R for Biologist - A basic journey towards analyzes biological data

Introduction and Overview:

Provide an introduction to R and its importance in data analysis and statistics. Give an overview of what students can expect to learn in the course.

1. Module 1: R Basics:

   • Start with setting up the R environment and basic syntax.
   • Move on to data types, variables, and operators.
   • Cover decision making, loops, and functions.
   • Progress to more advanced data structures like strings, vectors, lists, matrices, arrays, factors, and data frames.
   • Introduce packages and data reshaping techniques.

2. Module 2: R Data Interfaces:

• Begin with common data file formats such as CSV, Excel, and binary files.
• Proceed to handling XML and JSON files.
• Teach methods for extracting and processing web data.
• Conclude with accessing and manipulating databases using R.

3. Day 1: Foundations of R Programming

   • Module 1:
     • R - Environment Setup
     • R - Basic Syntax
     • R - Data Types
     • R - Variables
     • R - Operators

   Day 2: Essential Concepts and Data Structures

   • Module 1 (continued):
     • R - Decision Making
     • R - Loops
     • R - Functions
     • R - Strings
     • R - Vectors
     • R - Lists
     • R - Matrices
     • R - Arrays
     • R - Factors
     • R - Data Frames

   Day 3: Statistical Analysis with R

   • Module 1 (continued):
     • R - Mean, Median & Mode
     • R - Linear Regression
     • R - Multiple Regression
     • R - Logistic Regression
     • R - Normal Distribution
     • R - Binomial Distribution
     • R - Poisson Regression

   Day 4: Advanced Techniques in R

   • Module 1 (continued):
     • R - Analysis of Covariance
     • R - Time Series Analysis
     • R - Nonlinear Least Square
     • R - Decision Tree
     • R - Random Forest
     • R - Survival Analysis

   Day 5: Data Interfaces and Handling

   • Module 2:
     • R - CSV Files
     • R - Excel Files
     • R - Binary Files
     • R - XML Files
     • R - JSON Files
     • R - Web Data
     • R - Database

   Day 6: Practical Applications and Projects

   • Apply the concepts learned in the previous days to solve real-world problems.
   • Work on hands-on projects, analyze datasets, and build predictive models using R.

   Day 7: Review and Assessment

   • Review key concepts and techniques covered throughout the week.
   • Take quizzes or assessments to evaluate your understanding.
   • Identify any areas for further study or practice.