If I’m being honest, I would personally advice you to look at the dataviz project instead, if you haven’t heard of that one yet.
However, OriginLab might win in terms of sentiment. It has this nostalgic look of the ’90s, and apparently people really used it during that time. Nevertheless, despite looking old, the repo seems to be quite extensive, with nearly 400 different types of data visualizations:
Quantity isn’t everything though, as some of the 400 entries are disgustingly horrible:
What I do like about this OriginLab repo is that it has an option to sort its contents using a random order. This really facilitates discovery of new pearls:
Thanks to Maarten Lambrechts for sharing this resource on twitter a while back!
Last week I cohosted a professional learning course on data visualization at JADS. My fellow host was prof. Jack van Wijk, and together we organized an amazing workshop and poster event. Jack gave two lectures on data visualization theory and resources, and mentioned among others treevis.net, a resource I was unfamiliar with up until then.
treevis.net is a lot like the dataviz project in the sense that it is an extensive overview of different types of data visualizations. treevis is unique, however, in the sense that it is focused on specifically visualizations of hierarchical data: multi-level or nested data structures.
Hans-Jörg Schulz — professor of Computer Science at Aarhus University in Denmark — maintains the treevis repo. At the moment of writing, he has compiled over 300 different types of hierachical data visualizations and displays them on this website.
As an added bonus, the repo is interactive as there are several ways to filter and look for the visualization type that best fits your data and needs.
Most resources come with added links to the original authors and the original papers they were first published in, so this is truly a great resources for those interested in doing a deep dive into data visualization. Do have a look yourself!
This last trick, I learned in this recent blog post I came across, by Chisato. She explored all colors() base R incorporates, using the new ggforce and ggraph packages (thank you Thomas Lin Petersen!). Her exploration resulted in some nice visual overviews, which you can view in more detail in the original blog here.
Browse through hundreds of helpful data visualization tools, programs, and services. All neatly organized by Andy Kirk in categories: data handling, applications, programming, web-based, qualitative, mapping, specialist, and colour. What a great repository!