geom_point {ggplot2} | R Documentation |
The point geom is used to create scatterplots.
geom_point(mapping = NULL, data = NULL, stat = "identity", position = "identity", na.rm = FALSE, ...)
mapping |
The aesthetic mapping, usually constructed
with |
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 |
... |
other arguments passed on to
|
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)
.
geom_point
understands the following aesthetics (required aesthetics are in bold):
x
y
alpha
colour
fill
shape
size
scale_size
to see scale area of points,
instead of radius, geom_jitter
to jitter
points to reduce (mild) overplotting
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"))