The primary purpose of this lesson is to learn how to customize our ggplot2 plots. Load a different data set using new load functions Learn how to make and modify scatter plots to make fairly different overall plot representations. Learn to customize your ggplot with labels, axes, text annotations, and themes. Scatter plots and plot customization Objectives Practice plotting using ggplot2: Lesson 3 Practice plotting using ggplot2: Lesson 2 Lesson5: Visualizing clusters with heatmap and dendrogram Lesson 4: Stat Transformations: Bar plots, box plots, and histograms p + facet_grid(.Lesson 3: Scatter plots and ggplot2 customizationĪdd a stat to our plot with stat_ellipse().Ĭreating a publication ready volcano plot You can also have panels displayed in a other geometries, although they are defined with multiple variables. p <- ggplot(data = mtcars, aes(mpg, wt)) + geom_point() Plot weight versus mpg for each value of vs and carb. Here, the panels are determined by the values of multiple variables. # Warning: invalid factor level, NAs generated Q + geom_point(data = cycl6, color = "red") p <- ggplot(data = mpg, aes(x = displ, y = hwy)) + geom_point() Sometimes we may want to add features to a single facet. Other scales options are "free_x" and "free_xy" Decorating facets The relative sizes between the bins are not so different, though. Visually, it looks like the histograms are about the same and they aren't in actual counts. p + facet_wrap(~color, scales = "free_y") We can get a better plot by letting the y axes vary freely. p <- ggplot(data = diamonds, aes(x = price)) + geom_histogram(binwidth = 1000) Some of the subsets may exhibit extreme bahavior of a variable causing other facets to plot in uncommunicative ways. p <- ggplot(data = mpg, aes(x = displ, y = hwy, color = drv)) + geom_point() We can add an aesthetic for another variable and get one legend. We can control the layout with options to the facet_wrap function. P <- ggplot(data = mpg, aes(x = displ, y = hwy)) + geom_point() # $ manufacturer: Factor w/ 15 levels "audi","chevrolet".: 1 1 1 1 1 1 1 1 1 1. The panels are calculated in a 1 dimensional ribbon that can be wrapped to multiple rows. Here, a single categorical variable defines subsets of the data. setwd("~/Documents/Computing with Data/13_Facets/") Each panel plot corresponds to a set value of the variable. The faceting is defined by a categorical variable or variables. This is a very useful feature of ggplot2. In some circumstances we want to plot relationships between set variables in multiple subsets of the data with the results appearing as panels in a larger figure. Plotting multiple groups with facets in ggplot2
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