of a node's total collaboration. This
argument has no impact on |
org_count |
Optional data frame to provide the size of each organization
in the
|
subtitle |
String to override default plot subtitle. |
return |
String specifying what to return. This must be one of the following strings:
See |
Value
A different output is returned depending on the value passed to the return
argument:
-
"plot": 'ggplot' object. A group-to-group network plot. -
"table": data frame. An interactive matrix of the network. -
"network: 'igraph' object used for creating the network plot. -
"data": data frame. A long table of the underlying data.
See Also
Other Network:
g2g_data,
network_p2p(),
network_summary(),
p2p_data,
p2p_data_sim()
Examples
# Return a network plot g2g_data %>% network_g2g() # Return a network plot - Meeting hours and 5% threshold network_g2g( data = g2g_data, primary = "PrimaryCollaborator_Organization", secondary = "SecondaryCollaborator_Organization", exc_threshold = 0.05 ) # Return a network plot - custom-specific colours # Get labels of orgs and assign random colours org_str <- unique(g2g_data$PrimaryCollaborator_Organization) col_str <- sample( x = heat_colours(n = length(org_str)), # generate colour codes for each one size = length(org_str), replace = TRUE ) # Create and supply a named vector to `node_colour` names(col_str) <- org_str g2g_data %>% network_g2g(node_colour = col_str) # Return a network plot with circle layout # Vary node colours and add org sizes org_tb <- data.frame( Organization = c( "G&A East", "G&A West", "G&A North", "South Sales", "North Sales", "G&A South" ), n = sample(30:1000, size = 6) ) g2g_data %>% network_g2g(algorithm = "circle", node_colour = "vary", org_count = org_tb) # Return an interaction matrix # Minimum arguments specified g2g_data %>% network_g2g(return = "table")# Return a network plot g2g_data %>% network_g2g() # Return a network plot - Meeting hours and 5% threshold network_g2g( data = g2g_data, primary = "PrimaryCollaborator_Organization", secondary = "SecondaryCollaborator_Organization", exc_threshold = 0.05 ) # Return a network plot - custom-specific colours # Get labels of orgs and assign random colours org_str <- unique(g2g_data$PrimaryCollaborator_Organization) col_str <- sample( x = heat_colours(n = length(org_str)), # generate colour codes for each one size = length(org_str), replace = TRUE ) # Create and supply a named vector to `node_colour` names(col_str) <- org_str g2g_data %>% network_g2g(node_colour = col_str) # Return a network plot with circle layout # Vary node colours and add org sizes org_tb <- data.frame( Organization = c( "G&A East", "G&A West", "G&A North", "South Sales", "North Sales", "G&A South" ), n = sample(30:1000, size = 6) ) g2g_data %>% network_g2g(algorithm = "circle", node_colour = "vary", org_count = org_tb) # Return an interaction matrix # Minimum arguments specified g2g_data %>% network_g2g(return = "table")
Perform network analysis with the person-to-person query
Description
Analyse a person-to-person (P2P) network query, with multiple visualisation and analysis output options. Pass a data frame containing a person-to-person query and return a network visualization. Options are available for community detection using either the Louvain or the Leiden algorithms.
Usage
network_p2p( data, hrvar = "Organization", return = "plot", centrality = NULL, community = NULL, weight = NULL, comm_args = NULL, layout = "mds", path = paste("p2p", community, sep = "_"), style = "igraph", bg_fill = "#FFFFFF", font_col = "grey20", legend_pos = "right", palette = "rainbow", node_alpha = 0.7, edge_alpha = 1, edge_col = "#777777", node_sizes = c(1, 20), seed = 1 )network_p2p( data, hrvar = "Organization", return = "plot", centrality = NULL, community = NULL, weight = NULL, comm_args = NULL, layout = "mds", path = paste("p2p", community, sep = "_"), style = "igraph", bg_fill = "#FFFFFF", font_col = "grey20", legend_pos = "right", palette = "rainbow", node_alpha = 0.7, edge_alpha = 1, edge_col = "#777777", node_sizes = c(1, 20), seed = 1 )
Arguments
data |
Data frame containing a person-to-person query. |
hrvar |
String containing the label for the HR attribute. |
return |
A different output is returned depending on the value passed to the
|
centrality |
string to determines which centrality measure is used to
scale the size of the nodes. All centrality measures are automatically
calculated when it is set to one of the below values, and reflected in the
When |
community |
String determining which community detection algorithms to apply. Valid values include:
These values map to the community detection algorithms offered by |
weight |
String to specify which column to use as weights for the
network. To create a graph without weights, supply |
comm_args |
list containing the arguments to be passed through to igraph's clustering algorithms. Arguments must be named. See examples section on how to supply arguments in a named list. |
layout |
String to specify the node placement algorithm to be used.
Defaults to |
path |
File path for saving the PDF output. Defaults to a timestamped path based on current parameters. |
style |
String to specify which plotting style to use for the network plot. Valid values include:
|
bg_fill |
String to specify background fill colour. |
font_col |
String to specify font colour. |
legend_pos |
String to specify position of legend. Defaults to
|
palette |
String specifying the function to generate a colour palette
with a single argument |
node_alpha |
A numeric value between 0 and 1 to specify the transparency of the nodes. Defaults to 0.7. |
edge_alpha |
A numeric value between 0 and 1 to specify the transparency of the edges (only for 'ggraph' mode). Defaults to 1. |
edge_col |
String to specify edge link colour. |
node_sizes |
Numeric vector of length two to specify the range of node
sizes to rescale to, when |
seed |
Seed for the random number generator passed to either
|
Value
A different output is returned depending on the value passed to the return
argument:
-
'plot': return a network plot, interactively within R. -
'plot-pdf': save a network plot as PDF. This option is recommended when the graph is large, which make take a long time to run ifreturn = 'plot'is selected. Use this together withpathto control the save location. -
'sankey': return a sankey plot combining communities and HR attribute. This is only valid if a community detection method is selected atcommunity. -
'table': return a vertex summary table with counts in communities and HR attribute. Whencentralityis non-NULL, the average centrality values are calculated per group. -
'data': return a vertex data file that matches vertices with communities and HR attributes. -
'network': return 'igraph' object.
