Scatterplot | the R Graph Gallery

Using the ggplot2 package

Scatterplots are built with ggplot2 thanks to the geom_point() function. Discover a basic use case in graph #272, and learn how to custom it with next examples below.

Basic scatterplot

The most basic scatterplot you can build with R and ggplot2.

Simply explains how to call the geom_point() function.

Custom marker features

The geom_point() function has option to custom color, stroke, shape, size and more. Learn how to call them.

Map marker feature to variable

Ggplot2 makes it a breeze to map a variable to a marker feature. Here is an example where marker color depends on its category.

Map to several features

Extension of the previous concept: several features can be mapped to variables in the same time

Annotate with geom_text

geom_text() allows to add annotation to one, several or all markers of your chart.

Annotate with geom_label

Very close to geom_text, geom_label produces a label wrapped in a rectangle. This example also explains how to apply labels to a selection of markers.

Scatterplot with rug

Add rug on X and Y axis to describe the numeric variable distribution. Show how geom_rug() works.

Marginal distribution

Add marginal distribution around your scatterplot with ggExtra and the ggMarginal function.

Linear trend

geom_smooth allows to add the result of a model to your scatterplot, with confidence interval as well.

Using base R

Base R is also a good option to build a scatterplot, using the plot() function. The chart #13 below will guide you through its basic usage. Following examples allow a greater level of customization.

Most basic scatterplot

The most basic scatterplot you can build with R, using the plot() function.

plot() parameters

Custom your scatterplot with the arguments of the plot() function.

Polynomial curve

Set a linear model with lm(), and plot it on top of your scatterplot with line().

Confidence interval

Add a confidence interval around the polynomial model with polygon().

Lattice XYplot()

The lattice XYplot() allows to build one scatterplot for each level of a factor automatically.

Correlation of discrete variables

Make the circle size proportional to number of data points when working with discrete variables.

the mtext() function

mtext() allows to add text in margin. Useful to add an unique title for several charts.

split_screen() function

Split screen allows to split the chart window in several sections.

Split screen with par(mfrow())

The easiest way to split the graphic window is to use par(mfrow()).

Parameters reminder

A cheatsheet to quickly reminder what option to use with what value to customize your chart.

Special use case: Manhattan plots

A Manhattan plot is a particular type of scatterplot used in genomics. The X axis displays the position of a genetic variant on the genome. Each chromosome is usually represented using a different color. The Y axis shows p-value of the association test with a phenotypic trait.

Most basic scatterplot

The most basic scatterplot you can build with R, using the plot() function.

plot() parameters

Custom your scatterplot with the arguments of the plot() function.

Polynomial curve

Set a linear model with lm(), and plot it on top of your scatterplot with line().

Confidence interval

Add a confidence interval around the polynomial model with polygon().

🌐 From the web

The web is full of astonishing R charts made by awesome bloggers. The R graph gallery tries to display some of the best creations and explain how their source code works. If you want to display your work here, please drop me a word or even better, submit a Pull Request!

ggRepel

ggRepel allows to add multiple labels with no overlap automatically. Here is a good looking scatterplot using it!

Scatterplot with regression fit and automatic text repel

A scatterplot with a regression fit to highlight the main trend, a clean color palette, a customized legend and some greatly selected labels with no overlap