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Preview 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 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.

[•] 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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Publication date :
Jul 29, 2016

Length
452 pages

Edition :
1st Edition

Language :
English

ISBN-13 :
9781784390815

Category :

Languages :

Concepts :

Tools :

R for Data Science Cookbook

Credits

About the Author

About the Reviewer

www.PacktPub.com

Preface

1. Functions in R

2. Data Extracting, Transforming, and Loading

3. Data Preprocessing and Preparation

4. Data Manipulation

5. Visualizing Data with ggplot2

6. Making Interactive Reports

7. Simulation from Probability Distributions

8. Statistical Inference in R

9. Rule and Pattern Mining with R

10. Time Series Mining with R

11. Supervised Machine Learning

12. Unsupervised Machine Learning

Index

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