Further Reading: Relational and Categorical Visualization

Tier 1: Essential Reading

The seaborn Relational Tutorial. seaborn.pydata.org/tutorial/relational.html The official seaborn tutorial on relational visualization. Covers relplot, scatterplot, lineplot, and faceting. Essential as a direct API reference alongside this chapter.

The seaborn Categorical Tutorial. seaborn.pydata.org/tutorial/categorical.html The official tutorial for the categorical function family. Covers all seven categorical chart types with examples and when to use each.

Weissgerber, Tracey L., Natasa M. Milic, Stacey J. Winham, and Vesna D. Garovic. "Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm." PLOS Biology 13, no. 4 (2015): e1002128. The paper that pushed the dynamite plot critique into mainstream biomedical publication. Freely available through PLOS. Read alongside Case Study 1 of this chapter.

Wilke, Claus O. Fundamentals of Data Visualization. O'Reilly Media, 2019. Wilke's chapters on "Visualizing relationships" and "Visualizing amounts" cover the design principles that seaborn's relational and categorical families implement. Freely available at clauswilke.com/dataviz.

Wickham, Hadley. ggplot2: Elegant Graphics for Data Analysis. 3rd ed. Springer, 2016. Wickham's ggplot2 book covers the same chart types (relational and categorical) with ggplot2 syntax. Good for understanding the grammar of graphics origin of seaborn's API.

The #BarBarPlots Campaign. Twitter archives from 2016. A hashtag-based Twitter campaign advocating against dynamite plots in biomedical publication. The archives contain many before-and-after redesign examples.

Cleveland, William S., and Robert McGill. "Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods." Journal of the American Statistical Association 79, no. 387 (1984): 531-554. The classic empirical work on graphical perception that underlies the choice of chart types for relational visualization. Essential context for why scatter plots beat pie charts for comparison questions.

Knaflic, Cole Nussbaumer. Storytelling with Data. Wiley, 2015. Already recommended, Knaflic's book includes specific guidance on bar charts, line charts, and scatter plots for business audiences. Pair with seaborn documentation for the practical implementation.

Tier 3: Tools and Online Resources

Resource URL / Source Description
seaborn Examples Gallery seaborn.pydata.org/examples/ Visual gallery of seaborn plots with source code. Filter by chart type for specific examples.
The Python Graph Gallery — seaborn python-graph-gallery.com/seaborn/ Additional examples organized by chart type. Complements the official seaborn gallery.
Gapminder data gapminder.org/data/ The Gapminder dataset used in Case Study 2. Freely available for download.
statsmodels for regression statsmodels.org Python library for statistical modeling. Use for serious regression analysis beyond seaborn's built-in overlays.
scipy.stats docs.scipy.org/doc/scipy/reference/stats.html Scientific Python's statistical functions. Useful for custom statistical tests to accompany categorical visualizations.
pingouin (statistical testing) pingouin-stats.org A Python statistical testing library designed for easy use. Complements seaborn's visualization with appropriate statistical tests.
sns.boxplot documentation seaborn.pydata.org/generated/seaborn.boxplot.html Official API reference for boxplot with all parameter details.
sns.violinplot documentation seaborn.pydata.org/generated/seaborn.violinplot.html Official API reference for violinplot.

A note on reading order: If you want one additional source, read Weissgerber et al.'s "Beyond Bar and Line Graphs" paper. It is short, freely available, and directly relevant to the dynamite plot critique. For design principles, pair with Wilke's Fundamentals of Data Visualization. For practice, use the seaborn examples gallery daily.