There was some weirdness with the install/uninstall ( /usr/local/lib/R left over), I don’t know if I’ll need the optional features, and homebrew r now uses openblas. Update: I tested homebrew-r-srf, and am going with homebrew r. And, of course, actually using R for data analysis… What’s next? At some point I plan to try to integrate GitHub actions for testing, and create a CI/CD pipeline of sorts for Pages, using GitHub actions. I haven’t tried it yet, but I may go with homebrew-r-srf as suggested by Luis (or a fork of it). Interestingly, I also found that Homebrew’s R doesn’t include all R features, but the same author, Luis Puerto offered a solution to install all the things. Poking around further, I found that the cask method was sub-optimal as it installs in /usr/local which causes issues with brew doctor. This left one final question: how exactly to install r? Homebrew itself offers 2 methods: install the official binaries using brew cask install r, and just brew install r.

A blog post, renv: Project Environments for R.

A talk at rstudio::conf 2020, renv: Project Environments for R.

Setting it up is as simple as install.packages('renv') and renv::init(), and RStudio has published: I first found Packrat and then its replacement, renv (Packrat is maintained, but all new development has shifted to renv). (the only gem I install outside a project is bundler) So I went looking for the R equivalent… Which got me thinking, what about package management? While it seems that R doesn’t have the level of challenge posed by Python or Ruby, managing packages on a per-project basis is a best practice I learned from using Bundler to manage the code of this site. Working through this, I noted that RStudio is quite good at automatically downloading and installing packages as needed it triggered installation of rmarkdown and supporting pacakges when creating a new R Notebook, and also readr when importing data from csv. nb.html into git, and using GitHub Pages so that you can view the rendered HTML instead of just the HTML code. After more searching, I was able to get Notebooks working on GitHub, but I used the method described in rstudio/rmarkdown #1020 - checking in the. Side note: the consensus was to use html_notebook as a working document, and html_document to publish.

Quickly I discovered that while the tutorial was helpful, it wasn’t quite the setup I wanted it published R Markdown through GitHub pages, but wouldn’t directly support the automatically generated html of R Notebooks. GitHub published a tutorial in 2018 on getting RStudio integrated with GitHub, and I started working on that. Installation on macOS was easy enough, just brew install r and brew cask install rstudio. Of course, being a sysadmin at heart, I set out to figure out how exactly to best install R and RStudio, and manage the notebooks in git.

The SIRA community was quite supportive of this idea when I asked for suggestions on getting started in the BOF session, and recommended Teacup Giraffes and Tidy Tuesday for learning R, and on my own I found RStudio recommendations. I did more searching and reading, and finally settled on R Notebooks for a few reasons: R Notebooks are more disciplined (no strange side effects from running code out of order), fewer environment problems, the support of the RStudio company, better visualizations, and just because R is the more data-sciency language. After reading a thread on the SIRA mailing list, I was leaning towards R, and re-watched Elliot Murphy’s 2019 SIRAcon presentation on using notebooks, which led me to consider both R Markdown and Python Jupyter Notebooks. RStudio and R have a tougher learning curve, but are free and open source, under active development, and have a large online community. The tradeoffs between the two were pretty clear: SPSS is very easy to use, but expensive, proprietary, and old. Originally I considered SPSS (free to students) and RStudio. I needed a data analysis tool that would allow me to conduct traditional statistical analysis, and I wanted a tool that would be valuable to learn and one that would allow me to do exploratory analysis as well. the time of SIRACon 2020, I decided to start using R.

Packrat r on jupyter notebook