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Materials of the presentation are available here.
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Please talk about our R group with your colleagues.
You can also print this poster and put it up somewhere at work.
Join our R working sessions in Grenoble
Each month, we will organize one working session of 2 hours (on thursdays, from 16h-18h, at the IMAG building).
Please find the guidelines for these sessions:
Everyone is welcome (beginners to advanced R users, just bring your laptop).
Presentations/tutorials will last 30 to 60 min, on some R topics that can be useful to many people, with practical examples.
We want to have a lightning talk (5 min) after the main session so that people can quickly present how they use R in their work (or a specific package, etc.).
During the second part of the session, people could ask and answer some questions about specific problems they encounter when coding in R.
During this time, food and sodas will be offered by the Grenoble Alpes Data Institute.
If you wish to share your R experience during a working session and/or to co-animate a working session, please contact us.
Schedule of 2018-2019
Click on the title to see the session description
In this presentation, we will see the potential amazing applications of R. Then, we will compile links to where you can learn R (from scratch or to become more advanced). Finally, we will announce the coming sessions for this academic year.
imager is an R package for image processing. It is meant to be fast, flexible and to integrate well with standard R functions and data structures. imager is based on CImg, a C++ library for image processing with a clean and easy-to-use API.
During this presentation I will present the lm and glm functions for handling linear models with R. I will show how to make analysis with linear models using an original dataset containing the result of the French presidential 2017 election as well as socio-economic variables.
Drake is a general-purpose workflow manager for data-driven tasks. It rebuilds intermediate data objects when their dependencies change, and it skips work when the results are already up to date. Not every runthrough starts from scratch, and completed workflows have tangible evidence of reproducibility. Package drake also supports scalability, parallel computing, and a smooth user experience when it comes to setting up, deploying, and maintaining data science projects.
Materials from previous sessions