geom_point {ggplot2}R Documentation

geom_point

Description

Points, as for a scatterplot

Usage

geom_point(mapping=NULL, data=NULL, stat="identity", position="identity", na.rm=FALSE, ...)

Arguments

mapping mapping between variables and aesthetics generated by aes
data dataset used in this layer, if not specified uses plot dataset
stat statistic used by this layer
position position adjustment used by this layer
na.rm NULL
... ignored

Details

The point geom is used to create scatterplots.

This page describes geom_point, see layer and qplot for how to create a complete plot from individual components.

Value

A layer

Aesthetics

The following aesthetics can be used with geom_point. Aesthetics are mapped to variables in the data with the aes function: geom\_point(\code{aes}(x = var))

Advice

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(colour=alpha('black', 0.05))

Author(s)

Hadley Wickham, http://had.co.nz/

See Also

Examples

## Not run: 
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, legend = TRUE) + 
  geom_point(colour="pink", size = 4, legend = 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"))
## End(Not run)

[Package ggplot2 version 0.8.3 Index]