R Programming

Master R for statistical computing and data analysis.

Module 1: Introduction to R and RStudio

  • What is R? History and Overview
  • Installing R and RStudio
  • RStudio Interface and Basic Operations
  • R Syntax, Variables, and Data Types

Module 2: Data Structures in R

  • Vectors, Lists, Matrices, Arrays
  • Data Frames and Factors
  • Importing and Exporting Data (CSV, Excel, etc.)
  • Data Type Conversions

Module 3: Control Flow and Functions

  • Conditional Statements (if, else, ifelse)
  • Loops (for, while, repeat)
  • Writing Custom Functions in R
  • Applying Functions (apply, lapply, sapply)

Module 4: Data Manipulation with dplyr

  • Introduction to the Tidyverse and dplyr
  • Filtering and Selecting Data (filter, select)
  • Arranging and Mutating Data (arrange, mutate)
  • Summarizing and Grouping Data (summarise, group_by)
  • Joining Data Frames

Module 5: Data Visualization with ggplot2

  • Principles of Data Visualization
  • Introduction to ggplot2
  • Creating Various Plot Types (Scatter, Bar, Line, Boxplot)
  • Customizing Plots: Aesthetics, Labels, Themes
  • Advanced Visualization Techniques

Module 6: Statistical Analysis in R

  • Descriptive Statistics: Summary Measures, Frequency Distributions
  • Inferential Statistics: Hypothesis Testing (t-tests, ANOVA, Chi-squared)
  • Correlation and Regression Analysis
  • Linear Models (lm function)

Module 7: Introduction to Machine Learning with R

  • Overview of Machine Learning Concepts
  • Supervised Learning: Classification and Regression
  • Unsupervised Learning: Clustering
  • Using R Packages for ML (e.g., caret, randomForest)
  • Model Evaluation and Validation

Module 8: Text Mining and Web Scraping in R

  • Working with Text Data (tm package)
  • Text Preprocessing: Tokenization, Stemming, Stop Word Removal
  • Sentiment Analysis
  • Web Scraping with rvest

Module 9: Reporting and Reproducibility with R Markdown

  • Introduction to R Markdown
  • Creating Dynamic Reports
  • Integrating Code, Text, and Visualizations
  • Exporting to HTML, PDF, and Word

Module 10: Capstone Project and Best Practices

  • End-to-End Data Analysis Project using R
  • Best Practices in R Programming
  • Debugging and Error Handling
  • Review and Q&A

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