Contents: Data Analysis and Visualization

A selection of articles and code examples

Author

Pedro J. Aphalo

Published

2026-04-30

Modified

2026-04-30

Abstract

This Contents page lists pages that I have written for course IPS-003 and for the R-peer-support meetings at the Viikki Campus of the University of Helsinki. The level of difficulty level varies from introductory to intermediate. I update these pages from time to time and I will add new pages from time to time.

1 R and programing

R is a language and an environment for data analysis and visualization. It has become the standard for data analysis and visualisation in many fields. R can be extended by means of code packages which can be locally installed in a library. R can “talk” with code written in most other programming languages. This is important for code reuse and for performance in numerical and other computtaions.

1.1 Introducion to R

Articles related to simple data manipulation, basic plotting, ANOVA and regression.

Moderately advanced R learning material is available as a free on-line course at intro2R.

The second edition of my book Learn R: As a Language will be published on 26 April 2024. A dedicated website provides additional information and some free extra chapters. My book does not assume previous programing and focuses on the R language itself rather than on using R for specific purposes.

1.2 Advanced plots with R

Creating informative and elegant plots for inclusion in publications, reports and theses requires the same kind of approach than text. Design, drafting and frequently several rounds of revision. Plots are also very important for exploration and quality control of data. The requirements for exploration and publicationare rather different with respect to the graphical design, but not in relation to highlighting different features of a data set. Articles and code examples in this section, describe how to create specific types of plots or even how to add specific features to a plot. They assume familiarity with the basics of plotting in R with package ‘ggplot2’. The ‘ggplot2’ book is available on-line as an open-access web site.

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.
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.
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’.
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.
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.
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’.
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.
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.
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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1.3 Data analysis and design of experiments

Data analysis and design of experiments are very tightly dependent on each other. Statistics gives theoretical support to data analysis methods, but efficiently extracting information from observations from experiments and surveys is in many ways like detective work or solving puzzles. Modern data analysis makes heavy use of visual data displays (plots, diagrams, graphs). Material I have used for the course IPS-003 at the University of Helsinki, and for some earlier courses or individual classes is in the pages listed below, in part updated.

Research as a process
Design, realization, data analysis, and communication of research
Edited 2023-10-19
Abstract Fortuitously, in the last few days I have several times have had to ponder about the roles of hypothesis-based and descriptive approaches to research. Being an agronomist and ecologist rather than a philosopher, the question that worries me is how data analysis differs between these two approaches and how we should interpret the results of the analysis in each case. This page collects some of my thoughts and opinions on the subject at the time of writing.
Design of Experiments
Principles and practice
Edited 2023-10-21
Abstract An introduction to design and planning of experiemnts. The roles replication, randomization, and range of validity.
Randomization and independent replicates
Restricted randomization in design of experiments
Edited 2023-11-27
Abstract In this page I give a brief presentation on how randomization can be constrained to match the properties of the systems under study without compromising the validity of conclusions. This gives origin to different formal designs for experiments, and corresponding data analysis approaches. I also mention pseudoreplication as a problem that needs to be avoided whenever possible, and otherwise the additional implicit assumptions shown to be true or at least highly plausible.
Theoretical probability distributions
Interactive applications using R package Shiny
Edited 2023-11-21
Abstract Shiny Apps or dashboards for interactive exploration on the dependence of the t and F distributions on the degrees of freedom.
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.
EDA with ‘ggplot2’
EDA = Exploratory Data Analysis
Edited 2023-07-27
Abstract EDA and ‘ggplot2’ have a common ancestry. EDA as originally proposed by John W. Tukey included data visualization as a key tool. In this page I show examples of some of the data visualizations that are used in EDA, together with the R code I used to create them. I also briefly describe EDA’s position in the scientific research process and mention its origins.
Linear Models
An introduction using lm()
Edited 2023-11-30
Abstract In this page I give a brief presentation on the mechanics of fitting statistical models to observed data using R. I use a lineal model (LM), polynomial regression, as example. I describe the most important values contained in the model fit object returned by function lm() and how to extract them. I rely heavily on diagrams and data plots.
Introduction to Mixed Effects Models
Concepts and examples in R with package ‘nlme’
Edited 2025-01-24
Abstract An introduction to linear- and non-linear mixed effects models (LME and NLME) based on overheads. The concepts of fixed and random effects are presentsed using examples. Step by step model building is illustrated with a data set of the growth of individual leaves of birch sapplings. These same data are use to illustrate both LME and NLME fits.
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NoteExperimental error and replication

Interactive dashboards can help understand how the amount uncontrolled variation in observations and the number of replicates affect both tests of significance and parameter estimates when fitting models.

The interactive web page Design of Experiments: Playing with numbers is hosted at the ShinyApps server.

TipReuse

These pages are under a Creative Commons licence and can be reused with certain constraints, mainly that derived work must be licenced in the same way. The source files are available at GitHub.