Disentangling Data Science
Tag: repository
Rob Gilmore curates a github repo listing resources for working with Shiny, the R web framework and dashboarding tool.
Nan Xiao curates a second repository, listing awesome R packages offer that extensions to Shiny, like extended UI or server components.
They should be your go-to resources when looking for anything Shiny!
The Institute for Ethical Machine Learning compiled this amazing curated list of open source libraries that will help you deploy, monitor, version, scale, and secure your production machine learning.
The Institute for Ethical Machine Learning is a think-tank that brings together with technology leaders, policymakers & academics to develop standards for ML.
I used to keep a repository of links to interesting datasets to learn data science. However, that page I can retire, as Google has launched its new service Dataset Search.
The “world wide web” hosts millions of datasets, on nearly any topic you can think of. Google’s Dataset Search has indexed almost 25 million of these datasets, giving you a single entry point to search for datasets online. After a year of testing, Dataset Search is now officially out of beta.
After alpha testing, Dataset Search now includes filter based on the types of dataset that you want (e.g., tables, images, text), on whether the dataset is open source/access. For dataset on geographic area’s, you can see the map. The quality of dataset’s descriptions has improved greatly, and the tool now has a mobile version.
Anyone who publishes data can make their datasets discoverable in Dataset Search by describe the properties of their dataset using a special schema on their own web page.
Continuing my recent line of posts on data visualization resources, I found another repository in my inbox: OriginLab’s GraphGallery!
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!
Amy Cesal put together (1) this great overview of style guides for data visualization practice. Moreover, in the original tweet, Amy refers to other great repositories such as (2) this PolicyViz one and (3) this humongous one by Adele.
Amy’s list includes many references to the best practices used by some of the leading data journalism companies, such as the BBC, or professional data companies like Salesforce and IBM.
As I’m worried that this great repository may not stand the test of time on the current Google Docs location, here are the base URLs once more:
If you have any resources or style guides to contribute to Amy’s list, you can do so via this link.
Looking for a custom typeface to use in your data visualizations? Google Fonts is an awesome databank of nearly a thousands font families you can access, download, and use for free.
If you’re into design, the website includes a blog featuring articles on font design.
Google Fonts among others provided the font for my dissertation cover so I definitely recommend it.
paulvanderlaken.com 