Introduction
Here is our first plot which is just simply all the values of
Sepal.Width in the iris dataset. For the function to work the value you
want to plot must be given to the y argument in ggplot.
You can then flip the plot with + coord_flip() as we
demonstrate below.
ggplot(iris, aes(1, Sepal.Width)) +
geom_rain() +
theme_classic() +
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(), axis.ticks.x = element_blank())
Let’s see what is happening over the 3 Species of flowers. The fill must be a factor or a character vector!
ggplot(iris, aes(1, Sepal.Width, fill = Species)) +
geom_rain(alpha = .5) +
theme_classic() +
scale_fill_brewer(palette = 'Dark2')
Let’s color the dots by Species, we do this by adding
color = Species to ggplot. The default behavior of
geom_boxplot is to color the lines in the boxplot showing
the median and IQR. Therefore, we need to add a
boxplot.args list to re-color the boxplot to black. When we
do this all defaults are lost therefore we must add the options to not
show outliers.
ggplot(iris, aes(1, Sepal.Width, fill = Species, color = Species)) +
geom_rain(alpha = .6,
boxplot.args = list(color = "black", outlier.shape = NA)) +
theme_classic() +
scale_fill_brewer(palette = 'Dark2') +
scale_color_brewer(palette = 'Dark2')
It is also possible to nudge the box plots so they are not overlapping
with boxplot.args.pos. We will also flip them by setting
the rain.side argument to left (i.e., 'l').
ggplot(iris, aes(1, Sepal.Width, fill = Species, color = Species)) +
geom_rain(alpha = .5, rain.side = 'l',
boxplot.args = list(color = "black", outlier.shape = NA),
boxplot.args.pos = list(
position = ggpp::position_dodgenudge(x = .1, width = 0.1), width = 0.1
)) +
theme_classic() +
scale_fill_brewer(palette = 'Dark2') +
scale_color_brewer(palette = 'Dark2') +
guides(fill = 'none', color = 'none')
It could be even more useful to see the different species of flowers side by side rather than overlapping. The y value must be a factor or a character vector!
ggplot(iris, aes(Species, Sepal.Width, fill = Species)) +
geom_rain(alpha = .5) +
theme_classic() +
scale_fill_brewer(palette = 'Dark2') +
guides(fill = 'none', color = 'none')
We can flip the plots by adding coord_flip().
ggplot(iris, aes(Species, Sepal.Width, fill = Species)) +
geom_rain(alpha = .5) +
theme_classic() +
scale_fill_brewer(palette = 'Dark2') +
guides(fill = 'none', color = 'none') +
coord_flip()
This plot is a bit crammed, lets spread stuff out using the
boxplot.args.pos & violin.args.pos
arguments.
ggplot(iris, aes(Species, Sepal.Width, fill = Species)) +
geom_rain(alpha = .5,
boxplot.args.pos = list(
width = 0.05, position = position_nudge(x = 0.13)),
violin.args.pos = list(
side = "r",
width = 0.7, position = position_nudge(x = 0.2))) +
theme_classic() +
scale_fill_brewer(palette = 'Dark2') +
guides(fill = 'none', color = 'none') +
coord_flip()
Instead of coloring the dots by Species, the package offers the
ability to color to another variable such as Sepal.Length.
This will allow us to visualize how Sepal.Width and
Sepal.Length relate to each other in each
Species of flower. We can do this by adding them as a
covariate with the cov argument. At the current time the
argument must be given as a string.
ggplot(iris, aes(Species, Sepal.Width, fill = Species)) +
geom_rain(alpha = .6,
cov = "Sepal.Length") +
theme_classic() +
scale_fill_brewer(palette = 'Dark2') +
guides(fill = 'none', color = 'none') +
scale_color_viridis_c(option = "A", direction = -1)
We will now take the species versicolor and virginica and make longitudinal data. We are going to see what fertilizer does to the Sepal.Width of both species!
