Mastering R

Introduction
1.Getting Started with R
1.1.Introduction to R
1.2.Installing R and RStudio
1.3.R Basics and Syntax
1.4.Working with Data Types
1.5.Vectors, Matrices, and Arrays
1.6.Data Frames and Tibbles
1.7.Importing and Exporting Data

2.Data Manipulation with dplyr
2.1.Introduction to the Tidyverse
2.2.Filtering and Sorting Data
2.3.Selecting and Renaming Variables
2.4.Mutating Data
2.5.Grouping and Summarizing Data
2.6.Joining Data Sets

3.Data Visualization with ggplot2
3.1.Introduction to ggplot2
3.2.Creating Scatter Plots and Line Charts
3.3.Customizing Plot Aesthetics
3.4.Bar Charts and Histograms
3.5.Boxplots and Violin Plots
3.6.Faceting and Themes

4.Statistical Analysis with R
4.1.Descriptive Statistics
4.2.Hypothesis Testing
4.3.Analysis of Variance (ANOVA)
4.4.Linear Regression
4.5.Logistic Regression
4.6.Time Series Analysis
4.7.Principal Component Analysis (PCA)

5.Machine Learning with R
5.1.Introduction to Machine Learning
5.2.Supervised Learning: Regression
5.3.Supervised Learning: Classification
5.4.Unsupervised Learning: Clustering
5.5.Model Evaluation and Selection
5.6.Feature Engineering and Selection
5.7.Model Tuning and Optimization

6.Advanced R Programming
6.1.Functional Programming with purrr
6.2.Working with Dates and Times
6.3.Handling Missing Data
6.4.Regular Expressions in R
6.5.Writing Efficient R Code
6.6.Creating R Packages

7.Interfacing with Data Sources
7.1.Working with Databases in R
7.2.Web Scraping with rvest
7.3.API Integration with httr
7.4.Reading and Writing External File Formats
7.5.Spatial Data Analysis with sf

8.Reproducible Research with R Markdown
8.1.Introduction to R Markdown
8.2.Document Formatting and Styling
8.3.Embedding Code and Output
8.4.Creating Interactive Documents
8.5.Publishing and Sharing Reports

9.Big Data Analytics with SparkR
9.1.Introduction to SparkR
9.2.Setting Up Spark Environment
9.3.Working with Spark DataFrames
9.4.Machine Learning with SparkR
9.5.SparkR in a Clustered Environment

10.R in Production and Deployment
10.1.Building Shiny Web Applications
10.2.Deploying Shiny Apps
10.3.Batch Processing with R and cron
10.4.Creating APIs with Plumber
10.5.Containerization with Docker

11.Appendix
11.1.R Syntax Cheat Sheet
11.2.Glossary of Terms
11.3.Recommended Resources
11.4.About the author