See Also
Other Network:
g2g_data,
network_g2g(),
network_summary(),
p2p_data,
p2p_data_sim()
Examples
p2p_df <- p2p_data_sim(dim = 1, size = 100) # default - ggraph visual network_p2p(data = p2p_df, style = "ggraph") # return vertex table network_p2p(data = p2p_df, return = "table") # return vertex table with community detection network_p2p(data = p2p_df, community = "leiden", return = "table") # leiden - igraph style with custom resolution parameters network_p2p(data = p2p_df, community = "leiden", comm_args = list("resolution" = 0.1)) # louvain - ggraph style, using custom palette network_p2p( data = p2p_df, style = "ggraph", community = "louvain", palette = "heat_colors" ) # leiden - return a sankey visual with custom resolution parameters network_p2p( data = p2p_df, community = "leiden", return = "sankey", comm_args = list("resolution" = 0.1) ) # using `fluid_communities` algorithm with custom parameters network_p2p( data = p2p_df, community = "fluid_communities", comm_args = list("no.of.communities" = 5) ) # Calculate centrality measures and leiden communities, return at node level network_p2p( data = p2p_df, centrality = "betweenness", community = "leiden", return = "data" ) %>% dplyr::glimpse()p2p_df <- p2p_data_sim(dim = 1, size = 100) # default - ggraph visual network_p2p(data = p2p_df, style = "ggraph") # return vertex table network_p2p(data = p2p_df, return = "table") # return vertex table with community detection network_p2p(data = p2p_df, community = "leiden", return = "table") # leiden - igraph style with custom resolution parameters network_p2p(data = p2p_df, community = "leiden", comm_args = list("resolution" = 0.1)) # louvain - ggraph style, using custom palette network_p2p( data = p2p_df, style = "ggraph", community = "louvain", palette = "heat_colors" ) # leiden - return a sankey visual with custom resolution parameters network_p2p( data = p2p_df, community = "leiden", return = "sankey", comm_args = list("resolution" = 0.1) ) # using `fluid_communities` algorithm with custom parameters network_p2p( data = p2p_df, community = "fluid_communities", comm_args = list("no.of.communities" = 5) ) # Calculate centrality measures and leiden communities, return at node level network_p2p( data = p2p_df, centrality = "betweenness", community = "leiden", return = "data" ) %>% dplyr::glimpse()
Summarise node centrality statistics with an igraph object
Description
Pass an igraph object to the function and obtain centrality statistics for each node in the object as a data frame. This function works as a wrapper of the centralization functions in 'igraph'.
Usage
network_summary(graph, hrvar = NULL, return = "table")network_summary(graph, hrvar = NULL, return = "table")
Arguments
graph |
'igraph' object that can be returned from |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
return |
String specifying what output to return. Valid inputs include:
See |
Value
By default, a data frame containing centrality statistics. Available statistics include:
-
betweenness: number of shortest paths going through a node.
-
closeness: number of steps required to access every other node from a given node.
-
degree: number of connections linked to a node.
-
eigenvector: a measure of the influence a node has on a network.
-
pagerank: calculates the PageRank for the specified vertices. Please refer to the igraph package documentation for the detailed technical definition.
When "network" is passed to "return", an 'igraph' object is returned with
additional node attributes containing centrality scores.
When "plot" is passed to "return", a summary table is returned showing
the average centrality scores by HR attribute. This is currently available if
there is a valid HR attribute.
See Also
Other Network:
g2g_data,
network_g2g(),
network_p2p(),
p2p_data,
p2p_data_sim()
Examples
# Simulate a p2p network p2p_data <- p2p_data_sim(size = 100) g <- network_p2p(data = p2p_data, return = "network") # Return summary table network_summary(graph = g, return = "table") # Return network with node centrality statistics network_summary(graph = g, return = "network") # Return summary plot network_summary(graph = g, return = "plot", hrvar = "Organization") # Simulate a g2g network and return table g2 <- g2g_data %>% network_g2g(return = "network") network_summary(graph = g2, return = "table")# Simulate a p2p network p2p_data <- p2p_data_sim(size = 100) g <- network_p2p(data = p2p_data, return = "network") # Return summary table network_summary(graph = g, return = "table") # Return network with node centrality statistics network_summary(graph = g, return = "network") # Return summary plot network_summary(graph = g, return = "plot", hrvar = "Organization") # Simulate a g2g network and return table g2 <- g2g_data %>% network_g2g(return = "network") network_summary(graph = g2, return = "table")
Distribution of Manager 1:1 Time as a 100% stacked bar
Description
Analyze Manager 1:1 Time distribution. Returns a stacked bar plot of different buckets of 1:1 time. Additional options available to return a table with distribution elements.
Usage
one2one_dist( data, hrvar = "Organization", mingroup = 5, dist_colours = c("#facebc", "#fcf0eb", "#b4d5dd", "#bfe5ee"), return = "plot", cut = c(5, 15, 30) )one2one_dist( data, hrvar = "Organization", mingroup = 5, dist_colours = c("#facebc", "#fcf0eb", "#b4d5dd", "#bfe5ee"), return = "plot", cut = c(5, 15, 30) )
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
dist_colours |
A character vector of length four to specify colour codes for the stacked bars. |
return |
String specifying what to return. This must be one of the following strings:
See |
cut |
A numeric vector of length three to specify the breaks for the distribution, e.g. c(10, 15, 20) |
Value
A different output is returned depending on the value passed to the return argument:
-
"plot": 'ggplot' object. A stacked bar plot for the metric. -
"table": data frame. A summary table for the metric.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other Managerial Relations:
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Examples
# Return plot one2one_dist(pq_data, hrvar = "Organization", return = "plot") # Return summary table one2one_dist(pq_data, hrvar = "Organization", return = "table")# Return plot one2one_dist(pq_data, hrvar = "Organization", return = "plot") # Return summary table one2one_dist(pq_data, hrvar = "Organization", return = "table")
Distribution of Manager 1:1 Time (Fizzy Drink plot)
Description
Analyze weekly Manager 1:1 Time distribution, and returns a 'fizzy' scatter plot by default. Additional options available to return a table with distribution elements.
