Nicolas Rougier and friends have prepared an excellent outline of how to prepare superior data graphics. Their focus is on preparing effective data charts, and their article dwells on how to prepare individual charts for academic publication. However, their points are valid in a wider sense (and overlap with some of Edward Tufte’s guidelines.)
I am synopsizing and expanding for my own benefit.
Rule 1: Know Your Audience
Rule 2: Identify Your Message
Rule 3: Adapt The Figure To The Support Medium
Rule 4: Captions Are Not Optional
Rule 5: Do Not Trust The Defaults
Rule 6: Use Color Effectively
Rule 7: Do Not Mislead The Reader
Rule 8: Avoid “Chartjunk”
Rule 9: Message Trumps Beauty
Rule 10: Get The Right Tool
In that last rule, they inventory a number of open source tools useful to preparing data graphics for presentation and publication. I want to capture these for my own skills planning purposes.
MatPlotLib is a python plotting library that comes with a huge gallery of examples that cover virtually all scientific domains.
R provides a wide variety of statistical and graphical techniques, and is highly extensible.
Inkscape is a professional vector graphics editor. It cab also read a PDF file in order to extract figures and transform them.
TikZ and PGF are TeX packages for creating graphics programmatically.
GIMP is a photo compositing application that can quickly retouch an image or add some legends or labels.
ImageMagick is a software suite to create, edit, compose, or convert bitmap images. It can be used to quickly convert an image into another format.
D3.js offers an easy way to create and control interactive data-based graphical forms which run in web browsers.
Cytoscape is for visualizing complex networks and integrating these with any type of attribute data.
Circos was originally designed for visualizing genomic data but can create figures from data in any field.
(Image courtesy of cocoparisenne at Pixabay)