aes_group_order {ggplot2} | R Documentation

## Aesthetics: group

### Description

Aesthetics: group

### Examples

    # 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")

[Package ggplot2 version 2.1.0 Index]