Book Image

R Data Analysis Cookbook - Second Edition

By : Kuntal Ganguly, Shanthi Viswanathan, Viswa Viswanathan
Book Image

R Data Analysis Cookbook - Second Edition

By: Kuntal Ganguly, Shanthi Viswanathan, Viswa Viswanathan

Overview of this book

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 book, you will have all the knowledge you need to become an expert in data analysis with R, and put your skills to test in real-world scenarios.

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Table of Contents (14 chapters)
Preface
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Free Chapter
1
Acquire and Prepare the Ingredients - Your Data
Acquire and Prepare the Ingredients - Your Data
Introduction
Working with data
Reading data from CSV files
Reading XML data
Reading JSON data
Reading data from fixed-width formatted files
Reading data from R files and R libraries
Removing cases with missing values
Replacing missing values with the mean
Removing duplicate cases
Rescaling a variable to specified min-max range
Normalizing or standardizing data in a data frame
Binning numerical data
Creating dummies for categorical variables
Handling missing data
Correcting data
Imputing data
Detecting outliers
2
What's in There - Exploratory Data Analysis
What's in There - Exploratory Data Analysis
Introduction
Creating standard data summaries
Extracting a subset of a dataset
Splitting a dataset
Creating random data partitions
Generating standard plots, such as histograms, boxplots, and scatterplots
Generating multiple plots on a grid
Creating plots with the lattice package
Creating charts that facilitate comparisons
Creating charts that help to visualize possible causality
3
Where Does It Belong? Classification
Where Does It Belong? Classification
Introduction
Generating error/classification confusion matrices
Principal Component Analysis
Generating receiver operating characteristic charts
Building, plotting, and evaluating with classification trees
Using random forest models for classification
Classifying using the support vector machine approach
Classifying using the Naive Bayes approach
Classifying using the KNN approach
Using neural networks for classification
Classifying using linear discriminant function analysis
Classifying using logistic regression
Text classification for sentiment analysis
4
Give Me a Number - Regression
Give Me a Number - Regression
Introduction
Computing the root-mean-square error
Building KNN models for regression
Performing linear regression
Performing variable selection in linear regression
Building regression trees
Building random forest models for regression
Using neural networks for regression
Performing k-fold cross-validation
Performing leave-one-out cross-validation to limit overfitting
5
Can you Simplify That? Data Reduction Techniques
Can you Simplify That? Data Reduction Techniques
Introduction
Performing cluster analysis using hierarchical clustering
Performing cluster analysis using partitioning clustering
Image segmentation using mini-batch K-means
Partitioning around medoids
Clustering large application
Performing cluster validation
Performing Advance clustering
Model-based clustering with the EM algorithm
Reducing dimensionality with principal component analysis
6
Lessons from History - Time Series Analysis
Lessons from History - Time Series Analysis
Introduction
Exploring finance datasets
Creating and examining date objects
Operating on date objects
Performing preliminary analyses on time series data
Using time series objects
Decomposing time series
Filtering time series data
Smoothing and forecasting using the Holt-Winters method
Building an automated ARIMA model
7
How does it look? - Advanced data visualization
How does it look? - Advanced data visualization
Introduction
Creating scatter plots
Creating line graphs
Creating bar graphs
Making distributions plots
Creating mosaic graphs
Making treemaps
Plotting a correlations matrix
Creating heatmaps
Plotting network graphs
Labeling and legends
Coloring and themes
Creating multivariate plots
Creating 3D graphs and animation
Selecting a graphics device
8
This may also interest you - Building Recommendations
This may also interest you - Building Recommendations
Introduction
Building collaborative filtering systems
Performing content-based systems
Building hybrid systems
Performing similarity measures
Application of ML algorithms - image recognition system
Evaluating models and optimization
A practical example - fraud detection system
9
It's All About Your Connections - Social Network Analysis
It's All About Your Connections - Social Network Analysis
Introduction
Downloading social network data using public APIs
Creating adjacency matrices and edge lists
Plotting social network data
Computing important network metrics
Cluster analysis
Force layout
YiFan Hu layout
10
Put Your Best Foot Forward - Document and Present Your Analysis
Put Your Best Foot Forward - Document and Present Your Analysis
Introduction
Generating reports of your data analysis with R Markdown and knitr
Creating interactive web applications with shiny
Creating PDF presentations of your analysis with R presentation
Generating dynamic reports
11
Work Smarter, Not Harder - Efficient and Elegant R Code
Work Smarter, Not Harder - Efficient and Elegant R Code
Introduction
Exploiting vectorized operations
Processing entire rows or columns using the apply function
Applying a function to all elements of a collection with lapply and sapply
Applying functions to subsets of a vector
Using the split-apply-combine strategy with plyr
Slicing, dicing, and combining data with data tables
12
Where in the World? Geospatial Analysis
Where in the World? Geospatial Analysis
Introduction
Downloading and plotting a Google map of an area
Overlaying data on the downloaded Google map
Importing ESRI shape files to R
Using the sp package to plot geographic data
Getting maps from the maps package
Creating spatial data frames from regular data frames containing spatial and other data
Creating spatial data frames by combining regular data frames with spatial objects
Adding variables to an existing spatial data frame
Spatial data analysis with R and QGIS
13
Playing Nice - Connecting to Other Systems
Playing Nice - Connecting to Other Systems
Introduction
Using Java objects in R
Using JRI to call R functions from Java
Using Rserve to call R functions from Java
Executing R scripts from Java
Using the xlsx package to connect to Excel
Reading data from relational databases - MySQL
Reading data from NoSQL databases - MongoDB
Working with in-memory data processing with Apache Spark

Principal Component Analysis

Principal Component Analysis (PCA) is a statistical technique for dimensionality reduction that transforms data with high-dimensional space into low-dimensional space. Its goal is to replace a large number of correlated variables with a smaller number of uncorrelated variables while retaining as much information in the original variables as possible, hence playing a significant role in feature engineering tasks of the machine learning pipeline.

Getting ready

In this recipe, we will illustrate the technique of PCA using the USArrests dataset that contains crime-related statistics, such as Assault, Murder, Rape, and UrbanPop, per 100,000 residents in 50 states in the US.

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