The R packages in a data scientist's toolbox
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 Matlab, Ruby 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