Function reference • wpa

All functions
`GetResiduals()`	Extract Residuals from ARIMA, VAR, or any Simulated Fitted Time Series Model
`IV_by_period()`	Identify the WPA metrics that have the biggest change between two periods.
`IV_report()`	Generate a Information Value HTML Report
`LjungBox()`	Ljung and Box Portmanteau Test
`afterhours_dist()`	Distribution of After-hours Collaboration Hours as a 100% stacked bar
`afterhours_fizz()`	Distribution of After-hours Collaboration Hours (Fizzy Drink plot)
`afterhours_line()`	After-hours Collaboration Time Trend - Line Chart
`afterhours_rank()`	Rank groups with high After-Hours Collaboration Hours
`afterhours_summary()` `afterhours_sum()`	Summary of After-Hours Collaboration Hours
`afterhours_trend()`	After-Hours Time Trend
`anonymise()` `anonymize()`	Anonymise a categorical variable by replacing values
`calculate_IV()`	Calculate Weight of Evidence (WOE) and Information Value (IV) between a single predictor and a single outcome variable.
`camel_clean()`	Convert "CamelCase" to "Camel Case"
`capacity_report()`	Generate a Capacity report in HTML
`check_inputs()`	Check whether a data frame contains all the required variable
`check_query()`	Check a query to ensure that it is suitable for analysis
`coaching_report()`	Generate a Coaching report in HTML
`collaboration_area()` `collab_area()`	Collaboration - Stacked Area Plot
`collaboration_dist()` `collab_dist()`	Distribution of Collaboration Hours as a 100% stacked bar
`collaboration_fizz()` `collab_fizz()`	Distribution of Collaboration Hours (Fizzy Drink plot)
`collaboration_line()` `collab_line()`	Collaboration Time Trend - Line Chart
`collaboration_rank()` `collab_rank()`	Collaboration Ranking
`collaboration_report()`	Generate a Collaboration Report in HTML
`collaboration_sum()` `collab_sum()` `collaboration_summary()` `collab_summary()`	Collaboration Summary
`collaboration_trend()`	Collaboration Time Trend
`combine_signals()`	Combine signals from the Hourly Collaboration query
`comma()`	Add comma separator for thousands
`connectivity_report()`	Generate a Connectivity report in HTML
`copy_df()`	Copy a data frame to clipboard for pasting in Excel
`create_ITSA()`	Estimate an effect of intervention on every Viva Insights metric in input file by applying single-group Interrupted Time-Series Analysis (ITSA)
`create_IV()`	Calculate Information Value for a selected outcome variable
`create_bar()`	Mean Bar Plot for any metric
`create_bar_asis()`	Create a bar chart without aggregation for any metric
`create_boxplot()`	Box Plot for any metric
`create_bubble()`	Create a bubble plot with two selected Viva Insights metrics (General Purpose), with size representing the number of employees in the group.
`create_density()`	Create a density plot for any metric
`create_dist()`	Horizontal 100 percent stacked bar plot for any metric
`create_dt()`	Create interactive tables in HTML with 'download' buttons.
`create_fizz()`	Fizzy Drink / Jittered Scatter Plot for any metric
`create_hist()`	Create a histogram plot for any metric
`create_inc()` `create_incidence()`	Create an incidence analysis reflecting proportion of population scoring above or below a threshold for a metric
`create_line()`	Time Trend - Line Chart for any metric
`create_line_asis()`	Create a line chart without aggregation for any metric
`create_period_scatter()`	Period comparison scatter plot for any two metrics
`create_rank()`	Rank all groups across HR attributes on a selected Viva Insights metric
`create_rank_combine()`	Create combination pairs of HR variables and run 'create_rank()'
`create_sankey()`	Create a sankey chart from a two-column count table
`create_scatter()`	Create a Scatter plot with two selected Viva Insights metrics (General Purpose)
`create_stacked()`	Horizontal stacked bar plot for any metric
