Welcome to R Crash Course

A beginner-focused R programming course

This repository contains all the course materials for learning R programming from scratch, specially designed for students in biology, life sciences, and health-related fields.

What’s Included?

• Step-by-step session summaries
• Hands-on R scripts and datasets for practice
• Base R, ggplot2, and dplyr cheatsheets for quick reference while codin
• Practical examples relevant to biological datasets

Course Instructor

Instructor: Itrat Zehrh

Session 1: Getting start with R

Topic covered in this session:

• R, RStudio & RTools downlaoding & installation
• Set up working directory
• Created R project

Session Guide

Session 2: Basics of R Programming language (Part I)

In this session, we covered:

• Importance of syntax in R programming.
• How to use variables, comments and keywords?
• Writing if, else statement. Session Guide R Script

Session 3: Basics of R Programming language (Part II)

In this session we covered:

• Fundamentals of R functions
• Learned how to create user-defined R functions Here you can find: Session Summary

R Script

Session 4: Basics of R Programming language (Part III)

In this session, we explored some of the operators in R. session summary R Script

Session 5: Basics of R Programming language (Part IV)

In this session we learned the core data types in R, and perform data type conversions in R. Here you can find: Session Summary R Script

Session 6: Basics of R Programming language (Part V)

This session is about about data structure in R, and data assesment. R Script

Session 7: Exploratory Data Analysis

In this session, we covered the core concepts of exploratory data analysis using base R functions and the DataExplorer package. We also learned about control flow and loop functions in R. R Script

Session 8: Data Manipulation

In this session, we explored data manipulation using the tidyverse package. Participants practiced cleaning, transforming, and preparing datasets efficiently for analysis. R Script

Session 9: Statistical Analysis & Data Visualization

In this session, we performed basic statistical analyses, including: Descriptive statistics Frequency tables Checking normality t-tests Correlation analysis Chi-square tests Logistic regression

We also introduced data visualization using the ggplot2 package, practicing the creation of clear and informative plots. R Script

Practice with the provided scripts and materials to reinforce your learning.
Use these resources to start analyzing your own biological datasets confidently.
Follow us for free tutorials, resources, and omics workflows.

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Contact us: 📧 ai.biotechinfo@gmail.com