stat_summary {ggplot2} | R Documentation |
Summarise y values at every unique x
stat_summary(mapping=NULL, data=NULL, geom="pointrange", position="identity", ...)
mapping |
mapping between variables and aesthetics generated by aes |
data |
dataset used in this layer, if not specified uses plot dataset |
geom |
geometric used by this layer |
position |
position adjustment used by this layer |
... |
other arguments |
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 data) or on a vector. 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.
This page describes stat_summary, see layer
and qplot
for how to create a complete plot from individual components.
A layer
The following aesthetics can be used with stat_summary. Aesthetics are mapped to variables in the data with the aes
function: stat\_summary(\code{aes}(x = var))
x
: x position (required)
y
: y position (required)
Hadley Wickham, http://had.co.nz/
geom_errorbar
: error bars
geom_pointrange
: range indicated by straight line, with point in the middle
geom_linerange
: range indicated by straight line
geom_crossbar
: hollow bar with middle indicated by horizontal line
stat_smooth
: for continuous analog
## Not run: # Basic operation on a small dataset c <- qplot(cyl, mpg, data=mtcars) c + stat_summary() # 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, ...) } c + stat_sum_single(mean) c + stat_sum_single(mean, geom="line") c + stat_sum_single(median) c + stat_sum_single(sd) c + stat_summary(fun.y = mean, fun.ymin = min, fun.ymax = max, colour = "red") # 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, ...) } c + stat_sum_df("mean_cl_boot") c + stat_sum_df("mean_sdl") c + stat_sum_df("mean_sdl", mult=1) c + stat_sum_df("median_hilow") # There are lots of different geoms you can use to display the summaries c + stat_sum_df(fun.data="mean_cl_normal") c + stat_sum_df(fun.data="mean_cl_normal", geom = "errorbar") c + stat_sum_df(fun.data="mean_cl_normal", geom = "pointrange") c + stat_sum_df(fun.data="mean_cl_normal", geom = "smooth") # Summaries are much more useful with a bigger data set: m <- ggplot(movies, aes(x=round(rating), y=votes)) + geom_point() m2 <- m + stat_summary(fun.data = "mean_cl_boot", geom = "crossbar", colour = "red", width = 0.3) m2 # Notice how the overplotting skews off visual perception of the mean # supplementing the raw data with summary statisitcs is _very_ important # Next, we'll look at votes on a log scale. # Transforming the scale performs the transforming before the statistic. # This means we're calculating the summary on the logged data m2 + scale_y_log10() # Transforming the coordinate system performs the transforming after the # statistic. 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") ## End(Not run)