Tinkering with R and Social Network Analysis

My interest in Social Network Analysis (SNA) began when I was studying the Data, Analytics, and Learning MOOC (DALMOOC) through edX a couple of years ago (see my posts from during the course here). During the course it was mentioned that Twitter lends itself to SNA, so I did some fiddling around with analysing the Twitter streams of various library conferences. I used some of the tools that I was introduced to during the DALMOOC, such as Gephi and NodeXL, and managed to produce some graphs. However I put this on the backburner while I focussed on preparing my poster for the EBLIP8 Conference.

Earlier this year, though, I got the urge to start learning more about the R programming language. Although I have absolutely no background in coding or programming (unless you count copying BASIC programs out of a book for my Commodore 128 when I was a kid), I’d heard about the R programming language, and wanted to find out a bit more about it. I came across the free Datacamp course on R and did the first few lessons, but haven’t worked on it for a while now. I started looking around to see if there were any R packages that could do SNA on Twitter data, and I found that there were a few that I could use. There were websites which had some example code which I was able to copy and do some tweaking on (such as this one and this one), and before long I was collecting and analysing my own data.

I still wouldn’t call myself a coder or programmer, but I’m starting to get the hang of using R. It’s pretty easy to use, especially when you’re using code that is freely available and not having to develop your own. In my next post I’ll show some examples of SNA that I prepared based on the tweets sent at the 2016 Medical Library Association conference.