Introduction to R

• Overview of R and its features
• Installation and setup of R
• Introduction to RStudio and the R environment

R Basics

• R syntax and data types
• Variables, vectors, and matrices in R
• Working with data frames and lists

Data Manipulation and Transformation

• Importing and exporting data in R
• Data cleaning and preprocessing
• Working with missing values and outliers

Exploratory Data Analysis (EDA)

• Summarizing and visualizing data in R
• Descriptive statistics and data distributions
• Creating plots, charts, and graphs

Data Visualization

• Advanced data visualization techniques
• Customizing plots and adding annotations
• Creating interactive visualizations

Statistical Analysis with R

• Hypothesis testing and statistical inference
• Parametric and non-parametric tests in R
• Regression analysis and model building

Data Wrangling with dplyr and tidyr

• Introduction to the dplyr and tidyr packages
• Manipulating and transforming data frames
• Grouping, aggregating, and summarizing data

Machine Learning with R

• Introduction to machine learning algorithms in R
• Supervised learning (classification, regression)
• Unsupervised learning (clustering, dimensionality reduction)

Text Mining and Natural Language Processing (NLP)

• Analyzing text data in R
• Text preprocessing and tokenization
• Sentiment analysis and text classification

Time Series Analysis

• Working with time series data in R
• Time series decomposition and forecasting
• Seasonality and trend analysis

R Packages and Libraries

• Exploring popular R packages (ggplot2, dplyr, etc.)
• Using external libraries for specific tasks
• Creating and sharing R packages

Web Scraping and Data Extraction

• Scraping data from websites using R
• Parsing HTML and XML data
• Web automation and API integration

R Markdown and Reproducible Research

• Creating dynamic reports and documents with R Markdown
• Combining code, text, and visualizations
• Generating HTML, PDF, and Word documents

R Best Practices and Tips

• Writing clean and efficient R code
• Debugging and troubleshooting common issues
• Performance optimization techniques