{ "_id": "6a0f6d4bacfb0bcc41c5eb7f", "Package": "wpa", "Type": "Package", "Title": "Tools for Analysing and Visualising Viva Insights Data", "Version": "1.10.1", "Authors@R": "c(\nperson(given = \"Martin\", family = \"Chan\", role = c(\"aut\", \"cre\"), email = \"martin.chan@microsoft.com\"),\nperson(given = \"Carlos\", family = \"Morales\", role = \"aut\", email = \"carlos.morales@microsoft.com\"),\nperson(given = \"Mark\", family = \"Powers\", role = \"ctb\", email = \"mark.powers@microsoft.com\"),\nperson(given = \"Ainize\", family = \"Cidoncha\", role = \"ctb\", email = \"ainize.cidoncha@microsoft.com\"),\nperson(given = \"Rosamary\", family = \"Ochoa Vargas\", role = \"ctb\", email = \"rosamary.ochoa@microsoft.com\"),\nperson(given = \"Tannaz\", family = \"Sattari\", role = \"ctb\", email = \"tannaz.sattari@microsoft.com\"),\nperson(given = \"Lucas\", family = \"Hogner\", role = \"ctb\", email = \"lucas.hogner@microsoft.com\"),\nperson(given = \"Jasminder\", family = \"Thind\", role = \"ctb\", email = \"jasminder.thind@microsoft.com\"),\nperson(given = \"Simone\", family = \"Liebal\", role = \"ctb\", email = \"simone.liebal@microsoft.com\"),\nperson(given = \"Aleksey\", family = \"Ashikhmin\", role = \"ctb\", email = \"alashi@microsoft.com\"),\nperson(given = \"Ellen\", family = \"Trinklein\", role = \"ctb\"),\nperson(given = \"Microsoft Corporation\", role = \"cph\")\n)", "Description": "Opinionated functions that enable easier and faster\nanalysis of Viva Insights data. There are three main types of\nfunctions in 'wpa': (i) Standard functions create a 'ggplot'\nvisual or a summary table based on a specific Viva Insights\nmetric; (2) Report Generation functions generate HTML reports\non a specific analysis area, e.g. Collaboration; (3) Other\nmiscellaneous functions cover more specific applications (e.g.\nSubject Line text mining) of Viva Insights data. This package\nadheres to 'tidyverse' principles and works well with the pipe\nsyntax. 'wpa' is built with the beginner-to-intermediate R\nusers in mind, and is optimised for simplicity.", "URL": "https://github.com/microsoft/wpa/,\nhttps://microsoft.github.io/wpa/", "BugReports": "https://github.com/microsoft/wpa/issues/", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "LazyData": "true", "RoxygenNote": "7.3.2", "Roxygen": "list(markdown = TRUE)", "Language": "en-US", "Config/testthat/edition": "3", "Config/pak/sysreqs": "cmake libfontconfig1-dev libfreetype6-dev\nlibglpk-dev make libicu-dev libjpeg-dev libpng-dev libuv1-dev\nlibxml2-dev libssl-dev", "Repository": "https://microsoft.r-universe.dev", "Date/Publication": "2026-01-19 09:37:01 UTC", "RemoteUrl": "https://github.com/microsoft/wpa", "RemoteRef": "HEAD", "RemoteSha": "e8f0f0700160ffd98a39897906832989d2ba61f1", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-21 08:06:24 UTC", "User": "root" }, "Author": "Martin Chan [aut, cre],\nCarlos Morales [aut],\nMark Powers [ctb],\nAinize Cidoncha [ctb],\nRosamary Ochoa Vargas [ctb],\nTannaz Sattari [ctb],\nLucas Hogner [ctb],\nJasminder Thind [ctb],\nSimone Liebal [ctb],\nAleksey Ashikhmin [ctb],\nEllen Trinklein [ctb],\nMicrosoft Corporation [cph]", "Maintainer": "Martin Chan ", "MD5sum": "9d6c7aa9684d27acbffa9c7d1acdabf6", "_user": "microsoft", "_type": "src", "_file": "wpa_1.10.1.tar.gz", "_fileid": "ab10c714da6cd9e2033f80298436b8f905e8a506949fdef5a0a8193609a594f2", "_filesize": 2280338, "_sha256": "ab10c714da6cd9e2033f80298436b8f905e8a506949fdef5a0a8193609a594f2", "_created": "2026-05-21T08:06:24.000Z", "_published": "2026-05-21T20:38:35.125Z", "_distro": "noble", "_jobs": [ { "job": 77263905713, "time": 313, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7130556332" }, { "job": 77263906271, "time": 253, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7130536685" }, { "job": 77263905688, "time": 226, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "7130527937" }, { "job": 77263905884, "time": 229, "config": "macos-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7130528724" }, { "job": 77263905501, "time": 286, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7130453184" }, { "job": 77263905074, "time": 141, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7146639961" }, { "job": 77263905876, "time": 268, "config": "windows-devel", "r": "4.7.0", "check": "OK", "artifact": "7130541719" }, { "job": 77263905963, "time": 262, "config": "windows-oldrel", "r": "4.5.3", "check": "OK", "artifact": "7130539522" }, { "job": 77263905990, "time": 261, "config": "windows-release", "r": "4.6.0", "check": "OK", "artifact": "7130539321" } ], "_buildurl": "https://github.com/r-universe/microsoft/actions/runs/26213481155", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/microsoft/wpa", "_commit": { "id": "e8f0f0700160ffd98a39897906832989d2ba61f1", "author": "Martin Chan ", "committer": "GitHub ", "message": "Merge pull request #248 from DavisVaughan/fix/dplyr\n\nAdd `id` global", "time": 1768815421 }, "_maintainer": { "name": "Martin Chan", "email": "martin.chan@microsoft.com" }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 3.1.2", "role": "Depends" }, { "package": "dplyr", "role": "Imports" }, { "package": "stats", "role": "Imports" }, { "package": "utils", "role": "Imports" }, { "package": "tidyr", "role": "Imports" }, { "package": "tidyselect", "version": ">= 1.0.0", "role": "Imports" }, { "package": "magrittr", "role": "Imports" }, { "package": "purrr", "role": "Imports" }, { "package": "reshape2", "role": "Imports" }, { "package": "ggplot2", "role": "Imports" }, { "package": "ggrepel", "role": "Imports" }, { "package": "scales", "role": "Imports" }, { "package": "htmltools", "role": "Imports" }, { "package": "markdown", "role": "Imports" }, { "package": "rmarkdown", "role": "Imports" }, { "package": "networkD3", "role": "Imports" }, { "package": "DT", "role": "Imports" }, { "package": "tidytext", "role": "Imports" }, { "package": "ggraph", "role": "Imports" }, { "package": "igraph", "role": "Imports" }, { "package": "proxy", "role": "Imports" }, { "package": "ggwordcloud", "role": "Imports" }, { "package": "methods", "role": "Imports" }, { "package": "data.table", "role": "Imports" }, { "package": "knitr", "role": "Suggests" }, { "package": "extrafont", "role": "Suggests" }, { "package": "lifecycle", "role": "Suggests" }, { "package": "fst", "role": "Suggests" }, { "package": "glue", "role": "Suggests" }, { "package": "flexdashboard", "role": "Suggests" }, { "package": "lmtest", "role": "Suggests" }, { "package": "sandwich", "role": "Suggests" }, { "package": "testthat", "version": ">= 3.0.0", "role": "Suggests" } ], "_owner": "microsoft", "_selfowned": true, "_usedby": 1, "_updates": [ { "week": "2025-22", "n": 2 }, { "week": "2025-32", "n": 2 }, { "week": "2025-35", "n": 3 }, { "week": "2026-04", "n": 1 } ], "_tags": [ { "name": "v1.9.2", "date": "2025-05-28" }, { "name": "v1.10.0", "date": "2025-08-26" }, { "name": "v1.10.1", "date": "2026-01-19" } ], "_topics": [ "workplace-analytics" ], "_stars": 32, "_contributors": [ { "user": "martinctc", "count": 1134, "uuid": 17925865 }, { "user": "moralec", "count": 191, "uuid": 62895857 }, { "user": "copilot", "count": 19, "uuid": 198982749 }, { "user": "m-m-powers", "count": 13, "uuid": 63008952 }, { "user": "jukasper", "count": 8, "uuid": 42241691 }, { "user": "alashi-ms", "count": 7, "uuid": 80425523 }, { "user": "davisvaughan", "count": 4, "uuid": 19150088 }, { "user": "olivroy", "count": 1, "uuid": 52606734 } ], "_userbio": { "uuid": 6154722, "type": "organization", "name": "Microsoft", "description": "Open source projects and samples from Microsoft" }, "_downloads": { "count": 1396, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/wpa" }, "_devurl": "https://github.com/microsoft/wpa", "_pkgdown": "https://microsoft.github.io/wpa/", "_searchresults": 42, "_rbuild": "4.6.0", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/NEWS.html", "extra/NEWS.txt", "extra/readme.html", "extra/readme.md", "extra/wpa.html", "manual.pdf" ], "_homeurl": "https://github.com/microsoft/wpa", "_realowner": "microsoft", "_cranurl": true, "_releases": [ { "version": "1.4.3", "date": "2021-04-06" }, { "version": "1.5.0", "date": "2021-05-11" }, { "version": "1.6.0", "date": "2021-07-06" }, { "version": "1.6.1", "date": "2021-09-01" }, { "version": "1.6.2", "date": "2021-10-15" }, { "version": "1.6.3", "date": "2021-11-21" }, { "version": "1.6.4", "date": "2022-01-19" }, { "version": "1.7.0", "date": "2022-06-09" }, { "version": "1.8.0", "date": "2022-07-05" }, { "version": "1.8.1", "date": "2023-01-30" }, { "version": "1.9.0", "date": "2023-08-21" }, { "version": "1.9.1", "date": "2024-06-06" }, { "version": 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"create_line", "create_line_asis", "create_period_scatter", "create_rank", "create_rank_combine", "create_sankey", "create_scatter", "create_stacked", "create_tracking", "create_trend", "cut_hour", "email_dist", "email_fizz", "email_line", "email_rank", "email_sum", "email_summary", "email_trend", "export", "external_dist", "external_fizz", "external_line", "external_network_plot", "external_rank", "external_sum", "external_summary", "extract_date_range", "extract_hr", "flag_ch_ratio", "flag_em_ratio", "flag_extreme", "flag_outlooktime", "flex_index", "g2g_network", "generate_report", "generate_report2", "GetResiduals", "heat_colors", "heat_colours", "hr_trend", "hrvar_count", "hrvar_count_all", "hrvar_trend", "identify_churn", "identify_datefreq", "identify_holidayweeks", "identify_inactiveweeks", "identify_nkw", "identify_outlier", "identify_privacythreshold", "identify_query", "identify_shifts", "identify_shifts_wp", "identify_tenure", "import