`create_tracking()`	Create a line chart that tracks metrics over time with a 4-week rolling average
`create_trend()`	Heat mapped horizontal bar plot over time for any metric
`cut_hour()`	Convert a numeric variable for hours into categorical
`dv_data`	Sample Standard Person Query dataset for Data Validation
`em_data`	Sample Hourly Collaboration data
`email_dist()`	Distribution of Email Hours as a 100% stacked bar
`email_fizz()`	Distribution of Email Hours (Fizzy Drink plot)
`email_line()`	Email Time Trend - Line Chart
`email_rank()`	Email Hours Ranking
`email_summary()` `email_sum()`	Email Summary
`email_trend()`	Email Hours Time Trend
`export()`	Export 'wpa' outputs to CSV, clipboard, or save as images
`external_dist()`	Distribution of External Collaboration Hours as a 100% stacked bar
`external_fizz()`	Distribution of External Collaboration Hours (Fizzy Drink plot)
`external_line()`	External Collaboration Hours Time Trend - Line Chart
`external_network_plot()`	Plot External Network Breadth and Size as a scatter plot
`external_rank()`	Rank groups with high External Collaboration Hours
`external_sum()` `external_summary()`	External Collaboration Summary
`extract_date_range()`	Extract date period
`extract_hr()`	Extract HR attribute variables
`flag_ch_ratio()`	Flag unusual high collaboration hours to after-hours collaboration hours ratio
`flag_em_ratio()`	Flag Persons with unusually high Email Hours to Emails Sent ratio
`flag_extreme()`	Warn for extreme values by checking against a threshold
`flag_outlooktime()`	Flag unusual outlook time settings for work day start and end time
`flex_index()`	Compute a Flexibility Index based on the Hourly Collaboration Query
`g2g_data`	Sample Group-to-Group dataset
`generate_report()`	Generate HTML report with list inputs
`generate_report2()`	Generate HTML report based on existing RMarkdown documents
`heat_colours()` `heat_colors()`	Generate a vector of `n` contiguous colours, as a red-yellow-green palette.
`hr_trend()`	Employee count over time
`hrvar_count()` `analysis_scope()`	Create a count of distinct people in a specified HR variable
`hrvar_count_all()`	Create count of distinct fields and percentage of employees with missing values for all HR variables
`hrvar_trend()`	Track count of distinct people over time in a specified HR variable
`identify_churn()`	Identify employees who have churned from the dataset
`identify_datefreq()`	Identify date frequency based on a series of dates
`identify_holidayweeks()`	Identify Holiday Weeks based on outliers
`identify_inactiveweeks()`	Identify Inactive Weeks
`identify_nkw()`	Identify Non-Knowledge workers in a Person Query using Collaboration Hours
`identify_outlier()`	Identify metric outliers over a date interval
`identify_privacythreshold()`	Identify groups under privacy threshold
`identify_query()`	Identify the query type of the passed data frame
`identify_shifts()`	Identify shifts based on outlook time settings for work day start and end time
`identify_shifts_wp()`	Identify shifts based on binary activity
`identify_tenure()`	Tenure calculation based on different input dates, returns data summary table or histogram
`import_to_fst()`	Read a Workplace Analytics query in '.csv' using and create a '.fst' file in the same directory for faster reading
`import_wpa()`	Import a Workplace Analytics Query
`internal_network_plot()`	Plot Internal Network Breadth and Size as a scatter plot
`is_date_format()`	Identify whether string is a date format
`jitter_metrics()`	Jitter metrics in a data frame
`keymetrics_scan()`	Run a summary of Key Metrics from the Standard Person Query data
`keymetrics_scan_asis()`	Run a summary of Key Metrics without aggregation
`map_IV()`	Calculate Weight of Evidence (WOE) and Information Value (IV) between multiple predictors and a single outcome variable, returning a list of statistics.
