# Monthly Archives: February 2014

## dplyr 0.1.2

February 25, 2014
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We’re pleased to announce a new minor version of dplyr. This fixes a number of bugs that crashed R, and considerably improves the functionality of select(). You can now use named arguments to rename existing variables, and use new functions starts_with(), ends_with(), contains(),  matches() and num_range() to select variables based on their names. Finally, select() now

## Genetic data, large matrices and glmnet()

February 25, 2014
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Recently talking to a colleague, had contact with a problem that I had never worked with before: modeling with genetic The post Genetic data, large matrices and glmnet() appeared first on Flavio Barros .

## Next Kölner R User Meeting: 26 February 2014

February 25, 2014
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The next Cologne R user group meeting is scheduled for tomorrow, 26 February 2014. We are delighted to welcome: Diego de Castillo: R and databases Kim Kuen Tang: Hands on using R and kdb+ together Frank Celler: ArangoDB (Lightning Talk) Further details and the agenda are available on our KölnRUG Meetup site. Please sign up if you would like to come along....

## Job Trends in the Analytics Market: New, Improved, now Fortified with C, Java, MATLAB, Python, Julia and Many More!

February 24, 2014
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I’m expanding the coverage of my article, The Popularity of Data Analysis Software. This is the first installment, which includes a new opening and a greatly expanded analysis of the analytics job market. Here it is, from the abstract onward … Continue reading →

## 2013-11 Improving the ‘gridGraphviz’ package in R

February 24, 2014
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The gridGraphviz package renders node-and-edge graphs in R using the grid graphics package. Graphs are laid out using the Rgraphviz package to interface with the graph layout algorithms in graphviz. This article details the improvements made between gridGraphviz versions 0.2 … Continue reading →

## The forecast mean after back-transformation

February 24, 2014
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Many functions in the forecast package for R will allow a Box-Cox transformation. The models are fitted to the transformed data and the forecasts and prediction intervals are back-transformed. This preserves the coverage of the prediction intervals, and the back-transformed point forecast can be considered the median of the forecast densities (assuming the forecast densities on the transformed scale...

## Bayesian First Aid: Two Sample t-test

February 24, 2014
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As spring follows winter once more here down in southern Sweden, the two sample t-test follows the one sample t-test. This is a continuation of the Bayesian First Aid alternative to the one sample t-test where I’ll introduce the two sample alternative. It will be a quite short post as the two sample alternative is just more of the...

## Brief introduction on Sweave and Knitr for reproducible research

February 24, 2014
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$Brief introduction on Sweave and Knitr for reproducible research$

A few weeks ago I gave a presentation on using Sweave and Knitr under the guise of promoting reproducible research. I humbly offer this presentation to the blog with full knowledge that there are already loads of tutorials available online. This presentation is specific and slightly biased towards Windows OS, so it probably has limited

## Slidify: Modern, simple presentations written in R Markdown

February 24, 2014
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As a LaTeX fan I’m used to using Beamer for presentations, but the built-in themes are definitely starting to show their age — and writing a custom .sty file looks like a nightmare — so for a while I’ve been looking … Continue reading →

## Adding color to R plot: a function

February 24, 2014
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A friend of mine told me that she was spending her day colouring R plot because she never understood how to put color in them. This triggered a nerdy reaction in me that I had to put in a basic function. This was actually a funny exercise for two reasons: forced me to think at