Over 100 hands-on recipes to effectively solve real-world data problems using the most popular R packages and techniques
About This Book
- Gain insight into how data scientists collect, process, analyze, and visualize data using some of the most popular R packages
- Understand how to apply useful data analysis techniques in R for real-world applications
- An easy-to-follow guide to make the life of data scientist easier with the problems faced while performing data analysis
This book is for those who are already familiar with the basic operation of R, but want to learn how to efficiently and effectively analyze real-world data problems using practical R packages.
What You Will Learn
- Get to know the functional characteristics of R language
- Extract, transform, and load data from heterogeneous sources
- Understand how easily R can confront probability and statistics problems
- Get simple R instructions to quickly organize and manipulate large datasets
- Create professional data visualizations and interactive reports
- Predict user purchase behavior by adopting a classification approach
- Implement data mining techniques to discover items that are frequently purchased together
- Group similar text documents by using various clustering methods
This cookbook offers a range of data analysis samples in simple and straightforward R code, providing step-by-step resources and time-saving methods to help you solve data problems efficiently.
The first section deals with how to create R functions to avoid the unnecessary duplication of code. You will learn how to prepare, process, and perform sophisticated ETL for heterogeneous data sources with R packages. An example of data manipulation is provided, illustrating how to use the “dplyr” and “data.table” packages to efficiently process larger data structures. We also focus on “ggplot2” and show you how to create advanced figures for data exploration.
In addition, you will learn how to build an interactive report using the “ggvis” package. Later chapters offer insight into time series analysis on financial data, while there is detailed information on the hot topic of machine learning, including data classification, regression, clustering, association rule mining, and dimension reduction.
By the end of this book, you will understand how to resolve issues and will be able to comfortably offer solutions to problems encountered while performing data analysis.
Style and approach
This easy-to-follow guide is full of hands-on examples of data analysis with R. Each topic is fully explained beginning with the core concept, followed by step-by-step practical examples, and concluding with detailed explanations of each concept used.
|Sold by:||Barnes & Noble|
|File size:||20 MB|
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About the Author
Yu-Wei Chiu (David Chiu) is the founder of LargitData Company (LargitData.com), which mainly focuses on delivering data-driven products. LargitData has many years' experience in serving opinion mining products and recommending solutions for both the government and private sectors in the Asia-Pacific region.
In addition to being a start-up entrepreneur and data scientist, he specializes in using Spark and Hadoop to process big data and apply data mining techniques for data analysis. Yu-Wei is also a professional lecturer, and has delivered talks on Python, R, Hadoop, and tech talks at a variety of conferences.
In 2015, Yu-Wei wrote Machine Learning with R Cookbook, a book compiled for Packt Publishing. In 2013, he reviewed Bioinformatics with R Cookbook. For more information, please visit his personal website: ywchiu.com.
He feels immense gratitude to his family and friends for supporting and encouraging him to complete this book. Here, he sincerely says thanks to his mother, Ming-Yang Huang (Miranda Huang); his mentor, Man-Kwan Shan; proofreader of this book, Brendan Fisher; and more friends who have offered their support.