stat_summary

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

Summarise y values at every unique x

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.

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Aesthetics

The following aesthetics can be used with stat_summary. Aesthetics are mapped to variables in the data with the aes function: stat_summary(aes(x = var)). Note that you do not need quotes around the variable name.

Scales control how the variable is mapped to the aesthetic and are listed after each aesthetic.

Aesthetic	Default	Related scales
x	required	continuous, date, datetime, discrete
y	required	continuous, date, datetime, discrete

Layers are divided into groups by the group aesthetic. By default this is set to the interaction of all categorical variables present in the plot.

New variables produced by the statistic

To use these variables in an aesthetic mapping, you need to surrond them with .., like aes(x = ..output..). This tells ggplot that the variable isn't the original dataset, but has been created by the statistic.

Parameters

Parameters control the appearance of the stat. In addition to the parameters listed below (if any), any aesthetic can be used as a parameter, in which case it will override any aesthetic mapping.

...: other arguments

Returns

This function returns a layer object.

Examples

> # Basic operation on a small dataset 
> c <- qplot(cyl, mpg, data=mtcars) 
> c + 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) 
Warning: Removed 9 rows containing missing values (stat_summary).
  
> # 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, ...) 
+ } 
>  
> 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") 
  
>  
> c + 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, ...) 
+ } 
>  
> 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("mean_cl_normal") 
  
> c + stat_sum_df("mean_cl_normal", geom = "errorbar") 
  
> c + stat_sum_df("mean_cl_normal", geom = "pointrange") 
  
> c + stat_sum_df("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")