Data Analysis in R: Summary and Assessment

Descriptive analysis is a core part of clinical research data management. It helps the study team understand enrollment, completeness, data quality, participant characteristics, safety reporting, follow-up status, and analysis readiness. In R, descriptive analysis can be performed reproducibly using functions such as `count()`, `summarise()`, `group_by()`, `tabyl()`, and table-building packages such as `gtsummary`. The chapter emphasized that descriptive summaries must be interpreted in context. Counts and percentages depend on denominators. Numeric summaries depend on distributions, missing values, and units. Cross-tabulations depend on the correct unit of observation and grouping variables. Missing data should be visible when they are relevant to data management decisions. Learners should now be able to create categorical summaries, numeric summaries, grouped summaries, cross-tabulations, and simple report-ready tables. They should also understand common risks: wrong denominators, silent exclusion of missing values, accidental row multiplication after joins, unexpected categories, unit inconsistencies, and overinterpretation of small samples. The broader lesson is that descriptive analysis is not merely calculation. It is a disciplined way of making study data visible so that the research team can act responsibly.