The R programming language is a powerful tool used in data science for business (DS4B), but R can be unnecessarily challenging to learn. We believe you can learn R quickly by taking an 80/20 approach to learning the most in-demand functions and packages. In this article, we seek to ultimately understand what techniques are most critical to a beginners success through analyzing a master data scientist’s code base. Half of this article covers the web scraping procedure (using rvest and purrr) we used to collect our data (if new to R, you can skip this). The second half covers the insights gained from analyzing a master’s code base. In the next article in our series, we’ll develop a strategic learning plan built on our knowledge of the master. Last, there’s a bonus at the end of the article that shows how you can analyze your own code base using the new fs package. Enjoy.
We’re happy to announce the third release of the tibbletime package. This is a huge update, mainly due to a complete rewrite of the package. It contains a ton of new functionality and a number of breaking changes that existing users need to be aware of. All of the changes have been well documented in the NEWS file, but it’s worthwhile to touch on a few of them here and discuss the future of the package. We’re super excited so let’s check out the vision for tibbletime and its new functionality!
The EARL Boston 2017 conference was held November 1 - 3 in Boston, Mass. There were some excellent presentations illustrating how R is being embraced in enterprises, especially in the financial and pharmaceutical industries. Matt Dancho, founder of Business Science, presented on using machine learning to predict and explain employee turnover, a hot topic in HR! We’ve uploaded the HR Analytics presentation to YouTube. Check out the presentation, and don’t forget to follow us on social media to stay up on the latest Business Science news, events and information!
