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

(Phase I remote)

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.

• 01 - Getting Started with R
  Introduction to R and its environment, setting up your workspace, and understanding basic 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 tabular data.
  
• 07 - Data Manipulation with dplyr
  Introduction to data frames, R’s most widely used structure for handling tabular data.
  
• 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, etc.) to ensure consistency.
  
• 11 - Conditional Statements in R
  Using if, else, and switch statements for decision-making in your R scripts.
  
• 12 - Loops and Iterations
  Implementing for loops, while loops, and alternatives for iterative programming in R.
  
• 13 - Data Visualization with ggplot2: Basics
  Introduction to ggplot2, setting up plots, and understanding the grammar of graphics.
  
• 14 - Customizing ggplot2 Visualizations
  Modifying themes, axes, colors, and labels to enhance your visualizations.
  
• 15 - Advanced ggplot2 Techniques
  Faceting, combining multiple plots, and working with advanced aesthetics in ggplot2.
  
• 16 - ggplot2: Interactive and Specialized Plots
  Creating interactive and specialized plots for deeper data exploration.
  
• 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
  

If you have any questions, comments, suggestions, or issues, please submit them on the RTrainer GitHub page: https://github.com/dputhier/rtrainer. To do this, create a free GitHub account, navigate to the RTrainer GitHub page, click on the "Issues" tab, and select "New Issue."