Interactive R learning

For those who are still learning the basics of the R programming language, the following website might be interesting:

Another thing that might be interesting is the recently released R-library Swirl. The swirl R package is designed to simultaneously teach users statistics and the R programming language. In a typical swirl session, the user is required to load a package from the R console and chose from a menu of options the course he would like to take. Then he works through 10-15 minutes interactive modules, each covering a particular topic.

To install it, this is all you have to do in R:

#Install libraries
install_github(repo=”swirl”, username=”ncarchedi”)

#Call swirl()

Note however that the package is yet released and constantly fixed and updated. It is therefore recommended to update the version at least one ca month so you have full access to the latest features.

This is the official website of the package:
You can find a blog post on the release of the new package on Simply Statistics:

Monte carlo simulations in R

After a short summer break, the third meeting oft he TRUG took place on October 7th.

Henk Broekhuizen presented how R can be used for Monte Carlo simulations. Henk introduced the problem of combining probability distributions, then introduced the main ideas behind Monte Carlo simulations and finished with some outcomes from his own work in probabilistic MCDA models. In this kind of decision analysis, model outcomes are a complex function of the inputs. When these inputs are probability distributions, calculating the outcomes analytically becomes hard and sometimes impossible. Monte Carlo simulations are a useful and straightforward approach to approximate and visualize these model outcomes.  A pdf file of the presentation can be downloaded here: Third_TRUG_meeting

Resampling methods in R

The second meeting of the TRUG took place on July 11th.

Inga Schwabe gave an introduction into the world of resampling methods. Resampling methods can be a good alternative when classical statistical methods that are largely based on idealized assumptions (e.g. normal distribution) cannot be used for statistical inference. This was shown by means of a simple example: The empirical p value was calculated for the difference between the scores of a treatment and a control group by using a resampling method called ‘Permutation’. A pdf file of the presentation can be downloaded here: Second_TRUG_meeting