• Stepping Stone IDIBAPS R ggplot2 Data Vizualization workshop 2026
  • 1 Welcome
  • 2 Setup
    • 2.1 Install R and RStudio/Posit
    • 2.2 Install R packages
    • 2.3 Check setup
  • 3 R
    • 3.1 What is R ?
    • 3.2 Functions & packages
      • 3.2.1 Functions
      • 3.2.2 Packages
  • 4 What is RStudio (Posit)?
    • 4.1 RStudio interface
    • 4.2 The R console
  • 5 Paths and directories
    • 5.1 Path and home directory
    • 5.2 RStudio “Projects”
      • 5.2.1 Advantages
      • 5.2.2 Project for the course
      • 5.2.3 Leave the project
      • 5.2.4 Re-open the project
  • 6 R basics
    • 6.1 Arithmetic operators
    • 6.2 Objects in R
    • 6.3 Assigning data to an object
    • 6.4 Data types
  • 7 Import data / read files / scripts
    • 7.1 Fetch workshop files
      • 7.1.1 Download files from GitHub repository
      • 7.1.2 Dataset description
    • 7.2 Import / read in data
      • 7.2.1 Load package into environment
      • 7.2.2 from CSV
      • 7.2.3 from Excel
    • 7.3 Scripts
  • 8 ggplot2: get started
    • 8.1 Base structure
  • 9 Scatter plots: focus on individual data points
    • 9.1 Base plot
    • 9.2 Customize the points
  • 10 Additional layers
    • 10.1 ggtitle()
    • 10.2 Backgrounds
  • 11 Export graphs
    • 11.1 From the RStudio interface
    • 11.2 From the console
    • 11.3 Exercise 1 - scatter plots
  • 12 Scatter plots: more features
    • 12.1 Labels
    • 12.2 Mapping variables
    • 12.3 Additional ticks
    • 12.4 Density estimates
    • 12.5 Regression line
  • 13 Barplots: visualize categorical data
    • 13.1 Exercise 2 - Barplots
    • 13.2 Bar positions
  • 14 Boxplots: visualize data distribution
  • 15 Text fine-tuning
  • 16 Data wrangling: select and filter
    • 16.1 filter()
    • 16.2 select()
    • 16.3 rename()
    • 16.4 Exercise 3
  • 17 Dotplots
  • 18 Colors
    • 18.1 All R colors
    • 18.2 Base R palettes
    • 18.3 RColorBrewer
    • 18.4 ggsci
  • 19 Faceting
    • 19.1 Exercise 4 - Scatter plots
  • 20 Additional graphs
    • 20.1 Histograms and density plots
      • 20.1.1 Histogram
      • 20.1.2 Density plot
      • 20.1.3 Histogram + density
    • 20.2 Marginal plots
  • 21 More advanced data manipulation
    • 21.1 The pipe operator
    • 21.2 From wide to long format
    • 21.3 Join tables
  • 22 Interactive plots with {plotly}
  • 23 Selected non-ggplot packages
    • 23.1 Heatmaps with {pheatmap}
    • 23.2 Volcano plots with EnhancedVolcano package
  • 24 Resources
  • Published with bookdown

R for beginners: Introduction to data visualization with R and ggplot2: from data to publication-quality figures

Part24 Resources

  • RStudio/Tidyverse cheatsheets:
    • RStudio
    • ggplot2
    • dplyr
    • tidyr
    • readr
  • ggplot2 / R graphics:
    • ggplot elegant graphics for data analysis
    • R graph gallery
    • R graphics cookbook
    • ggplot2 extensions
    • ggplotly
    • Intro to animations in ggplot2
    • Curated list of awesome ggplot2 packages, tutorials, etc.
    • complete guide on how to modify the ggplot2 theme.
    • ggplot2 theme elements demonstration (source of above image)
    • ggplot2 styling
  • Online courses:
    • R programming Coursera course (Johns Hopkins University)
    • Official up-to-date introduction to R (from the R Core Team)
    • CRG Biocore intro course to the Tidyverse