Introduction to R

Introduction to R: A Beginner’s Guide

Course Description

This course is designed for anyone interested in learning the fundamentals of R, a powerful language widely used for statistical analysis, data science, and visualizations. Whether you are completely new to programming or already familiar with data analysis, this course offers a structured approach to learning R, broken into two parts: Basic (A) and Advanced (B).

Course Structure:

Part A: Basics of R

1. Reading the Data in R
   Learn how to import, manage, and read various data formats in R.
2. R Help and Packages
   Understand how to access R’s extensive help system and install/use packages that enhance R’s functionality.
3. Different Types of Objects
   Explore R’s data types such as vectors, matrices, lists, and data frames, which are the core building blocks.
4. Basic Manipulations
   Discover how to manipulate data and perform fundamental operations with R’s built-in functions.
5. Graphics and Data Visualization
   Visualize data using R’s powerful plotting systems like base graphics and ggplot2.
6. Simple Statistical Tests
   Conduct basic statistical tests like t-tests and chi-square tests to analyze data.
7. Regression Models
   Gain an introduction to linear and multiple regression modeling to understand relationships between variables.
8. R vs RStudio
   Compare and understand the difference between R and its popular IDE, RStudio, to enhance your workflow.

Part B: Advanced Topics in R

1. Loops and Functions
   Dive into loops and conditional statements to automate repetitive tasks, and learn to write custom functions in R.
2. Writing Your Own Program
   Build your own small R programs, solidifying your understanding of the language’s syntax and logic.
3. Monte Carlo Simulations
   Use simulations to estimate probabilities and model complex systems.
4. Optimization Methods
   Learn optimization techniques to minimize or maximize functions in various applications, such as in economics or engineering.
5. Non-parametric Models
   Explore advanced models that don’t rely on standard parametric assumptions, such as the Wilcoxon test or kernel density estimation.

This course will take you from basic data handling to creating custom statistical models and simulations, providing you with a solid foundation in R for various applications in research, business, and more.