R Data Analysis Cookbook, 2nd Edition

Key Features

• Analyse your data using the popular R packages with ready-to-use and customizable recipes
• Find meaningful insights from your data and generate dynamic reports
• A practical guide to help you put your data analysis skills in R to practical use

Book Description

This book will show you how you can put your data analysis skills in R to practical use, with recipes catering to the basic as well as advanced data analysis tasks. Right from acquiring your data and preparing it for analysis to the more complex data analysis techniques, the book will show you how you can implement each technique in the best possible manner. You will also visualize your data using the popular R packages like ggplot2 and gain hidden insights from it. Starting with implementing the basic data analysis concepts like handling your data to creating basic plots, you will master the more advanced data analysis techniques like performing cluster analysis, and generating effective analysis reports and visualizations. Throughout the book, you will get to know the common problems and obstacles you might encounter while implementing each of the data analysis techniques in R, with ways to overcoming them in the easiest possible way.

By the end of this book, you will have all the knowledge you need to become an expert in data analysis with R, and put your skills to test in real-world scenarios.

What you will learn

• Acquire, format and visualize your data using R
• Using R to perform an Exploratory data analysis
• Introduction to machine learning algorithms such as classification and regression
• Get started with social network analysis
• Generate dynamic reporting with Shiny
• Get started with geospatial analysis
• Handling large data with R for Spark and MongoDB

Table of Contents

Chapter 1. Acquire and Prepare the Ingredients – Your Data
Chapter 2. Acquire And Prepare The Ingredients – Your Data
Chapter 3. What’S In There – Exploratory Data Analysis
Chapter 4. Where Does It Belong? Classification
Chapter 5. Give Me A Number – Regression
Chapter 6. Can You Simplify That? Data Reduction Techniques
Chapter 7. Lessons From History – Time Series Analysis
Chapter 8. How Does It Look? – Advanced Data Visualization
Chapter 9. This May Also Interest You – Building Recommendations
Chapter 10. It’S All About Your Connections – Social Network Analysis
Chapter 11. Put Your Best Foot Forward – Document And Present Your Analysis
Chapter 12. Work Smarter, Not Harder – Efficient And Elegant R Code
Chapter 13. Where In The World? Geospatial Analysis
Chapter 14. Playing Nice – Connecting To Other Systems