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), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping.

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.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See fortify for which variables will be created.

A function will be called with a single argument, the plot data. The return value must be a data.frame., and will be used as the layer data.

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.

...

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 geom/stat.

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. borders.

Aesthetics

geom_point understands the following aesthetics (required aesthetics are in bold):

x
y
alpha
colour
fill
shape
size
stroke

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).

Examples

Run this code

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)