Visualization techniques

This is an open and hopefully ever growing collection of links to favorite tools (libraries) and techniques that help us produce informative figures from complex models.

Plain, useful figures

First you should select the main environment from which you do your analytics and want to produce figures. We have collected a few libraries for each of them.

R

• The main R graph gallery
• GGplot - a famous engine to produce figures in R. Provides the opportunity to customize every detail.
• Plotly for R - makes graphs available in a cloud and enables interactive collaboration
• Another way to make the figures interactive R shiny
• Some links to spatial analyses in R: Rspatial or Spatial data analysis

Python

• The main python graph gallery
• Seaborn - a widely used library for pretty scientific figures, next to
• Matplotlib - the classical standard for matlab-alike figures + the tutorial
• Plotly for python - makes graphs available in a cloud and enables interactive collaboration + the tutorial to integrate this in a jupyter notebook
• A way for a 'shiny' figure production within notebooks are provided by widgets, and a way showing how to do this is here

Matlab

• Matlab official gallery

Tips and tricks for polishing

• Export figures from the original environment in vector graph format (pdf, svg, ...) so that you can customize all elements by hand, which were not accessible via code. This helps also to align multiple figures of different types to produce one overview. An environment to load and adjust vector graphics is inkscape.