'of the variables sepal length and width', Text <- paste('iris data set gives the measurements in cm', Stable.p <- ggtexttable(stable, rows = NULL, # Summary table plot, medium orange theme Stable <- desc_statby(iris, measure.var = 'Sepal.Length', # Compute descriptive statistics by groups We finish by arranging/combining the three plots using the function ggarrange() # Density plot of 'Sepal.Length'ĭensity.p <- ggdensity(iris, x = 'Sepal.Length',

R function to draw a textual table: ggtexttable().

R function for computing descriptive statistics: desc_statby().

a plot of the summary table containing the descriptive statistics (mean, sd, … ) of Sepal.Length.

a density plot of the variable “Sepal.Length”.

We start by creating the following plots: In this section, we’ll show how to plot a table and text alongside a chart. Print(bp + rremove('x.text'), vp = define_region(row = 3, col = 1:2)) Print(dp, vp = define_region(row = 2, col = 2)) Print(bxp, vp = define_region(row = 2, col = 1)) Print(sp, vp = define_region(row = 1, col = 1:2)) # Span over two columns # A helper function to define a region on the layout PushViewport(viewport(layout = grid.layout(nrow = 3, ncol = 2)))

Define a grid viewport : a rectangular region on a graphics device.

Create a layout 2X2 - number of columns = 2 number of rows = 2.

Move to a new page on a grid device using the function grid.newpage().

The different steps can be summarized as follow : The function print() is used to place plots in a specified region. It provides also the helper function viewport() to define a region or a viewport on the layout. The grid R package can be used to create a complex layout with the help of the function grid.layout(). grid.arrange(bp, # bar plot spaning two columns The first row is all 1s, that’s where the first plot lives, spanning the two columns the second row contains plots 2 and 3 each occupying one column.

In the R code below layout_matrix is a 2x2 matrix (2 columns and 2 rows). It’s also possible to use the argument layout_matrix in the grid.arrange() function, to create a complex layout. Grid.arrange(sp, # First row with one plot spaning over 2 columnsĪrrangeGrob(bxp, dp, ncol = 2), # Second row with 2 plots in 2 different columns the box plot (bxp) and the dot plot (dp) will live in the second row with two plots in two different columns.the scatter plot (sp) will live in the first row and spans over two columns.The function arrangeGrop() helps to change the row/column span of a plot. size: Font size of the label to be drawnįor example, you can combine multiple plots, with particular locations and different sizes, as follow: library('cowplot')ĭraw_plot(bxp, x = 0, y =.x, y: Vector containing the x and y position of the labels, respectively.draw_plot_label(label, x = 0, y = 1, size = 16. It can handle vectors of labels with associated coordinates. Adds a plot label to the upper left corner of a graph. width, height: the width and the height of the plotĭraw_plot_label().x, y: The x/y location of the lower left corner of the plot.plot: the plot to place (ggplot2 or a gtable).

Places a plot somewhere onto the drawing canvas: draw_plot(plot, x = 0, y = 0, width = 1, height = 1) Note that, by default, coordinates run from 0 to 1, and the point (0, 0) is in the lower left corner of the canvas (see the figure below).ĭraw_plot(). Stat_cor(aes(color = cyl), label.x = 3) # Add correlation coefficient Shape = 'cyl' # Change point shape by groups 'cyl' Sp <- ggscatter(mtcars, x = 'wt', y = 'mpg',Ĭonf.int = TRUE, # Add confidence intervalĬolor = 'cyl', palette = 'jco', # Color by groups 'cyl' X.text.angle = 90 # Rotate vertically x axis texts Sort.val = 'asc', # Sort the value in ascending order Palette = 'jco', # jco journal color palett. # Bar plot (bp)īp <- ggbarplot(mtcars, x = 'name', y = 'mpg',Ĭolor = 'white', # Set bar border colors to white Sorting will be done globally, but not by groups. Change the fill color by the grouping variable “cyl”. Create an ordered bar plot and a scatter plot:Ĭreate ordered bar plots.You’ll learn how to combine these plots in the next sections using specific functions.īxp <- ggboxplot(ToothGrowth, x = 'dose', y = 'len',ĭp <- ggdotplot(ToothGrowth, x = 'dose', y = 'len',Ĭolor = 'dose', palette = 'jco', binwidth = 1) Bar plots and scatter plots using the mtcars data set.Box plots and dot plots using the ToothGrowth data set.We’ll start by creating 4 different plots: You can use any ggplot2 functions to create the plots that you want for arranging them later. Here, we’ll use ggplot2-based plotting functions available in ggpubr.

Multiple scatter plots in r