ggplot2 (version 0.9.1)

stat_summary: Summarise y values at every unique x.

Description

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).

Usage

stat_summary(mapping = NULL, data = NULL,
    geom = "pointrange", position = "identity", ...)

Arguments

mapping

The aesthetic mapping, usually constructed with aes or aes_string. Only needs to be set at the layer level if you are overriding the plot defaults.

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 layer. This can include aesthetics whose values you want to set, not map. See layer for more details.

Value

• 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)

Details

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.

See Also

geom_errorbar, geom_pointrange, geom_linerange, geom_crossbar for geoms to display summarised data

Examples

# 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")