| stat_summary {ggplot2} | R Documentation |
stat_summary allows for tremendous flexibilty in
the specification of summary functions. The summary
function can either operate on a data frame (with
argument name fun.data) or on a vector
(fun.y, fun.ymax, fun.ymin).
stat_summary(mapping = NULL, data = NULL,
geom = "pointrange", position = "identity", ...)
mapping |
The aesthetic mapping, usually constructed
with |
data |
A layer specific dataset - only needed if you want to override the plot defaults. |
geom |
The geometric object to use display the data |
position |
The position adjustment to use for overlappling points on this layer |
... |
other arguments passed on to
|
A simple vector function is easiest to work with as you can return a single number, but is somewhat less flexible. If your summary function operates on a data.frame it should return a data frame with variables that the geom can use.
a data.frame with additional columns:
fun.data |
Complete summary function. Should take data frame as input and return data frame as output |
fun.ymin |
ymin summary function (should take numeric vector and return single number) |
fun.y |
y summary function (should take numeric vector and return single number) |
fun.ymax |
ymax summary function (should take numeric vector and return single number) |
stat_summary understands the following aesthetics (required aesthetics are in bold):
x
y
geom_errorbar,
geom_pointrange,
geom_linerange, geom_crossbar
for geoms to display summarised data
# Basic operation on a small dataset
d <- qplot(cyl, mpg, data=mtcars)
d + stat_summary(fun.data = "mean_cl_boot", colour = "red")
p <- qplot(cyl, mpg, data = mtcars, stat="summary", fun.y = "mean")
p
# Don't use ylim to zoom into a summary plot - this throws the
# data away
p + ylim(15, 30)
# Instead use coord_cartesian
p + coord_cartesian(ylim = c(15, 30))
# You can supply individual functions to summarise the value at
# each x:
stat_sum_single <- function(fun, geom="point", ...) {
stat_summary(fun.y=fun, colour="red", geom=geom, size = 3, ...)
}
d + stat_sum_single(mean)
d + stat_sum_single(mean, geom="line")
d + stat_sum_single(median)
d + stat_sum_single(sd)
d + stat_summary(fun.y = mean, fun.ymin = min, fun.ymax = max,
colour = "red")
d + aes(colour = factor(vs)) + stat_summary(fun.y = mean, geom="line")
# Alternatively, you can supply a function that operates on a data.frame.
# A set of useful summary functions is provided from the Hmisc package:
stat_sum_df <- function(fun, geom="crossbar", ...) {
stat_summary(fun.data=fun, colour="red", geom=geom, width=0.2, ...)
}
d + stat_sum_df("mean_cl_boot")
d + stat_sum_df("mean_sdl")
d + stat_sum_df("mean_sdl", mult=1)
d + stat_sum_df("median_hilow")
# There are lots of different geoms you can use to display the summaries
d + stat_sum_df("mean_cl_normal")
d + stat_sum_df("mean_cl_normal", geom = "errorbar")
d + stat_sum_df("mean_cl_normal", geom = "pointrange")
d + stat_sum_df("mean_cl_normal", geom = "smooth")
# Summaries are more useful with a bigger data set:
mpg2 <- subset(mpg, cyl != 5L)
m <- ggplot(mpg2, aes(x=cyl, y=hwy)) +
geom_point() +
stat_summary(fun.data = "mean_sdl", geom = "linerange",
colour = "red", size = 2, mult = 1) +
xlab("cyl")
m
# An example with highly skewed distributions:
set.seed(596)
mov <- movies[sample(nrow(movies), 1000), ]
m2 <- ggplot(mov, aes(x= factor(round(rating)), y=votes)) + geom_point()
m2 <- m2 + stat_summary(fun.data = "mean_cl_boot", geom = "crossbar",
colour = "red", width = 0.3) + xlab("rating")
m2
# Notice how the overplotting skews off visual perception of the mean
# supplementing the raw data with summary statistics is _very_ important
# Next, we'll look at votes on a log scale.
# Transforming the scale means the data are transformed
# first, after which statistics are computed:
m2 + scale_y_log10()
# Transforming the coordinate system occurs after the
# statistic has been computed. This means we're calculating the summary on the raw data
# and stretching the geoms onto the log scale. Compare the widths of the
# standard errors.
m2 + coord_trans(y="log10")