Last week I spent four amazing days in Orlando at the first ever rstudio::conf. I learned a ton, met some really cool people, connected with several I hadn’t seen in a while, and came out feeling ready to take on the world. I’ve divided my summary of the conference into two parts. This first one shares my personal experience and some more general learnings, while the other one has quick, bullet-pointed lists on writing functions, packages, tools, and functions I learned, and general tips and tricks.
This is the second part of my posts on the rstudio::conf. If you’re interested in more general thoughts on the conference and some personal notes, check out my other post. This post is to gather, as succintly and organized as possible, the practical and technical things I learned at the conference. While I did a whole training day on writing R Packages, I haven’t included most of what I’ve learned here.
When Donald Trump first entered the Republican presidential primary on June 16, 2015, no media outlet seemed to take him seriously as a contender. He is a highly unusual candidate, and some in the media have admitted that they, and the media more generally, don’t know how to cover him, both in the primary and now in the general election. Trump himself has criticized the media’s coverage of him: center
The Bright Blue Horror Coming into Metis, I knew one of the hardest parts would be switching from R to Python. Beyond simply having much more experience in R, I had come to rely on Hadley Wickham’s fantastic set of R packages for data science. One of these is ggplot2, a data visualization package. While there is a version of ggplot2 for python, I decided to learn the main plotting system in Python, matplotlib.
As I mentioned in my previous post, I was fortunate to enter graduate school with a few years of programming experience in R. I learned R exclusively through my Statistics classes; while I took the graduate-level psychology statistics course at Rice and was a research assistant in multiple departments, all used SPSS. As this discrepancy suggests, the social sciences are often lagging behind in teaching and using open-source software. Fortunately, there is some effort to change this.