Usage
one2one_fizz(data, hrvar = "Organization", mingroup = 5, return = "plot")one2one_fizz(data, hrvar = "Organization", mingroup = 5, return = "plot")
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
return |
String specifying what to return. This must be one of the following strings:
See |
Value
A different output is returned depending on the value passed to the return argument:
-
"plot": 'ggplot' object. A jittered scatter plot for the metric. -
"table": data frame. A summary table for the metric.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other Managerial Relations:
one2one_dist(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Examples
# Return plot one2one_fizz(pq_data, hrvar = "Organization", return = "plot") # Return a summary table one2one_fizz(pq_data, hrvar = "Organization", return = "table")# Return plot one2one_fizz(pq_data, hrvar = "Organization", return = "plot") # Return a summary table one2one_fizz(pq_data, hrvar = "Organization", return = "table")
Frequency of Manager 1:1 Meetings as bar or 100% stacked bar chart
Description
This function calculates the average number of weeks (cadence) between of 1:1 meetings between an employee and their manager. Returns a distribution plot for typical cadence of 1:1 meetings. Additional options available to return a bar plot, tables, or a data frame with a cadence of 1 on 1 meetings metric.
Usage
one2one_freq( data, hrvar = "Organization", mingroup = 5, return = "plot", mode = "dist", sort_by = NULL )one2one_freq( data, hrvar = "Organization", mingroup = 5, return = "plot", mode = "dist", sort_by = NULL )
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
return |
String specifying what to return. This must be one of the following strings:
|
mode |
String specifying what method to use. This must be one of the following strings:
|
sort_by |
String to specify the bucket label to sort by. Defaults to
|
Value
A different output is returned depending on the value passed to the return argument:
-
"plot": 'ggplot' object. A stacked bar plot for the metric. -
"table": data frame. A summary table for the metric.
Distribution view
For this view, there are four categories of cadence:
-
Weekly (once per week)
-
Twice monthly or more (up to 3 weeks)
-
Monthly (3 - 6 weeks)
-
Every two months (6 - 10 weeks)
-
Quarterly or less (> 10 weeks)
In the occasion there are zero 1:1 meetings with managers, this is included
into the last category, i.e. 'Quarterly or less'. Note that when mode is
set to "sum", these rows are simply excluded from the calculation.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other Managerial Relations:
one2one_dist(),
one2one_fizz(),
one2one_line(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Examples
# Return plot, mode dist one2one_freq(pq_data, hrvar = "Organization", return = "plot", mode = "dist") # Return plot, mode sum one2one_freq(pq_data, hrvar = "Organization", return = "plot", mode = "sum") # Return summary table one2one_freq(pq_data, hrvar = "Organization", return = "table")# Return plot, mode dist one2one_freq(pq_data, hrvar = "Organization", return = "plot", mode = "dist") # Return plot, mode sum one2one_freq(pq_data, hrvar = "Organization", return = "plot", mode = "sum") # Return summary table one2one_freq(pq_data, hrvar = "Organization", return = "table")
Manager 1:1 Time Trend - Line Chart
Description
Provides a week by week view of 1:1 time with managers, visualised as line charts. By default returns a line chart for 1:1 meeting hours, with a separate panel per value in the HR attribute. Additional options available to return a summary table.
Usage
one2one_line(data, hrvar = "Organization", mingroup = 5, return = "plot")one2one_line(data, hrvar = "Organization", mingroup = 5, return = "plot")
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
return |
String specifying what to return. This must be one of the following strings:
See |
Details
Uses the metric Meeting_hours_with_manager_1_1.
Value
A different output is returned depending on the value passed to the return argument:
-
"plot": 'ggplot' object. A faceted line plot for the metric. -
"table": data frame. A summary table for the metric.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Other Managerial Relations:
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_rank(),
one2one_sum(),
one2one_trend()
Examples
# Return a line plot one2one_line(pq_data, hrvar = "LevelDesignation") # Return summary table one2one_line(pq_data, hrvar = "LevelDesignation", return = "table")# Return a line plot one2one_line(pq_data, hrvar = "LevelDesignation") # Return summary table one2one_line(pq_data, hrvar = "LevelDesignation", return = "table")
Manager 1:1 Time Ranking
Description
This function scans a standard query output for groups with high levels of 'Manager 1:1 Time'. Returns a plot by default, with an option to return a table with a all of groups (across multiple HR attributes) ranked by manager 1:1 time.
Usage
one2one_rank( data, hrvar = extract_hr(data), mingroup = 5, mode = "simple", plot_mode = 1, return = "plot" )one2one_rank( data, hrvar = extract_hr(data), mingroup = 5, mode = "simple", plot_mode = 1, return = "plot" )
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
mode |
String to specify calculation mode. Must be either:
|
plot_mode |
Numeric vector to determine which plot mode to return. Must
be either
|
return |
String specifying what to return. This must be one of the following strings:
See |
Details
Uses the metric Meeting_hours_with_manager_1_1.
See create_rank() for applying the same analysis to a different metric.
Value
A different output is returned depending on the value passed to the return
argument:
-
"plot": 'ggplot' object. A bubble plot where the x-axis represents the metric, the y-axis represents the HR attributes, and the size of the bubbles represent the size of the organizations. Note that there is no plot output ifmodeis set to"combine". -
"table": data frame. A summary table for the metric.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_sum(),
one2one_trend()
Other Managerial Relations:
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_sum(),
one2one_trend()
Examples
# Return rank table one2one_rank(data = pq_data, return = "table") # Return plot one2one_rank(data = pq_data, return = "plot")# Return rank table one2one_rank(data = pq_data, return = "table") # Return plot one2one_rank(data = pq_data, return = "plot")
Manager 1:1 Time Summary
Description
Provides an overview analysis of Manager 1:1 Time. Returns a bar plot showing average weekly minutes of Manager 1:1 Time by default. Additional options available to return a summary table.
Usage
one2one_sum(data, hrvar = "Organization", mingroup = 5, return = "plot") one2one_summary(data, hrvar = "Organization", mingroup = 5, return = "plot")one2one_sum(data, hrvar = "Organization", mingroup = 5, return = "plot") one2one_summary(data, hrvar = "Organization", mingroup = 5, return = "plot")
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
return |
String specifying what to return. This must be one of the following strings:
See |
Value
A different output is returned depending on the value passed to the return argument:
-
"plot": 'ggplot' object. A bar plot for the metric. -
"table": data frame. A summary table for the metric.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_trend()
Other Managerial Relations:
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_trend()
Examples
# Return a ggplot bar chart one2one_sum(pq_data, hrvar = "LevelDesignation") # Return a summary table one2one_sum(pq_data, hrvar = "LevelDesignation", return = "table")# Return a ggplot bar chart one2one_sum(pq_data, hrvar = "LevelDesignation") # Return a summary table one2one_sum(pq_data, hrvar = "LevelDesignation", return = "table")
Manager 1:1 Time Trend
Description
Provides a week by week view of scheduled manager 1:1 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.
