Welcome

Are you ready to embark on an exciting journey into the world of data analysis and statistical exploration? Imagine having the power to unlock the hidden insights within vast datasets, create stunning visualizations, and make data-driven decisions that can shape the future. Welcome to the world of R programming! R is not just a language; it’s your key to uncovering the stories data has to tell. Whether you’re a budding data scientist, a curious researcher, or someone who simply loves solving puzzles, R offers you a thrilling adventure where you’ll learn to command data with precision and creativity. Get ready to be amazed by the endless possibilities of R and join a global community of data enthusiasts who are reshaping the world, one analysis at a time.

In this website, you will have resources, examples and practice to master R.

Objectives

Session 1: R Basics (2 hours)

Part 1: Introduction to R

1. What is R and why use it?
2. Installing R and RStudio
3. Basic RStudio layout and functionality

Part 2: R Fundamentals

1. R as a calculator
2. Variables and data types (numeric, character)
3. Basic arithmetic operations

Part 3: Data Structures

1. Vectors: Creating, indexing, and operations
2. Data frames: Creating and exploring data frames
3. Importing and exporting data (CSV files)

Part 4: Data Manipulation

1. Subsetting and filtering data
2. Adding, removing, and renaming columns
3. Basic data summary and exploration

Part 5: Basic Data Visualization

1. Creating simple plots using plot(), hist(), pie(),barplot(),boxplot()

Session 2: Intermediate R I

Part 1: Functions and Control Structures

1. Writing and using functions
2. If statements and loops (for and while)

Part 2: Data Wrangling: Cleaning and Transformation

1. Reshaping data using dplyr functions (filter, arrange, mutate, summarize)
2. Handling missing data

Session 3: Intermediate R II

Part 1: Advanced Data Visualization

1. Creating customized plots with ggplot2
2. Adding titles, labels, and themes to plots

Part 2: Statistical Analysis

1. Introduction to hypothesis testing and statistical tests
2. Performing t-tests and chi-squared tests

(Optional) Part 3: Working with Dates and Times

1. Handling date and time data in R
2. Common date and time functions

(optional) Session 4: Advanced R

Part 1: Advanced Data Manipulation with dplyr

1. Grouping and summarizing data
2. Joining and merging datasets

Part 2: Text Data Processing

1. Manipulating and analyzing text data using regular expressions
2. Text mining basics

Part 3: Building Predictive Models

1. Introduction to machine learning in R
2. Creating predictive models with caret

Part 4: Bookdown: Using R markdown to create books

1. Introduction to Bookdown Package
2. Creating a simple bookdown book

Part 5: Version Control and Collaboration

1. Using Git and GitHub for version control and collaboration in R projects