Related
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")
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")
I have bar charts faceted by stock symbol name:
I would like to be able to add a little "subplot" of an indicator below each plot, like this:
Is this possible with ggplot2? I thought of a secondary axis, but most often there is no linear relation between the main plot axis and the indicator axis (so using sec_axis is not an option).
Thanks!
Since these appear to be categorically different types of plots, I think you'll have better luck creating separate plots and then rendering them together. Here's one solution using cowplot package:
library(ggplot2)
library(cowplot)
#sample data
df <- data.frame(x = 1:100, y = cumsum(rnorm(100)), volume = sample(1:10, 100, replace = TRUE))
p1 <- ggplot(df, aes(x,y)) +
geom_line()
p2 <- ggplot(df, aes(x,volume)) +
geom_bar(stat = "identity")
plot_grid(p1, p2, align = "v", ncol = 1, rel_heights = c(.8, .2))
Created on 2019-01-25 by the reprex package (v0.2.1)
Edit
Continuing to build on this kludgy example to support the concept of faceting. Ignore the ugly graph, it's smooshed due to image size constraints.
library(ggplot2)
library(cowplot)
library(gridExtra)
#sample data
df <- data.frame(x = 1:100, y = cumsum(rnorm(100)), volume = sample(1:10, 400, replace = TRUE), group = letters[1:4])
plots <- list()
for (j in unique(df$group)){
plot_df <- df[df$group == j, ]
p1 <- ggplot(plot_df, aes(x,y)) +
geom_line() +
facet_wrap(~group) +
xlab("")
p2 <- ggplot(plot_df, aes(x,volume)) +
geom_bar(stat = "identity")
p_out <- plot_grid(p1, p2, align = "v", ncol = 1, rel_heights = c(.7, .3))
plots[[j]] <- p_out
}
do.call(grid.arrange, plots)
Created on 2019-01-25 by the reprex package (v0.2.1)
I want to create a ggplot figure with six panels in R. The first five facets should represent five different subsets of data in bar charts, and the final facet should represent the whole data. I further want to have a fixed y-axis scale across the first five facets, but a different scale in the final facet. I am aware that it is currently not possible to specify individual ylims for each facet within the ggplot functionality (https://github.com/hadley/ggplot2/issues/187), but am wondering if I can do something similar using grid and possibly gtable packages, neither of which I'm very familiar with at the moment.
The following is my attempt. I replace the final facet with a facet in another figure.
library("ggplot2")
library("dplyr")
library("grid")
# create data
set.seed(1)
d1 <- data_frame(
value = rnorm(3 * 5, mean = 30, sd = 10),
f = rep(LETTERS[1:3], 5),
p = rep(paste("Panel", 1:5), each = 3)
)
d2 <- d1 %>%
mutate(p = "Total") %>%
rbind(d1)
# make initial figures
plot1 <- ggplot(d2, aes(f, value)) +
geom_bar(stat = "identity") +
facet_wrap(~ p) +
coord_cartesian(ylim = c(0, 50))
plot2 <- ggplot(d2, aes(f, value)) +
geom_bar(stat = "identity") +
facet_wrap(~ p, scales = "free_y")
# extract their grobs
g1 <- ggplotGrob(plot1)
g2 <- ggplotGrob(plot2)
# replace the final facet of plot1 with the final facet of plot2
g1[["grobs"]][[7]] <- g2[["grobs"]][[7]]
g1[["grobs"]][[19]] <- g2[["grobs"]][[19]]
g1[["grobs"]][[25]] <- g2[["grobs"]][[25]]
# draw the figure
grid.newpage()
grid.draw(g1)
And here's what I get.
As can be seen, however, the y-axis label of the final facet overlaps with the preceding facet. Does anyone know a way to avoid the overlap e.g., by making the final facet smaller?
One approach is to extract the "Total" plot from "g2", then insert it into "g1", but first remove the "Total" plot from "g1". But you will notice that the x-axis tick mark labels do not align across the facets.
