Title: | A Rainclouds Geom for 'ggplot2' |
Version: | 0.0.4 |
Description: | The 'geom_rain()' function adds different geoms together using 'ggplot2' to create raincloud plots. |
License: | MIT + file LICENSE |
Encoding: | UTF-8 |
Depends: | ggplot2 (≥ 3.4.0), R (≥ 3.4.0) |
Imports: | grid, gghalves, ggpp (≥ 0.5.6), rlang, vctrs (≥ 0.5.0), cli |
RoxygenNote: | 7.3.0 |
URL: | https://github.com/njudd/ggrain |
BugReports: | https://github.com/njudd/ggrain/issues |
Suggests: | knitr, rmarkdown |
VignetteBuilder: | knitr |
NeedsCompilation: | no |
Packaged: | 2024-01-23 10:46:27 UTC; njudd |
Author: | Nicholas Judd |
Maintainer: | Nicholas Judd <nickkjudd@gmail.com> |
Repository: | CRAN |
Date/Publication: | 2024-01-23 11:50:02 UTC |
Paired raincloud plot
Description
Taking from https://raw.githubusercontent.com/yjunechoe/geom_paired_raincloud/master/geom_paired_raincloud.R on 30-10-22 attribution to https://yjunechoe.github.io/
Usage
geom_paired_raincloud(
mapping = NULL,
data = NULL,
stat = "ydensity",
position = "dodge",
trim = TRUE,
scale = "area",
show.legend = NA,
inherit.aes = TRUE,
...
)
Arguments
mapping |
Set of aesthetic mappings created by |
data |
The data to be displayed in this layer. There are three options: If A A |
stat |
The statistical transformation to use on the data for this
layer, either as a |
position |
Position adjustment, either as a string naming the adjustment
(e.g. |
trim |
If |
scale |
if "area" (default), all violins have the same area (before trimming the tails). If "count", areas are scaled proportionally to the number of observations. If "width", all violins have the same maximum width. |
show.legend |
logical. Should this layer be included in the legends?
|
inherit.aes |
If |
... |
Other arguments passed on to |
Details
Create a paired raincloud plot (useful for visualizing difference between experimental conditions tested on the same subjects or items).
Adopted from the geom_violinhalf() source code from the see package
See Also
https://github.com/easystats/see/blob/master/R/geom_violinhalf.R
Examples
library(ggplot2)
Raincloud Plots
Description
This function displays individual data points, a boxplot and half a violin plot. It also has the option to connect data points with lines across groups by specifying an id to connect by. Lastly, if desired one can color the dots based of another variable.
Usage
geom_rain(
mapping = NULL,
data = NULL,
inherit.aes = TRUE,
id.long.var = NULL,
cov = NULL,
rain.side = NULL,
likert = FALSE,
seed = 42,
...,
point.args = rlang::list2(...),
point.args.pos = rlang::list2(position = position_jitter(width = 0.04, height = 0, seed
= seed)),
line.args = rlang::list2(alpha = 0.2, ...),
line.args.pos = rlang::list2(position = position_jitter(width = 0.04, height = 0, seed
= seed), ),
boxplot.args = rlang::list2(outlier.shape = NA, ...),
boxplot.args.pos = rlang::list2(width = 0.05, position = position_nudge(x = 0.1), ),
violin.args = rlang::list2(...),
violin.args.pos = rlang::list2(side = "r", width = 0.7, position = position_nudge(x =
0.15), )
)
Arguments
mapping |
Set of aesthetic mappings created by |
data |
The data to be displayed in this layer. There are three options: If A A |
inherit.aes |
If |
id.long.var |
A group to connect the lines by - must be a string (e.g., "id"). |
cov |
A covariate to color the dots by - must be as a string (e.g., "cov") |
rain.side |
How you want the rainclouds displayed, right ("r"), left ("l") or flanking ("f"), for a 1-by-1 flanking raincloud use ("f1x1") and for a 2-by-2 use ("f2x2"). |
likert |
Currently developing, right now just addes y-jitter. |
seed |
For the jittering in point & line to match. |
... |
Other arguments passed on to |
point.args |
A list of args for the dots |
point.args.pos |
A list of positional args for the points |
line.args |
A list of args for the lines, you need to specify a group to connect them with id.long.var |
line.args.pos |
A list of positional args for the lines |
boxplot.args |
A list of args for the boxplot |
boxplot.args.pos |
A list of positional args for the boxplot |
violin.args |
A list of args for the violin |
violin.args.pos |
A list of positional args for the violin |
Value
Returns a list of three environments to be used with the 'ggplot()' function in the 'ggplot2' package.
If the id.long.var argument is used the output will be a list of 4 environments.