Usage
one2one_trend(data, hrvar = "Organization", mingroup = 5, return = "plot")one2one_trend(data, hrvar = "Organization", mingroup = 5, return = "plot")
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
hrvar |
String containing the name of the HR Variable by which to split
metrics. Defaults to |
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
return |
Character vector specifying what to return, defaults to
|
Details
Uses the metric Meeting_hours_with_manager_1_1.
Value
Returns a 'ggplot' object by default, where 'plot' is passed in return.
When 'table' is passed, a summary table is returned as a data frame.
See Also
Other Visualization:
afterhours_dist(),
afterhours_fizz(),
afterhours_line(),
afterhours_rank(),
afterhours_summary(),
afterhours_trend(),
collaboration_area(),
collaboration_dist(),
collaboration_fizz(),
collaboration_line(),
collaboration_rank(),
collaboration_sum(),
collaboration_trend(),
create_bar(),
create_bar_asis(),
create_boxplot(),
create_bubble(),
create_dist(),
create_fizz(),
create_inc(),
create_line(),
create_line_asis(),
create_period_scatter(),
create_rank(),
create_sankey(),
create_scatter(),
create_stacked(),
create_tracking(),
create_trend(),
email_dist(),
email_fizz(),
email_line(),
email_rank(),
email_summary(),
email_trend(),
external_dist(),
external_fizz(),
external_line(),
external_rank(),
external_sum(),
hr_trend(),
hrvar_count(),
hrvar_trend(),
keymetrics_scan(),
meeting_dist(),
meeting_fizz(),
meeting_line(),
meeting_rank(),
meeting_summary(),
meeting_trend(),
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum()
Other Managerial Relations:
one2one_dist(),
one2one_fizz(),
one2one_freq(),
one2one_line(),
one2one_rank(),
one2one_sum()
Examples
# Run plot one2one_trend(pq_data) # Run table one2one_trend(pq_data, hrvar = "LevelDesignation", return = "table")# Run plot one2one_trend(pq_data) # Run table one2one_trend(pq_data, hrvar = "LevelDesignation", return = "table")
Sample person-to-person dataset
Description
A demo dataset representing a person-to-person query, structured as an
edgelist. The identifier variable for each person is PersonId, where the
variables have been prefixed with PrimaryCollaborator_ and
SecondaryCollaborator_ to represent the direction of collaboration.
Usage
p2p_datap2p_data
Format
A data frame with 11550 rows and 13 variables:
- PrimaryCollaborator_PersonId
- SecondaryCollaborator_PersonId
- MetricDate
- Diverse_tie_score
- Diverse_tie_type
- Strong_tie_score
- Strong_tie_type
- PrimaryCollaborator_Organization
- SecondaryCollaborator_Organization
- PrimaryCollaborator_LevelDesignation
- SecondaryCollaborator_LevelDesignation
- PrimaryCollaborator_FunctionType
- SecondaryCollaborator_FunctionType
...
Value
data frame.
Source
https://analysis.insights.viva.office.com/analyst/analysis/
See Also
Other Data:
g2g_data,
mt_data,
p2p_data_sim(),
pq_data
Other Network:
g2g_data,
network_g2g(),
network_p2p(),
network_summary(),
p2p_data_sim()
Simulate a person-to-person query using a Watts-Strogatz model
Description
Generate an person-to-person query / edgelist based on the graph
according to the Watts-Strogatz small-world network model. Organizational
data fields are also simulated for Organization, LevelDesignation, and
City.
Usage
p2p_data_sim(dim = 1, size = 300, nei = 5, p = 0.05)p2p_data_sim(dim = 1, size = 300, nei = 5, p = 0.05)
Arguments
dim |
Integer constant, the dimension of the starting lattice. |
size |
Integer constant, the size of the lattice along each dimension. |
nei |
Integer constant, the neighborhood within which the vertices of the lattice will be connected. |
p |
Real constant between zero and one, the rewiring probability. |
Details
This is a wrapper around igraph::watts.strogatz.game(). See igraph
documentation for details on methodology. Loop edges and multiple edges are
disabled. Size of the network can be changing the arguments size and nei.
Value
data frame with the same column structure as a person-to-person flexible
query. This has an edgelist structure and can be used directly as an input
to network_p2p().
See Also
Other Data:
g2g_data,
mt_data,
p2p_data,
pq_data
Other Network:
g2g_data,
network_g2g(),
network_p2p(),
network_summary(),
p2p_data
Examples
# Simulate a p2p dataset with 800 edges p2p_data_sim(size = 200, nei = 4)# Simulate a p2p dataset with 800 edges p2p_data_sim(size = 200, nei = 4)
Create the two-digit zero-padded format
Description
Create the two-digit zero-padded format
Usage
pad2(x)pad2(x)
Arguments
x |
numeric value or vector with maximum two characters. |
Value
Numeric value containing two-digit zero-padded values.
Perform a pairwise count of words by id
Description
This is a 'data.table' implementation that mimics the output of
pairwise_count() from 'widyr' to reduce package dependency. This is used
internally within tm_cooc().
Usage
pairwise_count(data, id = "line", word = "word")pairwise_count(data, id = "line", word = "word")
Arguments
data |
Data frame output from |
id |
String to represent the id variable. Defaults to |
word |
String to represent the word variable. Defaults to |
Value
data frame with the following columns representing a pairwise count:
-
"item1" -
"item2" -
"n"
See Also
Other Support:
any_idate(),
camel_clean(),
check_inputs(),
cut_hour(),
extract_date_range(),
extract_hr(),
heat_colours(),
is_date_format(),
maxmin(),
read_preamble(),
rgb2hex(),
totals_bind(),
totals_col(),
tstamp(),
us_to_space(),
wrap()
Other Text-mining:
meeting_tm_report(),
tm_clean(),
tm_cooc(),
tm_freq(),
tm_wordcloud()
Examples
td <- data.frame(line = c(1, 1, 2, 2), word = c("work", "meeting", "catch", "up")) pairwise_count(td, id = "line", word = "word")td <- data.frame(line = c(1, 1, 2, 2), word = c("work", "meeting", "catch", "up")) pairwise_count(td, id = "line", word = "word")
Sample Person Query dataset
Description
A dataset generated from a Person Query from Viva Insights.
