R Data Visualization Cheat Sheet
An R data visualization reference for ggplot2 mappings, layers, facets, scales, coordinates and export review.
What this reference covers
This reference organizes R data visualization around the question a chart should answer. It brings together data mappings, geometric layers, scales, facets, coordinates, themes and output checks so a reader can move from a small working plot to a clear published figure.
Build the smallest useful plot
Start with the data, the variables that explain the question and one geometric layer. Check that continuous and categorical fields are mapped to the intended axes, groups and visual channels before adding labels, colors or annotations. A simple plot makes missing values, unexpected groups and wrong units easier to see.
Refine structure before decoration
Use scales to document units and ranges, facets to separate meaningful categories and coordinates to support the comparison being made. Keep comparable panels on a consistent scale unless a different scale is explicitly explained. Add themes and annotation only after the data structure is correct.
Export and maintenance note
Review titles, legends, labels, contrast, canvas size and resolution at the actual report or screen width. This sheet is an operation index, not a guarantee of statistical validity or visual accessibility. Content review date: 2026-08-23.
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R data visualization study guide
Start from the analysis question, validate the data mapping with one layer, then add scales, facets, coordinates and output styling in small steps.
Before you start
- Prepare a PDF reader and a small data frame with documented variables.
- Know the difference between continuous values, categories, groups and missing values.
- Decide whether the final figure is for a report, web page, presentation or narrow screen.
Quick start
- 01
Write the chart question
Decide whether the figure compares groups, shows a distribution, explains a relationship or presents change over time before choosing a geometry.
- 02
Create a minimal layer
Set the data, mappings and one main layer, then check axes, groups, units and missing values before adding decoration.
- 03
Tune scales and facets
Use scales to explain units and ranges, and facets to split meaningful categories without hiding important differences.
- 04
Review the target export
Open the exported figure at its final size and check text, legend, contrast, annotations and resolution before keeping the script and data version.
Usage tips
- Give every color, shape or size mapping a clear meaning and avoid encoding too many variables at once.
- Keep scales consistent for comparisons; document any intentional change of scale in the caption.
- Solve data and mapping issues before changing themes, fonts or decorative details.
Troubleshooting and uninstall
Why is the expected grouping missing?
Check the group field type, missing values and mapping position, then specify the group explicitly when the geometry needs it.
Why is exported text clipped?
Increase the canvas or adjust margins, legend layout and long labels, then inspect the file at the final display size.
Frequently asked questions
Which R visualization topics are covered?
The sheet covers mappings, geometric layers, scales, facets, coordinates, themes and export review for common ggplot2 workflows.
Why start with a minimal chart?
A minimal chart verifies data, mappings, grouping and missing values before style changes make the real issue harder to locate.
How should facets be selected?
Use facets for categories that clarify the comparison, keep panel labels readable and maintain a consistent scale when panels are compared.
Does this sheet prove that a chart is statistically correct?
No. It is a plotting reference; sampling, units, uncertainty, outliers, grouping definitions and interpretation still need separate review.