_to_fst", "import_wpa", "internal_network_plot", "is_date_format", "IV_by_period", "IV_report", "jitter_metrics", "keymetrics_scan", "keymetrics_scan_asis", "LjungBox", "maxmin", "meeting_dist", "meeting_extract", "meeting_fizz", "meeting_line", "meeting_quality", "meeting_rank", "meeting_skim", "meeting_sum", "meeting_summary", "meeting_tm_report", "meeting_trend", "meetingtype_dist", "meetingtype_dist_ca", "meetingtype_dist_mt", "meetingtype_sum", "meetingtype_summary", "mgrcoatt_dist", "mgrrel_matrix", "network_describe", "network_g2g", "network_p2p", "network_summary", "one2one_dist", "one2one_fizz", "one2one_freq", "one2one_line", "one2one_rank", "one2one_sum", "one2one_summary", "one2one_trend", "p_test", "p2p_data_sim", "pad2", "pairwise_count", "period_change", "personas_hclust", "plot_flex_index", "plot_hourly_pat", "plot_WOE", "read_preamble", "remove_outliers", "rgb2hex", "standardise_pq", "standardize_pq", "subject_classify", "subject_scan", "subject_validate", "subject_validate_report", "theme_wpa", "theme_wpa_basic", "tm_clean", "tm_cooc", "tm_freq", "tm_scan", "tm_wordcloud", "totals_bind", "totals_col", "totals_reorder", "track_HR_change", "tstamp", "us_to_space", "validation_report", "wellbeing_report", "workloads_dist", "workloads_fizz", "workloads_line", "workloads_rank", "workloads_sum", "workloads_summary", "workloads_trend", "workpatterns_area", "workpatterns_classify", "workpatterns_hclust", "workpatterns_rank", "workpatterns_report", "wrap", "wrap_text" ], "_datasets": [ { "name": "dv_data", "title": "Sample Standard Person Query dataset for Data Validation", "object": "dv_data", "class": [ "data.frame" ], "fields": [ "PersonId", "Date", "Workweek_span", "Meetings_with_skip_level", "Meeting_hours_with_skip_level", "Generated_workload_email_hours", "Generated_workload_email_recipients", "Generated_workload_instant_messages_hours", "Generated_workload_instant_messages_recipients", "Generated_workload_call_hours", "Generated_workload_call_participants", "Generated_workload_calls_organized", "External_network_size", "Internal_network_size", "Networking_outside_company", "Networking_outside_organization", "After_hours_meeting_hours", "Open_1_hour_block", "Open_2_hour_blocks", "Total_focus_hours", "Low_quality_meeting_hours", "Total_emails_sent_during_meeting", "Meetings", "Meeting_hours", "Conflicting_meeting_hours", "Multitasking_meeting_hours", "Redundant_meeting_hours__lower_level_", "Redundant_meeting_hours__organizational_", "Time_in_self_organized_meetings", "Meeting_hours_during_working_hours", "Generated_workload_meeting_attendees", "Generated_workload_meeting_hours", "Generated_workload_meetings_organized", "Manager_coaching_hours_1_on_1", "Meetings_with_manager", "Meeting_hours_with_manager", "Meetings_with_manager_1_on_1", "Meeting_hours_with_manager_1_on_1", "After_hours_email_hours", "Emails_sent", "Email_hours", "Working_hours_email_hours", "After_hours_instant_messages", "Instant_messages_sent", "Instant_Message_hours", "Working_hours_instant_messages", "After_hours_collaboration_hours", "Collaboration_hours", "Collaboration_hours_external", "Working_hours_collaboration_hours", "After_hours_in_calls", "Total_calls", "Call_hours", "Working_hours_in_calls", "Domain", "FunctionType", "LevelDesignation", "Layer", "Region", "Organization", "zId", "attainment", "TimeZone", "HourlyRate", "IsInternal", "IsActive", "HireDate", "WorkingStartTimeSetInOutlook", "WorkingEndTimeSetInOutlook" ], "rows": 877, "table": true, "tojson": true }, { "name": "em_data", "title": "Sample Hourly Collaboration data", "object": "em_data", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "PersonId", "Date", "Unscheduled_calls_23_24", "Unscheduled_calls_22_23", "Unscheduled_calls_21_22", "Unscheduled_calls_20_21", "Unscheduled_calls_19_20", "Unscheduled_calls_18_19", "Unscheduled_calls_17_18", "Unscheduled_calls_16_17", "Unscheduled_calls_15_16", "Unscheduled_calls_14_15", "Unscheduled_calls_13_14", "Unscheduled_calls_12_13", "Unscheduled_calls_11_12", "Unscheduled_calls_10_11", "Unscheduled_calls_09_10", "Unscheduled_calls_08_09", "Unscheduled_calls_07_08", "Unscheduled_calls_06_07", "Unscheduled_calls_05_06", "Unscheduled_calls_04_05", "Unscheduled_calls_03_04", "Unscheduled_calls_02_03", "Unscheduled_calls_01_02", "Unscheduled_calls_00_01", "IMs_sent_23_24", "IMs_sent_22_23", "IMs_sent_21_22", "IMs_sent_20_21", "IMs_sent_19_20", "IMs_sent_18_19", "IMs_sent_17_18", "IMs_sent_16_17", "IMs_sent_15_16", "IMs_sent_14_15", "IMs_sent_13_14", "IMs_sent_12_13", "IMs_sent_11_12", "IMs_sent_10_11", "IMs_sent_09_10", "IMs_sent_08_09", "IMs_sent_07_08", "IMs_sent_06_07", "IMs_sent_05_06", "IMs_sent_04_05", "IMs_sent_03_04", "IMs_sent_02_03", "IMs_sent_01_02", "IMs_sent_00_01", "Emails_sent_23_24", "Emails_sent_22_23", "Emails_sent_21_22", "Emails_sent_20_21", "Emails_sent_19_20", "Emails_sent_18_19", "Emails_sent_17_18", "Emails_sent_16_17", "Emails_sent_15_16", "Emails_sent_14_15", "Emails_sent_13_14", "Emails_sent_12_13", "Emails_sent_11_12", "Emails_sent_10_11", "Emails_sent_09_10", "Emails_sent_08_09", "Emails_sent_07_08", "Emails_sent_06_07", "Emails_sent_05_06", "Emails_sent_04_05", "Emails_sent_03_04", "Emails_sent_02_03", "Emails_sent_01_02", "Emails_sent_00_01", "Meetings_23_24", "Meetings_22_23", "Meetings_21_22", "Meetings_20_21", "Meetings_19_20", "Meetings_18_19", "Meetings_17_18", "Meetings_16_17", "Meetings_15_16", "Meetings_14_15", "Meetings_13_14", "Meetings_12_13", "Meetings_11_12", "Meetings_10_11", "Meetings_09_10", "Meetings_08_09", "Meetings_07_08", "Meetings_06_07", "Meetings_05_06", "Meetings_04_05", "Meetings_03_04", "Meetings_02_03", "Meetings_01_02", "Meetings_00_01", "LevelDesignation", "Organization", "TimeZone", "IsActive", "WorkingStartTimeSetInOutlook", "WorkingEndTimeSetInOutlook", "WorkingDaysSetInOutlook" ], "rows": 2000, "table": true, "tojson": true }, { "name": "g2g_data", "title": "Sample Group-to-Group dataset", "object": "g2g_data", "class": [ "spec_tbl_df", "tbl_df", "tbl", "data.frame" ], "fields": [ "TimeInvestors_Organization", "Collaborators_Organization", "Date", "Meetings", "Meeting_hours", "Email_hours", "Collaboration_hours" ], "rows": 1417, "table": true, "tojson": true }, { "name": "mt_data", "title": "Sample Meeting Query dataset", "object": "mt_data", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "MeetingId", "StartDate", "StartTimeUTC", "EndDate", "EndTimeUTC", "Attendee_meeting_hours", "Attendees", "Organizer_Domain", "Organizer_FunctionType", "Organizer_LevelDesignation", "Organizer_Layer", "Organizer_Region", "Organizer_Organization", "Organizer_zId", "Organizer_attainment", "Organizer_TimeZone", "Organizer_HourlyRate", "Organizer_IsInternal", "Organizer_PersonId", "IsCancelled", "DurationHours", "IsRecurring", "Subject", "TotalAccept", "TotalNoResponse", "TotalDecline", "TotalNoEmailsDuringMeeting", "TotalNoDoubleBooked", "TotalNoAttendees", "MeetingResources", "Attendees_with_conflicting_meetings", "Invitees", "Emails_sent_during_meetings", "Attendees_multitasking", "Redundant_attendees", "Total_meeting_cost", "Total_redundant_hours" ], "rows": 2001, "table": true, "tojson": true }, { "name": "sq_data", "title": "Sample Standard Person Query dataset", "object": "sq_data", "class": [ "spec_tbl_df", "tbl_df", "tbl", "data.frame" ], "fields": [ "PersonId", "Date", "Workweek_span", "Meetings_with_skip_level", "Meeting_hours_with_skip_level", "Generated_workload_email_hours", "Generated_workload_email_recipients", "Generated_workload_instant_messages_hours", "Generated_workload_instant_messages_recipients", "Generated_workload_call_hours", "Generated_workload_call_participants", "Generated_workload_calls_organized", "External_network_size", "Internal_network_size", "Networking_outside_company", "Networking_outside_organization", "After_hours_meeting_hours", "Open_1_hour_block", "Open_2_hour_blocks", "Total_focus_hours", "Low_quality_meeting_hours", "Total_emails_sent_during_meeting", "Meetings", "Meeting_hours", "Conflicting_meeting_hours", "Multitasking_meeting_hours", "Redundant_meeting_hours__lower_level_", "Redundant_meeting_hours__organizational_", "Time_in_self_organized_meetings", "Meeting_hours_during_working_hours", "Generated_workload_meeting_attendees", "Generated_workload_meeting_hours", "Generated_workload_meetings_organized", "Manager_coaching_hours_1_on_1", "Meetings_with_manager", "Meeting_hours_with_manager", "Meetings_with_manager_1_on_1", "Meeting_hours_with_manager_1_on_1", "After_hours_email_hours", "Emails_sent", "Email_hours", "Working_hours_email_hours", "After_hours_instant_messages", "Instant_messages_sent", "Instant_Message_hours", "Working_hours_instant_messages", "After_hours_collaboration_hours", "Collaboration_hours", "Collaboration_hours_external", "Working_hours_collaboration_hours", "After_hours_in_calls", "Total_calls", "Call_hours", "Working_hours_in_calls", "Domain", "FunctionType", "LevelDesignation", "Layer", "Region", "Organization", "zId", "attainment", "TimeZone", "HourlyRate", "IsInternal", "IsActive" ], "rows": 4403, "table": true, "tojson": true } ], "_help": [ { "page": "afterhours_dist", "title": "Distribution of After-hours Collaboration Hours as a 100% stacked bar", "concept": [ "After-hours Collaboration", "Visualization" ], "topics": [ "afterhours_dist" ] }, { "page": "afterhours_fizz", "title": "Distribution of After-hours Collaboration Hours (Fizzy Drink plot)", "concept": [ "After-hours Collaboration", "Visualization" ], "topics": [ "afterhours_fizz" ] }, { "page": "afterhours_line", "title": "After-hours Collaboration Time Trend - Line Chart", "concept": [ "After-hours Collaboration", "Visualization" ], "topics": [ "afterhours_line" ] }, { "page": "afterhours_rank", "title": "Rank groups with high After-Hours Collaboration Hours", "concept": [ "After-hours Collaboration", "Visualization" ], "topics": [ "afterhours_rank" ] }, { "page": "afterhours_summary", "title": "Summary of After-Hours Collaboration Hours", "concept": [ "After-hours Collaboration", "Visualization" ], "topics": [ "afterhours_sum", "afterhours_summary" ] }, { "page": "afterhours_trend", "title": "After-Hours Time Trend", "concept": [ "After-hours Collaboration", "Visualization" ], "topics": [ "afterhours_trend" ] }, { "page": "anonymise", "title": "Anonymise a categorical variable by replacing values", "topics": [ "anonymise", "anonymize" ] }, { "page": "calculate_IV", "title": "Calculate Weight of Evidence (WOE) and Information Value (IV) between a single predictor and a single outcome variable.", "topics": [ "calculate_IV" ] }, { "page": "camel_clean", "title": "Convert \"CamelCase\" to \"Camel Case\"", "concept": [ "Support" ], "topics": [ "camel_clean" ] }, { "page": "capacity_report", "title": "Generate a Capacity report in HTML", "concept": [ "Reports" ], "topics": [ "capacity_report" ] }, { "page": "check_inputs", "title": "Check whether a data frame contains all the required variable", "concept": [ "Support" ], "topics": [ "check_inputs" ] }, { "page": "check_query", "title": "Check a query to ensure that it is suitable for analysis", "concept": [ "Data Validation" ], "topics": [ "check_query" ] }, { "page": "coaching_report", "title": "Generate a Coaching report in HTML", "concept": [ "Reports" ], "topics": [ "coaching_report" ] }, { "page": "collaboration_area", "title": "Collaboration - Stacked Area Plot", "concept": [ "Collaboration", "Visualization" ], "topics": [ "collaboration_area", "collab_area" ] }, { "page": "collaboration_dist", "title": "Distribution of Collaboration Hours as a 100% stacked bar", "concept": [ "Collaboration", "Visualization" ], "topics": [ "collaboration_dist", "collab_dist" ] }, { "page": "collaboration_fizz", "title": "Distribution of Collaboration Hours (Fizzy Drink plot)", "concept": [ "Collaboration", "Visualization" ], "topics": [ "collaboration_fizz", "collab_fizz" ] 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"topics": [ "combine_signals" ] }, { "page": "comma", "title": "Add comma separator for thousands", "topics": [ "comma" ] }, { "page": "connectivity_report", "title": "Generate a Connectivity report in HTML", "concept": [ "Reports" ], "topics": [ "connectivity_report" ] }, { "page": "copy_df", "title": "Copy a data frame to clipboard for pasting in Excel", "concept": [ "Import and Export" ], "topics": [ "copy_df" ] }, { "page": "create_bar", "title": "Mean Bar Plot for any metric", "concept": [ "Flexible", "Visualization" ], "topics": [ "create_bar" ] }, { "page": "create_bar_asis", "title": "Create a bar chart without aggregation for any metric", "concept": [ "Flexible", "Visualization" ], "topics": [ "create_bar_asis" ] }, { "page": "create_boxplot", "title": "Box Plot for any metric", "concept": [ "Flexible", "Visualization" ], "topics": [ "create_boxplot" ] }, { "page": "create_bubble", "title": "Create a bubble plot with two selected Viva Insights metrics (General Purpose), with 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scoring above or below a threshold for a metric", "concept": [ "Flexible", "Visualization" ], "topics": [ "create_inc", "create_incidence" ] }, { "page": "create_ITSA", "title": "Estimate an effect of intervention on every Viva Insights metric in input file by applying single-group Interrupted Time-Series Analysis (ITSA)", "concept": [ "Flexible Input", "Interrupted Time-Series Analysis" ], "topics": [ "create_ITSA" ] }, { "page": "create_IV", "title": "Calculate Information Value for a selected outcome variable", "concept": [ "Information Value", "Variable Association" ], "topics": [ "create_IV" ] }, { "page": "create_line", "title": "Time Trend - Line Chart for any metric", "concept": [ "Flexible", "Time-series", "Visualization" ], "topics": [ "create_line" ] }, { "page": "create_line_asis", "title": "Create a line chart without aggregation for any metric", "concept": [ "Flexible", "Time-series", "Visualization" ], "topics": [ "create_line_asis" ] }, { "page": "create_period_scatter", "title": "Period comparison scatter plot for any two metrics", "concept": [ "Flexible", "Time-series", "Visualization" ], "topics": [ "create_period_scatter" ] }, { "page": "create_rank", "title": "Rank all groups across HR attributes on a selected Viva Insights metric", "concept": [ "Flexible", "Visualization" ], "topics": [ "create_rank" ] }, { "page": "create_rank_combine", "title": "Create combination pairs of HR variables and run 'create_rank()'", "topics": [ "create_rank_combine" ] }, { "page": "create_sankey", "title": "Create