statar (version 0.6.1)

stat_binmean: Plot the mean of y over the mean of x within bins of x.

Description

Plot the mean of y over the mean of x within bins of x.

Usage

stat_binmean(mapping = NULL, data = NULL, geom = "point",
  position = "identity", show.legend = NA, inherit.aes = TRUE,
  na.rm = FALSE, n = 20, ...)

Arguments

mapping

Set of aesthetic mappings created by aes or aes_. If specified and inherit.aes = TRUE (the default), it is combined with the default mapping at the top

data

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.fra

geom

The geometric object to use display the data

position

Position adjustment, either as a string, or the result of a call to a position adjustment function.

show.legend

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.

inherit.aes

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.

na.rm

If FALSE (the default), removes missing values with a warning. If TRUE silently removes missing values.

n

number of x-bins. Default to 20. Set to zero if you want to use distinct value of x for grouping.

...

other arguments passed on to layer. These are often aesthetics, used to set an aesthetic to a fixed value, like color = "red" or size = 3. They may also be parameters to the paired g

Value

• a data.frame with additional columns:
• xtile
  bins for x
• x
  mean of x
• y
  mean of y

Examples

library(ggplot2)
g <- ggplot(iris, aes(x = Sepal.Width , y = Sepal.Length)) 
g + stat_binmean(n = 10)
g + stat_binmean(n = 10) + stat_smooth(method = "lm", se = FALSE)
g + stat_binmean(n = 0) 
g <- ggplot(iris, aes(x = Sepal.Width , y = Sepal.Length, color = Species))
g + stat_binmean(n = 10)
g + stat_binmean(n = 10) + stat_smooth(method = "lm", se = FALSE)