| Type: | Package |
| Title: | Boxplot and Significance Test for Two Groups |
| Version: | 0.1.0 |
| Description: | Provides functions to create side-by-side boxplots for a continuous variable grouped by a two-level categorical variable, check normality assumptions using the Shapiro-Wilk test (Shapiro and Wilk (1965) <doi:10.2307/2333709>), and perform appropriate statistical tests such as the independent two-sample t-test (Student (1908) <doi:10.1093/biomet/6.1.1>) or the Mann–Whitney U test ( Mann–Whitney (1947) <doi:10.1214/aoms/1177730491>). Returns a publication-ready plot and test statistics including test statistic, degrees of freedom, and p-value. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| Depends: | R (≥ 4.1.0) |
| Imports: | ggplot2, dplyr, stats, rlang |
| Suggests: | testthat (≥ 3.0.0), knitr, rmarkdown |
| VignetteBuilder: | knitr |
| RoxygenNote: | 7.3.3 |
| Config/testthat/edition: | 3 |
| NeedsCompilation: | no |
| Packaged: | 2025-09-24 11:39:52 UTC; arka |
| Author: | Arkaprabha Sau [aut, cre], Santanu Phadikar [aut], Ishita Bhakta [aut] |
| Maintainer: | Arkaprabha Sau <arka.doctor@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2025-10-01 07:00:16 UTC |
Compare Two Groups with Boxplot and Significance Test
Description
Generates side-by-side boxplots and runs Shapiro-Wilk tests by group to check for normality. If both groups are normally distributed, an independent two-sample t-test is applied. Otherwise, a Mann–Whitney U test (Wilcoxon rank-sum test) is performed. Returns a structured list with plots and a clear test summary.
Usage
compare_two_groups(data, continuous, group)
Arguments
data |
A data.frame containing the variables. |
continuous |
Name of continuous variable (string). |
group |
Name of categorical variable with exactly 2 levels (string). |
Value
A list containing:
- plot
A ggplot object of the boxplot with jittered points.
- normality
A data.frame showing Shapiro-Wilk test results by group.
- test_summary
A data.frame summarizing the statistical test used, statistic, df (if applicable), and p-value.
Examples
res <- compare_two_groups(mtcars, "mpg", "am")
res$plot
res$normality
res$test_summary