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**.

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.

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 Wickhamfor the great contribution of material and tutorials made available on the web and toBill VenablesandStefano Iacusfor their kind reviews.

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.

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

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.

In the previous post we saw how much convenient could be **GenABEL** in the management of genotypic/phenotypic data.

We introduced the import of genotypic data from an Illumina format file:

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

but what happens if you're analysing your data with **PLINK**, the open source toolset for GWAS?

This article gives a brief overview of the data.table package written by M. Dowle, T. Short, S. Lianoglou.

A data.table is an extension of a data.frame created to reduce the working time of the user in two ways:

- programming time
- compute time

Here is a little overview on **GenABEL** library developed by Yurii Aulchenko (**www.genabel.org/***)*.

GenABEL is a full-featured R library for dealing with Genome-Wide Association analysis of binary and quantitative traits.

Andrea Spanò, founder and partner at Quantide, held two seminars about Industrial Statistics at the University of Bergamo.

**Process Capability Analysis** and **Gage R&R** were topics of seminars.

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