**Statistical Modeling, Causal Inference, and Social Science » R**, and kindly contributed to R-bloggers)

Hadley writes:

I [Hadley] am going to be teaching an R development master class in New York City on Dec

12-13. The basic idea of the class is to help you write better code,

focused on the mantra of “do not repeat yourself”. In day one you will

learn powerful new tools of abstraction, allowing you to solve a wider

range of problems with fewer lines of code. Day two will teach you how

to make packages, the fundamental unit of code distribution in R,

allowing others to save time by allowing them to use your code.To get the most out of this course, you should have some experience

programming in R already: you should be familiar with writing

functions, and the basic data structures of R: vectors, matrices,

arrays, lists and data frames. You will find the course particularly

useful if you’re an experienced R user looking to take the next step,

or if you’re moving to R from other programming languages and you want

to quickly get up to speed with R’s unique features. A couple session

outline is available here.Both days will incorporate a mix of lectures and hands-on learning.

Expect to learn about a topic and then immediately put it into

practice with a small example. Plenty of help will be available if you

get stuck. You’ll receive a printed copy of all slides, as well as

electronic access to the slides, code and data. The material covered

in the course is currently being turned into a book. You can access

the current draft here.

This looks great, and I imagine that what you’d learn there would be useful for other data programming, not just for R.

The post Wickham R short course appeared first on Statistical Modeling, Causal Inference, and Social Science.

**leave a comment**for the author, please follow the link and comment on their blog:

**Statistical Modeling, Causal Inference, and Social Science » R**.

R-bloggers.com offers

**daily e-mail updates**about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...