Plot list of plots in GGiraph - r

I am trying to plot a list of plots using GGiraph in Rstudio.
The solution to plot multiple plots is either through Cowplot (ggobj = plot_grid(plot1, plot2)) or Patchwork (code = print(plot / plot)). This works if you individually print single plots. However, it looks like it does not accept a list of plots. I want the plots to be arranged in one column with multiple rows.
Does anyone have a solution to this?


You could try the plotlist argument in plot_grid.
#Using the example from giraffe
library(ggiraph)
library(ggplot2)
dataset <- mtcars
dataset$carname = row.names(mtcars)
gg_point = ggplot( data = dataset,
mapping = aes(x = wt, y = qsec, color = disp,
tooltip = carname, data_id = carname) ) +
geom_point_interactive() + theme_minimal()
#using the plotlist argument
library(cowplot)
girafe(ggobj = plot_grid(plotlist=list(gg_point, gg_point), ncol=1))


You can also do this with functional assembly in patchwork, using the wrap_plots() function and the ncol or nrow arguments. wrap_plots() can take either a series of graphs or a list.
library(ggiraph)
library(ggplot2)
library(patchwork)
# Demo figure
g <- ggplot(data = mpg, aes(x = cyl, y = hwy, color = class,
tooltip = model, data_id = model)) +
geom_point_interactive()
# Use wrap_plots from patchwork
g <- wrap_plots(list(g, g), ncol = 1)
girafe(ggobj = g)

Related

create single ggplot figure with a standalone ggplot and a ggplot created with facet_wrap()?

I have my an empty panel in my facetted ggplot. I would like to insert my standalone plot into this. Is this possible? See below for example code.
I found a possible solution Here, but can't get it to 'look nice'. To 'look nice' I want the standalone plot to have the same dimensions as one of the facetted plots.
library(ggplot2)
library(plotly)
data("mpg")
first_plot = ggplot(data = mpg, aes(x = trans, y = cty)) +
geom_point(size= 1.3)
facet_plot = ggplot(data = mpg, aes(x = year, y = cty)) +
geom_point(size = 1.3) +
facet_wrap(~manufacturer)
facet_plot # room for one more panel which I want first_plot to go?
# try an merge but makes first plot huge, compared with facetted plots.
subplot(first_plot, facet_plot, which_layout = 2)

Besides the options to manipulate the gtable or using patchwork one approach to achieve your desired result would be via some data wrangling to add the standalone plot as an additional facet. Not sure whether this will work for your real data but at least for mpg you could do:
library(ggplot2)
library(dplyr)
mpg_bind <- list(standalone = mpg, facet = mpg) %>%
bind_rows(.id = "id") %>%
mutate(x = ifelse(id == "standalone", trans, year),
facet = ifelse(id == "standalone", "all", manufacturer),
facet = forcats::fct_relevel(facet, "all", after = 1000))
ggplot(data = mpg_bind, aes(x = x, y = cty)) +
geom_point(size = 1.3) +
facet_wrap(~facet, scales = "free_x")

How can I repeat the x label for each panel in facet_wrap in ggplot?

My problem is simple: Instead of having a single axis label for the entire plot on x (y is optional; my label is ""), I want to repeat it for each panel in facet_wrap() (even with multiple lines). What I mean is: the normal output is this (tryind to write as little unnecessary code as possible):
ggplot(iris, aes(x = Petal.Width, y = Petal.Length))+
geom_point()+
facet_wrap(vars(Species), scales = "free")
What I want is this (plus the panel names; because of how I "faked" the graph, they aren't there):

This could be achieved by making separate plots for each catgeory of your facteting variable and glueing them together via cowplot or patchwork:
library(ggplot2)
library(dplyr)
library(cowplot)
library(patchwork)
plot <- function(x) {
iris %>%
filter(Species == x) %>%
ggplot(aes(x = Petal.Width, y = Petal.Length))+
geom_point()+
facet_wrap(vars(Species), scales = "free")
}
# Using cowplot
lapply(unique(iris$Species), plot) %>%
plot_grid(plotlist = ., nrow = 1)
# Using patchwork
lapply(unique(iris$Species), plot) %>%
wrap_plots(nrow = 1)

Outliers appearing in box plot when I use plotly with ggplot2 (R)?

