Objectifs :
The aim of this training is to acquire basic skills in Rstudio, Tidyverse and Rmarkdown for data wrangling, plots production and report making. This training focuses on the data analysis steps before statistical analysis (data wrangling and plots) and is entirely based on the Tidyverse package and language, specifically designed for data manipulation and plot production. It also uses a teaching method designed for beginners.

Programme :
DAY 1
Sequence 1 : RStudio, R basics and Rmarkdown
- RStudio : the four windows, basic drop down menus
Object creation and assignation, functions, object classes (numeric and character vectors, dataframes) and variable types (factor, date)
- Creation and use of a Rmarkdown document

Sequence 2 : Tidyverse syntax
- plot syntax with the ggplot2 package
- Dataframe manipulation syntax and columns manipulation with the dplyr package

Sequence 3 : data analysis steps
- Data importing, raw data visualisation, type of variable specification, data transformation (mean computation, row like selection), transformed data visualization

Sequence 4 : dataframes with many variables
- Conditional operations, dealing with missing data
- Plotting combination of variables on the same graphs, plotting standard error bars

DAY 2
Day one reminder
- Code reading exercise to revise basics functions

Sequence 5 : improve graphs
- the four types of layers in a ggplot and the different types of modifications (colours, axes, legend, fonts)


Sequence 6 : dataframes merging
- The two essential ways of joining dataframes (bound and merged)

Sequence 7 : Re-structure and tidy dataframes
- Restructuring dataframes (go from large to long format) with the tidyr package
- Tidy a character string (stringr package) and reorganise the orders of a factor (forcats package)

Memory reactivation and knowledge mapping
- Individual memory reactivation and group knowledge mapping (mind map with the xmind software)

Training evaluations :
- Self assessment of objectives achievement (quiz and exercises graphs)
- Satisfaction evaluation (online questionnaire)

Pré-requis :
Bring your computer

Instructions for sofwares and packages installation, datasets used during the training along with the pdf to take notes on and the rmarkdown template are sent to every participant a week before the training. People with disabilities can contact me so that we can find a way to adapt the training to suit their needs

Méthode pédagogique :
The training uses the user-friendly interface of RStudio, which simplifies the use of R, and the Rmarkdown package, which allows for the automatic generation of reports (HTML, Word, PDF) from the code. The teaching method consists of a series of progressive sequences made up of a demonstration (theoretical input), followed by a quiz and then an exercise. In addition, there is a teaching method designed for beginners that involves splitting the demonstration sequence as follows:


• A first demonstration, during which the instructor presents the knowledge and skills to be acquired by typing the code, while the participants, without typing the code themselves, focus their attention on understanding the information, answering questions, and taking notes on the PDF document sent beforehand and to be printed. This document contains the code typed by the instructor (which greatly facilitates note-taking).

• A second demonstration, during which the code is typed together, allowing participants to practice and make mistakes that are then corrected by the instructor.

This method is used on the first day; the demonstration is then conducted in a single session once the participants are comfortable with the language and its syntax. This method is well- suited to training in IT tools, which is mostly based on hands-on learning (the trainer demonstrates the sequence of operations step by step) and in which participants are quickly overwhelmed by the need to multitask: looking at the whiteboard to see the operations typed by the trainer, then looking at their own screen to type them in, listening to the trainer's explanations, taking notes, and correcting their mistakes. This mixing of tasks leads to a significant decrease in the learners' listening and comprehension skills, and a significant reduction in the training's pedagogical effectiveness.

Compétences acquises à l'issue de la formation :
At the end of the training, participants will be able to:

• Install R and RStudio, packages and use the RStudio four windows, create numeric and character vectors and dataframes
• Load a dataframe, verify and modify variables types (factor, date)
• Do the most common dataframes manipulation : filter rows, select columns, do grouped operations (mean and standard deviation computations), add columns, make a computation between columns
• Enhance plots aesthetics for publication (colours, axes, font, legend)
• Join dataframes of different sizes
• Re-structure and tidy dataframes (correct character strings, reorganise factor levels)
• Make a html report out of the code with Rmarkdown

Les Compétences et capacités visées à l'issue de la formation (fiches RNCP)

Arrêté du 22 février 2019 définissant les compétences des diplômés du doctorat et inscrivant le doctorat au répertoire national de la certification professionnelle. https://www.legifrance.gouv.fr/loda/id/JORFTEXT000038200990/

Bloc 1 : Conception et élaboration d’une démarche de recherche et développement, d’études et prospective

- Disposer d'une expertise scientifique tant générale que spécifique d'un domaine de recherche et de travail déterminé

- Identifier et résoudre des problèmes complexes et nouveaux impliquant une pluralité de domaines, en mobilisant les connaissances et les savoir-faire les plus avancés

Compétences sociales

- Adaptation ; Persévérance ; Résilience ; Gestion du changement et de l'échec ; Engagement

La formation participe à l'objectif suivant :être directement utile pour la réalisation des travaux personnels de recherche