I’ve been thinking about many ideas lately dealing with data and data science (this is, I’m sure, not news to anyone). I’ve also had several people encourage me to pick my blog back up, and I’ve recently made my den into a cute and comfy little office, so, why not put all this together and resume blogging with a little post about my thoughts on data! In particular, in this post I’m going to talk about coding.
Early on in my library career when I first got interested in data, I was talking to one of my first bosses and told her I thought I should learn R, which is essentially a scripting language, very useful for data processing, analysis, statistics, and visualization. She gave me a sort of dubious look, and even as I said it, I was thinking in my head, yeah, I’m probably not going to do that. I’m no computer scientist. Fast forward a few years later, and not only have I actually learned R, it’s probably the single most important skill in my professional toolbox.
Here’s the thing – you don’t have to be a computer scientist to code, especially in R. It’s actually remarkably straightforward, once you get over the initial strangeness of it and get a feel for the syntax. I started offering R classes around the beginning of this year and I call my introductory classes “Introduction to R for Non-programmers.” I had two reasons for selecting this name: one, I had only been using R for less than a year myself and didn’t (and still don’t) consider myself an expert. When I started thinking about getting up in front of a room of people and teaching them to code, I had horrifying visions of experienced computer scientists calling me out on my relative lack of expertise, mocking my class exercises, or correcting me in front of everyone. So, I figured, let’s set the bar low. 🙂 More importantly, I wanted to emphasize that R is approachable! It’s not scary! I can learn it, you can learn it. Hell, young children can (and do) learn it. Not only that, but you can learn it from one of a plethora of free resources without ever cracking a book or spending a dime. All it takes is a little time, patience, and practice.
The payoff? For one thing, you can impress your friends with your nerdy awesome skills! (Or at least that’s what I keep telling myself.) If you work with data of any kind, you can simplify your work, because using R (or other scientific programming languages) is faaaaar more efficient than using other point and click tools like Excel. You can create super awesome visualizations, do crazy data analysis in a snap, and work with big huge data sets that would break Excel. And you can do all of this for free! If you’re a research and/or medical librarian, you will also make yourself an invaluable resource to your user community. I believe that I could teach an R class every day at my library and there would still be people showing up. We regularly have waitlists of 20 or more people. Scientists are starting to catch on to all the reasons I’ve mentioned above, but not all of them have the time or inclination to use one of the free online resources. Plus, since I’m a real human person who knows my users and their research and their data, I know what they probably want to do, so my classes are more tailored to them.
I was being introduced to Hadley Wickham yesterday, who is a pretty big deal in the R world, as he created some very important R packages (kind of like apps), and my friend and colleague who introduced me said, “this is Lisa; she is our prototypical data scientist librarian.” I know there are other librarian coders out there because I’m on mailing lists with some of them, but I’m not currently aware of any other data librarians or medical librarians who know R. I’m sure there are others and I would be very interested in knowing them. And if it is fair to consider me a “prototype,” I wonder how many other librarians will be interested in becoming data scientist librarians. I’m really interested in hearing from the librarians reading this – do you want to code? Do you think you can learn to code? And if not, why not?