Articles - R Graphics Essentials

Combine Multiple GGPlots in One Graph

  |   69  |  Post a comment  |  R Graphics Essentials

This chapter describes, step by step, how to combine multiple ggplots in one graph, as well as, over multiple pages, using helper functions available in the ggpubr R package. We’ll also describe how to save the arranged plots and how to save multiple ggplots in one pdf file.

Contents:


Prerequisites

Load required packages and set the theme function theme_pubr() [in ggpubr] as the default theme:

library(ggplot2)
library(ggpubr)
theme_set(theme_pubr())

Arrange on one page

  • Create some basic plots as follow:
# 0. Define custom color palette and prepare the data
my3cols <- c("#E7B800", "#2E9FDF", "#FC4E07")
ToothGrowth$dose <- as.factor(ToothGrowth$dose)
# 1. Create a box plot (bp)
p <- ggplot(ToothGrowth, aes(x = dose, y = len))
bxp <- p + geom_boxplot(aes(color = dose)) +
  scale_color_manual(values = my3cols)
# 2. Create a dot plot (dp)
dp <- p + geom_dotplot(aes(color = dose, fill = dose), 
                       binaxis='y', stackdir='center') +
  scale_color_manual(values = my3cols) + 
  scale_fill_manual(values = my3cols)
# 3. Create a line plot
lp <- ggplot(economics, aes(x = date, y = psavert)) + 
  geom_line(color = "#E46726") 
  • Combine multiple ggplot on one page. Use the function ggarrange()[ggpubr package], a wrapper around the function plot_grid() [cowplot package]. Compared to plot_grid(), ggarange() can arrange multiple ggplots over multiple pages.
figure <- ggarrange(bxp, dp, lp,
                    labels = c("A", "B", "C"),
                    ncol = 2, nrow = 2)
figure

Annotate the arranged figure

Key R function: annotate_figure() [in ggpubr].

annotate_figure(
  figure,
  top = text_grob("Visualizing len",
                  color = "red", face = "bold", size = 14),
  bottom = text_grob("Data source: \n ToothGrowth", color = "blue",
                     hjust = 1, x = 1, face = "italic", size = 10),
  left = text_grob("Fig arranged using ggpubr",
                   color = "green", rot = 90),
  right = "I'm done, thanks :-)!",
  fig.lab = "Figure 1", fig.lab.face = "bold"
  )

Change column and row span of a plot

We’ll use nested ggarrange() functions to change column/row span of plots. For example, using the R code below:

  • the line plot (lp) will live in the first row and spans over two columns
  • the box plot (bxp) and the dot plot (dp) will be first arranged and will live in the second row with two different columns
ggarrange(
  lp,                # First row with line plot
  # Second row with box and dot plots
  ggarrange(bxp, dp, ncol = 2, labels = c("B", "C")), 
  nrow = 2, 
  labels = "A"       # Label of the line plot
  ) 

Use shared legend for combined ggplots

To place a common unique legend in the margin of the arranged plots, the function ggarrange() [in ggpubr] can be used with the following arguments:

  • common.legend = TRUE: place a common legend in a margin
  • legend: specify the legend position. Allowed values include one of c(“top”, “bottom”, “left”, “right”)
ggarrange(
  bxp, dp, labels = c("A", "B"),
  common.legend = TRUE, legend = "bottom"
  )

Mix table, text and ggplot2 graphs

In this section, we’ll show how to plot a table and text alongside a chart. The iris data set will be used.

We start by creating the following plots:

  1. a density plot of the variable “Sepal.Length”. R function: ggdensity() [in ggpubr]
  2. a plot of the summary table containing the descriptive statistics (mean, sd, … ) of Sepal.Length.
    • R function for computing descriptive statistics: desc_statby() [in ggpubr].
    • R function to draw a textual table: ggtexttable() [in ggpubr].
  3. a plot of a text paragraph. R function: ggparagraph() [in ggpubr].

We finish by arranging/combining the three plots using the function ggarrange() [in ggpubr]

# Density plot of "Sepal.Length"
#::::::::::::::::::::::::::::::::::::::
density.p <- ggdensity(iris, x = "Sepal.Length", 
                       fill = "Species", palette = "jco")
# Draw the summary table of Sepal.Length
#::::::::::::::::::::::::::::::::::::::
# Compute descriptive statistics by groups
stable <- desc_statby(iris, measure.var = "Sepal.Length",
                      grps = "Species")
stable <- stable[, c("Species", "length", "mean", "sd")]
# Summary table plot, medium orange theme
stable.p <- ggtexttable(stable, rows = NULL, 
                        theme = ttheme("mOrange"))
# Draw text
#::::::::::::::::::::::::::::::::::::::
text <- paste("iris data set gives the measurements in cm",
              "of the variables sepal length and width",
              "and petal length and width, respectively,",
              "for 50 flowers from each of 3 species of iris.",
             "The species are Iris setosa, versicolor, and virginica.",
             sep = " ")
text.p <- ggparagraph(text = text, face = "italic", size = 11, color = "black")
# Arrange the plots on the same page
ggarrange(density.p, stable.p, text.p, 
          ncol = 1, nrow = 3,
          heights = c(1, 0.5, 0.3))

Arrange over multiple pages

If you have a long list of ggplots, say n = 20 plots, you may want to arrange the plots and to place them on multiple pages. With 4 plots per page, you need 5 pages to hold the 20 plots.

The function ggarrange() [ggpubr] provides a convenient solution to arrange multiple ggplots over multiple pages. After specifying the arguments nrow and ncol,ggarrange()` computes automatically the number of pages required to hold the list of the plots. It returns a list of arranged ggplots.

For example the following R code,

multi.page <- ggarrange(bxp, dp, lp, bxp,
                        nrow = 1, ncol = 2)

returns a list of two pages with two plots per page. You can visualize each page as follow:

multi.page[[1]] # Visualize page 1
multi.page[[2]] # Visualize page 2

You can also export the arranged plots to a pdf file using the function ggexport() [ggpubr]:

ggexport(multi.page, filename = "multi.page.ggplot2.pdf")

See the PDF file: Multi.page.ggplot2

Export the arranged plots

R function: ggexport() [in ggpubr].

  • Export the arranged figure to a pdf, eps or png file (one figure per page).
ggexport(figure, filename = "figure1.pdf")
  • It’s also possible to arrange the plots (2 plot per page) when exporting them.

Export individual plots to a pdf file (one plot per page):

ggexport(bxp, dp, lp, bxp, filename = "test.pdf")

Arrange and export. Specify the nrow and ncol arguments to display multiple plots on the same page:

ggexport(bxp, dp, lp, bxp, filename = "test.pdf",
         nrow = 2, ncol = 1)

See also