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This 3-day hands-on workshop will build on the previous R workshop and will largely deal with statistics. After starting with a brief repetition of the basics (in order to bring everyone up to speed), we will lay the foundation for reproducible analyses with RStudio by looking at how to organize code across multiple projects, how to have both code and explanatory text in one file, and how to use version control (if everyone is familiar with git/github). We will then spend some time on creating publication-ready graphics with ggplot2 and start with the basic building blocks of statistical inference, also learning how to simulate data in a very simple way. The largest part of the workshop will then tackle the following topics: i) assumptions of parametric tests, ii) correlation, iii) regression, iv) comparing means, v) bootstrapping, and vi) exploratory data analysis; for each topic we will move from the theory and the most basic implementation to more complex approaches. [more]

This 3-day hands-on workshop will give a thorough introduction to R, without assuming any prior knowledge of programming (in R or any other language). We will start with a general overview of R, look at the various ways of how to get help in R and will use it as a simple pocket calculator, introducing variables along the way. We will then start writing little scripts and dealing with different data types in R, such as matrices, arrays, lists, data frames, etc. Next, we will learn how to use control flow statements and how to expand our basic R capabilities by making use of packages (and learn about their management). We will then spend quite some time on learning how to write and debug functions (with an excursion into the apply family). Finally, some time will be spent on R’s basic plotting capabilities and basic statistical tools. [more]

This 3-day hands-on workshop will build on the Matlab I workshop and will cover a wide variety of intermediate/advanced topics. After starting with a brief repetition of the basics (in order to bring everyone up to speed), we will spend a large amount of time on how to write (and debug) more complex pieces of code, using Matlab functions. We will also cover many topics that can be helpful during research projects, such as i) integrating Matlab with other tools (Bash, R, Python), ii) data visualization, iii) using regular expressions for filtering text, iv) using different data types, and - if time permits - v) graphical user interfaces. Finally, we will spend a lot of time on data processing and analysis. [more]

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