Functional programming (FP) provides a rich set of tools for reducing duplication in your code. The goal of FP is to make it easy to express repeated actions using high-level verbs. I think that learning a little about FP is really important for data scientists, because it's a really good fit for many problems that you'll encounter in practice.
In this talk, I'll introduce you to the basics of functional programming in R, using the purrr package. I'll begin by briefly dissecting the for loop that you're already familiar with, then continue to show why functional programming provides elegant alternatives. I'll next dive into two examples showing where FP is particularly useful in data science: when ingesting unruly datasets spread across multiple files, and producing multiple reports for different stakeholders.
You'll get the most out of this talk if you're familiar with R, or you've done data science in other languages like Python.
Hadley is Chief Scientist at RStudio, a member of the R Foundation, and Adjunct Professor at Stanford University and the University of Auckland. He builds tools (both computational and cognitive) to make data science easier, faster, and more fun. His work includes packages for data science (the tidyverse: including ggplot2, dplyr, tidyr, purrr, and readr) and principled software development (roxygen2, testthat, devtools). He is also a writer, educator, and speaker promoting the use of R for data science. Learn more on his website, http://hadley.nz.
