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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