HR Analytics Starter Kit – Intro to R
We review tools to help you start performing HR analytics with a focus on R platform, and providing useful examples for the HR and Workforce analytics using R.
By Richard Rosenow, Vanderbilt Owen Graduate School of Business.
Welcome to part 2 of the HR Analytics Starter Kit! My goal in writing these has been to share with you some of the most substantial articles and resources I’ve found while exploring the HR analytics space to help you get up to speed. For those of you arriving here at part 2 first, there’s not a particular sequence to reading these, but here is the link to part I in case you feel compelled to start there.
In part 1 I walked through some of the most substantial articles I had found in three groupings that I labeled Excitement (get excited about HR analytics), Implementation (how to think about developing an HR analytics function), and Example (examples of HR analytics in action). Here in part 2 I’d like to review some of the tools to help you start performing HR analytics with a focus on R.
Tools for Performing HR Analytics
I’m a huge Excel nerd, but to perform analysis on large datasets, Excel starts to slow down. Setting up multiple statistical tests in Excel can also be time consuming and complicated. If you don’t have embedded analytics within your current HR management software then you might want to think about picking up a more robust tool to analyze, interpret, and visualize your data.
Some of the most popular tools that I’ve seen HR practitioners use are Excel, Tableau, R, SAS, SPSS and Python, with a few recent articles stating that R and Python are leading the pack as far as popularity. There’s not a golden standard for analytics work and in the end it’s a question of capability, cost, and even more so preference. My recommendation to someone just starting down this path would be to take a look at R.
R is an open source statistical programming language. To rephrase, R is FREE software that you could download today that lets you run analysis on large datasets quickly. Just to reiterate – free! I want to pause on that and recognize how rare it is for HR to get access to a cutting edge tool at no cost. It’s one of the benefits of HR being a last mover into the data science space . Well known companies have been using R for a long time now (Facebook, Twitter, and Google to name a few popular ones) and the applications of R across the business are profound.
I understand that asking you to consider learning a programming language to analyze data sounds intimidating and I’ll admit that R has a steep learning curve. So in the sections below, I want to lay out an on-ramp of resources to ease you into R, point you in the direction of some early technical resources, and then review the few substantial technical examples I have found that apply R to HR solutions.
Introduction to R
We’re starting off with two videos; the first 90 seconds and the second 45 minutes. Watch the first one now if you’re relatively new to R and bookmark the other for later.
This first one is put together by Revolution Analytics (which was recently purchased by Microsoft) and covers R at a high level. This puts some context around why you might be interested in learning or working with it. If this is one of the first times you’re hearing about R, it’d be worth watching before moving forward.
This second video is more technical and the speaker John Cook provides a great history and context for the language. As the title suggests he frames up the good, the bad, and the ugly of R and this mindset sets the stage well for someone starting down this path. He also provides some of the best advice I’ve heard for working with R’s steep learning curve. He says that:
“R is a domain specific language, and so you have to understand the domain. Learning R purely as a language would be like learning PHP as a language without being interested in Web Development”.
In other words, if you just learn R for R’s sake, you’re probably going to run out of motivation. My advice would be to find an HR problem you want to tackle, maybe one you’ve already worked through in excel, and try to apply R to it to get to the same results.
If I can add anything to his explanation, I would say that whatever it is you want to do in R, you can do it. The beauty of R being open source and modular is that experts in every field are working with the same system and adding to it every day. That and Google is your best friend (“i.e. How do I run regression in R?”). There’s a massive and incredibly helpful community out there working through every question you could possibly run into.
Code school is a fascinating website. They’ve built the coding prompts right into the lessons which allows for a seamless learning experience. For someone who just wants to see what coding in R is like, the introduction they provide is a low commitment learning tool that make the language accessible and reduces some of the fears around picking it up.
The best technical resource for learning R is to download the software and poke around. R is free to download and use and quick to set up. By itself, R will do what you need, but R Studio is a program that sits on top of R and provides you with more point and click options for analysis instead of just running code through text entry like you would in base R. From what I’ve seen R Studio is recommended almost across the board for R among beginners and experts.