Unit 748: Comprehensive R Programming for Data Science

1. Introduction to R Programming

• History and evolution of R
• Key features and strengths of R in data science
• Setting up R and RStudio environment
• Basic syntax and programming concepts
• Overview of R packages and community resources

2. Data Types and Data Structures in R

• Atomic data types: numeric, character, logical, integer, factor
• Data structures: vectors, matrices, arrays
• Data frames and tibbles
• Lists and nested data structures
• Type coercion and conversion
• Indexing, subsetting, and accessing data

3. Control Structures and Functions in R

• Conditional statements: if, if-else, switch
• Looping constructs: for, while, repeat
• Writing and using functions
• Scope and environments
• Error handling and debugging basics

4. Data Import and Export in R

• Reading and writing CSV, Excel files (readr, readxl, writexl)
• Working with databases (DBI, RSQLite, RMySQL)
• Accessing web APIs and handling JSON/XML data
• Web scraping techniques with rvest
• Best practices for data import/export

5. Data Wrangling and Cleaning

• Handling missing values and outliers
• Data transformation and reshaping (tidyr)
• Filtering, selecting, and sorting data
• Merging and joining datasets (dplyr joins)
• Data validation and consistency checks

6. Data Manipulation with dplyr

• Overview of dplyr grammar
• Key verbs: filter, select, arrange, mutate, summarise
• Grouping and aggregation operations
• Chaining operations with the pipe operator (%>%)
• Working with large datasets efficiently

7. Data Visualization with ggplot2

• Principles of the Grammar of Graphics
• Creating basic plots: scatter, bar, histogram, boxplot
• Customizing aesthetics and themes
• Faceting for multi-panel plots
• Interactive visualizations with ggplotly and plotly

8. Statistical Analysis with R

• Descriptive statistics and exploratory data analysis
• Hypothesis testing: t-tests, chi-square tests
• Regression analysis: linear and logistic regression
• Analysis of variance (ANOVA)
• Clustering and other multivariate techniques

9. Machine Learning Basics in R

• Introduction to machine learning concepts
• Supervised learning algorithms: classification, regression
• Unsupervised learning: clustering, dimensionality reduction
• Model training, validation, and evaluation metrics
• Using caret, randomForest, and other ML packages

10. Text Mining and Natural Language Processing in R

• Text data preprocessing: tokenization, stemming, stopwords
• Creating document-term matrices
• Sentiment analysis techniques
• Text classification models
• Visualization of textual data

11. Time Series Analysis in R

• Understanding time series data and components
• Time series visualization
• Forecasting methods: ARIMA, exponential smoothing
• Using forecast and timeSeries packages
• Model diagnostics and validation

12. R Markdown for Reproducible Research

• Introduction to R Markdown syntax
• Combining code, output, and narrative
• Creating reports, presentations, and dashboards
• Parameterized reports and interactive documents
• Best practices for reproducibility

13. Shiny App Development

• Basics of Shiny framework
• UI and server architecture
• Creating interactive dashboards
• Customizing layouts and inputs
• Deploying Shiny applications

14. Best Practices and Advanced Topics

• Performance optimization strategies
• Parallel computing and multicore processing
• Version control with Git and RStudio
• Package development basics
• Ethical considerations and reproducible research

Summary and Integration

• Combining techniques learned for end-to-end data science projects
• Case studies and practical application examples
• Preparing for further specialization or advanced study