Manipulation de chaînes de caractères avec stringr - Alea. High-Performance and Parallel Computing with R. This CRAN task view contains a list of packages, grouped by topic, that are useful for high-performance computing (HPC) with R.
In this context, we are defining 'high-performance computing' rather loosely as just about anything related to pushing R a little further: using compiled code, parallel computing (in both explicit and implicit modes), working with large objects as well as profiling. Unless otherwise mentioned, all packages presented with hyperlinks are available from CRAN, the Comprehensive R Archive Network. Several of the areas discussed in this Task View are undergoing rapid change. Please send suggestions for additions and extensions for this task view to the task view maintainer .
Direct support in R started with release 2.14.0 which includes a new package parallel incorporating (slightly revised) copies of packages multicore and snow. Parallel computing: Explicit parallelism Several packages provide the communications layer required for parallel computing. Analyzing Your Data on the AWS Cloud (with R) Guest post by Jonathan Rosenblatt Disclaimer: This post is not intended to be a comprehensive review, but more of a “getting started guide”.
If I did not mention an important tool or package I apologize, and invite readers to contribute in the comments. Introduction I have recently had the delight to participate in a “Brain Hackathon” organized as part of the OHBM2013 conference. Being supported by Amazon, the hackathon participants were provided with Amazon credit in order to promote the analysis using Amazon’s Web Services (AWS). While imaging genetics is an interesting research topic, and the hackathon was a great idea by itself, it is the AWS I wish to present in this post. Storing your data and analyzing it on the cloud, be it AWS, Azure, Rackspace or others, is a quantum leap in analysis capabilities.
As motivation for analysis in the cloud consider: Here is a quick FAQ before going into the setup stages. FasteR! HigheR! StrongeR! - A Guide to Speeding Up R Code for Busy People. This is an overview of tools for speeding up your R code that I wrote for the Davis R Users’ Group.
First, Ask “Why?” It’s customary to quote Donald Knuth at this point, but instead I’ll quote my twitter buddy Ted Hart to illustrate a point: I’m just going to say it.I like for loops in #Rstats, makes my code readable.All you [a-z]*ply snobs can shove it! — Ted Hart (@DistribEcology) March 12, 2013 Code optimization is a matter is a matter of personal taste and priorities. . (1) Do you want your code to be readable? If you need to explain your code to yourself or others, or you will need to return to it in a few months time and understand what you wrote, it’s important that you write it in a way that is easy to understand. Some optimal code can be hard to read. Analyzing Your Data on the AWS Cloud (with R) Guest post by Jonathan Rosenblatt Disclaimer: This post is not intended to be a comprehensive review, but more of a “getting started guide”.
If I did not mention an important tool or package I apologize, and invite readers to contribute in the comments. Introduction I have recently had the delight to participate in a “Brain Hackathon” organized as part of the OHBM2013 conference. Being supported by Amazon, the hackathon participants were provided with Amazon credit in order to promote the analysis using Amazon’s Web Services (AWS). While imaging genetics is an interesting research topic, and the hackathon was a great idea by itself, it is the AWS I wish to present in this post. R - Remove data.frame row names when using xtable. R to Latex packages: Coverage. There are now quite a few R packages to turn cross-tables and fitted models into nicely formatted latex.
In a previous post I showed how to use one of them to display regression tables on the fly. In this post I summarise what types of R object each of the major packages can deal with. Unsurprisingly, there’s quite some variation… The packages I’m looking at here are: apsrtable (v0.8-8), xtable (v1.7-1), stargazer (v3.0.1), memisc (v0.96-3) and texreg (v1.22). I should note that all of these packages also allow users to add their own latex representation for new R objects. Also, several of these packages can typeset data.frame and matrix objects too. Finally, I haven’t checked all of this information. Chaînes de caractères. / Le langage. / Aide mémoire R.
Aide-memoire-R > Le-langage > Chaines-de-caracteres Les chaînes de caractères peuvent être délimitées par '' ou "" nchar("toto") : longueur de la chaîne de caractères (ou vecteur des longueurs si vecteur de chaînes).
Par exemple nchar(c("truc", "bidule")) donne 4 6. Attention, ce n'est pas length ! Toupper(x), tolower(x) : pour convertir en minuscules ou majuscules. The Comprehensive R Archive Network. Groupe des utilisateurs du logiciel R. BioStatistique Lyon1. R news & tutorials from the web. R Frequently Asked Questions.
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