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 ORCID iD [aut, cre], Jordy van Langen ORCID iD [aut], Micah Allen ORCID iD [ctb], Rogier Kievit ORCID iD [aut]
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 aes(). If specified and inherit.aes = TRUE (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping.

data

The data to be displayed in this layer. There are three options:

If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot().

A data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See fortify() for which variables will be created.

A function will be called with a single argument, the plot data. The return value must be a data.frame, and will be used as the layer data. A function can be created from a formula (e.g. ~ head(.x, 10)).

stat

The statistical transformation to use on the data for this layer, either as a ggproto Geom subclass or as a string naming the stat stripped of the stat_ prefix (e.g. "count" rather than "stat_count")

position

Position adjustment, either as a string naming the adjustment (e.g. "jitter" to use position_jitter), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.

trim

If TRUE (default), trim the tails of the violins to the range of the data. If FALSE, don't trim the tails.

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? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

If FALSE, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. borders().

...

Other arguments passed on to layer(). These are often aesthetics, used to set an aesthetic to a fixed value, like colour = "red" or size = 3. They may also be parameters to the paired geom/stat.

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 aes(). If specified and inherit.aes = TRUE (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping.

data

The data to be displayed in this layer. There are three options:

If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot().

A data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See fortify() for which variables will be created.

A function will be called with a single argument, the plot data. The return value must be a data.frame, and will be used as the layer data. A function can be created from a formula (e.g. ~ head(.x, 10)).

inherit.aes

If FALSE, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. borders().

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 layer(). These are often aesthetics, used to set an aesthetic to a fixed value, like colour = "red" or size = 3. They may also be parameters to the paired geom/stat.

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 FALSE, the default, missing values are removed with a warning. If TRUE, missing values are silently removed.

...

Other arguments passed on to layer(). These are often aesthetics, used to set an aesthetic to a fixed value, like colour = "red" or size = 3. They may also be parameters to the paired geom/stat.

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)