BIP (Blended Intensive Programs) : "Rare Diseases at the Omics era: Current tools for frequent challenges" (Phase I remote)

Objectives

The objective of Phase I is to build a strong foundation in R programming, starting with fundamental concepts such as data structures, manipulation, and control flow. As the program progresses, learners will explore data visualization using ggplot2, mastering its advanced features to create insightful and customized visual representations. Finally, the phase will end up with some elements about object-oriented programming (OOP) in R, covering general concepts and methodologies for structuring user-defined classes.

Learning modules

This first remote phase of the BIP is in self-paced learning mode. This means you will progress through the course at own speed and your own schedule. However, some meetings with teachers are scheduled (see below). These are not mandatory but can provide an opportunity to discuss any issues encountered.

You should go through all the modules outlined below (links for module 11 to 18 will be provided later). Each topic is designed to enhance your understanding and practical skills, ensuring you can apply R effectively in data analysis and beyond.

Module	Description
01 - Getting Started with R	Introduction to understanding basic R syntax.
02 - Working with Vectors	Understanding vectors, R’s primary data structure, and performing basic operations.
03 - Indexing and Subsetting Vectors	Learn how to access, filter, and manipulate vector elements efficiently.
04 - Factors and Categorical Data	Handling categorical variables in R using factors, including ordering and labeling.
05 - Matrices: Working with 2D Data	Creating and manipulating matrices, performing operations, and understanding their structure.
06 - Data Frames: The Heart of R	Introduction to data frames, R’s most widely used structure for handling data.
07 - Data Manipulation with dplyr	Using dplyr to filter, summarize, and transform data efficiently.
08 - Lists: Flexible Data Storage	Understanding lists and how they store heterogeneous data types in R.
09 - Navigating the File System	Reading and writing files, setting working directories, and managing paths in R.
10 - Data Type Conversions	Converting between different data types (numeric, character, factor...).
11 - Conditional Statements in R	Using if/else statements for decision-making in your R scripts.
12 - Loops and Iterations	Implementing for loops loops for iterative tasks in R.
13 - Data Visualization with ggplot2: Basics	Introduction to ggplot2 - part 1
14 - Customizing ggplot2 Visualizations	Introduction to ggplot2 - part 2
15 - Advanced ggplot2 Techniques	Introduction to ggplot2 - part 3
16 - ggplot2: Interactive and Specialized Plots	Introduction to ggplot2 - part 4
17 - Writing Functions in R	Creating and using functions to make your R code more modular and reusable.
18 - Object-Oriented Programming in R	Introduction to OOP in R, including S3 and S4 classes for structured programming.

Scheduled meetings

Several meetings with teachers are planned (see below). Attendance is not mandatory, but they offer a chance to discuss any potential issues. We may discuss later of additional slots in April and on adjusting the propose dates.

Date	Time (CET)
Wed 5 March	6 PM CET
Sat 8 March	9 PM CET
Wed 12 March	6 PM CET
Sat 15 March	9 PM CET
Wed 19 March	6 PM CET
Wed 26 March	6 PM CET

The meeting room details will be sent via email.

Scheduled meetings

Several meetings with D. Puthier & A. Gonzalez are planned (see below). Attendance is not mandatory, but they offer a chance to discuss any potential issues. We may discuss later of additional slots in April and on adjusting the propose dates.

Date	Time (CET)
Wed 5 March	6 PM CET
Sat 8 March	9 PM CET
Wed 12 March	6 PM CET
Sat 15 March	9 PM CET
Wed 19 March	6 PM CET
Wed 26 March	6 PM CET

The meeting room will be send by email.

Learning evaluation

Your understanding of various aspects of R programming will be assessed through an online web form. The evaluation will primarily focus on concepts covered in learning modules 01-16. The questions will test your knowledge, problem-solving skills, and ability to apply R programming techniques effectively.

Running the tutorials on your own machine

The pedagological material that is provided as web pages (see "Learning modules" section) is based on the rtrainer R library that we have been developing during the last years for our students. You can install and run the tutorials on your own machine (and we encourage you to do so). The procedure should be rather straightforward:

• install a recent version of R (>4.2.3) from CRAN.

• install a recent version of RStudio from Posit.

• Start RStudio. In the panel named "Console", at the bottom of the graphical interface, copy and paste successively the following instructions:

  • install.packages("devtools")
  • devtools::install_github("dputhier/rtrainer")
  • library(learnr)
  • learnr::available_tutorials("rtrainer")

Look at the tutorial names and start one of them (here "01_preamble") using:

• learnr::run_tutorial("01_preamble", "rtrainer")

Questions, discussion, remarks, issues

Submit your questions, issues, or feedback by opening an issue in this github repository (GitHub account is required) at:

• https://github.com/dputhier/rare-disease-phase-I/issues

Links

• Rare Diseases at the Omics era: Current tools for frequent challenges (Civis)