set.seed(42) # the magic number
iris_subset <- iris[iris$Species %in% c('versicolor', 'virginica'),]
iris.long <- cbind(rbind(iris_subset, iris_subset, iris_subset),
data.frame(time = c(rep("t1", dim(iris_subset)[1]), rep("t2", dim(iris_subset)[1]), rep("t3", dim(iris_subset)[1])),
id = c(rep(1:dim(iris_subset)[1]), rep(1:dim(iris_subset)[1]), rep(1:dim(iris_subset)[1]))))
# adding .5 and some noise to the versicolor species in t2
iris.long$Sepal.Width[iris.long$Species == 'versicolor' & iris.long$time == "t2"] <- iris.long$Sepal.Width[iris.long$Species == 'versicolor' & iris.long$time == "t2"] + .5 + rnorm(length(iris.long$Sepal.Width[iris.long$Species == 'versicolor' & iris.long$time == "t2"]), sd = .2)
# adding .8 and some noise to the versicolor species in t3
iris.long$Sepal.Width[iris.long$Species == 'versicolor' & iris.long$time == "t3"] <- iris.long$Sepal.Width[iris.long$Species == 'versicolor' & iris.long$time == "t3"] + .8 + rnorm(length(iris.long$Sepal.Width[iris.long$Species == 'versicolor' & iris.long$time == "t3"]), sd = .2)
# now we subtract -.2 and some noise to the virginica species
iris.long$Sepal.Width[iris.long$Species == 'virginica' & iris.long$time == "t2"] <- iris.long$Sepal.Width[iris.long$Species == 'virginica' & iris.long$time == "t2"] - .2 + rnorm(length(iris.long$Sepal.Width[iris.long$Species == 'virginica' & iris.long$time == "t2"]), sd = .2)
# now we subtract -.4 and some noise to the virginica species
iris.long$Sepal.Width[iris.long$Species == 'virginica' & iris.long$time == "t3"] <- iris.long$Sepal.Width[iris.long$Species == 'virginica' & iris.long$time == "t3"] - .4 + rnorm(length(iris.long$Sepal.Width[iris.long$Species == 'virginica' & iris.long$time == "t3"]), sd = .2)
iris.long$Sepal.Width <- round(iris.long$Sepal.Width, 1) # rounding Sepal.Width so t2 data is on the same resolution
iris.long$time <- factor(iris.long$time, levels = c('t1', 't2', 't3'))Here we plot the species overlapping at each time point. We can see fertilizer caused the Sepal Width of the versicolor species to increase while the virginica species decreased slightly!
ggplot(iris.long[iris.long$time %in% c('t1', 't2'),], aes(time, Sepal.Width, fill = Species)) +
geom_rain(alpha = .5) +
theme_classic() +
scale_fill_manual(values=c("dodgerblue", "darkorange")) +
guides(fill = 'none', color = 'none')
We can easily flank them using the rain.side argument
for 2-by-2 flanking (i.e., 'f2x2'). This also automatically
uses ggpp::position_dodgenudge to dodge the boxplots. Yet,
for descriptive purposes with will use the flanking argument
rain.side = 'f' with the defaults from 'f2x2'.
The rain.side = 'f' argument defaults to a 2-by-2 yet throw
a warning if you don’t remap the violin.args.pos. When
using flanking with more groups or more time points you must give
specific boxplot.args.pos and violin.args.pos
for each element. The left elements must have negative x-axis nudging
values with the right ones have positive x-axis nudging values.
ggplot(iris.long[iris.long$time %in% c('t1', 't2'),], aes(time, Sepal.Width, fill = Species)) +
geom_rain(alpha = .5, rain.side = 'f',
boxplot.args.pos = list(width = .1,
position = ggpp::position_dodgenudge(width = .1, #width needed now in ggpp version 0.5.g
x = c(-.13, -.13, # pre versicolor, pre virginica
.13, .13))), # post; post
violin.args.pos = list(width = .7,
position = position_nudge(x = c(rep(-.2, 256*2), rep(-.2, 256*2),# pre; pre
rep(.2, 256*2), rep(.2, 256*2))))) + #post; post
theme_classic() +
scale_fill_manual(values=c("dodgerblue", "darkorange")) +
guides(fill = 'none', color = 'none')
#> Warning: Using the `size` aesthetic with geom_polygon was deprecated in ggplot2 3.4.0.
