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
Published on 8. July 2025
452 pages
978-1-78439-204-8 (ISBN)
Description
Over 100 hands-on recipes to effectively solve real-world data problems using the most popular R packages and techniques
Key Features
[] 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
Book DescriptionThis 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.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
Who this book is forThis 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.
Key Features
[] 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
Book DescriptionThis 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.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
Who this book is forThis 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.
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More details
Edition
Digital original
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
US School Grade: College Graduate Student
Edition type
Digital original
File size
18,81 MB
ISBN-13
978-1-78439-204-8 (9781784392048)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Prabhanjan Narayanachar Tattar | Yu-Wei Chiu (David Chiu)
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
Book
07/2016
Packt Publishing
€57.30
Shipment within 3-4 weeks
Persons
Chiu (David Chiu) Yu-Wei, :
Yu-Wei, Chiu (David Chiu) is the founder of LargitData (www.LargitData.com), a startup company that mainly focuses on providing big data and machine learning products. He has previously worked for Trend Micro as a software engineer, where he was responsible for building big data platforms for business intelligence and customer relationship management systems. 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 lectures on big data and machine learning in R and Python, and given tech talks at a variety of conferences. In 2015, Yu-Wei wrote Machine Learning with R Cookbook, Packt Publishing. In 2013, Yu-Wei reviewed Bioinformatics with R Cookbook, Packt Publishing. For more information, please visit his personal website at www.ywchiu.com. **********************************Acknowledgement************************************** I have immense gratitude for my family and friends for supporting and encouraging me to complete this book. I would like to sincerely thank my mother, Ming-Yang Huang (Miranda Huang); my mentor, Man-Kwan Shan; the proofreader of this book, Brendan Fisher; Members of LargitData; Data Science Program (DSP); and other friends who have offered their support.
Yu-Wei, Chiu (David Chiu) is the founder of LargitData (www.LargitData.com), a startup company that mainly focuses on providing big data and machine learning products. He has previously worked for Trend Micro as a software engineer, where he was responsible for building big data platforms for business intelligence and customer relationship management systems. 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 lectures on big data and machine learning in R and Python, and given tech talks at a variety of conferences. In 2015, Yu-Wei wrote Machine Learning with R Cookbook, Packt Publishing. In 2013, Yu-Wei reviewed Bioinformatics with R Cookbook, Packt Publishing. For more information, please visit his personal website at www.ywchiu.com. **********************************Acknowledgement************************************** I have immense gratitude for my family and friends for supporting and encouraging me to complete this book. I would like to sincerely thank my mother, Ming-Yang Huang (Miranda Huang); my mentor, Man-Kwan Shan; the proofreader of this book, Brendan Fisher; Members of LargitData; Data Science Program (DSP); and other friends who have offered their support.
Content
Table of Contents
Functions in R
Data Extracting, Transforming and Loading
Data Preprocess and Preparation
Data Manipulation
Visualizing Data with ggplot2
Making Interactive Reports
Simulation from Probability Distribution
Statistical Inference in R
Rule and Pattern Mining with R
Time Series Mining with R
Supervised Machine Learning
Unsupervised Machine Learning
Functions in R
Data Extracting, Transforming and Loading
Data Preprocess and Preparation
Data Manipulation
Visualizing Data with ggplot2
Making Interactive Reports
Simulation from Probability Distribution
Statistical Inference in R
Rule and Pattern Mining with R
Time Series Mining with R
Supervised Machine Learning
Unsupervised Machine Learning
