Anaconda (and these days miniconda) has been my go-to for getting Python and the scientific/data science software stack installed on my computer (even on my Arch linux machine!).
When it first came out it was the first time I was able to install and use pandas and the rest of the scipy stack. I’ve stuck with it ever since.
It was also the first time I used (and understood) virtual environments.
I’ve had a lot of time to think about my time as an RStudio intern. When I do, I usually end up with a few words in my head before I’m flooded with (good) emotions and struggle with finding the words to convey my thoughts. The last time I tried to write something like this went a little like this. What I can say is this: whatever you thought it was like working at RStudio, is (probably) true.
I’m at the R/Medicine conference (no it’s not a Reddit thing) and got to help Alison Hill with her R Markdown for Medicine workshop. One of the questions I got to tinker with was making tables used to report model results.
One technique I learned while doing my MPH was to add variables to your model in blocks. It reduces the number of tests you need to perform, and is more meaningful than saying “I ran stepwise”.
I’ve been asked a few times lately about whether one should learn R or Python.
Channeling David Robinson’s post, I’m writing a blog post about it.
When you’ve written the same code 3 times, write a function
I have a love-hate relationship with Git. It took me years of following cookbooks and following strict set of commands to get a sense of how it works. I still have to spend time wrangling Git at times, but I’ve gotten to the point where I’m just annoyed, instead of scared. Here is the branching-squash-and-merge workflow I’ve been using at RStudio.
Also, sorry I may be glossing over some of the Git basics in this post…
One of the cool things about working on gradethis (grader need to be renamed) is that we end up doing things that aren’t common in R (i.e., grading and comparing code).
I discovered an inconsistency with the == operator when comparing (long) R expressions.
A quick primer on expressions In R, you can create an expression using the quote() function. This is essentially the code that R will execute.
I was initially going to get some small bookshelves to put my Oculus sensors on, but you can take your Oculus sensors off the stand! This way you wont have an awkward way to mount the sensors against the wall.
I also have a 3rd rear sensor, but when I was (re)setting up my sensors, I ended up just leaving it on my desk. The parts list below was from me shopping at Home Depot assuming I was going to mount all 3 against the wall.
I finally got everything moved over to blogdown with the Hugo Academic theme. Thanks so much to Allison Hill, who ran the summer-of-blogdown tutorial for us RStudio interns.
The transition was pretty seamless. Mainly because I didn’t really have that much content to move over. The biggest change was I had to commout my categories tag in my YAML post headers becuase they were causing the site to not build.
The main topics and events of last week were:
Much git. Metaprogramming and non-standard evaluation (NSE) in R Four 1-hour workshops by Allison Hill on the summer-of-blogdown moving things over from jekyll will take some time So many of the random things I’ve tinkered with in the past have come front and center. As an educator, I know seeing these things again make learning and understanding them easier.