These 4 environments have a similar structure to 'geom_boxplot()', 'geom_violin()', 'geom_point()' and 'geom_line()' from 'ggplot2'. need library(rlang) need library(ggplot2) depends = ggplot2
References
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 Wellcome Open Research 2021, 4:63. https://doi.org/10.12688/wellcomeopenres.15191.2
Examples
e1 <- ggplot(iris, aes(Species, Sepal.Width, fill = Species))
e1 + geom_rain()
# x must be the discrete variable
# orinetation can be changed with coord_flip()
e1 + geom_rain(alpha = .5) + coord_flip()
# we can color the dots by a covariate
e1 + geom_rain(cov = "Sepal.Length")
# we can edit elements individually
e1 + geom_rain(violin.args = list(alpha = .3, color = NA))
# we can flip them
e1 + geom_rain(rain.side = 'l')
# and move them
e1 +
geom_rain(boxplot.args.pos = list(width = .1, position = position_nudge(x = -.2)))
# they also work longitudinally
e2 <- ggplot(sleep, aes(group, extra, fill = group))
e2 + geom_rain(id.long.var = "ID")
# we can add groups
sleep_dat <- cbind(sleep, data.frame(sex = c(rep("male", 5),
rep("female", 5), rep("male", 5), rep("female", 5))))
e3 <- ggplot(sleep_dat, aes(group, extra, fill = sex))
e3 + geom_rain(alpha = .6)
# add likert example
e4 <- ggplot(mpg, aes(1, hwy, fill = manufacturer))
e4 + geom_rain(likert= TRUE)
# lets make it look nicer
e4 + geom_rain(likert= TRUE,
boxplot.args.pos = list(position = ggpp::position_dodgenudge(x = .095), width = .1),
violin.args = list(color = NA, alpha = .5))
Points
Description
The point geom is used to create scatterplots. The scatterplot is most
useful for displaying the relationship between two continuous variables.
It can be used to compare one continuous and one categorical variable, or
two categorical variables, but a variation like geom_jitter()
,
geom_count()
, or geom_bin2d()
is usually more
appropriate. A bubblechart is a scatterplot with a third variable
mapped to the size of points.
Arguments
na.rm |
If |
... |
Other arguments passed on to |
Overplotting
The biggest potential problem with a scatterplot is overplotting: whenever
you have more than a few points, points may be plotted on top of one
another. This can severely distort the visual appearance of the plot.
There is no one solution to this problem, but there are some techniques
that can help. You can add additional information with
geom_smooth()
, geom_quantile()
or
geom_density_2d()
. If you have few unique x
values,
geom_boxplot()
may also be useful.
Alternatively, you can
summarise the number of points at each location and display that in some
way, using geom_count()
, geom_hex()
, or
geom_density2d()
.
Another technique is to make the points transparent (e.g.
geom_point(alpha = 0.05)
) or very small (e.g.
geom_point(shape = ".")
).
Examples
p <- ggplot(mtcars, aes(wt, mpg))
p + geom_point()
# Add aesthetic mappings
p + geom_point(aes(colour = factor(cyl)))
p + geom_point(aes(shape = factor(cyl)))
# A "bubblechart":
p + geom_point(aes(size = qsec))
# Set aesthetics to fixed value
ggplot(mtcars, aes(wt, mpg)) + geom_point(colour = "red", size = 3)
# Varying alpha is useful for large datasets
d <- ggplot(diamonds, aes(carat, price))
d + geom_point(alpha = 1/10)
d + geom_point(alpha = 1/20)
d + geom_point(alpha = 1/100)
# For shapes that have a border (like 21), you can colour the inside and
# outside separately. Use the stroke aesthetic to modify the width of the
# border
ggplot(mtcars, aes(wt, mpg)) +
geom_point(shape = 21, colour = "black", fill = "white", size = 5, stroke = 5)
# You can create interesting shapes by layering multiple points of
# different sizes
p <- ggplot(mtcars, aes(mpg, wt, shape = factor(cyl)))
p +
geom_point(aes(colour = factor(cyl)), size = 4) +
geom_point(colour = "grey90", size = 1.5)
p +
geom_point(colour = "black", size = 4.5) +
geom_point(colour = "pink", size = 4) +
geom_point(aes(shape = factor(cyl)))
# geom_point warns when missing values have been dropped from the data set
# and not plotted, you can turn this off by setting na.rm = TRUE
set.seed(1)
mtcars2 <- transform(mtcars, mpg = ifelse(runif(32) < 0.2, NA, mpg))
ggplot(mtcars2, aes(wt, mpg)) +
geom_point()
ggplot(mtcars2, aes(wt, mpg)) +
geom_point(na.rm = TRUE)