`maxmin()`	Max-Min Scaling Function
`meeting_dist()`	Distribution of Meeting Hours as a 100% stacked bar
`meeting_extract()`	Extract top low-engagement meetings from the Meeting Query
`meeting_fizz()`	Distribution of Meeting Hours (Fizzy Drink plot)
`meeting_line()`	Meeting Time Trend - Line Chart
`meeting_quality()`	Run a meeting habits / meeting quality analysis
`meeting_rank()`	Meeting Hours Ranking
`meeting_skim()`	Produce a skim summary of meeting hours
`meeting_summary()` `meeting_sum()`	Meeting Summary
`meeting_tm_report()`	Generate a Meeting Text Mining report in HTML
`meeting_trend()`	Meeting Hours Time Trend
`meetingtype_dist()`	Distribution of Meeting Types by number of Attendees and Duration
`meetingtype_dist_ca()`	Meeting Type Distribution (Ways of Working Assessment Query)
`meetingtype_dist_mt()`	Meeting Type Distribution (Meeting Query)
`meetingtype_summary()` `meetingtype_sum()`	Create a summary bar chart of the proportion of Meeting Hours spent in Long or Large Meetings
`mgrcoatt_dist()`	Manager meeting coattendance distribution
`mgrrel_matrix()`	Manager Relationship 2x2 Matrix
`mt_data`	Sample Meeting Query dataset
`network_describe()`	Uncover HR attributes which best represent a population for a Person to Person query
`network_g2g()` `g2g_network()`	Create a network plot with the group-to-group query
`network_leiden()`	Implement the Leiden community detection on a Person to Person network query
`network_louvain()`	Implement the Louvain community detection on a Person to Person network query
`network_p2p()`	Create a network plot with the person-to-person query
`network_summary()`	Summarise node centrality statistics with an igraph object
`one2one_dist()`	Distribution of Manager 1:1 Time as a 100% stacked bar
`one2one_fizz()`	Distribution of Manager 1:1 Time (Fizzy Drink plot)
`one2one_freq()`	Frequency of Manager 1:1 Meetings as bar or 100% stacked bar chart
`one2one_line()`	Manager 1:1 Time Trend - Line Chart
`one2one_rank()`	Manager 1:1 Time Ranking
`one2one_sum()` `one2one_summary()`	Manager 1:1 Time Summary
`one2one_trend()`	Manager 1:1 Time Trend
`p2p_data_sim()`	Simulate a person-to-person query using a Watts-Strogatz model
`p_test()`	Calculate the p-value of the null hypothesis that two outcomes are from the same dataset
`pad2()`	Create the two-digit zero-padded format
`pairwise_count()`	Perform a pairwise count of words by id
`period_change()`	Plot the distribution of percentage change between periods of a Viva Insights metric by the number of employees.
`personas_hclust()`	Create hierarchical clusters of selected metrics using a Person query
`plot_WOE()`	Plot WOE graphs with an IV object
`plot_flex_index()`	Plot a Sample of Working Patterns using Flexibility Index output
`plot_hourly_pat()`	Internal function for plotting the hourly activity patterns.
`read_preamble()`	Read preamble
`remove_outliers()`	Remove outliers from a person query across time
`rgb2hex()`	Convert rgb to HEX code
`sq_data`	Sample Standard Person Query dataset
`standardise_pq()` `standardize_pq()`	Standardise variable names to a Standard Person Query
`subject_classify()`	Create a new logical variable that classifies meetings by patterns in subject lines
`subject_scan()` `tm_scan()`	Count top words in subject lines grouped by a custom attribute
`subject_validate()`	Scan meeting subject and highlight items for review
`subject_validate_report()`	Generate Meeting Text Mining report in HTML for Common Exclusion Terms
`theme_wpa()`	Main theme for 'wpa' visualisations
`theme_wpa_basic()`	Basic theme for 'wpa' visualisations
`tm_clean()`	Clean subject line text prior to analysis
`tm_cooc()`	Analyse word co-occurrence in subject lines and return a network plot
`tm_freq()`	Perform a Word or Ngram Frequency Analysis and return a Circular Bar Plot
`tm_wordcloud()`	Generate a wordcloud with meeting subject lines
`totals_bind()`	Row-bind an identical data frame for computing grouped totals
`totals_col()`	Fabricate a 'Total' HR variable
`totals_reorder()`	Reorder a value to the top of the summary table
`track_HR_change()`	Sankey chart of organizational movement between HR attributes and missing values (outside company move) (Data Overview)
`tstamp()`	Generate a time stamp
`us_to_space()`	Replace underscore with space
`validation_report()`	Generate a Data Validation report in HTML
`wellbeing_report()`	Generate a Wellbeing Report in HTML
`workloads_dist()`	Distribution of Work Week Span as a 100% stacked bar
`workloads_fizz()`	Distribution of Work Week Span (Fizzy Drink plot)
`workloads_line()`	Workloads Time Trend - Line Chart
`workloads_rank()`	Rank all groups across HR attributes for Work Week Span
`workloads_summary()` `workloads_sum()`	Work Week Span Summary
`workloads_trend()`	Work Week Span Time Trend
`workpatterns_area()`	Create an area plot of emails and IMs by hour of the day
`workpatterns_classify()`	Classify working pattern personas using a rule based algorithm
`workpatterns_classify_bw()`	Classify working pattern week archetypes using a rule-based algorithm, using the binary week-based ('bw') method.
`workpatterns_classify_pav()`	Classify working pattern personas using a rule based algorithm, using the person-average volume-based ('pav') method.
`workpatterns_hclust()`	Create a hierarchical clustering of email or IMs by hour of day
`workpatterns_rank()`	Create a rank table of working patterns
`workpatterns_report()`	Generate a report on working patterns in HTML
`wrap()`	Add a character at the start and end of a character string
`wrap_text()`	Wrap text based on character threshold

Reference

All functions