R data analysis cookbook : a journey from data computation to data-driven insights / Kuntal Ganguly.

Format:

Book

Author/Creator:

Ganguly, Kuntal, author.

Language:

English

Subjects (All):

R (Computer program language).

Physical Description:

1 online resource (1 volume) : illustrations

Edition:

Second edition.

Place of Publication:

Birmingham, England ; Mumbai, [India] : Packt, 2017.

System Details:

text file

Biography/History:

Ganguly Kuntal: Kuntal Ganguly is a big data analytics engineer focused on building large-scale, data-driven systems using big data frameworks and machine learning. He has around 7 years experience of building big data and machine learning applications. Kuntal provides solutions to cloud customers in building real-time analytics systems using managed cloud services and open source Hadoop ecosystem technologies such as Spark, Kafka, Storm, Solr, and so on, along with machine learning and deep learning frameworks. Kuntal enjoys hands-on software development and has single-handedly conceived, architected, developed, and deployed several large-scale distributed applications. He is a machine learning and deep learning practitioner and is very passionate about building intelligent applications. Viswanathan Shanthi: nanViswanathan Viswa: Viswa Viswanathan is an associate professor of Computing and Decision Sciences at the Stillman School of Business in Seton Hall University. After completing his PhD in Artificial Intelligence, Viswa spent a decade in academia and then switched to a leadership position in the software industry for another decade during which he worked for Infosys, Igate, and Starbase. He embraced academia once again in 2001. Viswa has taught extensively in fields ranging from operations research, computer science, software engineering, management information systems, and enterprise systems. In addition to university teaching, Viswa has conducted training programs for industry professionals and has written several peer-reviewed research publications in journals such as Operations Research, IEEE Software, Computers and Industrial Engineering, and International Journal of Artificial Intelligence in Education. He has authored a book titled Data Analytics with R: A hands-on approach. Viswa thoroughly enjoys hands-on software development and has single-handedly conceived, architected, developed, and deployed several web-based applications. Apart from his deep interest in technical fields such as data analytics, artificial intelligence, computer science, and software engineering, Viswa harbors a deep interest in education with special emphasis on the roots of learning and methods to foster deeper learning. He has done research in this area and hopes to pursue the subject further. Viswa would like to express deep gratitude to professors Amitava Bagchi and Anup Sen, who were inspirational forces during his early research career. He is also grateful to several extremely intelligent colleagues, notable among them being Rajesh Venkatesh, Dan Richner, and Sriram Bala, who significantly shaped his thinking. His aunt, Analdavalli; his sister, Sankari; and his wife, Shanthi, taught him much about hard work, and even the little he has absorbed has helped him immensely. His sons, Nitin and Siddarth, have helped with numerous insightful comments on various topics.

Summary:

Over 80 recipes to help you breeze through your data analysis projects using R About This Book Analyse your data using the popular R packages like ggplot2 with ready-to-use and customizable recipes Find meaningful insights from your data and generate dynamic reports A practical guide to help you put your data analysis skills in R to practical use Who This Book Is For This book is for data scientists, analysts and even enthusiasts who want to learn and implement the various data analysis techniques using R in a practical way. Those looking for quick, handy solutions to common tasks and challenges in data analysis will find this book to be very useful. Basic knowledge of statistics and R programming is assumed. What You Will Learn Acquire, format and visualize your data using R Using R to perform an Exploratory data analysis Introduction to machine learning algorithms such as classification and regression Get started with social network analysis Generate dynamic reporting with Shiny Get started with geospatial analysis Handling large data with R using Spark and MongoDB Build Recommendation system- Collaborative Filtering, Content based and Hybrid Learn real world dataset examples- Fraud Detection and Image Recognition In Detail Data analytics with R has emerged as a very important focus for organizations of all kinds. R enables even those with only an intuitive grasp of the underlying concepts, without a deep mathematical background, to unleash powerful and detailed examinations of their data. This book will show you how you can put your data analysis skills in R to practical use, with recipes catering to the basic as well as advanced data analysis tasks. Right from acquiring your data and preparing it for analysis to the more complex data analysis techniques, the book will show you how you can implement each technique in the best possible manner. You will also visualize your data using the popular R packages like ggplot2 and gain hidden insights from it. Starting with implementing the basic data analysis concepts like handling your data to creating basic plots, you will master the more advanced data analysis techniques like performing cluster analysis, and generating effective analysis reports and visualizations. Throughout the book, you will get to know the common problems and obstacles you might encounter while implementing each of the data analysis techniques in R, with ways to overcoming them in the easiest possible way. By the end of this ...

