Clear figures, stronger stories: Fundamentals of Data Visualization
Description
Figures are essential for communicating research findings and data story telling, yet creating clear and engaging figures can be challenging for researchers. In this lecture, we’ll explore the fundamental principles of effective data visualization, emphasizing clarity, accuracy, and avoiding common pitfalls. Through practical examples, you’ll learn how strategic choices—such as selecting appropriate chart types, thoughtful use of color, and minimizing visual clutter—can enhance the quality of your figures and help you better communicate your research findings.
Resources and links
(Free) Books and Papers
- Data Visualization: A Practical Introduction (Healy): Very nice theoretical introduction with practical examples using R and
ggplot2 - Fundamentals of Data Visualization (Wilke): No coding, but with lots of examples and practical tips on how to make scientific figures more accurate, beautiful and effective
- ggplot2: Elegant Graphics for Data Analysis (Wickham et al.): Specific to learn the figure with
ggplot2 - The Truthful Art (Alberto Cairo) (New Riders, 2016): Really nice general intro to data visualization. With some googling effort, you can find the pdf for free.
- Beyond Bars & Lines (PLOS Biology)
Tools
- Snook.ca Color‑Contrast Tool: Test the contrast of your colors
- Viz Palette tool to check your color palette for accessibility
Inspiration for plot types & Galleries
- Data‑to‑Viz: Really cool tool with a decision tree depending on your data and question
- The DataViz Project: A collection with 100 different plot types
- FT Visual Vocabulary: Shows different visualisation options for different questions
- Datawrapper Examples: Cool for inspiration
R‑Specific Resources
- Coloring in R’s Blind Spot: Colorblind palettes for R
- Labeling Bar‑Graphs in ggplot2
- Blogpost on raincloud plots and how to make them with
ggplot2 - “The Evolution of a ggplot”: A nice tutorial on an alternative to boxplots with
ggplot2
Notable R Packages from the ggplot2 universe
- colorspace: Provides many colorblind friendly colors you can easily apply to ggplots
- ggdist: Visualize distributions in R
- ggridges: Create ridgeline plots with ggplot2
- ggforce: For plot annotations and shapes
- ggtext: Add rich text annotations to ggplots
- patchwork: Combine multiple ggplots into one figure