The R packages in a data scientist’s toolbox

July 17, 2012
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(This article was first published on Revolutions, and kindly contributed to R-bloggers)

John Myles White, self-described "statistics hacker" and co-author of "Machine Learning for Hackers" was interviewed recently by The Setup. In the interview, he describes his some of his go-to R packages for data science:

Most of my work involves programming, so programming languages and their libraries are the bulk of the software I use. I primarily program in R, but, if the situation calls for it, I'll use MatlabRuby or Python. ...

That said, for me the specific language I use is much less important than the libraries availble for that language. In R, I do most of my graphics using ggplot2, and I clean my data using plyr, reshape, lubridate and stringr. I do most of my analysis using rjags, which interfaces with JAGS, and I'll sometimes use glmnet for regression modeling. And, of course, I use ProjectTemplate to organize all of my statistical modeling work. To do text analysis, I'll use the tm and lda packages.

Also in JMW's toolbox: Julia, TextMate 2, MySQL, Dropbox and a beefy MacBook. Read the full interview linked below for an insightful look at how he uses these and other tools day to day.

The Setup / Interview: John Myles White

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