ggplot2 (version 0.9.1)

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, ...)

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.

stat

The statistical transformation to use on the data for this layer.

position

The position adjustment to use for overlappling points on this layer

na.rm

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

...

other arguments passed on to layer. This can include aesthetics whose values you want to set, not map. See layer for more details.

Aesthetics

geom_point understands the following aesthetics:

• x: horizontal position
• y: vertical position
• shape: point shape.
• colour: point colour.
• fill: fill colour, only affects solid points
• size: size.
• alpha: alpha transparency modifies colour.

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 stat_smooth, stat_quantile or stat_density2d. 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, geom_point(alpha = 0.05).

See Also

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

Examples

p <- ggplot(mtcars, aes(wt, mpg))
p + geom_point()

# Add aesthetic mappings
p + geom_point(aes(colour = qsec))
p + geom_point(aes(alpha = qsec))
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(size = qsec)) + scale_area()
p + geom_point(aes(shape = factor(cyl))) + scale_shape(solid = FALSE)

# Set aesthetics to fixed value
p + geom_point(colour = "red", size = 3)
qplot(wt, mpg, data = mtcars, colour = I("red"), size = I(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)

# You can create interesting shapes by layering multiple points of
# different sizes
p <- ggplot(mtcars, aes(mpg, wt))
p + geom_point(colour="grey50", size = 4) + geom_point(aes(colour = cyl))
p + aes(shape = factor(cyl)) +
  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_guide = TRUE) +
  geom_point(colour="pink", size = 4, show_guide = TRUE) +
  geom_point(aes(shape = factor(cyl)))

# Transparent points:
qplot(mpg, wt, data = mtcars, size = I(5), alpha = I(0.2))

# 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))
qplot(wt, mpg, data = mtcars2)
qplot(wt, mpg, data = mtcars2, na.rm = TRUE)

# Use qplot instead
qplot(wt, mpg, data = mtcars)
qplot(wt, mpg, data = mtcars, colour = factor(cyl))
qplot(wt, mpg, data = mtcars, colour = I("red"))