What's New: R and Python

What’s New: R and Python

Epifany recently hired a couple of Data Scientists. I decided to learn enough R and Python to be dangerous. I’ve always wanted to say that. The truth is, I’m just getting familiar with the two ecosystems to better support my team.


Learning Resources

I tried DataCamp for a a couple of hours. I thought it was great and it taught me fundamentals that allowed me to further explore R, but (there is always a but), I felt it moved too slow for developers trying to learn R.

R Packages

  • Plumber: An R package that converts your existing R code to a web API using a handful of special one-line comments.
  • Shiny: Shiny is an R package that makes it easy to build interactive web apps straight from R.
  • Tidyverse: The tidyverse is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures. They had me at opinionated, coming from a strong Rails background I want someone to take lead and show us a way to do things, if we like it we use it, if not we keep looking.
  • Leaflet for R: This R package makes it easy to integrate and control Leaflet maps in R


  • R Style Guide by the talented Jean Fan.
  • Hydrogen: Hydrogen is an interactive coding environment that supports Python, R, JavaScript and other Jupyter kernels. I’ve been a fan of “notebooks” (the jupyter kind), but the closest I had so far was RunKit. Now I can run R, Python and JavaScript, as notebooks within Atom (See screenshot below). The result of each cell is shown within the code.



I’ve done small bits of Python and even played with pandas before, but only now, after playing with R I get it. I’m currently replicating all my R experiments in Python, by the end of it I expect to choose one and expand my Data Science skills further.

Photo by Samuel Zeller | “Binders on a shelf.”