Guided Tour: Statistical Plot Annotations

Labels with ‘ggplot2’, ‘ggpp’ and ‘ggpmisc’

ggpp
ggpmisc
R
plotting
Author

Pedro J. Aphalo

Published

2026-04-30

Modified

2026-04-30

Abstract

This page contains an orderly collection of links to code examples making use of R packages ‘ggplot2’, ‘ggpp’, ‘ggpmisc’, ‘ggrepel’, and other packages, as well as links to the documentation web sites the packages used in the examples.

1 Anotations and data labels

Package ‘ggplot2’ documentation

‘ggplot2’ Basics
The Layered Grammar of Graphics
Edited 2026-04-21
Abstract Plot construction with the Layered Grammar of Graphics implemented in R package ‘ggplot2’ and extensions can feel like a puzzle unless the mechanics are understood. I explain how ‘ggplot2’ plots are assembled and later rendered. I focus on the grammar and its features.
Packages ‘ggpmisc’, ‘ggpp’ and ‘gginnards’
Data labels, annotations and insets for ‘ggplot2’
Edited 2026-04-22
Abstract R packages’ggpmisc’, ‘ggpp’ and ‘gginnards’ started as a single small R package some 10 years ago. In this page I briefly recount the history, describe the design aims and the most important extensions they provide to the Grammar of Graphics as implemented in package ‘ggplot2’. The geoms, stats, positions, scales and numerous utility functions these three packages provide can simplify the constructions of different types of plots. ‘ggpmisc’ has its focus on statistical annotations, such as fitted model equations and multiple comparisons. ‘ggpp’ implements geoms that support various types of plot insets, annotations and marginal marks. ‘gginnards’ gives access to the inner components of ‘gg’ objects (ggplot objects) allowing their study and manipulation.
Introduction to Data Visualization
Using R with package ‘ggplot2’ and extensions
Edited 2024-04-17
Abstract An introduction to data visualization using R. The same data are plotted in different ways to demonstrate how different approaches to plotting emphasize different aspects of the data. Plots suitable for different audiences are also created. The source code for all examples is included in the page.
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2 ‘ggpp’

Package ‘ggpp’ documentation

Pairwise labels with ‘ggpp’
Contrasts, multiple comparisons and simple labels
Edited 2024-04-22
Abstract Example R code for plots with pairwise labels created with geom_text_pairwise() and geom_label_pairwise() from R package ‘ggpp’ demonstrating their use to display the results from multiple comparison tests, fitted contrasts, and ranges.
Nudging + repulsion with ‘ggrepel’ and ‘ggpp’
Directed/ordered data labels’ positions avoiding overlaps
Edited 2023-12-12
Abstract Example R code for plots with labels without overlaps but with controlled position displacements away from specific plots features such as curves and observations’ density centroids.
ggplot insets with package ‘ggpp’
Plots, tables and graphical objects as ggplot insets
Edited 2025-01-16
Abstract Example R code for plots based on package ggplot2 using geometries defined in package ggpp to add insets. These geometries from package ggpp implement addition of plot layers with plots, tables or other graphical objects as insets to a base plot, through extension of the Grammar of Graphics.
Data labels in bar plots with ‘ggpp’
Nudging labels in stacked and dodged bars or columns
Edited 2023-02-25
Abstract Example R code for plots with labels using position functions from package ggpp that combine the actions of two separate position functions available in package ggplot2, such as simultaneous use of stack and nudge and dodge and nudge. The examples show how to easily add labels to stacked and dodged bar plots retaining full control of the labels’ positioning.
Timelines with ‘ggplot2’
Timelines as data plots
Edited 2023-08-01
Abstract Example R code to create time-line plots, uising packages ‘ggplot2’, ‘ggpp’, ‘ggrepel’ and ‘photobiology’. Although 2D plots using cartesian coordinates are the most comon data visualizations, time-line plots are also very useful. Being in most cases 1D plots, they can be efficiently described with the layered Grammar of Graphics.
Fitted-model labels with ‘ggpmisc’ and ‘plotly’
Interactive ggplots with model equations, R2, F, P, etc.
Edited 2023-06-26
Abstract Example R code for interactive plots based on package ggplot2 using geometries defined in package ggpp and statistics from package ggpmisc together with package plotly. This page is a draft and currently contains a single plot example.
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3 ‘ggpmisc’

Package ‘ggpmisc’ documentation

Fitted-model labels with ‘ggpmisc’
Polynomials, major axis, quantile and non-linear regression lines, bands and equations
Edited 2024-06-06
Abstract Example R code for plots with predictions from fitted models labelled with model fitted equations, parameter estimates, R2, F-value, P-value, etc. Polynomial regression, quantile regression, major-axis regression, robust linear model fits as well as non-linear regression are examplified. Labels make use of layer functions from package ‘ggpmisc’.
Multiple comparisons with ‘ggpmisc’
Letter labels and labelled bars
Edited 2024-04-15
Abstract Examples of plots with pairwise labels and letter labels created with stat_multcomp() from R package ‘ggpmisc’. Function stat_multcomp() computes and adds to ggplots the results from multiple comparison tests as labels. Multiple comparisons based on Tukey and Dunnet contrasts are demonstrated.
Volcano and quadrant plots with ‘ggpmisc’
Data labels, annotations and guides
Edited 2023-07-16
Abstract Example R code for volcano plots and quadrant plots built with packages ggplot2 (>= 3.4.2), ggpp (>= 0.5.3), ggpmisc (>= 0.5.3) and ggrepel (>= 0.9.1). The examples demonstrate the use different types of annotations and data labels. My packages ggpp and ggpmisc include new geometries, statistics and scale functions specific and/or useful when plotting of gene-expression data in volcano and quadrant plots.
Annotating Plot Matrices
Using ‘GGally’ Together with ‘ggpmisc’
Edited 2024-04-17
Abstract This page documents how to annotate plot matrices created with GGally::ggpairs() using the layer functions from package ‘ggpmisc’.
Fitted-model labels in Markdown
‘ggpmisc’ together with ‘ggtext’
Edited 2024-06-08
Abstract Example R code for plots with predictions from fitted models labelled with model fitted equations, parameter estimates, R2, F-value, P-value, etc. Polynomial regression, quantile regression, major-axis regression, robust linear model fits as well as non-linear regression are examplified. Markdown encoded labels make use of statistics from package ‘ggpmisc’ and geometries fron ‘ggtext’.
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