Computes descriptive statistics for a numeric outcome variable stratified by a grouping factor, providing both a comparison table and a narrative highlighting which group has the highest/lowest mean and variability.
Examples
result <- easy_group_summary(mpg ~ cyl, data = mtcars)
print(result)
#>
#> ================================================================================
#> EasyStat Result :: GROUP_SUMMARY
#> ================================================================================
#>
#> TABLE 1 — MAIN RESULTS
#> --------------------------------------------------------------------------------
#> Group N Mean Median SD SE Min Max IQR CV_pct Skewness CI_lower
#> 6 7 19.7429 19.7 1.4536 0.5494 17.8 21.4 2.35 7.3625 -0.2586 18.3985
#> 4 11 26.6636 26.0 4.5098 1.3598 21.4 33.9 7.60 16.9138 0.3485 23.6339
#> 8 14 15.1000 15.2 2.5600 0.6842 10.4 19.2 1.85 16.9540 -0.4558 13.6219
#> CI_upper
#> 21.0872
#> 29.6934
#> 16.5781
#>
#> TABLE 2 — MODEL FIT / SUMMARY
#> --------------------------------------------------------------------------------
#> Metric Value
#> Outcome variable mpg
#> Grouping variable cyl
#> Number of groups 3
#> Overall Mean 20.0906
#> Overall SD 6.0269
#> Overall Median 19.2
#>
#> ================================================================================
#> PLAIN-LANGUAGE INTERPRETATION
#> ================================================================================
#>
#> GROUP SUMMARY: mpg by cyl
#>
#> Descriptive statistics were computed for 'mpg' across 3 groups of 'cyl'. The
#> group with the highest mean is '4' (M = 26.6636), while the group with the
#> lowest mean is '8' (M = 15.1). The group with the greatest variability
#> (highest SD) is '4' (SD = 4.5098). Overall, the grand mean across all
#> groups is 20.0906 (SD = 6.0269, Median = 19.2). These group-level
#> statistics provide the foundation for inferential comparisons using ANOVA
#> or t-tests.
#>
#> ================================================================================
#>