I am just wrapping up a course (ST558 at NCSU) that covers advanced R programming topics, like parallelization, Shiny, and containerization. I started this blog for the course and as my final assignment am making this reflection post.

I just completed a project to learn the basics of automating reporting using Rmarkdown. The github pages link is here and the github repo link is here.

I just wrapped a project (github repo) that gives an example of how to write functions to intearct with an API. I used the NHL’s API to write the vignette.

My first experiences with R came in the summer of 2019. I read An Introduction to Statistical Learning and worked through the labs, which were written in R. My first programming language is MATLAB, although I have primarily used Python since 2016. I had heard R was difficult to learn, but I found it to be intuitive since most of my programming revolves around data manipulation and modeling/testing. Your mileage may vary though. R is very much a language written by statisticians for statisticians. I could see someone coming from a non-statistical programming background being somewhat confused by it.

Given this is my first blog post, I am blogging about something that has had more than enough digital ink spilled over it; data science vs. statistics.