1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
|
#' Aesthetics: group, order
#'
#' @name aes_group_order
#' @aliases group order
#'
#' @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
#' library(reshape2) # for melt
#' library(plyr) # for colwise
#' rescale01 <- function(x) (x - min(x)) / diff(range(x))
#' ec_scaled <- data.frame(
#' date = economics$date,
#' colwise(rescale01)(economics[, -(1:2)]))
#' ecm <- melt(ec_scaled, id = "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
#' library(nlme)
#' h <- ggplot(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(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")
#'
#' # Use the order aesthetic to change stacking order of bar charts
#' w <- ggplot(diamonds, aes(clarity, fill = cut))
#' w + geom_bar()
#' w + geom_bar(aes(order = desc(cut)))
#'
#' # Can also be used to change plot order of scatter plots
#' d <- ggplot(diamonds, aes(carat, price, colour = cut))
#' d + geom_point()
#' d + geom_point(aes(order = sample(seq_along(carat))))
#' }
NULL
|
