Green Biotec UG Bremen Germany
Introduction to R Programming
| Introduction to R Programming | |
|---|---|
| Course Description | This course provides a comprehensive introduction to the R programming language, a powerful tool for statistical computing, data visualization, and data analysis. Designed for beginners, the course focuses on building the skills necessary to manipulate and analyze data efficiently. By the end of the course, students will be proficient in R programming and its applications in various fields such as data science, bioinformatics, and machine learning. |
| Recommended Books | 1. “R for Data Science” by Hadley Wickham and Garrett Grolemund. 2. “The Art of R Programming: A Tour of Statistical Software Design” by Norman Matloff. 3. “Advanced R” by Hadley Wickham. 4. “Hands-On Programming with R” by Garrett Grolemund. |
| Course Learning Outcomes | After completing this course, students will be able to: 1. Understand the fundamentals of R programming, including syntax and data structures. 2. Perform data manipulation, cleaning, and transformation using R. 3. Create meaningful data visualizations using R libraries such as ggplot2. 4. Implement statistical analysis and build simple predictive models. 5. Write efficient and reusable R scripts for data analysis. |
| Assessment System | Quizzes: 10-15% Assignments: 5-10% Midterms: 30-40% End Semester Exam: 40-50% |
| Lecture Plan | ||
|---|---|---|
| S.No. | Description | Quizzes/Assignment |
| 1 | Introduction to R: Installation, IDE (RStudio), and Basic Syntax | |
| 2 | Data Structures in R: Vectors, Lists, Matrices, Data Frames | Quiz 1 |
| 3 | Basic Data Manipulation: Subsetting, Indexing, and Filtering | |
| 4 | Data Cleaning and Transformation Using dplyr | |
| 5 | Introduction to Data Visualization with ggplot2 | Assignment 1 |
| 6 | Exploratory Data Analysis: Descriptive Statistics and Visualizations | Quiz 2 |
| 7 | Working with Real-World Datasets in R | |
| 8 | Control Structures: Loops and Conditional Statements | |
| 9 | Functions in R: Writing and Using Custom Functions | Assignment 2 |
| 10 | Introduction to Statistical Analysis in R: Hypothesis Testing | |
| 11 | Building Simple Linear Regression Models in R | Quiz 3 |
| 12 | Time Series Analysis in R | |
| 13 | Introduction to Machine Learning with R | Assignment 3 |
| 14 | Debugging and Best Practices in R Programming | |
| 15 | Capstone Project: End-to-End Data Analysis and Visualization | |
