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