Mix multiple graphs on the same page

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Easy way to mix multiple graphs on the same page - R software and data visualization

Install and load required packages

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

Prepare some data

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df <- ToothGrowth
# Convert the variable dose from a numeric to a factor variable
df$dose <- as.factor(df$dose)
head(df)
##    len supp dose
## 1  4.2   VC  0.5
## 2 11.5   VC  0.5
## 3  7.3   VC  0.5
## 4  5.8   VC  0.5
## 5  6.4   VC  0.5
## 6 10.0   VC  0.5

Cowplot: Publication-ready plots

The cowplot package is an extension to ggplot2 and it can be used to provide a publication-ready plots.

Basic plots

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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.

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

Arranging multiple graphs using cowplot

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# 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):

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


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

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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.
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    plot.iris <- ggplot(iris, aes(Sepal.Length, Sepal.Width)) + 
      geom_point() + facet_grid(. ~ Species) + stat_smooth(method = "lm") +
      background_grid(major = &#39;y&#39;, 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)

    grid.arrange: Create and arrange multiple plots

    The R code below creates a box plot, a dot plot, a violin plot and a stripchart (jitter plot) :

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    library(ggplot2)
    # Create a box plot
    bp <- ggplot(df, aes(x=dose, y=len, color=dose)) +
      geom_boxplot() + 
      theme(legend.position = "none")

    # Create a dot plot
    # Add the mean point and the standard deviation
    dp <- ggplot(df, aes(x=dose, y=len, fill=dose)) +
      geom_dotplot(binaxis=&#39;y&#39;, stackdir=&#39;center&#39;)+
      stat_summary(fun.data=mean_sdl, mult=1, 
                     geom="pointrange", color="red")+
       theme(legend.position = "none")

    # Create a violin plot
    vp <- ggplot(df, aes(x=dose, y=len)) +
      geom_violin()+
      geom_boxplot(width=0.1)

    # Create a stripchart
    sc <- ggplot(df, aes(x=dose, y=len, color=dose, shape=dose)) +
      geom_jitter(position=position_jitter(0.2))+
      theme(legend.position = "none") +
      theme_gray()

    Combine the plots using the function grid.arrange() [in gridExtra] :

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    library(gridExtra)
    grid.arrange(bp, dp, vp, sc, ncol=2, 
                 main="Multiple plots on the same page")

    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


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

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      # 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 :

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      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 :

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      # 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(&#39;#999999&#39;,&#39;#E69F00&#39;)) + 
        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(&#39;#999999&#39;,&#39;#E69F00&#39;)) + 
        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(&#39;#999999&#39;,&#39;#E69F00&#39;)) + 
        theme(legend.position = "none")

      Create a blank placeholder plot :

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      lankPlot <- 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 :

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      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


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        # 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))

        Insert an external graphical element inside a ggplot

        The function annotation_custom() [in ggplot2] can be used for adding tables, plots or other grid-based elements. The simplified format is :

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

        • grob: the external graphical element to display

        • xmin, xmax : x location in data coordinates (horizontal location)

        • ymin, ymax : y location in data coordinates (vertical location)


          • The different steps are :

          1. Create a scatter plot of y = f(x)

          2. Add, for example, the box plot of the variables x and y inside the scatter plot using the function annotation_custom()


            3. As the inset box plot overlaps with some points, a transparent background is used for the box plots.
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            # Create a transparent theme object
            transparent_theme <- theme(
             axis.title.x = element_blank(),
             axis.title.y = element_blank(),
             axis.text.x = element_blank(), 
             axis.text.y = element_blank(),
             axis.ticks = element_blank(),
             panel.grid = element_blank(),
             axis.line = element_blank(),
             panel.background = element_rect(fill = "transparent",colour = NA),
             plot.background = element_rect(fill = "transparent",colour = NA))

            Create the graphs :

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            p1 <- scatterPlot # see previous sections for the scatterPlot

            # Box plot of the x variable
            p2 <- ggplot(df2, aes(factor(1), x))+
              geom_boxplot(width=0.3)+coord_flip()+
              transparent_theme

            # Box plot of the y variable
            p3 <- ggplot(df2, aes(factor(1), y))+
              geom_boxplot(width=0.3)+
              transparent_theme

            # Create the external graphical elements
            # called a "grop" in Grid terminology
            p2_grob = ggplotGrob(p2)
            p3_grob = ggplotGrob(p3)
               

            # Insert p2_grob inside the scatter plot
            xmin <- min(x); xmax <- max(x)
            ymin <- min(y); ymax <- max(y)
            p1 + annotation_custom(grob = p2_grob, xmin = xmin, xmax = xmax, 
                                   ymin = ymin-1.5, ymax = ymin+1.5)


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            # Insert p3_grob inside the scatter plot
            p1 + annotation_custom(grob = p3_grob,
                                   xmin = xmin-1.5, xmax = xmin+1.5, 
                                   ymin = ymin, ymax = ymax)


            If you have a solution to insert, at the same time, both p2_grob and p3_grob inside the scatter plot, please let me a comment. I got some errors trying to do this…

            Mix table, text and ggplot2 graphs

            The functions below are required :

            • tableGrob() [in the package gridExtra] : for adding a data table to a graphic device

            • splitTextGrob() [in the package RGraphics] : for adding a text to a graph


              • Make sure that the package RGraphics is installed.
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              library(RGraphics)
              library(gridExtra)

              # Table
              p1 <- tableGrob(head(ToothGrowth))

              # Text
              text <- "ToothGrowth data describes the effect of Vitamin C on tooth growth in Guinea pigs.  Three dose levels of Vitamin C (0.5, 1, and 2 mg) with each of two delivery methods [orange juice (OJ) or ascorbic acid (VC)] are used."
              p2 <- splitTextGrob(text)

              # Box plot
              p3 <- ggplot(df, aes(x=dose, y=len)) + geom_boxplot()

              # Arrange the plots on the same page
              grid.arrange(p1, p2, p3, ncol=1)

              Infos

              This analysis has been performed using R software (ver. 3.1.2) and ggplot2 (ver. 1.0.0)
              Contribution from ：http://www.sthda.com/english/wiki/ggplot2-easy-way-to-mix-multiple-graphs-on-the-same-page-r-software-and-data-visualization

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