# Blog

## Operating on files with R: copy and rename

Nowadays, routinary operations on files, such as renaming or copying, are performed with some mouse clicks. Sometimes, it is useful perform this operations in batch. Linux users perform this operations through the shell. Also Windows users can use the shell, but there are also a lot of utilities that simplify these operations.

Why someone should use R to copy or rename a (lot of) file(s)?

## Learning RStudio for R Statistical Computing

"Learning RStudio for R Statistical Computing" will teach you how to quickly and efficiently create and manage statistical analysis projects, import data, develop R scripts, and generate reports and graphics. R developers will learn about package development, coding principles, and version control with RStudio.

This book will help you to learn and understand RStudio features to effectively perform statistical analysis and reporting, code editing, and R development.

The book starts with a quick introduction where you will learn to load data, perform simple analysis, plot a graph, and generate automatic reports. You will then be able to explore the available features for effective coding, graphical analysis, R project management, report generation, and even project management.

Book review

Posted in R | Tagged , | 1 Comment

## Maps in R: choropleth maps

This is the third article of the Maps in R series. After having shown how to draw a map without placing data on it and how to plot point data on a map, in this installment the creation of a choropleth map will be presented.

A choropleth map is a thematic map featuring regions colored or shaded according to the value assumed by the variable of interest in that particular region.

Posted in R | Tagged , , | 5 Comments

## Maps in R: Plotting data points on a map

In the introductory post of this series I showed how to plot empty maps in R.
Today I'll begin to show how to add data to R maps. The topic of this post is the visualization of data points on a map.

Posted in R | Tagged , , , , | 6 Comments

## Maps in R: Introduction - Drawing the map of Europe

This post is a brief follow-up to a question that appeared some time ago on the “The R Project for Statistical Computing” LinkedIn group, which I’m reporting here:

### How can I draw a map of MODERN Europe?

Hi, I'm trying to draw a map of modern Europe but I've found only maps of twenty years ago, with Yugoslavia and Czechoslovakia still united!!!
Does anyone know where I can get a more recent map to be employed with packages such as 'sp' or 'maps'?
Thank you very much!

Two different solutions to the above question will be provided here, using two different R packages.

Posted in R | Tagged , , | 13 Comments

## Function Closures and S4 Methods

This brief tutorial illustrates how to combine S4 object oriented capabilities with function closures in order to develop classes with built in methods. Thanks to Hadley Wickham for the great contribution of material and tutorials made available on the web and to Bill Venables and Stefano Iacus for their kind reviews.

Posted in R | Tagged , , | 4 Comments

## Genome annotation with NCBI2R

It's very convenient manage data with R: you can import your dataset, you could find many packages which respond to your needs, then you could plot your results.
However it could be very bothersome retrieve the data from online databases. You need to use the specific API and maybe write your scripts using a new programming language, then you have to convert your data in a table format and finally import them with R.

## Power and Sample Size Analysis: Z test

### Abstract

This article provide a brief background about power and sample size analysis. Then, power and sample size analysis is computed for the Z test.
Next articles will describe power and sample size analysis for:

• one sample and two samples t test;,
• p test, chi-square test, correlation;
• one-way ANOVA;
• DOE $2^k$.

Finally, a PDF article showing both the underlying methodology and the R code here provided, will be published.

Posted in R | Tagged | 1 Comment

## Divide or Mix. Flexible Approaches to Data Analysis

A very interesting paradigm in data analysis comes from the necessity to model data where it is difficult to think of a single global function to be capable to represent adequately the data.

We could see a spectrum of models going from the global statistical model, with a single function and associated probability distribution, to the decision tree fitting a set of constants at each leaf of the tree.

This articles focuses in models which combine the two extrema to yield a more parsimonious solution and, at the same time, try to get the best of both approaches.

We shall present two of the best representatives of the above mentioned approach, the party package which combines the decision tree with local models fitted at the leaves of the tree and the Flexmix package, implementing a solution based on a mixture of models (a soft approach).

> convert.snp.illumina(inf = "gen.illu", out = "gen.raw", strand = "file")