Usage
pq_datapq_data
Format
A data frame with 1000 rows and 154 variables:
- PersonId
- MetricDate
- After_hours_call_hours
- After_hours_chat_hours
- After_hours_collaboration_hours
- After_hours_email_hours
- After_hours_meeting_hours
- After_hours_scheduled_call_hours
- After_hours_unscheduled_call_hours
- Call_hours
- Calls
- Chat_hours
- Chats_sent
- Collaboration_hours
- Email_hours
- Emails_sent
- Meeting_and_call_hours
- Meeting_hours
- Meetings
- Multitasking_hours
- Scheduled_call_hours
- Unscheduled_call_hours
- Urgent_email_hours
- Urgent_meeting_hours
- Collaboration_hours_on_Friday
- Collaboration_hours_on_Monday
- Collaboration_hours_on_Saturday
- Collaboration_hours_on_Sunday
- Collaboration_hours_on_Thursday
- Collaboration_hours_on_Tuesday
- Collaboration_hours_on_Wednesday
- Meeting_hours_on_Friday
- Meeting_hours_on_Monday
- Meeting_hours_on_Saturday
- Meeting_hours_on_Sunday
- Meeting_hours_on_Thursday
- Meeting_hours_on_Tuesday
- Meeting_hours_on_Wednesday
- Unscheduled_weekend_calls
- Weekend_chats_sent
- Weekend_emails_sent
- Weekend_meetings
- Chats_sent_00_01
- Chats_sent_01_02
- Chats_sent_02_03
- Chats_sent_03_04
- Chats_sent_04_05
- Chats_sent_05_06
- Chats_sent_06_07
- Chats_sent_07_08
- Chats_sent_08_09
- Chats_sent_09_10
- Chats_sent_10_11
- Chats_sent_11_12
- Chats_sent_12_13
- Chats_sent_13_14
- Chats_sent_14_15
- Chats_sent_15_16
- Chats_sent_16_17
- Chats_sent_17_18
- Chats_sent_18_19
- Chats_sent_19_20
- Chats_sent_20_21
- Chats_sent_21_22
- Chats_sent_22_23
- Chats_sent_23_24
- Emails_sent_00_01
- Emails_sent_01_02
- Emails_sent_02_03
- Emails_sent_03_04
- Emails_sent_04_05
- Emails_sent_05_06
- Emails_sent_06_07
- Emails_sent_07_08
- Emails_sent_08_09
- Emails_sent_09_10
- Emails_sent_10_11
- Emails_sent_11_12
- Emails_sent_12_13
- Emails_sent_13_14
- Emails_sent_14_15
- Emails_sent_15_16
- Emails_sent_16_17
- Emails_sent_17_18
- Emails_sent_18_19
- Emails_sent_19_20
- Emails_sent_20_21
- Emails_sent_21_22
- Emails_sent_22_23
- Emails_sent_23_24
- Meetings_00_01
- Meetings_01_02
- Internal_meeting_hours_with_3_to_8_attendees
- Internal_meeting_hours_without_manager_1_1
- Small_group_chats_sent__excluding_manager
- Small_group_emails_sent__excluding_manager
- Small_group_meeting__call__and_chat_hours
- Collaboration_hours_with_direct_reports
- Manager_coaching_hours_1_1
- Meeting_and_call_hours_with_manager
- Meeting_and_call_hours_with_manager_1_1
- Meeting_and_call_hours_with_skip_level
- Meeting_hours_with_manager
- Meeting_hours_with_manager_1_1
- Meeting_hours_with_skip_level
- Meetings_with_manager
- Meetings_with_manager_1_1
- Meetings_with_skip_level
- Small_group_chats_sent__including_manager
- Small_group_emails_sent__including_manager
- Unscheduled_call_hours_with_manager
- Unscheduled_call_hours_with_manager_1_1
- Unscheduled_call_hours_with_skip_level
- Internal_network_size
- Conflicting_meeting_hours
- Large_and_long_meeting_hours
- Large_and_long_recurring_meeting_hours
- Large_and_short_meeting_hours
- Large_and_short_recurring_meeting_hours
- Meeting_hours_ended_on_time
- Meeting_hours_joined_on_time
- Meeting_hours_not_ended_on_time
- Meeting_hours_not_joined_on_time
- Meeting_hours_with_12_to_24_hours_of_advanced_notice
- Meeting_hours_with_24_or_more_hours_of_advanced_notice
- Meeting_hours_with_six_or_fewer_hours_of_advanced_notice
- Meeting_hours_with_six_to_12_hours_of_advanced_notice
- Recurring_meeting_hours
- Small_and_long_meeting_hours
- Small_and_long_recurring_meeting_hours
- Small_and_short_meeting_hours
- Small_and_short_recurring_meeting_hours
- Available_to_focus_hours
- Interrupted_hours
- Uninterrupted_hours
- External_chat_hours
- External_collaboration_hours
- External_email_hours
- External_meeting_hours
- External_unscheduled_call_hours
- Working_hours_call_hours
- Working_hours_chat_hours
- Working_hours_collaboration_hours
- Working_hours_email_hours
- Working_hours_meeting_hours
- Working_hours_scheduled_call_hours
- Working_hours_unscheduled_call_hours
- LevelDesignation
- Layer
- SupervisorIndicator
- Organization
- FunctionType
- WeekendDays
- IsActive
Value
data frame.
Source
https://learn.microsoft.com/en-us/viva/insights/advanced/analyst/person-query/
See Also
Other Data:
g2g_data,
mt_data,
p2p_data,
p2p_data_sim()
Read preamble
Description
Read in a preamble to be used within each individual reporting function. Reads from the Markdown file installed with the package.
Usage
read_preamble(path)read_preamble(path)
Arguments
path |
Text string containing the path for the appropriate Markdown file. |
Value
String containing the text read in from the specified Markdown file.
