ggplot2: how to easily combine multiple graphs in one, part 3

This article will show step by step how to combine several ggplot graphs in one or several illustrations using the helper functions available in the R ggpubr , cowplot and gridExtra packages . We also describe how to export the resulting graphs to a file.
First part
The second part of

Mix table, text and ggplot2 graphics

In this section, we will show how to display a table and text along with a graph. We will use the iris dataset.

Start by creating these graphs:

1. density graph of Sepal.Length. R function: ggdensity() [in ggpubr ]
2. summary table containing descriptive statistics (mean, standard deviation, etc.) Sepal.Length
   
   • Function R for calculating descriptive statistics: desc_statby() [in ggpubr ]
   • R function to create a table with text: ggtexttable() [in ggpubr ]
3. paragraph text . R function: ggparagraph() [in ggpubr ]

We conclude by combining all three graphs using the ggarrange() function [in ggpubr ].
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 #   "Sepal.Length" #:::::::::::::::::::::::::::::::::::::: density.p <- ggdensity(iris, x = "Sepal.Length", fill = "Species", palette = "jco") #    Sepal.Length #:::::::::::::::::::::::::::::::::::::: #      stable <- desc_statby(iris, measure.var = "Sepal.Length", grps = "Species") stable <- stable[, c("Species", "length", "mean", "sd")] #    ,  "medium orange" ( ) stable.p <- ggtexttable(stable, rows = NULL, theme = ttheme("mOrange")) #   #:::::::::::::::::::::::::::::::::::::: 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") #     ggarrange(density.p, stable.p, text.p, ncol = 1, nrow = 3, heights = c(1, 0.5, 0.3)) 

Adding a graphic element to ggplot

To add tables, graphs or other elements based on tables to the ggplot workspace, there is a function annotation_custom() [in ggplot2 ]. Simplified format:

 annotation_custom(grob, xmin, xmax, ymin, ymax) 

• grob : external graphic element to display
• xmin , xmax : x-location in coordinates (horizontal location)
• ymin , ymax : y-location in coordinates (vertical location)

Place the table in ggplot

We use the density.p and stable.p graphics created in the previous section.

 density.p + annotation_custom(ggplotGrob(stable.p), xmin = 5.5, ymin = 0.7, xmax = 8) 

Place the scatterplot in ggplot

1. Create a scatter plot for y = “Sepal.Width” by x = “Sepal.Length” from the iris dataset. R: ggscatter() [ ggpubr ] function .
2. Create a separate scatterplot of x and y variables with a transparent background. R function: ggboxplot() [ ggpubr ].
3. Transform a scatterplot into a graphical object called “grob” in Grid terminology. R function: ggplotGrob() [ ggplot2 ].
4. Place the scatter plots inside the scatter plots. R: annotation_custom() [ ggplot2 ] function .

Since the scatterplot placed inside is superimposed at several points, a transparent background is used for it.

 #     ("Species") #:::::::::::::::::::::::::::::::::::::::::::::::::::::::::: sp <- ggscatter(iris, x = "Sepal.Length", y = "Sepal.Width", color = "Species", palette = "jco", size = 3, alpha = 0.6) #    x/y #:::::::::::::::::::::::::::::::::::::::::::::::::::::::::: #    x xbp <- ggboxplot(iris$Sepal.Length, width = 0.3, fill = "lightgray") + rotate() + theme_transparent() #     ybp <- ggboxplot(iris$Sepal.Width, width = 0.3, fill = "lightgray") + theme_transparent() #     #   “grob”   Grid xbp_grob <- ggplotGrob(xbp) ybp_grob <- ggplotGrob(ybp) #       #:::::::::::::::::::::::::::::::::::::::::::::::::::::::::: xmin <- min(iris$Sepal.Length); xmax <- max(iris$Sepal.Length) ymin <- min(iris$Sepal.Width); ymax <- max(iris$Sepal.Width) yoffset <- (1/15)*ymax; xoffset <- (1/15)*xmax #  xbp_grob    sp + annotation_custom(grob = xbp_grob, xmin = xmin, xmax = xmax, ymin = ymin-yoffset, ymax = ymin+yoffset) + #  ybp_grob    annotation_custom(grob = ybp_grob, xmin = xmin-xoffset, xmax = xmin+xoffset, ymin = ymin, ymax = ymax) 

Add a background image to ggplot2 graphics

Import background image. Use either the readJPEG() function [in the jpeg package] or the readPNG() function [in the png package] depending on the format of the background image.