a sankey chart from a two-column count table", "concept": [ "Flexible", "Visualization" ], "topics": [ "create_sankey" ] }, { "page": "create_scatter", "title": "Create a Scatter plot with two selected Viva Insights metrics (General Purpose)", "concept": [ "Flexible", "Visualization" ], "topics": [ "create_scatter" ] }, { "page": "create_stacked", "title": "Horizontal stacked bar plot for any metric", "concept": [ "Flexible", "Visualization" ], "topics": [ "create_stacked" ] }, { "page": "create_tracking", "title": "Create a line chart that tracks metrics over time with a 4-week rolling average", "concept": [ "Flexible", "Visualization" ], "topics": [ "create_tracking" ] }, { "page": "create_trend", "title": "Heat mapped horizontal bar plot over time for any metric", "concept": [ "Flexible", "Time-series", "Visualization" ], "topics": [ "create_trend" ] }, { "page": "cut_hour", "title": "Convert a numeric variable for hours into categorical", "concept": [ "Support" ], "topics": [ "cut_hour" ] }, { "page": "dv_data", "title": "Sample Standard Person Query dataset for Data Validation", "concept": [ "Data" ], "topics": [ "dv_data" ] }, { "page": "em_data", "title": "Sample Hourly Collaboration data", "concept": [ "Data" ], "topics": [ "em_data" ] }, { "page": "email_dist", "title": "Distribution of Email Hours as a 100% stacked bar", "concept": [ "Emails", "Visualization" ], "topics": [ "email_dist" ] }, { "page": "email_fizz", "title": "Distribution of Email Hours (Fizzy Drink plot)", "concept": [ "Emails", "Visualization" ], "topics": [ "email_fizz" ] }, { "page": "email_line", "title": "Email Time Trend - Line Chart", "concept": [ "Emails", "Visualization" ], "topics": [ "email_line" ] }, { "page": "email_rank", "title": "Email Hours Ranking", "concept": [ "Emails", "Visualization" ], "topics": [ "email_rank" ] }, { "page": "email_summary", "title": "Email Summary", "concept": [ "Emails", "Visualization" ], "topics": [ "email_sum", "email_summary" ] }, { "page": "email_trend", "title": "Email Hours Time Trend", "concept": [ "Emails", "Visualization" ], "topics": [ "email_trend" ] }, { "page": "export", "title": "Export 'wpa' outputs to CSV, clipboard, or save as images", "concept": [ "Import and Export" ], "topics": [ "export" ] }, { "page": "external_dist", "title": "Distribution of External Collaboration Hours as a 100% stacked bar", "concept": [ "External Collaboration", "Visualization" ], "topics": [ "external_dist" ] }, { "page": "external_fizz", "title": "Distribution of External Collaboration Hours (Fizzy Drink plot)", "concept": [ "External Collaboration", "Visualization" ], "topics": [ "external_fizz" ] }, { "page": "external_line", "title": "External Collaboration Hours Time Trend - Line Chart", "concept": [ "External Collaboration", "Visualization" ], "topics": [ "external_line" ] }, { "page": "external_network_plot", "title": "Plot External Network Breadth and Size as a scatter plot", "concept": [ "Network", "Visualization" ], "topics": [ "external_network_plot" ] }, { "page": "external_rank", "title": "Rank groups with high External Collaboration Hours", "concept": [ "After-hours Collaboration", "Visualization" ], "topics": [ "external_rank" ] }, { "page": "external_sum", "title": "External Collaboration Summary", "concept": [ "External Collaboration", "Visualization" ], "topics": [ "external_sum", "external_summary" ] }, { "page": "extract_date_range", "title": "Extract date period", "concept": [ "Support" ], "topics": [ "extract_date_range" ] }, { "page": "extract_hr", "title": "Extract HR attribute variables", "concept": [ "Data Validation", "Support" ], "topics": [ "extract_hr" ] }, { "page": "flag_ch_ratio", "title": "Flag unusual high collaboration hours to after-hours collaboration hours ratio", "concept": [ "Data Validation" ], "topics": [ "flag_ch_ratio" ] }, { "page": "flag_em_ratio", "title": "Flag Persons with unusually high Email Hours to Emails Sent ratio", "concept": [ "Data Validation" ], "topics": [ "flag_em_ratio" ] }, { "page": "flag_extreme", "title": "Warn for extreme values by checking against a threshold", "concept": [ "Data Validation" ], "topics": [ "flag_extreme" ] }, { "page": "flag_outlooktime", "title": "Flag unusual outlook time settings for work day start and end time", "concept": [ "Data Validation" ], "topics": [ "flag_outlooktime" ] }, { "page": "flex_index", "title": "Compute a Flexibility Index based on the Hourly Collaboration Query", "concept": [ "Working Patterns" ], "topics": [ "flex_index" ] }, { "page": "g2g_data", "title": "Sample Group-to-Group dataset", "concept": [ "Data", "Network" ], "topics": [ "g2g_data" ] }, { "page": "generate_report", "title": "Generate HTML report with list inputs", "concept": [ "Reports" ], "topics": [ "generate_report" ] }, { "page": "generate_report2", "title": "Generate