geom_point: Points, as for a scatterplot

Description

The point geom is used to create scatterplots.

Usage

geom_point(mapping = NULL, data = NULL, stat = "identity",
  position = "identity", na.rm = FALSE, show.legend = NA,
  inherit.aes = TRUE, ...)

Arguments

mapping

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

data

A data frame. If specified, overrides the default data frame defined at the top level of the plot.

stat

The statistical transformation to use on the data for this layer, as a string.

position

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

na.rm

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

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.

...

other arguments passed on to layer. There are three types of arguments you can use here:

Aesthetics: to set an aesthetic to a fixed value, likecolor = "red"orsize = 3.

Aesthetics

[results=rd,stage=build]{ggplot2:::rd_aesthetics("geom", "point")} p <- ggplot(mtcars, aes(wt, mpg)) p + geom_point()

# Add aesthetic mappings p + geom_point(aes(colour = factor(cyl))) p + geom_point(aes(shape = factor(cyl))) p + geom_point(aes(size = qsec))

# Change scales p + geom_point(aes(colour = cyl)) + scale_colour_gradient(low = "blue") p + geom_point(aes(shape = factor(cyl))) + scale_shape(solid = FALSE)

# Set aesthetics to fixed value ggplot(mtcars, aes(wt, mpg)) + geom_point(colour = "red", size = 3)

# Varying alpha is useful for large datasets d <- ggplot(diamonds, aes(carat, price)) d + geom_point(alpha = 1/10) d + geom_point(alpha = 1/20) d + geom_point(alpha = 1/100)

# For shapes that have a border (like 21), you can colour the inside and # outside separately. Use the stroke aesthetic to modify the width of the # border ggplot(mtcars, aes(wt, mpg)) + geom_point(shape = 21, colour = "black", fill = "white", size = 5, stroke = 5)

# You can create interesting shapes by layering multiple points of # different sizes p <- ggplot(mtcars, aes(mpg, wt, shape = factor(cyl))) p + geom_point(aes(colour = factor(cyl)), size = 4) + geom_point(colour = "grey90", size = 1.5) p + geom_point(colour = "black", size = 4.5) + geom_point(colour = "pink", size = 4) + geom_point(aes(shape = factor(cyl)))

# These extra layers don't usually appear in the legend, but we can # force their inclusion p + geom_point(colour = "black", size = 4.5, show.legend = TRUE) + geom_point(colour = "pink", size = 4, show.legend = TRUE) + geom_point(aes(shape = factor(cyl)))

# geom_point warns when missing values have been dropped from the data set # and not plotted, you can turn this off by setting na.rm = TRUE mtcars2 <- transform(mtcars, mpg = ifelse(runif(32) < 0.2, NA, mpg)) ggplot(mtcars2, aes(wt, mpg)) + geom_point() ggplot(mtcars2, aes(wt, mpg)) + geom_point(na.rm = TRUE)

scale_size to see scale area of points, instead of radius, geom_jitter to jitter points to reduce (mild) overplotting

Details

The scatterplot is useful for displaying the relationship between two continuous variables, although it can also be used with one continuous and one categorical variable, or two categorical variables. See geom_jitter for possibilities.

The bubblechart is a scatterplot with a third variable mapped to the size of points. There are no special names for scatterplots where another variable is mapped to point shape or colour, however.

The biggest potential problem with a scatterplot is overplotting: whenever you have more than a few points, points may be plotted on top of one another. This can severely distort the visual appearance of the plot. There is no one solution to this problem, but there are some techniques that can help. You can add additional information with geom_smooth, geom_quantile or geom_density_2d. If you have few unique x values, geom_boxplot may also be useful. Alternatively, you can summarise the number of points at each location and display that in some way, using stat_sum. Another technique is to use transparent points, e.g. geom_point(alpha = 0.05).