To test the example below, make sure the png package is installed. You can install it using the install.packages(“png”) command.

 #   img.file <- system.file(file.path("images", "background-image.png"), package = "ggpubr") img <- png::readPNG(img.file) 

Combine ggplot with background image. R function: background_image() [in ggpubr ].

 library(ggplot2) library(ggpubr) ggplot(iris, aes(Species, Sepal.Length))+ background_image(img)+ geom_boxplot(aes(fill = Species), color = "white")+ fill_palette("jco") 

Change the transparency of the fill in the scatter diagrams by specifying the argument alpha. The value must be between [0, 1], where 0 is full transparency, and 1 is lack of transparency.

 library(ggplot2) library(ggpubr) ggplot(iris, aes(Species, Sepal.Length))+ background_image(img)+ geom_boxplot(aes(fill = Species), color = "white", alpha = 0.5)+ fill_palette("jco") 

Another example, overlaying a map of France on ggplot2:

 mypngfile <- download.file("https://upload.wikimedia.org/wikipedia/commons/thumb/e/e4/France_Flag_Map.svg/612px-France_Flag_Map.svg.png", destfile = "france.png", mode = 'wb') img <- png::readPNG('france.png') ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width)) + background_image(img)+ geom_point(aes(color = Species), alpha = 0.6, size = 5)+ color_palette("jco")+ theme(legend.position = "top") 

We have graphics on several pages.

If you have a long list of ggplot-s, say, n = 20, you might want to organize them by placing them on several pages. If there are 4 graphics on the page, for 20 you will need 5 pages.

The ggarrange() function [in ggpubr ] provides a convenient solution to place several ggplot-s on several pages. After setting the nrow and ncol arguments, the ggarrange() function automatically calculates the number of pages it takes to place all the graphs. It returns a list of ordered ggplot-s.

For example, the code below

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

returns two pages with two graphs on each. You can display each page like this:

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

Ordered graphs can be exported to a pdf file using the ggexport() function [in ggpubr ]:

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

PDF file

Multipage output can be obtained with the marrangeGrob() function [in gridExtra ].

 library(gridExtra) res <- marrangeGrob(list(bxp, dp, bp, sp), nrow = 1, ncol = 2) #   pdf- ggexport(res, filename = "multi.page.ggplot2.pdf") #   res 

Nested positioning with ggarrange ()

Let us arrange the graphs created in the previous sections.

 p1 <- ggarrange(sp, bp + font("x.text", size = 9), ncol = 1, nrow = 2) p2 <- ggarrange(density.p, stable.p, text.p, ncol = 1, nrow = 3, heights = c(1, 0.5, 0.3)) ggarrange(p1, p2, ncol = 2, nrow = 1) 

Export graphs

Function R: ggexport() [in ggpubr ].

First, create a list of 4 ggplot-s corresponding to the variables Sepal.Length, Sepal.Width, Petal.Length and Petal.Width in the iris data set.

 plots <- ggboxplot(iris, x = "Species", y = c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width"), color = "Species", palette = "jco" ) plots[[1]] #    plots[[2]] #     .. 

You can then export individual graphs to a file (pdf, eps or png) (one graph per page). You can arrange the graphics (2 per page) when exporting.

Export individual graphs to pdf (one per page):

 ggexport(plotlist = plots, filename = "test.pdf") 

Arrange and export. Set nrow and ncol to display several graphs on the same page:

 ggexport(plotlist = plots, filename = "test.pdf", nrow = 2, ncol = 1)