I have a problem with outliers appearing when they shouldn't when I use plotly, and I'm wondering how to stop this from happening. I'll use the mtcars dataset as an example.
I'll set an obvious outlier:
mtcars[1,1] = 60
The first plot, with outliers included:
p <- ggplot(mtcars) +
+ geom_boxplot(
+ aes(x = cyl, y = mpg, group = cyl))
Now I make a plot with outliers removed:
p <- ggplot(mtcars) +
geom_boxplot(
aes(x = cyl, y = mpg, group = cyl),
outlier.shape = NA)
Now I convert the plot to ggplotly and save
p <- plotly::ggplotly(p)
The outliers are showing. Does anyone know how I can get around this problem / have a solution when using ggplotly?

Found a solution:
for (i in 1:3) {
p$x$data[[i]]$marker <- list(opacity = 0)
}

R: Remove border when plotting with ggplot2 + ggExtra + cowplot

I couldn't find the way not to plot the outer frame when combining graphs through ggplot2 + ggExtra + cowplot. I am not sure where I have to tell R, but suspect the issue to lie in ggExtra. Here is an example:
require(ggplot2)
require(cowplot)
require(ggExtra)
# Creat a graph
A <- ggplot(mpg, aes(x = cty, y = hwy, colour = factor(cyl))) + geom_point(size = 2.5)
# Add marginal histogram
B <- ggExtra::ggMarginal(A,type = 'histogram', margins = 'x', size = 9)
# Combine through cowplot
combo <- plot_grid(B,B,labels=c("A","B"))
plot(combo) # looks fine
# Re-combine through cowplot
plot_grid(B,combo,ncol=1,rel_heights = c(2,3)) # that's where I got an unwanted nasty frame around 'combo'
Any hint would be greatly appreciated!

p <- plot_grid(B,combo,ncol=1,rel_heights = c(2,3))
p <- p + panel_border(remove = TRUE)
https://rdrr.io/cran/cowplot/man/panel_border.html

Side-by-side plots with ggplot2

I would like to place two plots side by side using the ggplot2 package, i.e. do the equivalent of par(mfrow=c(1,2)).
For example, I would like to have the following two plots show side-by-side with the same scale.
x <- rnorm(100)
eps <- rnorm(100,0,.2)
qplot(x,3*x+eps)
qplot(x,2*x+eps)
Do I need to put them in the same data.frame?
qplot(displ, hwy, data=mpg, facets = . ~ year) + geom_smooth()

Any ggplots side-by-side (or n plots on a grid)
The function grid.arrange() in the gridExtra package will combine multiple plots; this is how you put two side by side.
require(gridExtra)
plot1 <- qplot(1)
plot2 <- qplot(1)
grid.arrange(plot1, plot2, ncol=2)
This is useful when the two plots are not based on the same data, for example if you want to plot different variables without using reshape().
This will plot the output as a side effect. To print the side effect to a file, specify a device driver (such as pdf, png, etc), e.g.
pdf("foo.pdf")
grid.arrange(plot1, plot2)
dev.off()
or, use arrangeGrob() in combination with ggsave(),
ggsave("foo.pdf", arrangeGrob(plot1, plot2))
This is the equivalent of making two distinct plots using par(mfrow = c(1,2)). This not only saves time arranging data, it is necessary when you want two dissimilar plots.
Appendix: Using Facets
Facets are helpful for making similar plots for different groups. This is pointed out below in many answers below, but I want to highlight this approach with examples equivalent to the above plots.
mydata <- data.frame(myGroup = c('a', 'b'), myX = c(1,1))
qplot(data = mydata,
x = myX,
facets = ~myGroup)
ggplot(data = mydata) +
geom_bar(aes(myX)) +
facet_wrap(~myGroup)
Update
the plot_grid function in the cowplot is worth checking out as an alternative to grid.arrange. See the answer by #claus-wilke below and this vignette for an equivalent approach; but the function allows finer controls on plot location and size, based on this vignette.

