Pbd: programming with big data in R. My R and Climate Change Learning Curve. Lexical scope and function closures in R. Introduction R is different to many “easy to use” statistical software packages – it expects to be given commands at the R command prompt.

This can be intimidating for new users, but is at the heart of its power. Most powerful software tools have an underlying scripting language. This is because scriptable tools are typically more flexible, and easier to automate, script, program, etc. R - Books. Missing Data. In R, missing values are represented by the symbol NA (not available) .

Impossible values (e.g., dividing by zero) are represented by the symbol NaN (not a number). Unlike SAS, R uses the same symbol for character and numeric data. Testing for Missing Values. R Tutorials. Preamble There is plenty to say about data frames because they are the primary data structure in R.

Some of what follows is essential knowledge. Some of it will be satisfactorily learned for now if you remember that "R can do that. " I will try to point out which parts are which. Set aside some time. Omegahat Statistical Computing. R Time Series Tutorial. The data sets used in this tutorial are available in astsa, the R package for the text.

A detailed tutorial (and more!) Is available in Appendix R of the text. Probability Distributions. For most of the classical distributions, base R provides probability distribution functions (p), density functions (d), quantile functions (q), and random number generation (r).

Beyond this basic functionality, many CRAN packages provide additional useful distributions. In particular, multivariate distributions as well as copulas are available in contributed packages. Ultimate bibles on probability distributions are different volumes of N. Say it in R with "by", "apply" and friends. R is a language, as Luis Apiolaza pointed out in his recent post.

This is absolutely true, and learning a programming language is not much different from learning a foreign language. It takes time and a lot of practice to be proficient in it. I started using R when I moved to the UK and I wonder, if I have a better understanding of English or R by now. Resources to help you learn and use R. R. Look what I found: two amazing charts. While doing some research for my statistics blog, I came across a beauty by Lane Kenworthy from almost a year ago (link) via this post by John Schmitt (link).

How embarrassing is the cost effectiveness of U.S. health care spending? When a chart is executed well, no further words are necessary. I'd only add that the other countries depicted are "wealthy nations". Even more impressive is this next chart, which plots the evolution of cost effectiveness over time. Plyr. Knitr: Elegant, flexible and fast dynamic report generation with R. Overview The knitr package was designed to be a transparent engine for dynamic report generation with R, solve some long-standing problems in Sweave, and combine features in other add-on packages into one package (knitr ≈ Sweave + cacheSweave + pgfSweave + weaver + animation::saveLatex + R2HTML::RweaveHTML + highlight::HighlightWeaveLatex + 0.2 * brew + 0.1 * SweaveListingUtils + more).

Short-refcard.pdf (application/pdf Object) Useful Links. ComputingPresentation.R.conditionals.pdf (application/pdf Object)

R tools. R tutoriels et des lésions.