ggplot2::geom_point()

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

Returns Layer· Updated May 20, 2026· Tidyverse

ggplot2scatterpointsgeomtidyversevisualisation

geom_point() draws a point for each observation. It’s the go-to geom for scatter plots — showing the relationship between two continuous variables, or the distribution of one variable against another.

Syntax

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

Most arguments are shared across all geoms. The ones you’ll use most:

Argument	What it does
`mapping`	Aesthetic mappings from `aes()`
`data`	Data frame for this layer (usually inherited from `ggplot()`)
`stat`	Statistical transformation — almost always `"identity"`
`position`	Position adjustment — `"identity"` by default, or `"jitter"` to spread overlapping points

Aesthetics

geom_point() supports all position aesthetics plus visual ones. The most common:

Aesthetic	What it controls
`x`	Position on x-axis
`y`	Position on y-axis
`colour`	Point colour (continuous or categorical)
`size`	Point size in mm
`shape`	Point shape (0–25)
`alpha`	Transparency (0–1)
`fill`	Fill colour for shape 21–25
`stroke`	Border width for shapes 21–25

Map aesthetics to variables to encode data in the plot. Set aesthetics to constants to style the points:

# Mapped: colour varies with a variable
ggplot(mtcars, aes(x = wt, y = mpg, colour = cyl)) + geom_point()

# Set: all points are the same size
ggplot(mtcars, aes(x = wt, y = mpg)) + geom_point(size = 3)

Basic Scatter Plot

library(ggplot2)

ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point()

A simple two-variable scatter. The x-axis shows car weight, the y-axis shows miles per gallon. You can immediately see the negative relationship.

Mapping Additional Variables

The power of geom_point() is encoding additional variables through aesthetics:

# Colour by a third variable
ggplot(mtcars, aes(x = wt, y = mpg, colour = hp)) +
  geom_point()

# Size and colour by different variables
ggplot(mtcars, aes(x = wt, y = mpg, size = hp, colour = factor(cyl))) +
  geom_point()

Continuous variables mapped to colour get a gradient scale. Categorical variables get a discrete colour palette. Use scale_colour_viridis_d() or similar for colourblind-safe palettes.

Shape Aesthetic

The shape aesthetic accepts integers 0–25. Different shapes have different capabilities:

# Use shape 21 (filled circle) to allow both colour AND fill
ggplot(mtcars, aes(x = wt, y = mpg, fill = factor(cyl), colour = factor(gear))) +
  geom_point(shape = 21, size = 3)

Shape 21–25 have a fill and a stroke (border colour). Shapes 0–20 either have no fill or no stroke, depending.

Overplotting and Jitter

Overplotting happens when many points share the same coordinates — a common problem with discrete or categorical data. The fix is position_jitter:

# Default: points at exact coordinates
ggplot(mtcars, aes(x = factor(cyl), y = mpg)) +
  geom_point()

# Jittered: small random offset spreads the points
ggplot(mtcars, aes(x = factor(cyl), y = mpg)) +
  geom_point(position = position_jitter(width = 0.3, height = 0))

width is the maximum jitter in the x direction, height in the y direction. For a categorical x-axis, horizontal jitter (width) spreads points without changing their y position.

geom_jitter() is a shorthand for geom_point(position = "jitter"):

# Equivalent to the jitter call above
ggplot(mtcars, aes(x = factor(cyl), y = mpg)) +
  geom_jitter(width = 0.3, height = 0)

Alpha for Large Data

When you have thousands of points, use alpha to reveal density:

ggplot(faithful, aes(x = eruptions, y = waiting)) +
  geom_point(alpha = 0.3)

alpha = 0.3 makes each point 30% opaque. Where points overlap, the density shows through as darker regions. Common values for large datasets: 0.1 to 0.5.

Size Legend and Scaling

When size is mapped to a continuous variable, ggplot2 creates a size legend showing the mapping:

ggplot(mtcars, aes(x = wt, y = mpg, size = hp)) +
  geom_point()

Control the legend appearance with scale_size_continuous():

ggplot(mtcars, aes(x = wt, y = mpg, size = hp)) +
  geom_point() +
  scale_size_continuous(range = c(1, 6))

range sets the minimum and maximum point sizes in mm.

Combining with Smoothers

A common pattern is points + a smoothing layer:

ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() +
  geom_smooth()

geom_smooth() adds a regression line with a confidence interval. method = "lm" forces a linear model; method = "loess" (default for small n) fits a local polynomial.

Colour by Group with Legend Merging

When multiple layers map the same aesthetic, ggplot2 merges legends intelligently:

ggplot(mtcars, aes(x = wt, y = mpg, colour = factor(cyl))) +
  geom_point(size = 2) +
  geom_smooth(se = FALSE, method = "lm")

The colour legend shows both the point colours and the line colours from geom_smooth().

Parameters

Parameter	Type	Default	Description
`mapping`	aesthetic	`NULL`	Aesthetic mappings from `aes()`
`data`	data.frame	`NULL`	Layer-specific data
`stat`	string	`"identity"`	Leave data as-is
`position`	string/position	`"identity"`	Identity or `"jitter"`
`na.rm`	logical	`FALSE`	Remove missing values silently
`show.legend`	logical/NA	`NA`	Show legend for this layer
`inherit.aes`	logical	`TRUE`	Inherit aesthetics from ggplot()

Additional parameters passed through ... go to layer().