Contents:

Cover

Copyright

Credits

About the Author

About the Reviewers

www.PacktPub.com

Customer Feedback

Table of Contents

Preface

Chapter 1: Acquire and Prepare the Ingredients - Your Data

Introduction

Working with data

Reading data from CSV files

Getting ready

How to do it...

How it works...

There's more...

Handling different column delimiters

Handling column headers/variable names

Handling missing values

Reading strings as characters and not as factors

Reading data directly from a website

Reading XML data

Extracting HTML table data from a web page

Extracting a single HTML table from a web page

Reading JSON data

Reading data from fixed-width formatted files

Files with headers

Excluding columns from data

Reading data from R files and R libraries

Saving all objects in a session

Saving objects selectively in a session

Attaching/detaching R data files to an environment

Listing all datasets in loaded packages

Removing cases with missing values

Eliminating cases with NA for selected variables

Finding cases that have no missing values

Converting specific values to NA

Excluding NA values from computations

Replacing missing values with the mean

Imputing random values sampled from non-missing values

Removing duplicate cases

There's more.

Identifying duplicates without deleting them

Rescaling a variable to specified min-max range

Rescaling many variables at once

See also

Normalizing or standardizing data in a data frame

Standardizing several variables simultaneously

Binning numerical data

Creating a specified number of intervals automatically

Creating dummies for categorical variables

Choosing which variables to create dummies for

Handling missing data

Understanding missing data pattern

Correcting data

Combining multiple columns to single columns

Splitting single column to multiple columns

Imputing data

Detecting outliers

Treating the outliers with mean/median imputation

Handling extreme values with capping

Transforming and binning values

Outlier detection with LOF

Chapter 2: What&amp

#x27

s in There - Exploratory Data Analysis

Creating standard data summaries

Using the str() function for an overview of a data frame

Computing the summary and the str() function for a single variable

Finding other measures

Extracting a subset of a dataset

Excluding columns

Selecting based on multiple values

Selecting using logical vector

Splitting a dataset

Creating random data partitions

Case 1 - Numerical target variable and two partitions

Case 2 - Numerical target variable and three partitions

Case 3 - Categorical target variable and two partitions

Case 4 - Categorical target variable and three partitions

Using a convenience function for partitioning

Sampling from a set of values

Generating standard plots, such as histograms, boxplots, and scatterplots

Creating histograms

Creating boxplots

Creating scatterplots

Creating scatterplot matrices

Histograms

Boxplots

Overlay a density plot on a histogram

Overlay a regression line on a scatterplot

Color specific points on a scatterplot

Generating multiple plots on a grid

Graphics parameters

Creating plots with the lattice package

Adding flair to your graphs

Creating charts that facilitate comparisons

Using base plotting system

Creating&amp

#160

beanplots with the beanplot package

Creating charts that help to visualize possible causality

Chapter 3: Where Does It Belong? Classification

Generating error/classification confusion matrices

Visualizing the error/classification confusion matrix

Comparing the model's performance for different classes

Principal Component Analysis

Generating receiver operating characteristic charts

Using arbitrary class labels

Building, plotting, and evaluating with classification trees

Computing raw probabilities

Creating the ROC chart

Using random forest models for classification

Generating the ROC chart

Specifying cutoffs for classification

Classifying using the support vector machine approach

Controlling the scaling of variables

Determining the type of SVM model

Assigning weights to the classes

Choosing the cost of SVM

Tuning the SVM

Classifying using the Naive Bayes approach

Classifying using the KNN approach

Automating the process of running KNN for many k values

Selecting appropriate values of k using caret

Using KNN to compute raw probabilities instead of classifications

Using neural networks for classification

Exercising greater control over nnet

Generating raw probabilities and plotting the ROC curve

Classifying using linear discriminant function analysis

Using the formula interface for lda

Classifying using logistic regression

Text classification for sentiment analysis

Chapter 4: Give Me a Number - Regression

Computing the root-mean-square error

Using a convenience function to compute the RMS error

Building KNN models for regression

Running KNN with cross-validation in place of a validation partition

Using a convenience function to run KNN

Using a convenience function to run KNN for multiple k values

Performing linear regression

Forcing lm to use a specific factor level as the reference

Using other options in the formula expression for linear models

Performing variable selection in linear regression

Building regression trees

Generating regression trees for data with categorical predictors

Generating regression trees using the ensemble method - Bagging and Boosting

Building random forest models for regression

Controlling forest generation

Using neural networks for regression

Performing k-fold cross-validation

Performing leave-one-out cross-validation to limit overfitting

How to do it.

How it works.

Notes:

Previous edition published: 2015.

Includes bibliographical references at the end of each chapters and index.

Description based on online resource; title from PDF title page (ebrary, viewed October 18, 2017).

OCLC:

1006894355