stat_summary operates on unique x; stat_summary_bin
operates on binned x. They are more flexible versions of
stat_bin(): instead of just counting, they can compute any
aggregate.
stat_summary_bin(mapping = NULL, data = NULL, geom = "pointrange",
position = "identity", ..., fun.data = NULL, fun.y = NULL,
fun.ymax = NULL, fun.ymin = NULL, fun.args = list(), bins = 30,
binwidth = NULL, breaks = NULL, na.rm = FALSE, show.legend = NA,
inherit.aes = TRUE)stat_summary(mapping = NULL, data = NULL, geom = "pointrange",
position = "identity", ..., fun.data = NULL, fun.y = NULL,
fun.ymax = NULL, fun.ymin = NULL, fun.args = list(),
na.rm = FALSE, show.legend = NA, inherit.aes = TRUE)
The data to be displayed in this layer. There are three options:
If NULL, the default, the data is inherited from the plot
data as specified in the call to ggplot().
A data.frame, or other object, will override the plot
data. All objects will be fortified to produce a data frame. See
fortify() for which variables will be created.
A function will be called with a single argument,
the plot data. The return value must be a data.frame, and
will be used as the layer data. A function can be created
from a formula (e.g. ~ head(.x, 10)).
Use to override the default connection between
geom_histogram()/geom_freqpoly() and stat_bin().
Position adjustment, either as a string, or the result of a call to a position adjustment function.
Other arguments passed on to layer(). These are
often aesthetics, used to set an aesthetic to a fixed value, like
colour = "red" or size = 3. They may also be parameters
to the paired geom/stat.
A function that is given the complete data and should
return a data frame with variables ymin, y, and ymax.
Alternatively, supply three individual functions that are each passed a vector of x's and should return a single number.
Optional additional arguments passed on to the functions.
Number of bins. Overridden by binwidth. Defaults to 30.
The width of the bins. Can be specified as a numeric value
or as a function that calculates width from unscaled x. Here, "unscaled x"
refers to the original x values in the data, before application of any
scale transformation. When specifying a function along with a grouping
structure, the function will be called once per group.
The default is to use bins
bins that cover the range of the data. You should always override
this value, exploring multiple widths to find the best to illustrate the
stories in your data.
The bin width of a date variable is the number of days in each time; the bin width of a time variable is the number of seconds.
Alternatively, you can supply a numeric vector giving
the bin boundaries. Overrides binwidth, bins, center,
and boundary.
If FALSE, the default, missing values are removed with
a warning. If TRUE, missing values are silently removed.
logical. Should this layer be included in the legends?
NA, the default, includes if any aesthetics are mapped.
FALSE never includes, and TRUE always includes.
It can also be a named logical vector to finely select the aesthetics to
display.
If FALSE, overrides the default aesthetics,
rather than combining with them. This is most useful for helper functions
that define both data and aesthetics and shouldn't inherit behaviour from
the default plot specification, e.g. borders().
stat_summary() understands the following aesthetics (required aesthetics are in bold):
x
y
group
Learn more about setting these aesthetics in vignette("ggplot2-specs").
You can either supply summary functions individually (fun.y,
fun.ymax, fun.ymin), or as a single function (fun.data):
Complete summary function. Should take numeric vector as input and return data frame as output
ymin summary function (should take numeric vector and return single number)
y summary function (should take numeric vector and return single number)
ymax summary function (should take numeric vector and return single number)
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 computes
multiple values at once (e.g. ymin and ymax), use fun.data.
If no aggregation functions are supplied, will default to
mean_se().
geom_errorbar(), geom_pointrange(),
geom_linerange(), geom_crossbar() for geoms to
display summarised data
# NOT RUN {
d <- ggplot(mtcars, aes(cyl, mpg)) + geom_point()
d + stat_summary(fun.data = "mean_cl_boot", colour = "red", size = 2)
# You can supply individual functions to summarise the value at
# each x:
d + stat_summary(fun.y = "median", colour = "red", size = 2, geom = "point")
d + stat_summary(fun.y = "mean", colour = "red", size = 2, geom = "point")
d + aes(colour = factor(vs)) + stat_summary(fun.y = mean, geom="line")
d + stat_summary(fun.y = mean, fun.ymin = min, fun.ymax = max,
colour = "red")
d <- ggplot(diamonds, aes(cut))
d + geom_bar()
d + stat_summary_bin(aes(y = price), fun.y = "mean", geom = "bar")
# }
# NOT RUN {
# Don't use ylim to zoom into a summary plot - this throws the
# data away
p <- ggplot(mtcars, aes(cyl, mpg)) +
stat_summary(fun.y = "mean", geom = "point")
p
p + ylim(15, 30)
# Instead use coord_cartesian
p + coord_cartesian(ylim = c(15, 30))
# 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 <- ggplot(mtcars, aes(cyl, mpg)) + geom_point()
# The crossbar geom needs grouping to be specified when used with
# a continuous x axis.
d + stat_sum_df("mean_cl_boot", mapping = aes(group = cyl))
d + stat_sum_df("mean_sdl", mapping = aes(group = cyl))
d + stat_sum_df("mean_sdl", fun.args = list(mult = 1), mapping = aes(group = cyl))
d + stat_sum_df("median_hilow", mapping = aes(group = cyl))
# An example with highly skewed distributions:
if (require("ggplot2movies")) {
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")
}
# }
Run the code above in your browser using DataLab