Visualization Links

The Chartmaker directory has a nice matrix showing which tools can be used to produce many kinds of chart, with examples of how.

Tools

No coding required

Two new tools allow you to interactively create bespoke chart layouts without programming

Adobe's Data Illustrator
Microsoft Research's Charticulator

RAWGraphs may also be useful for creating more standard visualization types.

There are also a couple of commercial tools aimed more at journalists: Datawrapper and Kiln's Flourish.

Python

The most commonly used plotting library in python is probably matplotlib; its pyplot module provides an interface similar to the MATLAB plotting interface. Check out its gallery for examples of what the output it can produce, and the corresponding code. However, there are many alternative libraries.

See the blog post Python Data Visualization 2018: Why So Many Libraries?. Also see this clickable adaptation of a slide from Jake VanderPlas' keynote at Pycon 2017 "The Python Visualization Landscape"

Some of these libraries (e.g. Plotly's Dash or Bokeh) can produce interactive plots to be viewed in a web browser.

The Python Graph Gallery is a useful reference with Matplotlib & Seaborne examples.

R

To produce plots in R, I suggest using the ggplot2 library. Many other libraries (typically with names beginning with gg) build upon ggplot2 and provide additional features.

The R Graph Gallery is a useful reference.

Colors

There are many tools for choosing colors for use in visualization. Cynthia Brewer's colorbrewer2 is widely used.

Viz Palette, from Elijah Meeks and Susie Lu, is a useful tools for identifying potential problems in a color scheme, and for previewing how it might look.

Chart types

Articles:

Catalogues:

There are also many literature browsers that review visualization techniques in a a particular area:

jamesscottbrown/VisLinks.md