Working with R

Update: I spoke at SIRAcon 2022 on my two-year journey learning R.

Around the time of SIRAcon 2020, I decided to start using R. 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. Originally I considered SPSS (free to students) and RStudio. The tradeoffs between the two were pretty clear: SPSS is very easy to use, but expensive, proprietary, and old. RStudio and R have a tougher learning curve, but are free and open source, under active development, and have a large online community. 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. 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.

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. 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.

Installation on macOS was easy enough, just brew install r and brew cask install rstudio. GitHub published a tutorial in 2018 on getting RStudio integrated with GitHub, and I started working on that. 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. Side note: the consensus was to use html_notebook as a working document, and html_document to publish. 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 .nb.html into git, and using GitHub Pages so that you can view the rendered HTML instead of just the HTML code.

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 packages when creating a new R Notebook, and also readr when importing data from csv. 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. (the only gem I install outside a project is bundler) So I went looking for the R equivalent…

I first found Packrat and then its replacement, renv (Packrat is maintained, but all new development has shifted to renv). Setting it up is as simple as install.packages("renv") and renv::init(), and RStudio has published:

• A talk at rstudio::conf 2020, renv: Project Environments for R
• A blog post, renv: Project Environments for R
• An Introduction to renv

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. Poking around further, I found that the cask method was sub-optimal as it installs in /usr/local which causes issues with brew doctor. 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. I haven’t tried it yet, but I may go with homebrew-r-srf as suggested by Luis (or a fork of it).

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. And, of course, actually using R for data analysis…

Update: I tested homebrew-r-srf, and am going with homebrew r. 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. If I find I actually need any of the missing capabilities, I’ll likely write my own formula.

CONOPS (Concept of Operations)

I recently came across a posting on Design Docs at Google. I was struck by the similarities between the design document, as described in the article, and a Concept of Operations (CONOPS). Traditionally, CONOPS are primarily used by the military, for very large and costly projects, such as the design of a new Coast Guard Cutter, created prior to official design documentation, and mainly serve to satisfy project requirements, and is not something you’d expect a modern software organization like Google to use.

In my own work, I’ve come to believe that a shared mental model of the application or service the team is building is essential for reliability and resilience, and there is research that suggests an agile CONOPS can help develop a shared mental model amongst stakeholders, by using visualization, models, and system thinking. My own brief experiment with CONOPS found that creating a visual diagram is most valuable, the formal CONOPS outline, defined in IEEE standard 1362, was less useful.

What’s interesting about the Google Design Doc is that it includes important elements of the CONOPS. The article identifies the following functions of the design document (emphasis mine):

• Early identification of design issues when making changes is still cheap.
• Achieving consensus around a design in the organization.
• Ensuring consideration of cross-cutting concerns.
• Scaling knowledge of senior engineers into the organization.
• Form the basis of an organizational memory around design decisions.
• Acts as a summary artifact in the technical portfolio of the software designer(s).

It’s notable that four of the six functions all relate to development of a shared mental model of the system being built - across the engineering organization, with security & privacy, senior engineers, and for posterity. Additionally, I argue that many of the features described would also be found in a good CONOPS: Goals and Non-Goals, visual diagrams, and existing constraints. Unsurprisingly, the post also recommends making the document only as long as needed, avoid creating an ‘implementation manual’, and iterate.

I’d agree with all of that, and would also suggest one additional lesson from well written CONOPS: adding operational scenarios, as included in the CPC CONOPS mentioned earlier, can be an effective tool for helping people understand what’s being proposed, and how the designers envision it being used. Having specific narratives helps ‘make it real’, and makes implicit assumptions more explicit.

Bottom Line: whether you call it a CONOPS or a design document, creating a high-level description of what you’re planning to build, without getting into the weeds, is an underutilized but effective way to build better software systems. Focus on visualization and creating a common mental model for the organization (including our future selves), iterate, and consider using scenarios to help build understanding.

Failover Conf

Back in April, I attended Failover Conf, a virtual conference hosted by Gremlin. Overall I thought the conference was pretty good, but as with all conferences, the usefulness of the talks varied. The influence of safety thinking was clear, especially Resilience Engineering, which was explicitly covered in two talks (Amy Tobey and J Paul Reed).

The highlights for me were two talks on Site Reliability Engineering (SRE) by Jennifer Petoff on SRE training at Google, and by Danyel Fisher & Liz Fong-Jones, on implementing SRE at honeycomb.io. SRE is an interesting practice; it’s essentially “how Google implemented operations at scale,” making the conference an interesting blend of theory (Resilience Engineering) and practice (SRE).

The downside of the conference was the unusually high number of marketing emails participants received; I mean, I know it’s a free conference, but even Gremlin admitted there were too many. Thankfully, you can watch all the talks without registration here.

The conference also had a dedicated Slack for discussion during and after the talks, which was for me at least as interesting as the talks themselves. From the Slack discussion, I got recommendations on some additional academic reading on Resilience Engineering from J Paul Reed, which I am sharing here:

• REdeploy
• SNAFUcatchers
• Approaching Overload: Diagnosis and Response to Anomalies in Complex and Automated Production Software Systems (masters thesis)
• Managing the Hidden Costs of Coordination
• ACM Queue Vol. 17 No. 6, Human Factors - includes the article above
• Maps, Context, and Tribal Knowledge: On the Structure and Use of Post-Incident Analysis Artifacts in Software Development and Operations (I think this was actually recommended by John Allspaw, also available at Lund)