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R for Data Science Cookbook (n)

Chapter 2. Data Extracting, Transforming, and Loading

This chapter covers the following topics:

Downloading open data
Reading and writing CSV files
Scanning text files
Working with Excel files
Reading data from databases
Scraping web data
Accessing Facebook data
Working with twitteR

Key benefits

• 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

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.

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

What do you get with Print?

Instant access to your digital copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Redeem a companion digital copy on all Print orders

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

Frequently bought together

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Rahul Raoniar Mar 09, 2017

Finally a book for engineering students. After searching 20+ books for Data science with R, finally got it. This book covers almost everything that an engineering student needs during master's or PhD. It covers almost every aspect of data processing like adding, dropping, filtering, merging, shorting and reshaping data set with fantastic libraries like dplyr, data.table and many more. It covers almost every statistical inference and testing techniques such as t-test, ANOVA, Various regression models (Linear, Multiple, Logistic, Step-wise regression etc), Distributions ( Normal, Poison, Binomial etc). This book also covers time series analysis with ARIMA and machine learning for both supervised and unsupervised with k-mean and other clustering techniques. It has one dedicated chapter on plotting functions like ggplot2, ggvis and mapping with spacial data, which provides awesome plots. This book is for all students and data scientist professional those want to explore the statistical characteristics of data sets. This book covers almost every part of data processing using R. The main advantage of this book is that after every code examples it provides detailed description of methodology involved in code. Like for Chi-Square test we can do it in few lines in R, this book provides the step wise code and at the end of the code it describes the theory and equation involved for calculation, which provides a great help for better understanding the underlying concepts.As a PhD scholar in Transportation Engineering, I can say, this is the best book available in market so far for data science with R.

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Sudipta Chakraborty Oct 06, 2017

Today i received it from amazon as expected. Thank you amazon. This book is really good and pretty much helpful for those who are seeking a career in data science or data analytics domain. As of now i am giving 4 star once i done with this book i will be reviewing once again. Thanks

Prashant Sharma Nov 07, 2018

Its a good book. Must have for all R programmers

R for Data Science Cookbook (n): Over 100 hands-on recipes to effectively solve real-world data problems using the most popular R packages and techniques

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