raincloudplots <img src="https://github.com/jorvlan/open-visualizations/blob/master/R/package_figures/rainclouds_highres.png" width="150" height="160" align="right"/>

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If you use this package for your research, please cite it, thank you.

Paper

<pre> - Allen, M., Poggiali, D., Whitaker, K., Marshall, T. R., van Langen, J., & Kievit, R. A. Raincloud plots: a multi-platform tool for robust data visualization [version 2; peer review: 2 approved] <b>Wellcome Open Research</b> 2021, 4:63. <a href="https://doi.org/10.12688/wellcomeopenres.15191.2">https://doi.org/10.12688/wellcomeopenres.15191.2</a> </pre>

Background

It all started with the pre-print of raincloudplots in 2019 and accompanying GitHub repository RainCloudPlots. In the beginning of 2020, a tutorial called ‘open-visualizations’ was released and turned out to be a valuable addition to the previously published pre-print. This tutorial provides detailed and extensive R code to create robust and transparent repeated measures visualizations, by showing the slope change for each individual data point over time. To date (13-01-2021), this tutorial has been cited in 20 scientific papers. However, using this tutorial requires sufficient R programming knowledge and might therefore not be suitable for non-R experts. Therefore, we have created this dedicated raincloudplots package. This package is tailored towards easy visualization of grouped and repeated measures data. Moreover, it also provides individually linked repeated measures visualizations, which add detail and richness to a multitude of between/within-subject designs.

Package demonstration

• Tested on macOS with R version >= 4.0.3
• Tested on Windows with R version >= 4.0.3
• Researchers that would like to visualize more complex repeated measures designs, for instance with more groups and more time-points, please see our extensive tutorials:
  • <a href="https://github.com/jorvlan/open-visualizations">https://github.com/jorvlan/open-visualizations</a>
  • <a href="https://github.com/RainCloudPlots/RainCloudPlots">https://github.com/RainCloudPlots/RainCloudPlots</a>

Updates

<pre> - <b> January 2024</b> We have written a ggplot2-extension R-package <a href="https://github.com/njudd/ggrain">https://github.com/njudd/ggrain</a> which allows users to create Raincloud plots - following the 'Grammar of Graphics'. Please visit our newest repository at: <a href="https://github.com/njudd/ggrain">https://github.com/njudd/ggrain</a> - <b>February 2021 (version 0.2.0)</b> It is now possible to make raincloudplots with unequal 'between-group' comparisons (e.g., group1: 50 data-points, group2: 40 data-points) This is not possible for 'repeated-measures' between-timepoints (e.g., pre-post) connected by intra-individual lines `raincloudplots`. </pre>

Installation

if (!require(remotes)) {
    install.packages("remotes")
}
remotes::install_github('jorvlan/raincloudplots')

library(raincloudplots)

Raincloud 1 x 1

Step 1: Initialize the data-format

• data_1x1 creates the long-format data.frame that is needed for 1-by-1 rainclouds.
• array_1 the first array of datapoints to be plotted
• array_2 the second array of datapoints to be plotted
• jit_distance the amount of distance between jittered datapoints (0.9 by default)
• jit_seed the amount used in set.seed() (321 by default)

df_1x1 <- data_1x1(
  array_1 = iris$Sepal.Length[1:50],
  array_2 = iris$Sepal.Length[51:100],
  jit_distance = .09,
  jit_seed = 321)

> head(df_1x1)
  y_axis x_axis id       jit
1    5.1      1  1 1.0820609
2    4.9      1  2 1.0787114
3    4.7      1  3 0.9528797
4    4.6      1  4 0.9559133
5    5.0      1  5 0.9802922
6    5.4      1  6 0.9714124
> tail(df_1x1)
    y_axis x_axis id      jit
95     5.6      2 45 2.059387
96     5.7      2 46 2.004848
97     5.7      2 47 2.066980
98     6.2      2 48 2.074479
99     5.1      2 49 1.939248
100    5.7      2 50 1.999004

Step 2: Create a vertical or horizontal 1 x 1 raincloudplot

• raincloud_1x1 creates the 1-by-1 comparison for grouped data.
• data the data.frame created with data_1x1
• colors concatenated string for both colors
• fills concatenated string for both both fills
• size size of the data
• alpha alpha of the data
• ort vertical or horizontal display of rainclouds

raincloud_1_h <- raincloud_1x1(
  data = df_1x1, 
  colors = (c('dodgerblue','darkorange')), 
  fills = (c('dodgerblue','darkorange')), 
  size = 1, 
  alpha = .6, 
  ort = 'h') +

scale_x_continuous(breaks=c(1,2), labels=c("Group1", "Group2"), limits=c(0, 3)) +
  xlab("Groups") + 
  ylab("Score") +
  theme_classic()

