Jumping Rivers

• Data science consultancy
  • Python / R, machine learning, dashboards, API’s
• Data engineering
  • Data pipelines, server health and security, managed Posit (RStudio) services
• Training
  • Python, R, Git, Tableau + many more
• Community
  • Conferences/meetups, blogs, open-source

Python plotting landscape

Matplotlib

• Stable plotting interface
• Flexible customisation
• Active development community
• Comprehensive documentation

Alternatives to Matplotlib

For all its strengths, Matplotlib does have a few downsides:

• The default appearance of plots is not particularly appealing.
• Complex figures are non-trivial and require many lines of code.

Alternatives to Matplotlib

• Javascript-related
  • Interactive visualisations
  • plotly
• OpenGL-related
  • Interactive 3D figures
  • PyOpenGL,
  • visvis
  • pyglet.

Alternatives to Matplotlib

• D3.js-related
  • Interactive data visualisations
  • Optimised for the web
  • mpld3
  • Vega-Altair
• Bulit on top of Matplotlib
  • Plotnine
  • Seaborn

What is Seaborn?

• Builds on Matplotlib
• Integrates with Pandas data structures
• Detailed statistical plots with few lines of code

What is Seaborn?

New in v0.12

• seaborn.objects interface
• More flexible customisation within Seaborn API
• Currently still experimental and not covered in this workshop

The plan

Part 1: Introduction to Seaborn

• First plots
• Seaborn and Matplotlib

Part 2: Statistical visualisations with Seaborn

• Bivariate relationships
• Distributions
• Categorical data
• Multi-panel plots

The Plan

09:30 - 11:00 Part 1: Introduction to Seaborn

11:00 - 11:15 Break

11:15 - 12:45 Part 2: Statistical visualisations with Seaborn

12:45 - 13:00 Q & A

Takeaways

• You’ll be able to download every file (including your exercise solutions)

• Materials are also available at https://github.com/jumpingrivers/2023-nhs-r-seaborn