One downside of the solutions based on grid.arrange is that they make it difficult to label the plots with letters (A, B, etc.), as most journals require.
I wrote the cowplot package to solve this (and a few other) issues, specifically the function plot_grid():
library(cowplot)
iris1 <- ggplot(iris, aes(x = Species, y = Sepal.Length)) +
geom_boxplot() + theme_bw()
iris2 <- ggplot(iris, aes(x = Sepal.Length, fill = Species)) +
geom_density(alpha = 0.7) + theme_bw() +
theme(legend.position = c(0.8, 0.8))
plot_grid(iris1, iris2, labels = "AUTO")
The object that plot_grid() returns is another ggplot2 object, and you can save it with ggsave() as usual:
p <- plot_grid(iris1, iris2, labels = "AUTO")
ggsave("plot.pdf", p)
Alternatively, you can use the cowplot function save_plot(), which is a thin wrapper around ggsave() that makes it easy to get the correct dimensions for combined plots, e.g.:
p <- plot_grid(iris1, iris2, labels = "AUTO")
save_plot("plot.pdf", p, ncol = 2)
(The ncol = 2 argument tells save_plot() that there are two plots side-by-side, and save_plot() makes the saved image twice as wide.)
For a more in-depth description of how to arrange plots in a grid see this vignette. There is also a vignette explaining how to make plots with a shared legend.
One frequent point of confusion is that the cowplot package changes the default ggplot2 theme. The package behaves that way because it was originally written for internal lab uses, and we never use the default theme. If this causes problems, you can use one of the following three approaches to work around them:
1. Set the theme manually for every plot. I think it's good practice to always specify a particular theme for each plot, just like I did with + theme_bw() in the example above. If you specify a particular theme, the default theme doesn't matter.
2. Revert the default theme back to the ggplot2 default. You can do this with one line of code:
theme_set(theme_gray())
3. Call cowplot functions without attaching the package. You can also not call library(cowplot) or require(cowplot) and instead call cowplot functions by prepending cowplot::. E.g., the above example using the ggplot2 default theme would become:
## Commented out, we don't call this
# library(cowplot)
iris1 <- ggplot(iris, aes(x = Species, y = Sepal.Length)) +
geom_boxplot()
iris2 <- ggplot(iris, aes(x = Sepal.Length, fill = Species)) +
geom_density(alpha = 0.7) +
theme(legend.position = c(0.8, 0.8))
cowplot::plot_grid(iris1, iris2, labels = "AUTO")
Updates:
As of cowplot 1.0, the default ggplot2 theme is not changed anymore.
As of ggplot2 3.0.0, plots can be labeled directly, see e.g. here.

Using the patchwork package, you can simply use + operator:
library(ggplot2)
library(patchwork)
p1 <- ggplot(mtcars) + geom_point(aes(mpg, disp))
p2 <- ggplot(mtcars) + geom_boxplot(aes(gear, disp, group = gear))
p1 + p2
Other operators include / to stack plots to place plots side by side, and () to group elements. For example you can configure a top row of 3 plots and a bottom row of one plot with (p1 | p2 | p3) /p. For more examples, see the package documentation.

You can use the following multiplot function from Winston Chang's R cookbook
multiplot(plot1, plot2, cols=2)
multiplot <- function(..., plotlist=NULL, cols) {
require(grid)
# Make a list from the ... arguments and plotlist
plots <- c(list(...), plotlist)
numPlots = length(plots)
# Make the panel
plotCols = cols # Number of columns of plots
plotRows = ceiling(numPlots/plotCols) # Number of rows needed, calculated from # of cols
# Set up the page
grid.newpage()
pushViewport(viewport(layout = grid.layout(plotRows, plotCols)))
vplayout <- function(x, y)
viewport(layout.pos.row = x, layout.pos.col = y)
# Make each plot, in the correct location
for (i in 1:numPlots) {
curRow = ceiling(i/plotCols)
curCol = (i-1) %% plotCols + 1
print(plots[[i]], vp = vplayout(curRow, curCol ))
}
}

Yes, methinks you need to arrange your data appropriately. One way would be this:
X <- data.frame(x=rep(x,2),
y=c(3*x+eps, 2*x+eps),
case=rep(c("first","second"), each=100))
qplot(x, y, data=X, facets = . ~ case) + geom_smooth()
I am sure there are better tricks in plyr or reshape -- I am still not really up to speed
on all these powerful packages by Hadley.

Using the reshape package you can do something like this.
library(ggplot2)
wide <- data.frame(x = rnorm(100), eps = rnorm(100, 0, .2))
wide$first <- with(wide, 3 * x + eps)
wide$second <- with(wide, 2 * x + eps)
long <- melt(wide, id.vars = c("x", "eps"))
ggplot(long, aes(x = x, y = value)) + geom_smooth() + geom_point() + facet_grid(.~ variable)

