This is one of the fastest papers I have ever written. It was a great collaboration with Tomás Lejarraga from the Universitat de les Illes Balears. Why was it great? Because it is one of the rare cases (at least in my academic life) where all people involved in a project contribute equally and quickly. Often, the weight of a contribution lies with one person which slows down things – with Tomás this was different – we were often sitting in front of a computer writing together (have never done this before, thought it would not work).
Before there was R, there was S. R was modeled on a language developed at AT&T Bell Labs starting in 1976 by Rick Becker and John Chambers (and, later, Alan Wilks) along with Doug Dunn, Jean McRae, and Judy Schilling. Here is a talk by Rick Becker telling the story of R. Good Stuff!
The friendly people from RStudio recently started a webinar series with talks on the following topics (among others):
Data wrangling with R and RStudio
The Grammar and Graphics of Data Science (both dplyr happiness)
RStudio and Shiny
… and many more.
Our friend Dr. Nathaniel D. Philipps also started a cool R course with videos, shiny apps and many other new goodies.
This is mainly a note to self:
There are several style guides for R out there. I particularly like the one from Google and the somewhat lighter version of Hadley (ggplot god).
All of that style guide thinking started after a question on about R workflow … How do we organize large R projects. Hadley (again) is favoring an Load-Clean-Func-Do approach which looks somewhat like that:
load.R # load data clean.
I had a discussion the other day on the re-appearing topic why one should learn R …
I took the list below from the R-Bloggers which argues why grad students should learn R:
R is free, and lets grad students escape the burdens of commercial license costs. R has really good online documentation; and the community is unparalleled. The command-line interface is perfect for learning by doing. R is on the cutting edge, and expanding rapidly.
Across the street at the Revolution blog a nice example of using R with data from the cloud (see another post on this topic here) shows us the distribution of fouls during the just finished World Cup in a nice barchart. Even more interesting than the fact that Holland rules this category is the way the data are collected from a Google spreadsheet page.
With the following simple code line:
Jeroen Ooms did for R what Google did for editing documents online. He created several software packages that help running R with a nice frontend over the Internet.
I first learned about Jeroen’s website through his implementation of ggplot2 – this page is useful to generate graphs with the powerful ggplot2 package without R knowledge, however it is even more helpful to learn ggplot2 code with the View-code panel function which displays the underlying R code.
I really liked Lattice for generating graphs in R until I saw what ggplot2 can do …
One of the big differences between the two is the theory on which ggplot2 is based upon. There are clear modular building blocks that can be applied in a consistent manner on any graph generated. Both packages are extremely versatile but at the end of the day I think ggplot2 provides a clearer structure and hence more flexibility …
From: The R Flashmob Project
Subject: R Flashmob #2
You are invited to take part in R Flashmob, the project that makes the
world a better place by posting helpful questions and answers about the
R statistical language to the programmer’s Q & A site stackoverflow.com
Please forward this to other people you know who might like to join.
Q. Why would I want to join an inexplicable R mob?