Book Image

R Data Analysis Cookbook

By : Viswa Viswanathan, Shanthi Viswanathan
Book Image

R Data Analysis Cookbook

By: Viswa Viswanathan, Shanthi Viswanathan

Overview of this book

<p>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.</p> <p>This book empowers you by showing you ways to use R to generate professional analysis reports. It provides examples for various important analysis and machine-learning tasks that you can try out with associated and readily available data. The book also teaches you to quickly adapt the example code for your own needs and save yourself the time needed to construct code from scratch.</p>

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R Data Analysis Cookbook
Viswa Viswanathan | Shanthi Viswanathan
May, 2015 | 342 pages
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Table of Contents (18 chapters)
R Data Analysis Cookbook
R Data Analysis Cookbook
Credits
Credits
About the Authors
About the Authors
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Acquire and Prepare the Ingredients – Your Data
Acquire and Prepare the Ingredients – Your Data
Introduction
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 [0,1]
Normalizing or standardizing data in a data frame
Binning numerical data
Creating dummies for categorical variables
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
Selecting a graphics device
Creating plots with the lattice package
Creating plots with the ggplot2 package
Creating charts that facilitate comparisons
Creating charts that help visualize a possible causality
Creating multivariate plots
3
Where Does It Belong? – Classification
Where Does It Belong? – Classification
Introduction
Generating error/classification-confusion matrices
Generating ROC charts
Building, plotting, and evaluating – classification trees
Using random forest models for classification
Classifying using Support Vector Machine
Classifying using the Naïve Bayes approach
Classifying using the KNN approach
Using neural networks for classification
Classifying using linear discriminant function analysis
Classifying using logistic regression
Using AdaBoost to combine classification tree models
4
Give Me a Number – Regression
Give Me a Number – Regression
Introduction
Computing the root mean squared 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 K-means clustering
Performing cluster analysis using hierarchical clustering
Reducing dimensionality with principal component analysis
6
Lessons from History – Time Series Analysis
Lessons from History – Time Series Analysis
Introduction
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
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
8
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
9
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
10
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 into 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
11
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
Index
Index

Chapter 9. Work Smarter, Not Harder – Efficient and Elegant R Code

In this chapter, we will cover recipes for doing the following without explicit iteration:

  • 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

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