Book Image

Machine Learning with R Cookbook

By : Yu-Wei, Chiu (David Chiu)
Book Image

Machine Learning with R Cookbook

By: Yu-Wei, Chiu (David Chiu)

Overview of this book

<p>The R language is a powerful open source functional programming language. At its core, R is a statistical programming language that provides impressive tools to analyze data and create high-level graphics.</p> <p>This book covers the basics of R by setting up a user-friendly programming environment and performing data ETL in R. Data exploration examples are provided that demonstrate how powerful data visualization and machine learning is in discovering hidden relationships. You will then dive into important machine learning topics, including data classification, regression, clustering, association rule mining, and dimension reduction.</p>

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Machine Learning with R Cookbook
Yu-Wei, Chiu (David Chiu)
Mar, 2015 | 442 pages
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Table of Contents (21 chapters)
Machine Learning with R Cookbook
Machine Learning with R Cookbook
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Practical Machine Learning with R
Practical Machine Learning with R
Introduction
Downloading and installing R
Downloading and installing RStudio
Installing and loading packages
Reading and writing data
Using R to manipulate data
Applying basic statistics
Visualizing data
Getting a dataset for machine learning
2
Data Exploration with RMS Titanic
Data Exploration with RMS Titanic
Introduction
Reading a Titanic dataset from a CSV file
Converting types on character variables
Detecting missing values
Imputing missing values
Exploring and visualizing data
Predicting passenger survival with a decision tree
Validating the power of prediction with a confusion matrix
Assessing performance with the ROC curve
3
R and Statistics
R and Statistics
Introduction
Understanding data sampling in R
Operating a probability distribution in R
Working with univariate descriptive statistics in R
Performing correlations and multivariate analysis
Operating linear regression and multivariate analysis
Conducting an exact binomial test
Performing student's t-test
Performing the Kolmogorov-Smirnov test
Understanding the Wilcoxon Rank Sum and Signed Rank test
Working with Pearson's Chi-squared test
Conducting a one-way ANOVA
Performing a two-way ANOVA
4
Understanding Regression Analysis
Understanding Regression Analysis
Introduction
Fitting a linear regression model with lm
Summarizing linear model fits
Using linear regression to predict unknown values
Generating a diagnostic plot of a fitted model
Fitting a polynomial regression model with lm
Fitting a robust linear regression model with rlm
Studying a case of linear regression on SLID data
Applying the Gaussian model for generalized linear regression
Applying the Poisson model for generalized linear regression
Applying the Binomial model for generalized linear regression
Fitting a generalized additive model to data
Visualizing a generalized additive model
Diagnosing a generalized additive model
5
Classification (I) – Tree, Lazy, and Probabilistic
Classification (I) – Tree, Lazy, and Probabilistic
Introduction
Preparing the training and testing datasets
Building a classification model with recursive partitioning trees
Visualizing a recursive partitioning tree
Measuring the prediction performance of a recursive partitioning tree
Pruning a recursive partitioning tree
Building a classification model with a conditional inference tree
Visualizing a conditional inference tree
Measuring the prediction performance of a conditional inference tree
Classifying data with the k-nearest neighbor classifier
Classifying data with logistic regression
Classifying data with the Naïve Bayes classifier
6
Classification (II) – Neural Network and SVM
Classification (II) – Neural Network and SVM
Introduction
Classifying data with a support vector machine
Choosing the cost of a support vector machine
Visualizing an SVM fit
Predicting labels based on a model trained by a support vector machine
Tuning a support vector machine
Training a neural network with neuralnet
Visualizing a neural network trained by neuralnet
Predicting labels based on a model trained by neuralnet
Training a neural network with nnet
Predicting labels based on a model trained by nnet
7
Model Evaluation
Model Evaluation
Introduction
Estimating model performance with k-fold cross-validation
Performing cross-validation with the e1071 package
Performing cross-validation with the caret package
Ranking the variable importance with the caret package
Ranking the variable importance with the rminer package
Finding highly correlated features with the caret package
Selecting features using the caret package
Measuring the performance of the regression model
Measuring prediction performance with a confusion matrix
Measuring prediction performance using ROCR
Comparing an ROC curve using the caret package
Measuring performance differences between models with the caret package
8
Ensemble Learning
Ensemble Learning
Introduction
Classifying data with the bagging method
Performing cross-validation with the bagging method
Classifying data with the boosting method
Performing cross-validation with the boosting method
Classifying data with gradient boosting
Calculating the margins of a classifier
Calculating the error evolution of the ensemble method
Classifying data with random forest
Estimating the prediction errors of different classifiers
9
Clustering
Clustering
Introduction
Clustering data with hierarchical clustering
Cutting trees into clusters
Clustering data with the k-means method
Drawing a bivariate cluster plot
Comparing clustering methods
Extracting silhouette information from clustering
Obtaining the optimum number of clusters for k-means
Clustering data with the density-based method
Clustering data with the model-based method
Visualizing a dissimilarity matrix
Validating clusters externally
10
Association Analysis and Sequence Mining
Association Analysis and Sequence Mining
Introduction
Transforming data into transactions
Displaying transactions and associations
Mining associations with the Apriori rule
Pruning redundant rules
Visualizing association rules
Mining frequent itemsets with Eclat
Creating transactions with temporal information
Mining frequent sequential patterns with cSPADE
11
Dimension Reduction
Dimension Reduction
Introduction
Performing feature selection with FSelector
Performing dimension reduction with PCA
Determining the number of principal components using the scree test
Determining the number of principal components using the Kaiser method
Visualizing multivariate data using biplot
Performing dimension reduction with MDS
Reducing dimensions with SVD
Compressing images with SVD
Performing nonlinear dimension reduction with ISOMAP
Performing nonlinear dimension reduction with Local Linear Embedding
12
Big Data Analysis (R and Hadoop)
Big Data Analysis (R and Hadoop)
Introduction
Preparing the RHadoop environment
Installing rmr2
Installing rhdfs
Operating HDFS with rhdfs
Implementing a word count problem with RHadoop
Comparing the performance between an R MapReduce program and a standard R program
Testing and debugging the rmr2 program
Installing plyrmr
Manipulating data with plyrmr
Conducting machine learning with RHadoop
Configuring RHadoop clusters on Amazon EMR
Resources for R and Machine Learning
Resources for R and Machine Learning
Dataset – Survival of Passengers on the Titanic
Dataset – Survival of Passengers on the Titanic
Index
Index

