Install and load the package gridExtra

install.packages("gridExtra")
library("gridExtra")

Install and load the package cowplot

cowplot can be installed as follow:

install.packages("cowplot")

OR

as follow using devtools package (devtools should be installed before using the code below):

devtools::install_github("wilkelab/cowplot")

Load cowplot:

library("cowplot")

Basic plots

library(cowplot)
# Default plot
bp <- ggplot(df, aes(x=dose, y=len, color=dose)) +
  geom_boxplot() + 
  theme(legend.position = "none")
bp
# Add gridlines
bp + background_grid(major = "xy", minor = "none")

Recall that, the function ggsave()[in ggplot2 package] can be used to save ggplots. However, when working with cowplot, the function save_plot() [in cowplot package] is preferred. It?s an alternative to ggsave with a better support for multi-figur plots.

save_plot("mpg.pdf", plot.mpg,
          base_aspect_ratio = 1.3 # make room for figure legend
          )

Arranging multiple graphs using cowplot

# Scatter plot
sp <- ggplot(mpg, aes(x = cty, y = hwy, colour = factor(cyl)))+ 
  geom_point(size=2.5)
sp
# Bar plot
bp <- ggplot(diamonds, aes(clarity, fill = cut)) +
  geom_bar() +
  theme(axis.text.x = element_text(angle=70, vjust=0.5))
bp

Combine the two plots (the scatter plot and the bar plot):

plot_grid(sp, bp, labels=c("A", "B"), ncol = 2, nrow = 1)

The function draw_plot() can be used to place graphs at particulat locations with a particular sizes. The format of the function is:

draw_plot(plot, x = 0, y = 0, width = 1, height = 1)

• plot: the plot to place (ggplot2 or a gtable)
• x: The x location of the lower left corner of the plot.
• y: The y location of the lower left corner of the plot.
• width, height: the width and the height of the plot

The function ggdraw() is used to initialize an empty drawing canvas.

plot.iris <- ggplot(iris, aes(Sepal.Length, Sepal.Width)) + 
  geom_point() + facet_grid(. ~ Species) + stat_smooth(method = "lm") +
  background_grid(major = 'y', minor = "none") + # add thin horizontal lines 
  panel_border() # and a border around each panel
# plot.mpt and plot.diamonds were defined earlier
ggdraw() +
  draw_plot(plot.iris, 0, .5, 1, .5) +
  draw_plot(sp, 0, 0, .5, .5) +
  draw_plot(bp, .5, 0, .5, .5) +
  draw_plot_label(c("A", "B", "C"), c(0, 0, 0.5), c(1, 0.5, 0.5), size = 15)

Add a common legend for multiple ggplot2 graphs

This can be done in four simple steps :

1. Create the plots : p1, p2, ?.
2. Save the legend of the plot p1 as an external graphical element (called a ?grob? in Grid terminology)
3. Remove the legends from all plots
4. Draw all the plots with only one legend in the right panel

To save the legend of a ggplot, the helper function below can be used :

library(gridExtra)
get_legend<-function(myggplot){
  tmp <- ggplot_gtable(ggplot_build(myggplot))
  leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
  legend <- tmp$grobs[[leg]]
  return(legend)
}

(The function above is derived from this forum. )

# 1. Create the plots
#++++++++++++++++++++++++++++++++++
# Create a box plot
bp <- ggplot(df, aes(x=dose, y=len, color=dose)) +
  geom_boxplot()
# Create a violin plot
vp <- ggplot(df, aes(x=dose, y=len, color=dose)) +
  geom_violin()+
  geom_boxplot(width=0.1)+
  theme(legend.position="none")
# 2. Save the legend
#+++++++++++++++++++++++
legend <- get_legend(bp)
# 3. Remove the legend from the box plot
#+++++++++++++++++++++++
bp <- bp + theme(legend.position="none")
# 4. Arrange ggplot2 graphs with a specific width
grid.arrange(bp, vp, legend, ncol=3, widths=c(2.3, 2.3, 0.8))

Scatter plot with marginal density plots

Step 1/3. Create some data :

set.seed(1234)
x <- c(rnorm(500, mean = -1), rnorm(500, mean = 1.5))
y <- c(rnorm(500, mean = 1), rnorm(500, mean = 1.7))
group <- as.factor(rep(c(1,2), each=500))
df2 <- data.frame(x, y, group)
head(df2)

##             x          y group
## 1 -2.20706575 -0.2053334     1
## 2 -0.72257076  1.3014667     1
## 3  0.08444118 -0.5391452     1
## 4 -3.34569770  1.6353707     1
## 5 -0.57087531  1.7029518     1
## 6 -0.49394411 -0.9058829     1

Step 2/3. Create the plots :

# Scatter plot of x and y variables and color by groups
scatterPlot <- ggplot(df2,aes(x, y, color=group)) + 
  geom_point() + 
  scale_color_manual(values = c('#999999','#E69F00')) + 
  theme(legend.position=c(0,1), legend.justification=c(0,1))
# Marginal density plot of x (top panel)
xdensity <- ggplot(df2, aes(x, fill=group)) + 
  geom_density(alpha=.5) + 
  scale_fill_manual(values = c('#999999','#E69F00')) + 
  theme(legend.position = "none")
# Marginal density plot of y (right panel)
ydensity <- ggplot(df2, aes(y, fill=group)) + 
  geom_density(alpha=.5) + 
  scale_fill_manual(values = c('#999999','#E69F00')) + 
  theme(legend.position = "none")

Create a blank placeholder plot :

blankPlot <- ggplot()+geom_blank(aes(1,1))+
  theme(
    plot.background = element_blank(), 
   panel.grid.major = element_blank(),
   panel.grid.minor = element_blank(), 
   panel.border = element_blank(),
   panel.background = element_blank(),
   axis.title.x = element_blank(),
   axis.title.y = element_blank(),
   axis.text.x = element_blank(), 
   axis.text.y = element_blank(),
   axis.ticks = element_blank(),
   axis.line = element_blank()
     )

Step 3/3. Put the plots together:

Arrange ggplot2 with adapted height and width for each row and column :

library("gridExtra")
grid.arrange(xdensity, blankPlot, scatterPlot, ydensity, 
        ncol=2, nrow=2, widths=c(4, 1.4), heights=c(1.4, 4))

Create a complex layout using the function viewport()

The different steps are :

1. Create plots : p1, p2, p3, ?.
2. Move to a new page on a grid device using the function grid.newpage()
3. Create a layout 2X2 - number of columns = 2; number of rows = 2
4. Define a grid viewport : a rectangular region on a graphics device
5. Print a plot into the viewport

# Move to a new page
grid.newpage()
# Create layout : nrow = 2, ncol = 2
pushViewport(viewport(layout = grid.layout(2, 2)))
# A helper function to define a region on the layout
define_region <- function(row, col){
  viewport(layout.pos.row = row, layout.pos.col = col)
} 
# Arrange the plots
print(scatterPlot, vp=define_region(1, 1:2))
print(xdensity, vp = define_region(2, 1))
print(ydensity, vp = define_region(2, 2))