raincloud: Examine the imbalance of continuous covariates

In vecmatch: Generalized Propensity Score Estimation and Matching for Multiple Groups

Examine the imbalance of continuous covariates

Description

The raincloud() function allows to generate distribution plots for continuous data in an easy and uncomplicated way. The function is based on the ggplot2 package, which must already be preinstalled Raincloud plots consist of three main elements:

• Distribution plots, specifically violin plots with the mean values and standard deviations of respective groups,

• Jittered point plots depicting the underlying distribution of the data in the rawest form,

• Boxplots, summarizing the most important statistics of the underlying distribution.

Usage

raincloud(
  data = NULL,
  y = NULL,
  group = NULL,
  facet = NULL,
  ncol = 1,
  significance = NULL,
  sig_label_size = 2L,
  sig_label_color = FALSE,
  smd_type = "mean",
  limits = NULL,
  jitter = 0.1,
  alpha = 0.4,
  plot_name = NULL,
  overwrite = FALSE,
  ...
)

Arguments

data	A non-empty data.frame containing at least one numeric column, as specified by the y argument. This argument must be provided and does not have a default value.
y	A single string or unquoted symbol representing the name of a numeric column in the data. In the vector matching workflow, it is typically a numeric covariate that requires balancing.
group	A single string or unquoted symbol representing the name of a factor or character column in data. In raincloud() plots, the groups specified by group argument will be distinguished by separate fill and color aesthetics. For clarity, it is recommended to plot fewer than 10 groups, though there is no formal limit.
facet	A single string or unquoted symbol representing the name of a variable in data to facet by. This argument is used in a call to ggplot2::facet_wrap(), creating separate distribution plots for each unique group in the facet variable.
ncol	A single integer. The value should be less than or equal to the number of unique categories in the facet variable. This argument is used only when facet is not NULL, specifying the number of columns in the ggplot2::facet_wrap() call. The distribution plots will be arranged into the number of columns defined by ncol.
significance	A single string specifying the method for calculating p-values in multiple comparisons between groups defined by the group argument. Significant comparisons are represented by bars connecting the compared groups on the left side of the boxplots. Note that if there are many significant tests, the plot size may adjust accordingly. For available methods refer to the Details section. If the significance argument is not NULL, standardized mean differences (SMDs) are also calculated and displayed on the right side of the jittered point plots.
sig_label_size	An integer specifying the size of the significance and SMD (standardized mean difference) labels displayed on the bars on the right side of the plot.
sig_label_color	Logical flag. If FALSE (default), significance and SMD bars and text are displayed in the default color (black). If TRUE, colors are applied dynamically based on value: nonsignificant tests and SMD values below 0.10 are displayed in green, while significant tests and SMD values of 0.10 or higher are displayed in red.
smd_type	A single string indicating the type of effect size to calculate and display on the left side of the jittered point plots: mean - Cohen's d is calculated, median - the Wilcoxon effect size (r) is calculated based on the Z statistic extracted from the Wilcoxon test.
limits	A numeric atomic vector of length two, specifying the y axis limits in the distribution plots. The first element sets the minimum value, and the second sets the maximum. This vector is passed to the ggplot2::xlim() function to adjust the axis scale.
jitter	A single numeric value between 0 and 1 that controls the amount of jitter applied to points in the ggplot2::geom_jitter() plots. Higher values of the jitter argument produce more jittered plot. It's recommended to keep this value low, as higher jitter can make the plot difficult to interpret.
alpha	A single numeric value between 0 and 1 that controls the transparency of the density plots, boxplots, and jittered point plots. Lower values result in higher transparency. It is recommended to keep this value relatively high to maintain the interpretability of the plots when using the group argument, as excessive transparency may cause overlap between groups, making it difficult to distinguish them visually.
plot_name	A string specifying a valid file name or path for the plot. If set to NULL, the plot is displayed to the current graphical device but not saved locally. If a valid name with .png or .pdf extension is provided, the plot is saved locally. Users can also include a subdirectory in plot_name. Ensure the file path follows the correct syntax for your operating system.
overwrite	A logical flag (default FALSE) that is evaluated only if the save.name argument is provided. If TRUE, the function checks whether a plot with the same name already exists. If it does, the existing plot will be overwritten. If FALSE and a plot with the same name exists, an error is thrown. If no such plot exists, the plot is saved normally.
...	Additional arguments passed to the function for calculating p-values when the significance argument is specified. For available functions associated with different significance methods, please refer to the Details section and consult the documentation for the relevant functions in the rstatix package.

Details

Available methods for the argument significance are:

• "t_test" - Performs a pairwise comparison using the two-sample t-test, with the default Holm adjustment for multiple comparisons. This test assumes normally distributed data and equal variances. The adjustment can be modified via the p.adjust.method argument. The test is implemented via rstatix::pairwise_t_test()

• "dunn_test" - Executes Dunn's test for pairwise comparisons following a Kruskal-Wallis test. It is a non-parametric alternative to the t-test when assumptions of normality or homogeneity of variances are violated. Implemented via rstatix::dunn_test().

• "tukeyHSD_test" - Uses Tukey's Honest Significant Difference (HSD) test for pairwise comparisons between group means. Suitable for comparing all pairs when the overall ANOVA is significant. The method assumes equal variance between groups and is implemented via rstatix::tukey_hsd().

• "games_howell_test" - A post-hoc test used after ANOVA, which does not assume equal variances or equal sample sizes. It’s particularly robust for data that violate homogeneity of variance assumptions. Implemented via rstatix::games_howell_test().

• "wilcoxon_test" - Performs the Wilcoxon rank-sum test (also known as the Mann-Whitney U test) for non-parametric pairwise comparisons. Useful when data are not normally distributed. Implemented via rstatix::pairwise_wilcox_test().

Value

A ggplot object representing the distribution of the y variable across the levels of the group and facet variables in data.

See Also

mosaic() which summarizes the distribution of discrete data

Examples

## Example: Creating a raincloud plot for the ToothGrowth dataset.
## This plot visualizes the distribution of the `len` variable by
## `dose` (using different colors) and facets by `supp`. Group
## differences by `dose` are calculated using a `t_test`, and standardized
## mean differences (SMDs) are displayed through jittered points.
library(ggplot2)
library(ggpubr)

p <- raincloud(ToothGrowth, len, dose, supp,
  significance = "t_test",
  jitter = 0.15, alpha = 0.4
)

## As `p` is a valid `ggplot` object, we can manipulate its
## characteristics usingthe `ggplot2` or `ggpubr` packages
## to create publication grade plot:
p <- p +
  theme_classic2() +
  theme(
    axis.line.y = element_blank(),
    axis.ticks.y = element_blank()
  ) +
  guides(fill = guide_legend("Dose [mg]")) +
  ylab("Length [cm]")

p

vecmatch documentation built on April 3, 2025, 8:46 p.m.