#> ℹ Please use the `linewidth` aesthetic instead.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
We can connect each plant with lines using the
id.long.var argument, we will also use the convient
rain.side = 'f2x2'. As with the cov argument,
it must be a string linking the ids of each observation across time.
ggplot(iris.long[iris.long$time %in% c('t1', 't2'),], aes(time, Sepal.Width, fill = Species)) +
geom_rain(alpha = .5, rain.side = 'f2x2', id.long.var = "id") +
theme_classic() +
scale_fill_manual(values=c("dodgerblue", "darkorange")) +
guides(fill = 'none', color = 'none')
#> Warning: Duplicated aesthetics after name standardisation: alpha
We can color the dots and the connecting lines by Species! We will
also remove the lines around the violins by specifying their
color = NA, yet we now must re-add the alpha argument.
ggplot(iris.long[iris.long$time %in% c('t1', 't2'),], aes(time, Sepal.Width, fill = Species, color = Species)) +
geom_rain(alpha = .5, rain.side = 'f2x2', id.long.var = "id",
violin.args = list(color = NA, alpha = .7)) +
theme_classic() +
scale_fill_manual(values=c("dodgerblue", "darkorange")) +
scale_color_manual(values=c("dodgerblue", "darkorange")) +
guides(fill = 'none', color = 'none')
#> Warning: Duplicated aesthetics after name standardisation: alpha
We can start to combine aspects, for example here is three timepoints with subjects connected, special flanking and a covariate mapped!
ggplot(iris.long, aes(time, Sepal.Width, fill = Species)) +
geom_rain(alpha = .5, rain.side = 'f', id.long.var = "id", cov = "Sepal.Length",
boxplot.args = list(outlier.shape = NA, alpha = .8),
violin.args = list(alpha = .8, color = NA),
boxplot.args.pos = list(width = .1,
position = ggpp::position_dodgenudge(width = .1,
x = c(-.13, -.13, # t1 old, t1 young
-.13, .13,
.13, .13))),
violin.args.pos = list(width = .7,
position = position_nudge(x = c(rep(-.2, 256*2), rep(-.2, 256*2),# t1
rep(-.2, 256*2), rep(.2, 256*2), # t2
rep(.2, 256*2), rep(.2, 256*2))))) +
theme_classic() +
scale_fill_manual(values=c("dodgerblue", "darkorange")) +
scale_color_viridis_c(option = "A", direction = -1) +
guides(fill = 'none', color = 'none')
#> Warning: Duplicated aesthetics after name standardisation: alpha
Lastly, we can add a mean trend line using stat_summary.
Accentuating the opposite effects fertilizers had on the two species of
flowers!
ggplot(iris.long[iris.long$time %in% c('t1', 't2'),], aes(time, Sepal.Width, fill = Species)) +
geom_rain(alpha = .5, rain.side = 'f2x2') +
theme_classic() +
stat_summary(fun = mean, geom = "line", aes(group = Species, color = Species)) +
stat_summary(fun = mean, geom = "point",
aes(group = Species, color = Species)) +
scale_fill_manual(values=c("dodgerblue", "darkorange")) +
scale_color_manual(values=c("dodgerblue", "darkorange")) +
guides(fill = 'none', color = 'none')
Here is some sample code on how to do a significance test on a 1-by-1
flanking raincloud with the package ggsignif. We will not
run it as we don’t want to add ggsignif as a package
dependency.
ggplot(iris.long[iris.long$Species == 'versicolor' & iris.long$time %in% c('t1', 't2'),], aes(time, Sepal.Width, fill = Species)) +
geom_rain(alpha = .5, rain.side = 'f1x1') +
ggsignif::geom_signif(
comparisons = list(c("t1", "t2")),
map_signif_level = TRUE) +
scale_fill_manual(values=c("darkorange", "darkorange")) +
theme_classic()