There is also multipanelfigure package that is worth to mention. See also this answer.
library(ggplot2)
theme_set(theme_bw())
q1 <- ggplot(mtcars) + geom_point(aes(mpg, disp))
q2 <- ggplot(mtcars) + geom_boxplot(aes(gear, disp, group = gear))
q3 <- ggplot(mtcars) + geom_smooth(aes(disp, qsec))
q4 <- ggplot(mtcars) + geom_bar(aes(carb))
library(magrittr)
library(multipanelfigure)
figure1 <- multi_panel_figure(columns = 2, rows = 2, panel_label_type = "none")
# show the layout
figure1
figure1 %<>%
fill_panel(q1, column = 1, row = 1) %<>%
fill_panel(q2, column = 2, row = 1) %<>%
fill_panel(q3, column = 1, row = 2) %<>%
fill_panel(q4, column = 2, row = 2)
figure1
# complex layout
figure2 <- multi_panel_figure(columns = 3, rows = 3, panel_label_type = "upper-roman")
figure2
figure2 %<>%
fill_panel(q1, column = 1:2, row = 1) %<>%
fill_panel(q2, column = 3, row = 1) %<>%
fill_panel(q3, column = 1, row = 2) %<>%
fill_panel(q4, column = 2:3, row = 2:3)
figure2
Created on 2018-07-06 by the reprex package (v0.2.0.9000).

ggplot2 is based on grid graphics, which provide a different system for arranging plots on a page. The par(mfrow...) command doesn't have a direct equivalent, as grid objects (called grobs) aren't necessarily drawn immediately, but can be stored and manipulated as regular R objects before being converted to a graphical output. This enables greater flexibility than the draw this now model of base graphics, but the strategy is necessarily a little different.
I wrote grid.arrange() to provide a simple interface as close as possible to par(mfrow). In its simplest form, the code would look like:
library(ggplot2)
x <- rnorm(100)
eps <- rnorm(100,0,.2)
p1 <- qplot(x,3*x+eps)
p2 <- qplot(x,2*x+eps)
library(gridExtra)
grid.arrange(p1, p2, ncol = 2)
More options are detailed in this vignette.
One common complaint is that plots aren't necessarily aligned e.g. when they have axis labels of different size, but this is by design: grid.arrange makes no attempt to special-case ggplot2 objects, and treats them equally to other grobs (lattice plots, for instance). It merely places grobs in a rectangular layout.
For the special case of ggplot2 objects, I wrote another function, ggarrange, with a similar interface, which attempts to align plot panels (including facetted plots) and tries to respect the aspect ratios when defined by the user.
library(egg)
ggarrange(p1, p2, ncol = 2)
Both functions are compatible with ggsave(). For a general overview of the different options, and some historical context, this vignette offers additional information.

Update: This answer is very old. gridExtra::grid.arrange() is now the recommended approach.
I leave this here in case it might be useful.
Stephen Turner posted the arrange() function on Getting Genetics Done blog (see post for application instructions)
vp.layout <- function(x, y) viewport(layout.pos.row=x, layout.pos.col=y)
arrange <- function(..., nrow=NULL, ncol=NULL, as.table=FALSE) {
dots <- list(...)
n <- length(dots)
if(is.null(nrow) & is.null(ncol)) { nrow = floor(n/2) ; ncol = ceiling(n/nrow)}
if(is.null(nrow)) { nrow = ceiling(n/ncol)}
if(is.null(ncol)) { ncol = ceiling(n/nrow)}
## NOTE see n2mfrow in grDevices for possible alternative
grid.newpage()
pushViewport(viewport(layout=grid.layout(nrow,ncol) ) )
ii.p <- 1
for(ii.row in seq(1, nrow)){
ii.table.row <- ii.row
if(as.table) {ii.table.row <- nrow - ii.table.row + 1}
for(ii.col in seq(1, ncol)){
ii.table <- ii.p
if(ii.p > n) break
print(dots[[ii.table]], vp=vp.layout(ii.table.row, ii.col))
ii.p <- ii.p + 1
}
}
}

Using tidyverse:
x <- rnorm(100)
eps <- rnorm(100,0,.2)
df <- data.frame(x, eps) %>%
mutate(p1 = 3*x+eps, p2 = 2*x+eps) %>%
tidyr::gather("plot", "value", 3:4) %>%
ggplot(aes(x = x , y = value)) +
geom_point() +
geom_smooth() +
facet_wrap(~plot, ncol =2)
df

Consider also ggarrange from the ggpubr package. It has many benefits, including options to align axes between plots and to merge common legends into one.

