Disentangling Data Science
Tag: reproducible
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.
Version control is an essential tool for any software developer. Hence, any respectable data scientist has to make sure his/her analysis programs and machine learning pipelines are reproducible and maintainable through version control.
Often, we use git for version control. If you don’t know what git is yet, I advise you begin here. If you work in R, start here and here. If you work in Python, start here.
This blog is intended for those already familiar working with git, but who want to learn how to write better, more informative git commit messages. Actually, this blog is just a summary fragment of this original blog by Chris Beams, which I thought deserved a wider audience.
For example:
Summarize changes in around 50 characters or less
More detailed explanatory text, if necessary. Wrap it to about 72
characters or so. In some contexts, the first line is treated as the
subject of the commit and the rest of the text as the body. The
blank line separating the summary from the body is critical (unless
you omit the body entirely); various tools like `log`, `shortlog`
and `rebase` can get confused if you run the two together.
Explain the problem that this commit is solving. Focus on why you
are making this change as opposed to how (the code explains that).
Are there side effects or other unintuitive consequences of this
change? Here's the place to explain them.
Further paragraphs come after blank lines.
- Bullet points are okay, too
- Typically a hyphen or asterisk is used for the bullet, preceded
by a single space, with blank lines in between, but conventions
vary here
If you use an issue tracker, put references to them at the bottom,
like this:
Resolves: #123
See also: #456, #789
If you’re having a hard time summarizing your commits in a single line or message, you might be committing too many changes at once. Instead, you should try to aim for what’s called atomic commits.
Cover image by XKCD#1296
Sharon Machlis is the author of Practical R for Mass Communication and Journalism. In writing this book, she obviously wrote a lot of R code. Now, Sharon has been nice enough to share all 195 tricks and tips she came across during her writing with us, via this handy table.
Sharon’s list contains many neat tricks, some of which less well-known base functions, others features of more niche packages. Here’s the ones I am definitely adding to my R tricks overview and want to highlight here as well:
cut() readr::parse_number(mydf$mycol)DT::datatable(mydf, filter = 'top')scales::percent(myfraction)seq_along(myvec)And as if one list was not enough, scrolling through her Twitter feed, I found another R tips and tricks list by Sharon:
Timo Grossenbacher works as reporter/coder for SRF Data, the data journalism unit of Swiss Radio and TV. He analyzes and visualizes data and investigates data-driven stories. On his website, he hosts a growing list of cool projects. One of his recent blogs covers categorical spatial interpolation in R. The end result of that blog looks amazing:
This map was built with data Timo crowdsourced for one of his projects. With this data, Timo took the following steps, which are covered in his tutorial:
sf object, reproject the geodata into the ETRS CRS, clip the point data to Germany, so data outside of Germany is discarded).kknn package. Since this is quite computationally and memory intensive, the resulting raster is split up into 20 batches, and each batch is computed by a single CPU core in parallel.ggplot2.All code for the above process can be accessed on Timo’s Github. The georeferenced points underlying the interpolation look like the below, where each point represents the location of a person who selected a certain pronunciation in an online survey. More details on the crowdsourced pronunciation project van be found here, .
Markdown is a great tool for integrating data analysis and report writing. Rosanna van Hespen wrote a great five-blog guide on how to write your thesis in R Markdown:
paulvanderlaken.com 