Call cowplot functions without attaching the package. You can do this with one line of code: theme_set(theme_gray())ģ. Revert the default theme back to the ggplot2 default. If you specify a particular theme, the default theme doesn't matter.Ģ. I think it's good practice to always specify a particular theme for each plot, just like I did with + theme_bw() in the example above. If this causes problems, you can use one of the following three approaches to work around them:ġ. The package behaves that way because it was originally written for internal lab uses, and we never use the default theme. One frequent point of confusion is that the cowplot package changes the default ggplot2 theme.
#GGPLOT BOXPLOT HOW TO#
There is also a vignette explaining how to make plots with a shared legend. (The ncol = 2 argument tells save_plot() that there are two plots side-by-side, and save_plot() makes the saved image twice as wide.)įor a more in-depth description of how to arrange plots in a grid see this vignette. The object that plot_grid() returns is another ggplot2 object, and you can save it with ggsave() as usual: p <- plot_grid(iris1, iris2, labels = "AUTO")Īlternatively, you can use the cowplot function save_plot(), which is a thin wrapper around ggsave() that makes it easy to get the correct dimensions for combined plots, e.g.: p <- plot_grid(iris1, iris2, labels = "AUTO") Iris2 <- ggplot(iris, aes(x = Sepal.Length, fill = Species)) +
Iris1 <- ggplot(iris, aes(x = Species, y = Sepal.Length)) + I wrote the cowplot package to solve this (and a few other) issues, specifically the function plot_grid(): library(cowplot) One downside of the solutions based on grid.arrange is that they make it difficult to label the plots with letters (A, B, etc.), as most journals require. See the answer by below and this vignette for an equivalent approach but the function allows finer controls on plot location and size, based on this vignette. The plot_grid function in the cowplot is worth checking out as an alternative to grid.arrange. This is pointed out below in many answers below, but I want to highlight this approach with examples equivalent to the above plots. This not only saves time arranging data, it is necessary when you want two dissimilar plots.įacets are helpful for making similar plots for different groups. This is the equivalent of making two distinct plots using par(mfrow = c(1,2)). Or, use arrangeGrob() in combination with ggsave(), ggsave("foo.pdf", arrangeGrob(plot1, plot2))
#GGPLOT BOXPLOT DRIVER#
To print the side effect to a file, specify a device driver (such as pdf, png, etc), e.g. This will plot the output as a side effect. This is useful when the two plots are not based on the same data, for example if you want to plot different variables without using reshape(). The function grid.arrange() in the gridExtra package will combine multiple plots this is how you put two side by side. Any ggplots side-by-side (or n plots on a grid)
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