raincloud_1_h

raincloud_1_v <- raincloud_1x1(
  data = df_1x1, 
  colors = (c('dodgerblue','darkorange')), 
  fills = (c('dodgerblue','darkorange')), 
  size = 1, 
  alpha = .6, 
  ort = 'v') +

scale_x_continuous(breaks=c(1,2), labels=c("Group1", "Group2"), limits=c(0, 3)) +
  xlab("Groups") + 
  ylab("Score") +
  theme_classic()

raincloud_1_v

Raincloud 1 x 1 repeated measures

• raincloud_1x1_repmes creates a 1-by-1 repeated measures raincloud.
• data the data.frame created with data_1x1
• colors concatenated string for both colors
• fills concatenated string for both fills
• line_color color of the lines
• line_alpha alpha of the lines
• size size of the data
• alpha alpha of the data
• align_clouds FALSE if spreaded on different x-axis ticks, TRUE if aligned on same x-axis tick

raincloud_2 <- raincloud_1x1_repmes(
  data = df_1x1,
  colors = (c('dodgerblue', 'darkorange')),
  fills = (c('dodgerblue', 'darkorange')),
  line_color = 'gray',
  line_alpha = .3,
  size = 1,
  alpha = .6,
  align_clouds = FALSE) +
 
scale_x_continuous(breaks=c(1,2), labels=c("Pre", "Post"), limits=c(0, 3)) +
  xlab("Time") + 
  ylab("Score") +
  theme_classic()

raincloud_2

raincloud_2_aligned <- raincloud_1x1_repmes(
  data = df_1x1,
  colors = (c('dodgerblue', 'darkorange')),
  fills = (c('dodgerblue', 'darkorange')),
  line_color = 'gray',
  line_alpha = .3,
  size = 1,
  alpha = .6,
  align_clouds = TRUE) +
 
scale_x_continuous(breaks=c(1,2), labels=c("Pre", "Post"), limits=c(0, 3)) +
  xlab("Time") + 
  ylab("Score") +
  theme_classic()

raincloud_2_aligned

Raincloud 2 x 2 repeated measures

Step 1: Initialize the data-format

• data_2x2 creates the long-format data.frame needed for 2 x 2 repeated measures rainclouds.
• array_1 the first array of datapoints to be plotted
• array_2 the second array of datapoints to be plotted
• array_3 the array of datapoints to be plotted
• array_4 the array of datapoints to be plotted
• array_5 the array of datapoints to be plotted (OPTIONAL: only needed for 2x3 repeated measures, see below)
• array_6 the array of datapoints to be plotted (OPTIONAL: only needed for 2x3 repeated measures, see below)
• labels concatenated string of both group labels
• spread_x_ticks FALSE if 2 x-ticks, TRUE if 4 x-ticks
• jit_distance the amount of distance between jittered datapoints (0 by default)
• jit_seed the amount used in set.seed() (321 by default)

df_2x2 <- data_2x2(
  array_1 = iris$Sepal.Length[1:50],
  array_2 = iris$Sepal.Length[51:100],
  array_3 = iris$Sepal.Length[101:150],
  array_4 = iris$Sepal.Length[81:130],
  labels = (c('congruent','incongruent')),
  jit_distance = .09,
  jit_seed = 321,
  spread_x_ticks = FALSE) 

> head(df_2x2)
  y_axis x_axis id     group       jit
1    5.1      1  1 congruent 1.0820609
2    4.9      1  2 congruent 1.0787114
3    4.7      1  3 congruent 0.9528797
4    4.6      1  4 congruent 0.9559133
5    5.0      1  5 congruent 0.9802922
6    5.4      1  6 congruent 0.9714124
> tail(df_2x2)
    y_axis x_axis id       group      jit
195    6.7   2.01 45 incongruent 2.056353
196    7.2   2.01 46 incongruent 1.975210
197    6.2   2.01 47 incongruent 2.011292
198    6.1   2.01 48 incongruent 2.013551
199    6.4   2.01 49 incongruent 1.961014
200    7.2   2.01 50 incongruent 2.086574

df_2x2_spread <- data_2x2(
  array_1 = iris$Sepal.Length[1:50],
  array_2 = iris$Sepal.Length[51:100],
  array_3 = iris$Sepal.Length[101:150],
  array_4 = iris$Sepal.Length[81:130],
  labels = (c('congruent','incongruent')),
  jit_distance = .09,
  jit_seed = 321,
  spread_x_ticks = TRUE) 

> head(df_2x2_spread)
  y_axis x_axis id     group       jit
1    5.1      1  1 congruent 1.0820609
2    4.9      1  2 congruent 1.0787114
3    4.7      1  3 congruent 0.9528797
4    4.6      1  4 congruent 0.9559133
5    5.0      1  5 congruent 0.9802922
6    5.4      1  6 congruent 0.9714124
> tail(df_2x2_spread)
    y_axis x_axis id       group      jit
195    6.7      4 45 incongru