R for Data Science Cookbook

Over 100 hands-on recipes to effectively solve real-world data problems using the most popular R packages and techniques

R for Data Science Cookbook

Yu-Wei, Chiu (David Chiu)

3 customer reviews
Over 100 hands-on recipes to effectively solve real-world data problems using the most popular R packages and techniques
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Book Details

ISBN 139781784390815
Paperback452 pages

Book Description

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.

Table of Contents

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

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Table of Contents

Book Details

ISBN 139781784390815
Paperback452 pages
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