Explore a continuous variable — dist

Summarises a continuous variable, returning a tibble of descriptive statistics and a plot. When a grouping variable is supplied, results are stratified by group.

For two groups, a t-test and Wilcoxon rank-sum test are reported. For three or more groups, a one-way ANOVA and Kruskal-Wallis test are reported.

Usage

dist_sum(data, var, by = NULL)

Arguments

data: The data frame or tibble
var: The continuous variable to summarise
by: Optional grouping variable

Value

A tibble with one row per group (or one row when ungrouped) containing the following columns:

n: Number of non-missing observations.
n_miss: Number of missing (NA) values.
median: Median value.
p25, p75: 25th and 75th percentiles (interquartile range boundaries).
mean: Arithmetic mean.
sd: Standard deviation.
ci_lower, ci_upper: Lower and upper bounds of the 95% confidence interval for the mean. Uses the t-distribution when n < 30, and the Z-distribution when n >= 30.
min, max: Minimum and maximum observed values.
n_outliers: Count of values more than 1.5 x IQR below Q1 or above Q3 (Tukey fence method).
shapiro_p: P-value from the Shapiro-Wilk test of normality. Returns NA when n < 3 or n > 5000 (outside the valid range of the test).
normal: Logical. TRUE if shapiro_p > 0.05, indicating no significant departure from normality at the 5% level.
p_ttest: Shown when two groups are compared. P-value from an independent samples t-test, testing whether the means of the two groups differ. Assumes approximately normal distributions or large samples. All p-values are reported on the first row only; remaining rows contain NA.
p_wilcox: Shown when two groups are compared. P-value from the Wilcoxon rank-sum test (Mann-Whitney U test), a non-parametric alternative to the t-test. Preferred over p_ttest when data are skewed, ordinal, or contain outliers, as it compares ranks rather than means and makes no distributional assumptions.
p_anova: Shown when three or more groups are compared. P-value from a one-way analysis of variance (ANOVA) F-test, testing whether at least one group mean differs from the others. Assumes approximately normal distributions and equal variances across groups.
p_kruskal: Shown when three or more groups are compared. P-value from the Kruskal-Wallis test, a non-parametric alternative to one-way ANOVA. Preferred over p_anova when data are skewed or the normality assumption is not met, as it compares rank distributions and makes no distributional assumptions.

Examples

example_data <- dplyr::tibble(id = 1:100, age = rnorm(100, mean = 30, sd = 10),
                              group = sample(c("a", "b", "c", "d"),
                              size = 100, replace = TRUE))
dist_sum(example_data, age, group)

#> # A tibble: 4 × 17
#>   group     n n_miss median   p25   p75  mean    sd   min   max n_outliers
#>   <chr> <int>  <int>  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>      <int>
#> 1 a        33      0   32.6  23.6  39.6  31.4 10.6  12.1   55.6          0
#> 2 b        25      0   29.2  23.7  37.1  29.3  8.90  9.36  43.2          0
#> 3 c        20      0   33.8  23.7  38.4  31.7 11.9   5.53  56.5          0
#> 4 d        22      0   26.9  20.3  36.4  28.8 11.9  11.0   52.1          0
#> # ℹ 6 more variables: shapiro_p <dbl>, ci_lower <dbl>, ci_upper <dbl>,
#> #   normal <lgl>, p_anova <dbl>, p_kruskal <dbl>
example_data <- dplyr::tibble(id = 1:100, age = rnorm(100, mean = 30, sd = 10),
                             sex = sample(c("male", "female"),
                             size = 100, replace = TRUE))
dist_sum(example_data, age, sex)

#> # A tibble: 2 × 17
#>   sex        n n_miss median   p25   p75  mean    sd   min   max n_outliers
#>   <chr>  <int>  <int>  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>      <int>
#> 1 female    54      0   29.0  24.5  35.9  30.3  9.30  10.2  51.3          0
#> 2 male      46      0   34.0  25.9  40.9  33.4 10.9   11.8  50.9          0
#> # ℹ 6 more variables: shapiro_p <dbl>, ci_lower <dbl>, ci_upper <dbl>,
#> #   normal <lgl>, p_ttest <dbl>, p_wilcox <dbl>
summary <- dist_sum(example_data, age, sex) # Save summary statistics as a tibble.