![]() ![]() 2, size = 1 ) + ggtitle ( "Fitted growth curve per diet" ) # Third plot P 2 <- ggplot ( ChickWeight, aes ( x = Time, y = weight, colour = Diet )) + geom_point ( alpha =. P 1 <- ggplot ( ChickWeight, aes ( x = Time, y = weight, colour = Diet, group = Chick )) + geom_line () + ggtitle ( "Growth curve for individual chicks" ) # Second plot Ymin <- min(iris$Sepal.Width) ymax <- max(iris$Sepal.Library ( ggplot2 ) # This example uses the ChickWeight dataset, which comes with ggplot2 Xmin <- min(iris$Sepal.Length) xmax <- max(iris$Sepal.Length) # Place box plots inside the scatter plot Ybp <- ggboxplot(iris$Sepal.Width, width = 0.3, fill = "lightgray") + Xbp <- ggboxplot(iris$Sepal.Length, width = 0.3, fill = "lightgray") + Sp <- ggscatter(iris, x = "Sepal.Length", y = "Sepal.Width", # Scatter plot colored by groups ("Species") Text.p <- ggparagraph(text = text, face = "italic", size = 11, color = "black")Īs the inset box plot overlaps with some points, a transparent background is used for the box plots. "The species are Iris setosa, versicolor, and virginica.", sep = " ") ![]() "for 50 flowers from each of 3 species of iris.", "and petal length and width, respectively,", "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(). 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(). ![]()
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