HTML report based on existing RMarkdown documents", "topics": [ "generate_report2" ] }, { "page": "GetResiduals", "title": "Extract Residuals from ARIMA, VAR, or any Simulated Fitted Time Series Model", "topics": [ "GetResiduals" ] }, { "page": "heat_colours", "title": "Generate a vector of 'n' contiguous colours, as a red-yellow-green palette.", "concept": [ "Support" ], "topics": [ "heat_colors", "heat_colours" ] }, { "page": "hr_trend", "title": "Employee count over time", "concept": [ "Data Validation", "Visualization" ], "topics": [ "hr_trend" ] }, { "page": "hrvar_count", "title": "Create a count of distinct people in a specified HR variable", "concept": [ "Data Validation", "Visualization" ], "topics": [ "analysis_scope", "hrvar_count" ] }, { "page": "hrvar_count_all", "title": "Create count of distinct fields and percentage of employees with missing values for all HR variables", "concept": [ "Data Validation" ], "topics": [ "hrvar_count_all" ] }, { "page": "hrvar_trend", "title": "Track count of distinct people over time in a specified HR variable", "concept": [ "Data Validation", "Visualization" ], "topics": [ "hrvar_trend" ] }, { "page": "identify_churn", "title": "Identify employees who have churned from the dataset", "concept": [ "Data Validation" ], "topics": [ "identify_churn" ] }, { "page": "identify_datefreq", "title": "Identify date frequency based on a series of dates", "topics": [ "identify_datefreq" ] }, { "page": "identify_holidayweeks", "title": "Identify Holiday Weeks based on outliers", "concept": [ "Data 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"Data Validation", "Working Patterns" ], "topics": [ "identify_shifts" ] }, { "page": "identify_shifts_wp", "title": "Identify shifts based on binary activity", "concept": [ "Data Validation", "Working Patterns" ], "topics": [ "identify_shifts_wp" ] }, { "page": "identify_tenure", "title": "Tenure calculation based on different input dates, returns data summary table or histogram", "concept": [ "Data Validation" ], "topics": [ "identify_tenure" ] }, { "page": "import_to_fst", "title": "Read a Workplace Analytics query in '.csv' using and create a '.fst' file in the same directory for faster reading", "concept": [ "Import and Export" ], "topics": [ "import_to_fst" ] }, { "page": "import_wpa", "title": "Import a Workplace Analytics Query", "concept": [ "Import and Export" ], "topics": [ "import_wpa" ] }, { "page": "internal_network_plot", "title": "Plot Internal Network Breadth and Size as a scatter plot", "concept": [ "Network", "Visualization" ], "topics": [ "internal_network_plot" ] }, { "page": "is_date_format", "title": "Identify whether string is a date format", "concept": [ "Support" ], "topics": [ "is_date_format" ] }, { "page": "IV_by_period", "title": "Identify the WPA metrics that have the biggest change between two periods.", "concept": [ "Information Value", "Time-series", "Variable Association" ], "topics": [ "IV_by_period" ] }, { "page": "IV_report", "title": "Generate a Information Value HTML Report", "concept": [ "Information Value", "Reports", "Variable Association" ], "topics": [ "IV_report" ] }, { "page": "jitter_metrics", "title": "Jitter metrics in a data frame", "topics": [ "jitter_metrics" ] }, { "page": "keymetrics_scan", "title": "Run a summary of Key Metrics from the Standard Person Query data", "concept": [ "Visualization" ], "topics": [ "keymetrics_scan" ] }, { "page": "keymetrics_scan_asis", "title": "Run a summary of Key Metrics without aggregation", "topics": [ "keymetrics_scan_asis" ] }, { "page": "LjungBox", "title": "Ljung and Box 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}, { "page": "meeting_quality", "title": "Run a meeting habits / meeting quality analysis", "concept": [ "Meetings", "Visualization" ], "topics": [ "meeting_quality" ] }, { "page": "meeting_rank", "title": "Meeting Hours Ranking", "concept": [ "Meetings", "Visualization" ], "topics": [ "meeting_rank" ] }, { "page": "meeting_skim", "title": "Produce a skim summary of meeting hours", "concept": [ "Meetings" ], "topics": [ "meeting_skim" ] }, { "page": "meeting_summary", "title": "Meeting Summary", "concept": [ "Meetings", "Visualization" ], "topics": [ "meeting_sum", "meeting_summary" ] }, { "page": "meeting_tm_report", "title": "Generate a Meeting Text Mining report in HTML", "concept": [ "Meetings", "Reports", "Text-mining" ], "topics": [ "meeting_tm_report" ] }, { "page": "meeting_trend", "title": "Meeting Hours Time Trend", "concept": [ "Meetings", "Visualization" ], "topics": [ "meeting_trend" ] }, { "page": "meetingtype_dist", "title": "Distribution of Meeting Types by number of Attendees and Duration", "concept": [ "Meetings", "Visualization" ], "topics": [ "meetingtype_dist" ] }, { "page": "meetingtype_dist_ca", "title": "Meeting Type Distribution (Ways of Working