I’ve been learning a bit of statistical computing with R lately on the side from Chris Paciorek’s Berkeley course. I just got introduced to knitr and it’s damned sweet! It’s an R package which takes a LaTeX file with embedded R, and produces a pure LaTeX file (similar to how Rails renders an `.html.erb`

file into an `.html`

file), where the resulting LaTeX file has the output of the R code. It makes it super easy to embed statistical calculations, graphs, and all the good stuff R gives you right into your TeX files. It let’s you put math in your math, so you can math while you math.

I’ve got a little project which:

- Runs a Python script which will use Selenium to scrape a web page for 2012 NFL passing statistics.
- “Knits” a TeX file with embedded R that cleans the raw scraped data, produces a histogram of touchown passes for teams, and displays the teams with the least and greatest number of touchdowns.
- Compiles the resulting TeX file and opens the resulting PDF.
- Cleans up any temporary work files.

Here’s what the pre-“knitted” LaTeX looks like with the embedded R:

\documentclass{article} \usepackage{graphicx} %% begin.rcode setup, include=FALSE % opts_chunk$set(fig.path='figure/latex-', cache.path='cache/latex-') %% end.rcode \begin{document} After scraping data for all passing TDs in 2012, we get the following histogram for number of TD passes by team. %% begin.rcode cache=TRUE % scrape <- read.csv('scrape.csv') % raw_data <- scrape[scrape[,"X"]!="",] % tds_for_passers <- transform(raw_data[c("Tm","TD")], TD = as.numeric(as.character(TD))) % tds_for_teams <- aggregate(tds_for_passers$TD, by=list(Team=tds_for_passers$Tm), FUN=sum) % hist(tds_for_teams$x) %% end.rcode The teams with the greatest and least TDs: %% begin.rcode % low_high <- c(which.min(tds_for_teams$x), which.max(tds_for_teams$x)) % tds_for_teams[low_high,"Team"] %% end.rcode \end{document}

You can comment out the line in the `factory`

script that deletes the `tds2012-out.tex`

file if you want to see what it looks like post-knit. The resulting TeX file basically contains a ton of new commonad definitions but the meat of it is what it does with your R code. It formats and displays the R code itself, and then it displays the output of the R code. Wherever the output is a graph, you’ll see `\includegraphics[...]{...}`

. knitr will do the R computation, render the graphics, create a `figures`

subdirectory and store them there for the `\includegraphics`

to reference. Whenever the output is simply text or mathematical expressions, you’ll see the R output translated to pure LaTeX markup.

Pretty cool stuff!