R Viva Insights Cheat Sheet for Activity Aggregation and Privacy
A practical R reference for activity data, time windows, grouped metrics, work-pattern analysis and privacy-aware reporting.
What this reference covers
This sheet uses R data frames and grouped workflows to organize activity dates, time windows and work-pattern metrics. A clear script can separate cleaning, definition, aggregation and export so the same periods can be rerun and reviewed.
Statistics and privacy must be considered together. A time zone, workday rule or group label can change a result, while overly fine grouping can reveal a person. Use group-level outputs, retain coverage and sample size, and do not treat the result as an efficiency score or an automatic decision.
A reproducible reporting workflow
Write a field dictionary and metric definition, run the grouped calculation on a sanitized sample, compare equivalent periods, and remove fine-grained identifiers and mappings before export.
Maintenance note
This reference does not replace an organization privacy policy, authorization process or employee notice. Activity records may be sensitive and should follow minimization, access, sanitization, retention and purpose limits. Content review date: 2026-08-23.
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R Viva Insights work-pattern study guide
Define authorized fields and metrics, build a repeatable grouped calculation, compare equivalent periods and apply privacy thresholds before exporting results.
Before you start
- Know R data frames, date-time values, grouping and summaries.
- Prepare authorized, sanitized example data without direct identifiers.
- Write the field dictionary, time zone and reporting purpose before coding.
Quick start
- 01
Inspect input and definitions
Confirm time fields, time zone, activity type, group labels and coverage period, and record what each field represents.
- 02
Build a repeatable summary
Calculate grouped metrics by a documented time window while retaining sample size, missing count and data version.
- 03
Compare equivalent periods
Use identical workday, time zone and group rules, marking holidays and organizational changes instead of mixing them into a normal period.
- 04
Export privacy-aware results
Apply minimum-group thresholds, sanitization and access control, and publish only the aggregate conclusion needed for the question.
Usage tips
- Keep metric definitions in the script and report rather than relying on chart titles alone.
- Mark unusual weeks, holidays and organizational changes so they are not interpreted as ordinary behavior.
- Store raw data and result tables separately, and limit mapping access to the smallest necessary group.
Troubleshooting and uninstall
Why does a summary disagree with another report?
Compare time zone, date boundaries, activity filters, group labels and missing-value handling, then reproduce one small period step by step.
How can a result table be less identifiable?
Raise the minimum-group threshold, merge periods or remove fine-grained dimensions, then reassess combinations of fields for re-identification.
Frequently asked questions
What work fits the R Viva Insights cheat sheet?
It supports time aggregation, work-pattern exploration and group-level review of authorized activity data using R.
Why retain sample size and missing counts?
They indicate whether a metric is stable and coverage is sufficient, reducing the chance of reading a data gap as a behavior change.
Should reports show individual rankings?
Group aggregation is the preferred default; remove direct identifiers and apply minimum-group and organizational privacy requirements before publication.