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 :
Complete the prerequisite compliance table provided online by the trainer.

Méthode pédagogique :
The training uses the RStudio user-friendly interface and the Rmarkdown file format which allows to automatically generate reports (html, word, pdf) from the code.
The teaching method consists in a progressive sequences made of a theoretical input, a quiz and an exercise and uses knowledge producede by cognitive science research to reinforce memorisation.
A teaching method designed for beginners is also used and consists in splitting the theoretical input demonstration in two steps:
• A first demonstration, during which the trainer introduces the knowledge and skills of the sequence by typing the code, while participants are asked not to type the code but to focus on understanding, note taking (on the pdf of the training provided beforehand) and questions asking.
• A second demonstration, during which the code is typed again but all together this time.

This method is used the first day and dropped the second day, once participants are accusotmed to the syntax.
This method is well adapted to computer courses, which are too often based on the « do everything together » (the teacher shows step by step operations to do) and during which participants get quickly lost in the face of an overwhelming multitasking to perform: look at the screen to see what to do, type it on his/her own computer, listen to the teacher’s explanations, take notes, correct mistakes.
This uneasy multi-tasking leads to a drastic diminution of the listening and understanding capacity, and a drastic drop in learning efficiency.

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

Bloc 4 : Veille scientifique et technologique à l’échelle internationale

- Acquérir, synthétiser et analyser les données et informations scientifiques et technologiques d’avant-garde à l’échelle internationale

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