ggplot2 (version 0.9.0)

stat_sum: Sum unique values. Useful for overplotting on scatterplots.

Description

Sum unique values. Useful for overplotting on scatterplots.

Usage

stat_sum(mapping = NULL, data = NULL, geom = "point",
    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
• n
  number of observations at position
• prop
  percent of points in that panel at that position

See Also

ggfluctuation for a fluctuation diagram,

Examples

d <- ggplot(diamonds, aes(x = cut, y = clarity))
# By default, all categorical variables in the plot form grouping
# variables, and the default behavior in stat_sum is to show the
# proportion. Specifying stat_sum with no group identifier leads to
# a plot which is not meaningful:
d + stat_sum()
# To correct this problem and achieve a more desirable plot, we need
# to specify which group the proportion is to be calculated over.
# There are several ways to do this:

# by overall proportion
d + stat_sum(aes(group = 1))
d + stat_sum(aes(group = 1)) + scale_size(range = c(3, 10))
d + stat_sum(aes(group = 1)) + scale_area(range = c(3, 10))

# by cut
d + stat_sum(aes(group = cut))
d + stat_sum(aes(group = cut, colour = cut))

# by clarity
d + stat_sum(aes(group = clarity))
d + stat_sum(aes(group = clarity, colour = cut))

# Instead of proportions, can also use sums
d + stat_sum(aes(size = ..n..))

# Can also weight by another variable
d + stat_sum(aes(group = 1, weight = price))
d + stat_sum(aes(group = 1, weight = price, size = ..n..))

# Or using qplot
qplot(cut, clarity, data = diamonds)
qplot(cut, clarity, data = diamonds, stat = "sum", group = 1)