See Also
Other Support:
any_idate(),
camel_clean(),
check_inputs(),
cut_hour(),
extract_date_range(),
extract_hr(),
heat_colours(),
is_date_format(),
maxmin(),
pairwise_count(),
rgb2hex(),
totals_bind(),
totals_col(),
tstamp(),
us_to_space(),
wrap()
Other Reports:
IV_report(),
generate_report(),
meeting_tm_report(),
validation_report()
Convert rgb to HEX code
Description
Convert rgb to HEX code
Usage
rgb2hex(r, g, b)rgb2hex(r, g, b)
Arguments
r, g, b
|
Values that correspond to the three RGB parameters |
Value
Returns a string containing a HEX code.
See Also
Other Support:
any_idate(),
camel_clean(),
check_inputs(),
cut_hour(),
extract_date_range(),
extract_hr(),
heat_colours(),
is_date_format(),
maxmin(),
pairwise_count(),
read_preamble(),
totals_bind(),
totals_col(),
tstamp(),
us_to_space(),
wrap()
Main theme for 'vivainsights' visualisations
Description
A theme function applied to 'ggplot' visualisations in 'vivainsights'. Install and load 'extrafont' to use custom fonts for plotting.
Usage
theme_wpa(font_size = 12, font_family = "Segoe UI")theme_wpa(font_size = 12, font_family = "Segoe UI")
Arguments
font_size |
Numeric value that prescribes the base font size for the plot. The text elements are defined relatively to this base font size. Defaults to 12. |
font_family |
Character value specifying the font family
to be used in the plot. The default value is |
Value
Returns a ggplot object with the applied theme.
See Also
Other Themes:
theme_wpa_basic()
Basic theme for 'vivainsights' visualisations
Description
A theme function applied to 'ggplot' visualisations in 'vivainsights'.
Based on theme_wpa() but has no font requirements.
Usage
theme_wpa_basic(font_size = 12)theme_wpa_basic(font_size = 12)
Arguments
font_size |
Numeric value that prescribes the base font size for the plot. The text elements are defined relatively to this base font size. Defaults to 12. |
Value
Returns a ggplot object with the applied theme.
See Also
Other Themes:
theme_wpa()
Clean subject line text prior to analysis
Description
This function processes the Subject column in a Meeting Query by applying
tokenisation usingtidytext::unnest_tokens(), and removing any stopwords
supplied in a data frame (using the argument stopwords). This is a
sub-function that feeds into tm_freq(), tm_cooc(), and tm_wordcloud().
The default is to return a data frame with tokenised counts of words or
ngrams.
Usage
tm_clean(data, token = "words", stopwords = NULL, ...)tm_clean(data, token = "words", stopwords = NULL, ...)
Arguments
data |
A Meeting Query dataset in the form of a data frame. |
token |
A character vector accepting either |
stopwords |
A character vector OR a single-column data frame labelled
|
... |
Additional parameters to pass to |
Value
data frame with two columns:
-
line -
word
See Also
Other Text-mining:
meeting_tm_report(),
pairwise_count(),
tm_cooc(),
tm_freq(),
tm_wordcloud()
Examples
# words tm_clean(mt_data) # ngrams tm_clean(mt_data, token = "ngrams")# words tm_clean(mt_data) # ngrams tm_clean(mt_data, token = "ngrams")
Analyse word co-occurrence in subject lines and return a network plot
Description
This function generates a word co-occurrence network plot, with options to
return a table. This function is used within meeting_tm_report().
Usage
tm_cooc(data, stopwords = NULL, seed = 100, return = "plot", lmult = 0.05)tm_cooc(data, stopwords = NULL, seed = 100, return = "plot", lmult = 0.05)
Arguments
data |
A Meeting Query dataset in the form of a data frame. |
stopwords |
A character vector OR a single-column data frame labelled
|
seed |
A numeric vector to set seed for random generation. |
return |
String specifying what to return. This must be one of the following strings:
See |
lmult |
A multiplier to adjust the line width in the output plot. Defaults to 0.05. |
Details
This function uses tm_clean() as the underlying data wrangling function.
There is an option to remove stopwords by passing a data frame into the
stopwords argument.
Value
A different output is returned depending on the value passed to the return
argument:
-
"plot": 'ggplot' and 'ggraph' object. A network plot. -
"table": data frame. A summary table.
Example
The function can be run with subject lines from mt_data, as per below.
mt_data %>% tm_cooc(lmult = 0.01)
Author(s)
Carlos Morales [email protected]
See Also
Other Text-mining:
meeting_tm_report(),
pairwise_count(),
tm_clean(),
tm_freq(),
tm_wordcloud()
Examples
# Demo using a subset of `mt_data`# Demo using a subset of `mt_data`
Perform a Word or Ngram Frequency Analysis and return a Circular Bar Plot
Description
Generate a circular bar plot with frequency of words / ngrams.
This function is used within meeting_tm_report().
Usage
tm_freq(data, token = "words", stopwords = NULL, keep = 100, return = "plot")tm_freq(data, token = "words", stopwords = NULL, keep = 100, return = "plot")
Arguments
data |
A Meeting Query dataset in the form of a data frame. |
token |
A character vector accepting either |
stopwords |
A character vector OR a single-column data frame labelled
|
keep |
A numeric vector specifying maximum number of words to keep. |
return |
String specifying what to return. This must be one of the following strings:
See |
Details
This function uses tm_clean() as the underlying data wrangling function.
There is an option to remove stopwords by passing a data frame into the
stopwords argument.
Value
A different output is returned depending on the value passed to the return
argument:
-
"plot": 'ggplot' object. A circular bar plot. -
"table": data frame. A summary table.
See Also
Other Text-mining:
meeting_tm_report(),
pairwise_count(),
tm_clean(),
tm_cooc(),
tm_wordcloud()
Examples
# circular network plot with words tm_freq(mt_data, token = "words") # circular network plot with ngrams tm_freq(mt_data, token = "ngrams") # summary table of text frequency tm_freq(mt_data, token = "words", return = "table")# circular network plot with words tm_freq(mt_data, token = "words") # circular network plot with ngrams tm_freq(mt_data, token = "ngrams") # summary table of text frequency tm_freq(mt_data, token = "words", return = "table")
Generate a wordcloud with meeting subject lines
Description
Generate a wordcloud with the meeting query.
This is a sub-function that feeds into meeting_tm_report().
