![]() ![]() p2 <- ggplot(penguins, aes(x=body_mass_g, y=n, color=species))+ Plot 2 - Boxplots of body mass (g) separated by species. Geom_smooth(method=lm, color="black",se=T) + p1 <- ggplot(penguins,Īes(x=body_mass_g,y=bill_length_mm,color=species))+ Plot 1 - A scatterplot of body mass (g) and bill length (mm) with a linear regression line. These four plots will be looking at different relationships of body mass (g) and bill length (mm) separated by species(Adelie, Chinstrap, Gentoo). Now, we are going to create four plots using the ggplot2 package that will be used in the following examples. Further information on how to use color to your advantage in ggplots, see here and here. More information about how to use the RColorBrewer package can be found here. Library(Rcolorbrewer) # for multiple color palettes Library(ggthemes) # use themes to clean up the data visualizations Load in relevant libraries library(ggplot2) # to create data visualizations The next few sections cover multi-panel figure using plots created with the ggplot2 package, which allows more flexibilty when creating figures. While baseR can be useful for creating basic plots, it has limited functionality. The main downside of using par() parameter and layout() function in baseR, is that you can only use plots created using baseR code. ![]() Using layout() also changes the proportions of the graphs which may affect the final interpretation of results. Layout <- mtext("Bill Length and Bill Depth by Species", side=3, outer=T, line=-1)Īdditional examples of how to use the layout() function can be found here. Layout <- boxplot(penguins$bill_length_mm ~ penguins$species, Layout <- boxplot(penguins$bill_depth_mm ~ penguins$species, Layout <- plot(penguins$bill_length_mm, penguins$bill_depth_mm, Parameter <- boxplot(penguins$bill_length_mm ~ penguins$species,Ĭompared to par() parameter, the layout() function can give you more control over the proportions of the plotting space, for example: layout(matrix(c(1,1,2,3), 2, 2, byrow = T)) Parameter <- boxplot(penguins$bill_depth_mm ~ penguins$species, Parameter <- plot(penguins$bill_length_mm, penguins$bill_depth_mm, However, par() doesn’t allow you to control the proportions of the plotting space, as you can see in the following example: parameter <- par(mfrow=c(1,3)) #set up the plotting space Par() allows you to adjust plots in the horizontal or vertical plane. Using the par(mfrow=) parameter or layout() functions, we can adjust the plotting space to fit multiple plots. Tp fit multiple plots on the sample panel, there are a few ways to do this using baseR. Main="A: Bill Length by Bill Depth ") #plot titleĪ<- boxplot(penguins$bill_depth_mm ~ penguins$species,ī<- boxplot(penguins$bill_length_mm ~ penguins$species, Ylab="Bill Depth (mm)", #label for y-axis Xlab="Bill Length (mm)", #label for x-axis Individually, each baseR graph will look like this: plot(penguins$bill_length_mm, penguins$bill_depth_mm, In addition to this relationship, you also want to list information about the distribution of these two variables you are investigating. Say you are looking at the relationship between bill length and bill depth in penguins. However, this matrix can only support plots created using baseR frameworks, so its functionality is limited. ?penguins #can be used to find more information about the penguins datasetīaseR allows you to create a matrix of a plot in one space. In these example figures, we will be using theme_clean() from the ggthemes package to standardize the aesthetics in these figures. Before creating figures from these data, we omitted any rows where values in one or more cells was missing, using the na.omit() function.Īdditionally, themes are tools that can be used create different aesthetics for figures. Instructions to download this package can be found here. In all these examples, we will use the penguins dataset in the palmerpenguins package. This post outlines some possible methods that you can use to present multiple graphs and the potential benefits/drawbacks of each. There are many methods that we can use to present multiple graphs together in R. For example, you might want to compare or contrast different graphs or perhaps emphasize a group of graphs for a report or presentation. Presenting multiple graphs in one figure can be a valuable tool for visualizing data. ![]()
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