Two weeks into this course, my favorite component of R has by far been the versatile and wide-ranging array of packages that have been concocted to ease usage of the program. These packages’ slick and creative approaches to bundling and calling upon data sets and functions are genuinely enjoyable to discover, and I’m impressed with how many user-created packages there are. I also appreciate the ease of use pertaining to creating and combining data types and working with different data structures, though I’m sure we’ll be unearthing a plethora of idiosyncrasies over the coming weeks.

Overall, I find R to be more intuitive than my initial experience with SAS, and I prefer the R interface to the SAS windowing environment for its ease of use. In contrast to the somewhat underwhelming commenting option in SAS, I find the R Markdown system to be delightful, offering a level of personalization and expression that I didn’t think possible and making outputting a wide variety of objects (such as PDF and HTML documents) extremely easy. With what we’ve learned so far, I feel like R allows for more flexible approaches to programming, and perhaps greater level of customization in one’s outputs. However, this also has its disadvantages; I do miss having a more “definite” correct answer to a programming query, as has somewhat been the case in my experience with SAS. I also somewhat prefer the way that SAS steps are often explicitly bounded into sections by run; statements, with each line ending in a semicolon; somehow, I feel like this lends a somewhat “cleaner” feel to the code.

While SAS is perhaps more niche and can process particularly large datasets, I feel like R will be an excellent tool for collaboration and for communicating with non-data science professionals. There are some concepts that have been covered that will require additional review and effort on my end - but surely, that is the case with any new topic of study! Switching gears from one language to another is a bit tricky, given the unique quirks of each, but having a background in SAS is making it simpler to follow certain important concepts in R, especially when it comes to principles concerning logical operators and how to work with data. I’m intrigued to see what lies ahead!