Next session: Thursday April 6, 2023 @IMAG (Auditorium) at 17:30
Registration to the event here.
Attendance is totally free but registration is
mandatory and is on a first-come, first-serve basis.
No Zoom this time.
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Join our R working sessions in Grenoble
We organise monthly working sessions on Thursdays at 17:30, 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.
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 2022-2023
Click on the title to see the session description
The {gtsummary} package provides an elegant and flexible way to create publication-ready analytical and summary tables using R. This package can summarize datasets, regression models, and more, using sensible defaults with highly customizable capabilities. This session will be a very straightforward and hands on talk, presenting the {gtsummary} package that can be useful for any level of R user, wanting to easily and reproducibly produce nice looking tables that follow journal standards in many different formats (html, rtf, etc). The aim will be for attendees to understand the basics of the package, but we will also try to dig a little deeper in the structure of a gtsummary object and learn how to customize tables to fit your specific needs.
The {ggplot2} package is the perfect solution for making elegant and comprehensible graphics with a very short amount of code lines. Using the “Grammar of Graphics” as its core principle, this package provides a lot of easy to understand tools to help R users of any level to quickly and effortlessly produce publication-ready graphics. In this session, after a brief presentation of ggplot2, we will try to understand the “grammar” part of a ggplot2’s plot. Understanding this allow the user to create any plot fairly easily, because they know how each block works and interact with one another.
Git is a version control system independent from R, it provides a neat way to stop the spread of old scripts sent by e-mail, enhances scientific reproductibility and provides a framework to collaborate on writing code with other people. This software can be used by every R-User, within Rstudio or using a command line tool, at any step of a project or package development. This session will present the core principles when managing code files with Git, and why it may save you time when working in team. At the end of this session you will be able to use Git for saving your work and share it easily online on GitHub/GitLab. This may even be your first step in understanding how to contribute to your most loved open-source package on GitHub/GitLab.
The {dplyr} package is one of the core packages of the tidyverse ecosystem that provides a grammar of data manipulation with a consistent interface for working with data frames. It supports operations to filter, group, and summarize data. {dplyr} also includes functions for working with databases and can be used to efficiently manipulate large datasets. This session will focus on the {dplyr} basics, but we will also overview the {data.table} package which is usually much faster than {dplyr} and the {dtplyr} package that provides a data.table backend for dplyr.
Markup languages allow users to bypass a lot of limitations introduced by widespread "What you see is what you get" (WYSIWYG) based alternatives.
Contrary to the more sophisticated LaTeX or troff, markdown is extremely simple and easy to learn alternative with intuitive syntax.
Combined with pandoc back-end the resulting documents may be converted to nearly any derided format possible: HTML, PDF, DOCX and many more.
{Rmarkdown} introduces the possibility to use R, Python, C++ and other languages alongside markdown, simplifying the creation of reproducible reports and documents.
This session will focus on {Rmarkdown} based workflow, offering as well the introduction to {Quarto}, a novel alternative for classical {Rmarkdown}.
How to avoid writing slow R code? What are the strategies to write R code that is fast to run? I will provide some examples, and I will explain why some R codes are faster than others, so that you understand how to make your code faster. I'll also quickly talk about other strategies to make your code faster, such as Rcpp and parallelism.
Materials from previous sessions
2022 - 2023
Performance of R code: slides
Litterate programming with {Rmarkdown} and {Quarto}: slides
Publication ready tables with {gtsummary}: slides,
video
2021 - 2022
Unfortunately, the group was inactive during that period.