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.
For those of you interested in potentially picking up R, these are some of the best early resources I found.
How to transition from Excel to R by Tony Ojeda
One of the most helpful articles when I got started. I mentioned I love Excel, but it’s more of an addiction than anything else. Above all this article helped assure me that I could still do in R what I already knew how to do in Excel.
The R Podcast by Eric Nantz
The R podcast is thorough in it’s explanations. Eric Nantz is a statistician with an unmatched knack for explaining the nuances and verbiage that goes along with learning R. I spent a few months struggling through R before coming across his podcast. Listening to his breakdown of the core functions of the system helped pull the disparate pieces into place. He gives a great history of R, context for the work, and his website includes follow-along video applications to help bring you up to speed.
R Programming by Roger Peng
For a beginner there’s not a better book on R. This book by Roger D. Peng breaks down the fundamentals of R in an accessible format and his follow-along lesson are incredible. It’s available in print at Lulu.com or you can download it by donation on Leanpub.
R Programming Coursera – John Hopkins
Every “Starting R” resource or message board that I’ve seen recommends the John Hopkins Coursera course as the gold star of learning R. Somewhat of a duplicate of the last resource, this course is taught by Roger Peng and his lessons are accessible and easy to follow. The benefit of the video course is getting to see the code and then execute along with the video. Sample exercises and a capstone project take it to the next level.
I personally have a love – procrastinate relationship with Coursera. I have started so many courses only to let them drift. I’m sure it’s beneficial to sign up and go through the entire course, but picking out the parts you find most interesting or lectures covering troubles you’re having is also a very valid option. I’ve started this one again on March 15th and I’m hoping with this public display of commitment I can push through it.
HR examples of R
Here’s the core of why this is in the HR Analytics Starter Kit. From what I can tell there are unfortunately not many walkthrough examples of using R in HR that are publicly available, but that might be a question to explore for another post. Until then, here are the best ones I’ve found.
People Analytics – An Example Using R by Lyndon Sundmark
We start with the best. Lyndon Sundmark put together a mock data set to develop some examples of HR analytics in application. I thank him for the effort because it’s leaps and bounds the best public example of applying R to an HR problem that I’ve come across. His post includes links to his R code, a walk-through of the steps he took, and the .csv original data file.
For anyone looking to understand what performing HR analytics could look like with R, this is the first example to check out. For anyone looking to produce an HR analytics example to help the community learn the techniques, this article sets the bar for what should be included.
On the other hand this public presentation from Facebook is probably the best example of a company applying R to explore an HR issue. It doesn’t get as deep into explaining how or why they developed the code, but it still shows deep technical details for a real-world application. Many large corporations are applying R to problems like this, but very few have talked openly about it.
Is there a Gender Gap in Florida’s Government? by Charles McGuinness
RPubs is a public site where you can publish your work from R. I’m not sure if Charles McGuinness expected attention for it when he posted this to RPubs, but when I stumbled across it I bookmarked it immediately. Charles uses a public dataset from Florida state employees then cleans, analyzes, and visualizes the data. I thought this was an excellent and fairly straightforward example of how to use R to perform basic analysis on an employee dataset.
R Helps with Employee Churn by Pasha Roberts
This article reviewing the work and the original article by Pasha Roberts gives a glimpse into what you can do with HR analytics when you start getting more rigorous with your analysis. Pasha’s article on the methodology behind Employee Churn and the corresponding Github gives us a taste for what a predictive analytics consulting firm like Talent Analytics is able to produce.
To wrap up R resources for HR, I want to touch on Predictive Analytics World: Workforce. If you’re interested in HR analytics and want to attend a conference to network and learn about advancements, there are an amazing number of meetups and conferences and this would require a much longer post to touch on all of them in even a few sentences. PAW: Workforce stands out to me as one of the only conferences that has a dedicated track focused on analysts designed to provide them with the cutting edge technical skills needed to produce predictive insights.
The Predictive Analytics World series of conference as a whole are data science oriented conferences. In addition to the main conference track for PAW Workforce, there are standalone full day sessions on R, Predictive modeling, and even uplift modeling (a technique I fully believe HR needs to steal from marketing) which are taught by industry experts. If you’re going to be at PAW Workforce or if you’re thinking about going, let me know and I look forward to meeting up with you out there.
That wraps up this section of the HR Analytics Starter Kit for now. If you were new to R to start and you’re now interested in R, I’m excited for you! Being open source, there’s a huge community and a wealth of other resources out there for you to take advantage of in your learning. I’m still getting started on my journey here as well, so if you find other resources that worked well for you that you think should be included in this part of the Starter Kit, I’d love to hear about them in the comments.
As far as R in HR goes, I’ve found that there are still very few public resources available. There’s a chance I’ve missed some that are hanging out in the corners of the web and if I have please send them over, I’d love to hear more. There’s a lot of talk about HR analytics right now, but not a lot of step-by-step practical examples or public datasets for new learners looking to build their skill-set. If anyone reading this feels like they have the resources or knowledge to create more examples, please contact me. I’d love to help you publish.
To everyone, a huge thank you for the support on my first few articles. I hope you took something away that was helpful and please forward this to a friend or teammate who might enjoy it.
I’m also looking forward to hearing your general thoughts on these articles and the blog in general, so please reach out to me here or on Twitter with any comments, suggestions, or questions.
- What Types of Questions Can Data Science Answer
- Stop Hiring Data Scientists Until You’re Ready for Data Science
- Predictive Analytics World Workforce 2015: Highlights