Assessment Query)", "concept": [ "Meetings", "Visualization" ], "topics": [ "meetingtype_dist_ca" ] }, { "page": "meetingtype_dist_mt", "title": "Meeting Type Distribution (Meeting Query)", "concept": [ "Meetings", "Visualization" ], "topics": [ "meetingtype_dist_mt" ] }, { "page": "meetingtype_summary", "title": "Create a summary bar chart of the proportion of Meeting Hours spent in Long or Large Meetings", "concept": [ "Meetings", "Visualization" ], "topics": [ "meetingtype_sum", "meetingtype_summary" ] }, { "page": "mgrcoatt_dist", "title": "Manager meeting coattendance distribution", "concept": [ "Managerial Relations", "Visualization" ], "topics": [ "mgrcoatt_dist" ] }, { "page": "mgrrel_matrix", "title": "Manager Relationship 2x2 Matrix", "concept": [ "Managerial Relations", "Visualization" ], "topics": [ "mgrrel_matrix" ] }, { "page": "mt_data", "title": "Sample Meeting Query dataset", "concept": [ "Data" ], "topics": [ "mt_data" ] }, { "page": "network_describe", "title": "Uncover HR attributes which best represent a population for a Person to Person query", "concept": [ "Network" ], "topics": [ "network_describe" ] }, { "page": "network_g2g", "title": "Create a network plot with the group-to-group query", "concept": [ "Network" ], "topics": [ "g2g_network", "network_g2g" ] }, { "page": "network_p2p", "title": "Perform network analysis with the person-to-person query", "concept": [ "Network" ], "topics": [ "network_p2p" ] }, { "page": "network_summary", "title": "Summarise node centrality statistics with an igraph object", "concept": [ "Network" ], "topics": [ "network_summary" ] }, { "page": "one2one_dist", "title": "Distribution of Manager 1:1 Time as a 100% stacked bar", "concept": [ "Managerial Relations", "Visualization" ], "topics": [ "one2one_dist" ] }, { "page": "one2one_fizz", "title": "Distribution of Manager 1:1 Time (Fizzy Drink plot)", "concept": [ "Managerial Relations", "Visualization" ], "topics": [ "one2one_fizz" ] }, { "page": "one2one_freq", "title": "Frequency of Manager 1:1 Meetings as bar or 100% stacked bar chart", "concept": [ "Managerial Relations", "Visualization" ], "topics": [ "one2one_freq" ] }, { "page": "one2one_line", "title": "Manager 1:1 Time Trend - Line Chart", "concept": [ "Managerial Relations", "Visualization" ], "topics": [ "one2one_line" ] }, { "page": "one2one_rank", "title": "Manager 1:1 Time Ranking", "concept": [ "Managerial Relations", "Visualization" ], "topics": [ "one2one_rank" ] }, { "page": "one2one_sum", "title": "Manager 1:1 Time Summary", "concept": [ "Managerial Relations", "Visualization" ], "topics": [ "one2one_sum", "one2one_summary" ] }, { "page": "one2one_trend", "title": "Manager 1:1 Time Trend", "concept": [ "Managerial Relations", "Visualization" ], "topics": [ "one2one_trend" ] }, { "page": "p_test", "title": "Calculate the p-value of the null hypothesis that two outcomes are from the same dataset", "concept": [ "Support" ], "topics": [ "p_test" ] }, { "page": "p2p_data_sim", "title": "Simulate a person-to-person query using a Watts-Strogatz model", "concept": [ "Data", "Network" ], "topics": [ "p2p_data_sim" ] }, { "page": "pad2", "title": "Create the two-digit zero-padded format", "topics": [ "pad2" ] }, { "page": "pairwise_count", "title": "Perform a pairwise count of words by id", "concept": [ "Support", "Text-mining" ], "topics": [ "pairwise_count" ] }, { "page": "period_change", "title": "Plot the distribution of percentage change between periods of a Viva Insights metric by the number of employees.", "concept": [ "Flexible", "Flexible Input", "Time-series", "Visualization" ], "topics": [ "period_change" ] }, { "page": "personas_hclust", "title": "Create hierarchical clusters of selected metrics using a Person query", "concept": [ "Clustering" ], "topics": [ "personas_hclust" ] }, { "page": "plot_flex_index", "title": "Plot a Sample of Working Patterns using Flexibility Index output", "concept": [ "Working Patterns" ], "topics": [ "plot_flex_index" ] }, { "page": "plot_hourly_pat", "title": "Internal function for plotting the hourly activity patterns.", "topics": [ "plot_hourly_pat" ] }, { "page": "plot_WOE", "title": "Plot WOE graphs with an IV object", "concept": [ "Information Value", "Support", "Variable Association" ], "topics": [ "plot_WOE" ] }, { "page": "read_preamble", "title": "Read preamble", "concept": [ "Reports", "Support" ], "topics": [ "read_preamble" ] }, { "page": "remove_outliers", "title": "Remove outliers from a person query across time", "concept": [ "Data Validation" ], "topics": [ "remove_outliers" ] }, { "page": "rgb2hex", "title": "Convert rgb to HEX code", "concept": [ "Support" ], "topics": [ "rgb2hex" ] }, { "page": "sq_data", "title": "Sample Standard Person Query dataset", "concept": [ "Data" ], "topics": [ 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