Usage
tm_wordcloud( data, stopwords = NULL, seed = 100, keep = 100, return = "plot", ... )tm_wordcloud( data, stopwords = NULL, seed = 100, keep = 100, return = "plot", ... )
Arguments
data |
A Meeting Query dataset in the form of a data frame. |
stopwords |
A character vector OR a single-column data frame labelled
|
seed |
A numeric vector to set seed for random generation. |
keep |
A numeric vector specifying maximum number of words to keep. |
return |
String specifying what to return. This must be one of the following strings:
See |
... |
Additional parameters to be passed to
|
Details
Uses the 'ggwordcloud' package for the underlying implementation, thus
returning a 'ggplot' object. Additional layers can be added onto the plot
using a ggplot + syntax.
The recommendation is not to return over 100 words in a word cloud.
This function uses tm_clean() as the underlying data wrangling function.
There is an option to remove stopwords by passing a data frame into the
stopwords argument.
Value
A different output is returned depending on the value passed to the return
argument:
-
"plot": 'ggplot' object containing a word cloud. -
"table": data frame returning the data used to generate the word cloud.
See Also
Other Text-mining:
meeting_tm_report(),
pairwise_count(),
tm_clean(),
tm_cooc(),
tm_freq()
Examples
tm_wordcloud(mt_data, keep = 30) # Removing stopwords tm_wordcloud(mt_data, keep = 30, stopwords = c("weekly", "update"))tm_wordcloud(mt_data, keep = 30) # Removing stopwords tm_wordcloud(mt_data, keep = 30, stopwords = c("weekly", "update"))
Row-bind an identical data frame for computing grouped totals
Description
Row-bind an identical data frame and impute a specific
column with the target_value, which defaults as "Total". The purpose of
this is to enable to creation of summary tables with a calculated "Total"
row. See example below on usage.
Usage
totals_bind(data, target_col, target_value = "Total")totals_bind(data, target_col, target_value = "Total")
Arguments
data |
data frame |
target_col |
Character value of the column in which to impute |
target_value |
Character value to impute in the new data frame to
row-bind. Defaults to |
Value
data frame with twice the number of rows of the input data frame, where half
of those rows will have the target_col column imputed with the value from
target_value.
See Also
Other Support:
any_idate(),
camel_clean(),
check_inputs(),
cut_hour(),
extract_date_range(),
extract_hr(),
heat_colours(),
is_date_format(),
maxmin(),
pairwise_count(),
read_preamble(),
rgb2hex(),
totals_col(),
tstamp(),
us_to_space(),
wrap()
Examples
pq_data %>% totals_bind(target_col = "LevelDesignation", target_value = "Total") %>% create_bar(hrvar = "LevelDesignation", metric = "Email_hours", return = "table")pq_data %>% totals_bind(target_col = "LevelDesignation", target_value = "Total") %>% create_bar(hrvar = "LevelDesignation", metric = "Email_hours", return = "table")
Fabricate a 'Total' HR variable
Description
Create a 'Total' column of character type comprising exactly of
one unique value. This is a convenience function for returning a no-HR
attribute view when NULL is supplied to the hrvar argument in
functions.
Usage
totals_col(data, total_value = "Total")totals_col(data, total_value = "Total")
Arguments
data |
data frame |
total_value |
Character value defining the name and the value of the
|
Value
data frame containing an additional 'Total' column on top of the input data frame.
See Also
Other Support:
any_idate(),
camel_clean(),
check_inputs(),
cut_hour(),
extract_date_range(),
extract_hr(),
heat_colours(),
is_date_format(),
maxmin(),
pairwise_count(),
read_preamble(),
rgb2hex(),
totals_bind(),
tstamp(),
us_to_space(),
wrap()
Examples
# Create a visual without HR attribute breaks pq_data %>% totals_col() %>% create_fizz(hrvar = "Total", metric = "Email_hours")# Create a visual without HR attribute breaks pq_data %>% totals_col() %>% create_fizz(hrvar = "Total", metric = "Email_hours")
Sankey chart of organizational movement between HR attributes and missing values (outside company move) (Data Overview)
Description
Creates a list of everyone at a specified start date and a specified end date then aggregates up people who have moved between organizations between this to points of time and visualizes the move through a sankey chart.
Through this chart you can see:
-
The HR attribute/orgs that have the highest move out
-
The HR attribute/orgs that have the highest move in
-
The number of people that do not have that HR attribute or if they are no longer in the system
Usage
track_HR_change( data, start_date = min(data$MetricDate), end_date = max(data$MetricDate), hrvar = "Organization", mingroup = 5, return = "plot", NA_replacement = "Out of Company" )track_HR_change( data, start_date = min(data$MetricDate), end_date = max(data$MetricDate), hrvar = "Organization", mingroup = 5, return = "plot", NA_replacement = "Out of Company" )
Arguments
data |
A Person Query dataset in the form of a data frame. |
start_date |
A start date to compare changes. See |
end_date |
An end date to compare changes. See |
hrvar |
HR Variable by which to compare changes between, defaults to
|
mingroup |
Numeric value setting the privacy threshold / minimum group size. Defaults to 5. |
return |
Character vector specifying what to return, defaults to
|
NA_replacement |
Character replacement for NA defaults to "out of company" |
Value
Returns a 'NetworkD3' object by default, where 'plot' is passed in return.
When 'table' is passed, a summary table is returned as a data frame.
Author(s)
Tannaz Sattari Tabrizi [email protected]
See Also
Other Data Validation:
check_query(),
extract_hr(),
flag_ch_ratio(),
flag_em_ratio(),
flag_extreme(),
flag_outlooktime(),
hr_trend(),
hrvar_count(),
hrvar_count_all(),
hrvar_trend(),
identify_churn(),
identify_holidayweeks(),
identify_inactiveweeks(),
identify_nkw(),
identify_outlier(),
identify_privacythreshold(),
identify_shifts(),
identify_tenure(),
validation_report()
Examples
pq_data %>% track_HR_change()pq_data %>% track_HR_change()
Generate a time stamp
Description
This function generates a time stamp of the format 'yymmdd_hhmmss'.
This is a support function and is not intended for direct use.
Usage
tstamp()tstamp()
Value
String containing the timestamp in the format 'yymmdd_hhmmss'.