The above solutions may not be efficient if you want to plot multiple ggplot plots using a loop (e.g. as asked here: Creating multiple plots in ggplot with different Y-axis values using a loop), which is a desired step in analyzing the unknown (or large) data-sets (e.g., when you want to plot Counts of all variables in a data-set).
The code below shows how to do that using the mentioned above 'multiplot()', the source of which is here: http://www.cookbook-r.com/Graphs/Multiple_graphs_on_one_page_(ggplot2):
plotAllCounts <- function (dt){
plots <- list();
for(i in 1:ncol(dt)) {
strX = names(dt)[i]
print(sprintf("%i: strX = %s", i, strX))
plots[[i]] <- ggplot(dt) + xlab(strX) +
geom_point(aes_string(strX),stat="count")
}
columnsToPlot <- floor(sqrt(ncol(dt)))
multiplot(plotlist = plots, cols = columnsToPlot)
}
Now run the function - to get Counts for all variables printed using ggplot on one page
dt = ggplot2::diamonds
plotAllCounts(dt)
One things to note is that:
using aes(get(strX)), which you would normally use in loops when working with ggplot , in the above code instead of aes_string(strX) will NOT draw the desired plots. Instead, it will plot the last plot many times. I have not figured out why - it may have to do the aes and aes_string are called in ggplot.
Otherwise, hope you'll find the function useful.

In my experience gridExtra:grid.arrange works perfectly, if you are trying to generate plots in a loop.
Short Code Snippet:
gridExtra::grid.arrange(plot1, plot2, ncol = 2)
** Updating this comment to show how to use grid.arrange() within a for loop to generate plots for different factors of a categorical variable.
for (bin_i in levels(athlete_clean$BMI_cat)) {
plot_BMI <- athlete_clean %>% filter(BMI_cat == bin_i) %>% group_by(BMI_cat,Team) %>% summarize(count_BMI_team = n()) %>%
mutate(percentage_cbmiT = round(count_BMI_team/sum(count_BMI_team) * 100,2)) %>%
arrange(-count_BMI_team) %>% top_n(10,count_BMI_team) %>%
ggplot(aes(x = reorder(Team,count_BMI_team), y = count_BMI_team, fill = Team)) +
geom_bar(stat = "identity") +
theme_bw() +
# facet_wrap(~Medal) +
labs(title = paste("Top 10 Participating Teams with \n",bin_i," BMI",sep=""), y = "Number of Athletes",
x = paste("Teams - ",bin_i," BMI Category", sep="")) +
geom_text(aes(label = paste(percentage_cbmiT,"%",sep = "")),
size = 3, check_overlap = T, position = position_stack(vjust = 0.7) ) +
theme(axis.text.x = element_text(angle = 00, vjust = 0.5), plot.title = element_text(hjust = 0.5), legend.position = "none") +
coord_flip()
plot_BMI_Medal <- athlete_clean %>%
filter(!is.na(Medal), BMI_cat == bin_i) %>%
group_by(BMI_cat,Team) %>%
summarize(count_BMI_team = n()) %>%
mutate(percentage_cbmiT = round(count_BMI_team/sum(count_BMI_team) * 100,2)) %>%
arrange(-count_BMI_team) %>% top_n(10,count_BMI_team) %>%
ggplot(aes(x = reorder(Team,count_BMI_team), y = count_BMI_team, fill = Team)) +
geom_bar(stat = "identity") +
theme_bw() +
# facet_wrap(~Medal) +
labs(title = paste("Top 10 Winning Teams with \n",bin_i," BMI",sep=""), y = "Number of Athletes",
x = paste("Teams - ",bin_i," BMI Category", sep="")) +
geom_text(aes(label = paste(percentage_cbmiT,"%",sep = "")),
size = 3, check_overlap = T, position = position_stack(vjust = 0.7) ) +
theme(axis.text.x = element_text(angle = 00, vjust = 0.5), plot.title = element_text(hjust = 0.5), legend.position = "none") +
coord_flip()
gridExtra::grid.arrange(plot_BMI, plot_BMI_Medal, ncol = 2)
}
One of the Sample Plots from the above for loop is included below.
The above loop will produce multiple plots for all levels of BMI category.
Sample Image
If you wish to see a more comprehensive use of grid.arrange() within for loops, check out https://rpubs.com/Mayank7j_2020/olympic_data_2000_2016

The cowplot package gives you a nice way to do this, in a manner that suits publication.
x <- rnorm(100)
eps <- rnorm(100,0,.2)
A = qplot(x,3*x+eps, geom = c("point", "smooth"))+theme_gray()
B = qplot(x,2*x+eps, geom = c("point", "smooth"))+theme_gray()
cowplot::plot_grid(A, B, labels = c("A", "B"), align = "v")

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