If you use this package for your research, please cite it, thank you.
Paper
- 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]
Wellcome Open Research 2021, 4:63. https://doi.org/10.12688/wellcomeopenres.15191.2
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.
- 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:
- January 2024 We have written a ggplot2-extension R-package https://github.com/njudd/ggrain which allows users to create Raincloud plots - following the 'Grammar of Graphics'. Please visit our newest repository at: https://github.com/njudd/ggrain - February 2021 (version 0.2.0) 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`.
if (!require(remotes)) {
install.packages("remotes")
}
remotes::install_github('jorvlan/raincloudplots')
library(raincloudplots)
Step 1: Initialize the data-format
data_1x1creates the long-format data.frame that is needed for 1-by-1 rainclouds.array_1the first array of datapoints to be plottedarray_2the second array of datapoints to be plottedjit_distancethe amount of distance between jittered datapoints (0.9 by default)jit_seedthe 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 1.999004Step 2: Create a vertical or horizontal 1 x 1 raincloudplot
raincloud_1x1creates the 1-by-1 comparison for grouped data.datathe data.frame created withdata_1x1colorsconcatenated string for both colorsfillsconcatenated string for both both fillssizesize of the dataalphaalpha of the dataortvertical 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_1x1_repmescreates a 1-by-1 repeated measures raincloud.datathe data.frame created withdata_1x1colorsconcatenated string for both colorsfillsconcatenated string for both fillsline_colorcolor of the linesline_alphaalpha of the linessizesize of the dataalphaalpha of the dataalign_cloudsFALSE 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
Step 1: Initialize the data-format
data_2x2creates the long-format data.frame needed for 2 x 2 repeated measures rainclouds.array_1the first array of datapoints to be plottedarray_2the second array of datapoints to be plottedarray_3the array of datapoints to be plottedarray_4the array of datapoints to be plottedarray_5the array of datapoints to be plotted (OPTIONAL: only needed for 2x3 repeated measures, see below)array_6the array of datapoints to be plotted (OPTIONAL: only needed for 2x3 repeated measures, see below)labelsconcatenated string of both group labelsspread_x_ticksFALSE if 2 x-ticks, TRUE if 4 x-ticksjit_distancethe amount of distance between jittered datapoints (0 by default)jit_seedthe 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 2.086574df_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 incongruent 4.046353
196 7.2 4 46 incongruent 3.965210
197 6.2 4 47 incongruent 4.001292
198 6.1 4 48 incongruent 4.003551
199 6.4 4 49 incongruent 3.951014
200 7.2 4 50 incongruent 4.076574Step 2: Create a raincloud plot with 4 x-ticks or with 2 x-ticks
raincloud_2x2 <- raincloud_2x2_repmes(
data = df_2x2,
colors = (c('dodgerblue', 'darkorange', 'dodgerblue', 'darkorange')),
fills = (c('dodgerblue', 'darkorange', 'dodgerblue', 'darkorange')),
size = 1,
alpha = .6,
spread_x_ticks = FALSE) +
scale_x_continuous(breaks=c(1,2), labels=c("Pre", "Post"), limits=c(0, 3)) +
xlab("Time") +
ylab("Score") +
theme_classic()
raincloud_2x2
raincloud_2x2_spread <- raincloud_2x2_repmes(
data = df_2x2_spread,
colors = (c('dodgerblue', 'darkorange', 'dodgerblue', 'darkorange')),
fills = (c('dodgerblue', 'darkorange', 'dodgerblue', 'darkorange')),
line_color = 'gray',
line_alpha = .3,
size = 1,
alpha = .6,
spread_x_ticks = TRUE) +
scale_x_continuous(breaks=c(1,2,3,4), labels=c("Pre", "Post", "Pre", "Post"), limits=c(0, 5)) +
xlab("Time") +
ylab("Score") +
theme_classic()
raincloud_2x2_spread
Step 1: Initialize the data-format
df_2x3 <- 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],
array_5 = iris$Sepal.Length[21:70],
array_6 = iris$Sepal.Length[41:90],
labels = (c('congruent','incongruent')),
jit_distance = .05,
jit_seed = 321) > head(df_2x3)
y_axis x_axis id group jit
1 5.1 1 1 congruent 1.0455894
2 4.9 1 2 congruent 1.0437286
3 4.7 1 3 congruent 0.9738220
4 4.6 1 4 congruent 0.9755074
5 5.0 1 5 congruent 0.9890512
6 5.4 1 6 congruent 0.9841180
> tail(df_2x3)
y_axis x_axis id group jit
295 5.4 3.01 45 incongruent 3.055610
296 6.0 3.01 46 incongruent 3.047695
297 6.7 3.01 47 incongruent 3.058535
298 6.3 3.01 48 incongruent 3.005049
299 5.6 3.01 49 incongruent 2.978512
300 5.5 3.01 50 incongruent 2.967493Step 2: Create a vertical or horizontal 2 x 3 Raincloud
raincloud_2x3_vertical <- raincloud_2x3_repmes(
data = df_2x3,
colors = (c('dodgerblue', 'darkorange', 'dodgerblue',
'darkorange', 'dodgerblue', 'darkorange')),
fills = (c('dodgerblue', 'darkorange', 'dodgerblue',
'darkorange', 'dodgerblue', 'darkorange')),
size = 1,
alpha = .6,
ort = 'v') +
scale_x_continuous(breaks=c(1,2,3), labels=c("T-1", "T-2", "T-3"), limits=c(0, 4)) +
xlab("Time") +
ylab("Score") +
theme_classic()
raincloud_2x3_vertical
raincloud_2x3_horizontal <- raincloud_2x3_repmes(
data = df_2x3,
colors = (c('dodgerblue', 'darkorange', 'dodgerblue',
'darkorange', 'dodgerblue', 'darkorange')),
fills = (c('dodgerblue', 'darkorange', 'dodgerblue',
'darkorange', 'dodgerblue', 'darkorange')),
size = 1,
alpha = .6,
ort = 'h') +
scale_x_continuous(breaks=c(1,2,3), labels=c("T-1", "T-2", "T-3"), limits=c(0, 4)) +
xlab("Time") +
ylab("Score") +
theme_classic()
raincloud_2x3_horizontal