R/aes-group-order.r

In animint2: Animated Interactive Grammar of Graphics

## #' Aesthetics: group
## #'
## #' @name aes_group_order
## #' @aliases group
## #'
## #' @examples
## #' \donttest{
## #'
## #' # By default, the group is set to the interaction of all discrete variables in the
## #' # plot. This often partitions the data correctly, but when it does not, or when
## #' # no discrete variable is used in the plot, you will need to explicitly define the
## #' # grouping structure, by mapping group to a variable that has a different value
## #' # for each group.
## #'
## #' # For most applications you can simply specify the grouping with
## #' # various aesthetics (colour, shape, fill, linetype) or with facets.
## #'
## #' p <- ggplot(mtcars, aes(wt, mpg))
## #' # A basic scatter plot
## #' p + geom_point(size = 4)
## #' # The colour aesthetic
## #' p + geom_point(aes(colour = factor(cyl)), size = 4)
## #' # Or you can use shape to distinguish the data
## #' p + geom_point(aes(shape = factor(cyl)), size = 4)
## #'
## #' # Using fill
## #' a <- ggplot(mtcars, aes(factor(cyl)))
## #' a + geom_bar()
## #' a + geom_bar(aes(fill = factor(cyl)))
## #' a + geom_bar(aes(fill = factor(vs)))
## #'
## #' # Using linetypes
## #' rescale01 <- function(x) (x - min(x)) / diff(range(x))
## #' ec_scaled <- data.frame(
## #'   date = economics$date,
## #'   plyr::colwise(rescale01)(economics[, -(1:2)]))
## #' ecm <- reshape2::melt(ec_scaled, id.vars = "date")
## #' f <- ggplot(ecm, aes(date, value))
## #' f + geom_line(aes(linetype = variable))
## #'
## #' # Using facets
## #' k <- ggplot(diamonds, aes(carat, ..density..)) + geom_histogram(binwidth = 0.2)
## #' k + facet_grid(. ~ cut)
## #'
## #' # There are three common cases where the default is not enough, and we
## #' # will consider each one below. In the following examples, we will use a simple
## #' # longitudinal dataset, Oxboys, from the nlme package. It records the heights
## #' # (height) and centered ages (age) of 26 boys (Subject), measured on nine
## #' # occasions (Occasion).
## #'
## #' # Multiple groups with one aesthetic
## #' h <- ggplot(nlme::Oxboys, aes(age, height))
## #' # A single line tries to connect all the observations
## #' h + geom_line()
## #' # The group aesthetic maps a different line for each subject
## #' h + geom_line(aes(group = Subject))
## #'
## #' # Different groups on different layers
## #' h <- h + geom_line(aes(group = Subject))
## #' # Using the group aesthetic with both geom_line() and geom_smooth()
## #' # groups the data the same way for both layers
## #' h + geom_smooth(aes(group = Subject), method = "lm", se = FALSE)
## #' # Changing the group aesthetic for the smoother layer
## #' # fits a single line of best fit across all boys
## #' h + geom_smooth(aes(group = 1), size = 2, method = "lm", se = FALSE)
## #'
## #' # Overriding the default grouping
## #' # The plot has a discrete scale but you want to draw lines that connect across
## #' # groups. This is the strategy used in interaction plots, profile plots, and parallel
## #' # coordinate plots, among others. For example, we draw boxplots of height at
## #' # each measurement occasion
## #' boysbox <- ggplot(nlme::Oxboys, aes(Occasion, height))
## #' boysbox + geom_boxplot()
## #' # There is no need to specify the group aesthetic here; the default grouping
## #' # works because occasion is a discrete variable. To overlay individual trajectories
## #' # we again need to override the default grouping for that layer with aes(group = Subject)
## #' boysbox <- boysbox + geom_boxplot()
## #' boysbox + geom_line(aes(group = Subject), colour = "blue")
## #' }
## NULL