R Data Analysis Cookbook - More Than 80 Recipes to Help You Deliver Sharp Data Analysis
暫譯: R 數據分析食譜 - 超過 80 種食譜幫助您提供精確的數據分析

Key Features

• Analyse data with ready-to-use and customizable recipes
• Discover convenient functions to speed-up your work and data files
• Explore the leading R packages built for expert data analysis

Book Description

Data analysis has recently emerged as a very important focus for a huge range of organizations and businesses. R makes detailed data analysis easier, making advanced data exploration and insight accessible to anyone interested in learning it.

This book empowers you by showing you ways to use R to generate professional analysis reports. It provides examples for various important analysis and machine-learning tasks that you can try out with associated and readily available data. The book also teaches you to quickly adapt the example code for your own needs and save yourself the time needed to construct code from scratch.

What you will learn

• Get data into your R environment and prepare it for analysis
• Perform exploratory data analyses and generate meaningful visualizations of the data
• Apply several machine-learning techniques for classification and regression
• Get your hands around large data sets with the help of reduction techniques
• Extract patterns from time-series data and produce forecasts based on them
• Learn how to extract actionable information from social network data
• Implement geospatial analysis
• Present your analysis convincingly through reports and build an infrastructure to enable others to play with your data

About the Author

Viswa Viswanathan is an associate professor of Computing and Decision Sciences at the Stillman School of Business in Seton Hall University. After completing his PhD in artificial intelligence, Viswa spent a decade in academia and then switched to a leadership position in the software industry for a decade.

Shanthi Viswanathan is an experienced technologist who has delivered technology management and enterprise architecture consulting to many enterprise customers. She has worked for Infosys Technologies, Oracle Corporation, and Accenture. As a consultant, Shanthi has helped several large organizations, such as Canon, Cisco, Celgene, Amway, Time Warner Cable, and GE among others, in areas such as data architecture and analytics, master data management, service-oriented architecture, business process management, and modeling.

Table of Contents

1. Acquire and Prepare the Ingredients Your Data
2. What's in There? Exploratory Data Analysis
3. Where Does It Belong? Classification
4. Give Me a Number Regression
5. Can You Simplify That? Data Reduction Techniques
6. Lessons from History Time Series Analysis
7. It's All about Your Connections Social Network Analysis
8. Put Your Best Foot Forward Document and Present Your Analysis
9. Work Smarter, Not Harder Efficient and Elegant R Code
10. Where in the World? Geospatial Analysis
11. Playing Nice Connecting to Other Systems