See Also
Other Support:
any_idate(),
camel_clean(),
check_inputs(),
cut_hour(),
extract_date_range(),
extract_hr(),
heat_colours(),
is_date_format(),
maxmin(),
pairwise_count(),
read_preamble(),
rgb2hex(),
totals_bind(),
totals_col(),
us_to_space(),
wrap()
Replace underscore with space
Description
Convenience function to convert underscores to space
Usage
us_to_space(x)us_to_space(x)
Arguments
x |
String to replace all occurrences of |
Value
Character vector containing the modified string.
See Also
Other Support:
any_idate(),
camel_clean(),
check_inputs(),
cut_hour(),
extract_date_range(),
extract_hr(),
heat_colours(),
is_date_format(),
maxmin(),
pairwise_count(),
read_preamble(),
rgb2hex(),
totals_bind(),
totals_col(),
tstamp(),
wrap()
Examples
us_to_space("Meeting_hours_with_manager_1_on_1")us_to_space("Meeting_hours_with_manager_1_on_1")
Generate a Data Validation report in HTML
Description
The function generates an interactive HTML report using Standard Person Query data as an input. The report contains checks on Viva Insights query outputs to provide diagnostic information for the Analyst prior to analysis.
An additional Standard Meeting Query can be provided to perform meeting subject line related checks. This is optional and the validation report can be run without it.
Usage
validation_report( data, meeting_data = NULL, hrvar = "Organization", path = "validation report", hrvar_threshold = 150, timestamp = TRUE )validation_report( data, meeting_data = NULL, hrvar = "Organization", path = "validation report", hrvar_threshold = 150, timestamp = TRUE )
Arguments
data |
A Standard Person Query dataset in the form of a data frame. |
meeting_data |
An optional Meeting Query dataset in the form of a data frame. |
hrvar |
HR Variable by which to split metrics, defaults to "Organization" but accepts any character vector, e.g. "Organization" |
path |
Pass the file path and the desired file name, excluding the file extension. |
hrvar_threshold |
Numeric value determining the maximum number of unique
values to be allowed to qualify as a HR variable. This is passed directly
to the |
timestamp |
Logical vector specifying whether to include a timestamp in
the file name. Defaults to |
Details
For your input to data or meeting_data, please use the function
vivainsights::import_query() to import your csv query files into R. This function will
standardize format and prepare the data as input for this report.
For most variables, a note is returned in-line instead of an error if the variable is not available.
Value
An HTML report with the same file name as specified in the arguments is generated in the working directory. No outputs are directly returned by the function.
Checking functions within validation_report()
-
check_query() -
flag_ch_ratio() -
hrvar_count_all() -
identify_privacythreshold() -
identify_nkw() -
identify_holidayweeks() -
subject_validate()(available in 'wpa') -
identify_tenure() -
flag_outlooktime() -
identify_shifts() -
track_HR_change()
You can browse each individual function for details on calculations.
Creating a report
Below is an example on how to run the report.
validation_report(pq_data,
hrvar = "Organization")
See Also
Other Reports:
IV_report(),
generate_report(),
meeting_tm_report(),
read_preamble()
Other Data Validation:
check_query(),
extract_hr(),
flag_ch_ratio(),
flag_em_ratio(),
flag_extreme(),
flag_outlooktime(),
hr_trend(),
hrvar_count(),
hrvar_count_all(),
hrvar_trend(),
identify_churn(),
identify_holidayweeks(),
identify_inactiveweeks(),
identify_nkw(),
identify_outlier(),
identify_privacythreshold(),
identify_shifts(),
identify_tenure(),
track_HR_change()
Add a character at the start and end of a character string
Description
This function adds a character at the start and end of a character string, where the default behaviour is to add a double quote.
Usage
wrap(string, wrapper = "\"")wrap(string, wrapper = "\"")
Arguments
string |
Character string to be wrapped around |
wrapper |
Character to wrap around |
Value
Character vector containing the modified string.
See Also
Other Support:
any_idate(),
camel_clean(),
check_inputs(),
cut_hour(),
extract_date_range(),
extract_hr(),
heat_colours(),
is_date_format(),
maxmin(),
pairwise_count(),
read_preamble(),
rgb2hex(),
totals_bind(),
totals_col(),
tstamp(),
us_to_space()
Wrap text based on character threshold
Description
Wrap text in visualizations according to a preset character
threshold. The next space in the string is replaced with \n, which will
render as next line in plots and messages.
Usage
wrap_text(x, threshold = 15)wrap_text(x, threshold = 15)
Arguments
x |
String to wrap text |
threshold |
Numeric, defaults to 15. Number of character units by which
the next space would be replaced with |
Value
String output representing a processed version of x, with spaces replaced
by \n.
Examples
wrapped <- wrap_text( "The total entropy of an isolated system can never decrease." ) message(wrapped)wrapped <- wrap_text( "The total entropy of an isolated system can never decrease." ) message(wrapped)
Calculate Chatterjee's Rank Correlation Coefficient
Description
This function calculates Chatterjee's rank correlation coefficient, which measures the association between two variables. It is particularly useful for identifying monotonic relationships between variables, even if they are not linear.
Usage
xicor(x, y, ties = FALSE)xicor(x, y, ties = FALSE)
Arguments
x |
A numeric vector representing the independent variable. |
y |
A numeric vector representing the dependent variable. |
ties |
A logical value indicating whether to handle ties in the data. Default is FALSE. If If |
Details
Unlike Pearson's correlation (which measures linear relationships), Chatterjee's coefficient can handle non-linear monotonic relationships. It is robust to outliers and can handle tied ranks, making it versatile for datasets with ordinal data or tied ranks. This makes it a valuable alternative to Spearman's and Kendall's correlations, especially when the data may not meet the assumptions required by these methods.
By default, ties = FALSE is set to prioritize computational efficiency, as
handling ties requires additional processing. In cases where ties are present
or likely (such as when working with ordinal or categorical data), it is
recommended to set ties = TRUE.
Value
A numeric value representing Chatterjee's rank correlation coefficient.
Examples
xicor(x = pq_data$Collaboration_hours, y = pq_data$Internal_network_size, ties = TRUE) xicor(x = pq_data$Collaboration_hours, y = pq_data$Internal_network_size, ties = FALSE)xicor(x = pq_data$Collaboration_hours, y = pq_data$Internal_network_size, ties = TRUE) xicor(x = pq_data$Collaboration_hours, y = pq_data$Internal_network_size, ties = FALSE)