ggplot2::geom_jitter() adds random noise to points using a uniform distribution. When many points are plotted, they appear in a rectangle. This position jitters points using a normal distribution instead, resulting in more circular clusters.

position_jitternormal(sd_x = NULL, sd_y = NULL, seed = NA)

Arguments

sd_x, sd_y

Standard deviation to add along the x and y axes. The function uses stats::rnorm() with mean = 0 behind the scenes.

If omitted, defaults to 0.15. As with ggplot2::geom_jitter(), categorical data is aligned on the integers, so a standard deviation of more than 0.2 will spread the data so it's not possible to see the distinction between the categories.

seed

A random seed to make the jitter reproducible. Useful if you need to apply the same jitter twice, e.g., for a point and a corresponding label. The random seed is reset after jittering. If NA (the default value), the seed is initialised with a random value; this makes sure that two subsequent calls start with a different seed. Use NULL to use the current random seed and also avoid resetting (the behaviour of ggplot 2.2.1 and earlier).

Examples

# Example data
df <- data.frame(
x = sample(1:3, 1500, TRUE),
y = sample(1:3, 1500, TRUE)
)

# position_jitter results in rectangular clusters
ggplot(df, aes(x = x, y = y)) +
geom_point(position = position_jitter())

# geom_jitternormal results in more circular clusters
ggplot(df, aes(x = x, y = y)) +
geom_point(position = position_jitternormal())

# You can adjust the standard deviations along both axes
# Tighter circles
ggplot(df, aes(x = x, y = y)) +
geom_point(position = position_jitternormal(sd_x = 0.08, sd_y = 0.08))

# Oblong shapes
ggplot(df, aes(x = x, y = y)) +
geom_point(position = position_jitternormal(sd_x = 0.2, sd_y = 0.08))

# Only add random noise to one dimension
ggplot(df, aes(x = x, y = y)) +
geom_point(
position = position_jitternormal(sd_x = 0.15, sd_y = 0),
alpha = 0.1
)