Posts Tagged ‘ packages ’

Integrate data and reporting on the Web with knitr

September 11, 2012
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Today's guest post comes from Yihui Xie, author of the knitr package — ed. Hi, this is Yihui Xie, and I'm guest posting on the Revolutions blog to talk about one aspect of the knitr package: how we can integrate data analysis and reporting in R with the Web. This post includes both the work that has been done...

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ggplot2 0.9.2 has been released!

September 7, 2012
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ggplot2 0.9.2 has been released!

The main changes in this version are to the theming system. There are also a number of enhancements to the theming system that make it easier to modify themes and we’ve renamed a number of functions to have more informative names. Your existing code should continue to work, although you may receive warnings about functions

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RStan: Fast, multilevel Bayesian modeling in R

August 31, 2012
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For the last decade or so, the go-to software for Bayesian statisticians has been BUGS (and later the open-source incarnation, OpenBugs, or JAGS). BUGS is used for multi-level modeling: using a specialized notation, you can define random variables of various distributions, set Bayesian priors for their parameters, and create the network of relationships that describe how the random variables...

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Creating beautiful reports from R with knitr

August 21, 2012
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Creating beautiful reports from R with knitr

People use the R language every day to create the elements of reports: tables, charts, analyses, and forecasts. But assembling all of that information into a print-ready document laid out with text can a hassle. You can cut-and-paste all of the elements into Word, but then what do you do when the data file gets updated at the last...

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Getting Started with R and Hadoop

August 20, 2012
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Getting Started with R and Hadoop

Last week's meeting of the Chicago area Hadoop User Group (a joint meeting the Chicago R User Group, and sponsored by Revolution Analytics) focused on crunching Big Data with R and Hadoop. Jeffrey Breen, president of Atmosphere Research Group, frequently deals with large data sets in his airline consulting work, and R is his "go-to tool for anything data-related"....

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The top 10 critical packages on CRAN

August 8, 2012
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The top 10 critical packages on CRAN

While most R packages on CRAN are designed to be used by an R user directly, a few packages are designed to be used by other package developers. (And some packages are so useful that they're regularly used by both camps.) When a package author publishes a package to CRAN, she must list those packages that provide functions her...

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Faster R in Hadoop: rmr 1.3 now available

July 23, 2012
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The RHadoop project continues the Big Data integration of R and Hadoop, with a new update to its rmr package. Version 1.3 of rmr improves the performance of map-reduce jobs for Hadoop written in R. New features include: An optional vectorized API for efficient R programming when dealing with small records. Fast C implementations for serialization and deserialization from...

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R Journal, June 2012

July 20, 2012
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The June 2012 issue of the R Journal, the peer-reviewed open-journal about R packages and applications of R, is now available. This issue includes articles about: Efficiently calling C functions from R without the need for wrapper code Using clusters of Macs running Apple Xgrid for parallel distributed processing with R Semi-automated text classification with the 'maxent' package Two...

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The R packages in a data scientist’s toolbox

July 17, 2012
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John Myles White, self-described "statistics hacker" and co-author of "Machine Learning for Hackers" was interviewed recently by The Setup. In the interview, he describes his some of his go-to R packages for data science: Most of my work involves programming, so programming languages and their libraries are the bulk of the software I use. I primarily program in R,...

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Using integer programming in R to optimize cargo loads

July 16, 2012
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Using integer programming in R to optimize cargo loads

Linear Programming is a mathematical technique used to find the values of some variables (within the bounds of some defined constraints) to find the maximum value of a quantity. For example, consider this problem from the FishyOperations blog: A trading company is looking for a way to maximize profit per transportation of their goods. The company has a train...

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