# Load packages
library(ggplot2)
library(dplyr)
library(gtable)
library(grid)
# create data
set.seed(1)
d1 <- data.frame(
value = rnorm(3 * 5, mean = 30, sd = 10),
f = rep(LETTERS[1:3], 5),
p = rep(paste("Panel", 1:5), each = 3)
)
d2 <- d1 %>%
mutate(p = "Total") %>%
rbind(d1)
# make initial figures
plot1 <- ggplot(d2, aes(f, value)) +
geom_bar(stat = "identity") +
facet_wrap(~ p) +
coord_cartesian(ylim = c(0, 50))
plot2 <- ggplot(d2, aes(f, value)) +
geom_bar(stat = "identity") +
facet_wrap(~ p, scales = "free_y")
# Get the ggplot grobs
g1 <- ggplotGrob(plot1)
g2 <- ggplotGrob(plot2)
# Extract "Total" plot from g2
keep = g2$layout$name %in% c("panel-3-2", "axis-b-3-2", "axis-l-2-3", "strip-t-3-2")
pos = subset(g2$layout, keep, c(t,l,b,r))
g2 = g2[c(min(pos$t):max(pos$b)), c(min(pos$l):max(pos$r))]
# Remove "Total" plot from g1
keep = !g1$layout$name %in% c("panel-3-2", "axis-b-3-2", "strip-t-3-2")
pos = subset(g1$layout, !keep, c(t,l,b,r))
g1$grobs <- g1$grobs[keep]
g1$layout <- g1$layout[keep, ]
# Insert g2 into g1
g1 = gtable_add_grob(g1, g2, t=min(pos$t), b=max(pos$b), l=min(pos$l), r=max(pos$r))
# Draw it
grid.newpage()
grid.draw(g1)
Another approach is to extract the "Total" plot from "g2" as before, but to move its y-axis to the right side of the plot (using code borrowed from here. (I tweaked your "plot2" so that the tick mark labels are better aligned in the final plot.) In this way, the "Total" panel takes as much space as the other panels, and thus the x-axis tick mark labels align, but the y-axis for the "Total" panel sticks out to the right.
# Make initial figures
plot1 <- ggplot(d2, aes(f, value)) +
geom_bar(stat = "identity") +
facet_wrap(~ p) +
coord_cartesian(ylim = c(0, 50))
plot2 <- ggplot(d2, aes(f, value)) +
geom_bar(stat = "identity") +
facet_wrap(~ p, scales = "free_y") +
theme(axis.text.y = element_text(hjust = 0)) ## For better formatting of labels
# extract their grobs
g1 <- ggplotGrob(plot1)
g2 <- ggplotGrob(plot2)
# Extract "Total" plot from g2
keep = g2$layout$name %in% c("panel-3-2", "axis-b-3-2", "axis-l-2-3", "strip-t-3-2")
pos = subset(g2$layout, keep, c(t,l,b,r))
g2 = g2[c(min(pos$t):max(pos$b)), c(min(pos$l):max(pos$r))]
# Get the position of the panel in the layout
panel <- c(subset(g2$layout, grepl("panel", g2$layout$name), se = t:r))
# Get the row number of the y-axis in the layout
rn <- which(grepl("axis-l", g2$layout$name))
# Extract the axis (tick marks and axis text from the gtable)
axis.grob <- g2$grobs[[rn]]
axisl <- axis.grob$children[[2]] # Two children - get the second
axisl # Note: two grobs - tick marks and text
# Reverse the grobs and the widths
axisl$widths <- rev(axisl$widths)
axisl$grobs <- rev(axisl$grobs)
axisl$grobs[[1]]$x <- axisl$grobs[[1]]$x - unit(1, "npc") + unit(2.75, "pt")
axisl$grobs[[2]]$children[[1]]$x = unit(.15, "npc")
# Remove the column containing the left axis
g2 <- g2[, -(panel$r-1)]
## remove empty panels
keep = !g1$layout$name %in% c("panel-3-2", "axis-b-3-2", "strip-t-3-2")
pos = subset(g1$layout, !keep, c(t,l,b,r))
g1$grobs <- g1$grobs[keep]
g1$layout <- g1$layout[keep, ]
# Insert g2 into g1
g1 = gtable_add_grob(g1, g2, t = min(pos$t), b = max(pos$b), l = min(pos$l), r = max(pos$r))
# Add a new column to g1, and add the revised axisl grob to the new column.
pos = subset(g1$layout, grepl("panel", g1$layout$name), c(t,l,b,r)) # position of bottom right panel
g1 <- gtable_add_cols(g1, axisl$widths, max(pos$r))
g1 <- gtable_add_grob(g1, axisl, t = max(pos$b), l = max(pos$r)+1, r = max(pos$r)+2)
# Draw it
grid.newpage()
grid.draw(g1)
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")