Downloading and installing R

To use R, you must first install it on your computer. This recipe gives detailed instructions on how to download and install R.

Getting ready

If you are new to the R language, you can find a detailed introduction, language history, and functionality on the official website (http://www.r-project.org/). When you are ready to download and install R, please access the following link: http://cran.r-project.org/.

How to do it...

Please perform the following steps to download and install R for Windows and Mac users:

  1. Go to the R CRAN website, http://www.r-project.org/, and click on the download R link, that is, http://cran.r-project.org/mirrors.html):

  2. You may select the mirror location closest to you:

    CRAN mirrors

  3. Select the correct download link based on your operating system:

    Click on the download link based on your OS

As the installation of R differs for Windows and Mac, the steps required to install R for each OS are provided here.

For Windows users:

  1. Click on Download R for Windows, as shown in the following screenshot, and then click on base:

    Go to "Download R for Windows" and click "base"

  2. Click on Download R 3.x.x for Windows:

    Click "Download R 3.x.x for Windows"

  3. The installation file should be downloaded. Once the download is finished, you can double-click on the installation file and begin installing R:

  4. The Windows installation of R is quite straightforward; the installation GUI may instruct you on how to install the program step by step (public license, destination location, select components, startup options, startup menu folder, and select additional tasks). Leave all the installation options as the default settings if you do not want to make any changes.

  5. After successfully completing the installation, a shortcut to the R application will appear in your Start menu, which will open the R Console:

    The Windows R Console

For Mac OS X users:

  1. Go to Download R for (Mac) OS X, as shown in this screenshot.

  2. Click on the latest version (.pkg file extension) according to your Mac OS version:

  3. Double-click on the downloaded installation file (.pkg extension) and begin to install R. Leave all the installation options as the default settings if you do not want to make any changes:

  4. Follow the onscreen instructions, Introduction, Read Me, License, Destination Select, Installation Type, Installation, Summary, and click on continue to complete the installation.

  5. After the file is installed, you can use Spotlight Search or go to the application folder to find R:

    Use "Spotlight Search" to find R

  6. Click on R to open R Console:

As an alternative to downloading a Mac .pkg file to install R, Mac users can also install R using Homebrew:

  1. Download XQuartz-2.X.X.dmg from https://xquartz.macosforge.org/landing/.

  2. Double-click on the .dmg file to mount it.

  3. Update brew with the following command line:

    $ brew update

  4. Clone the repository and symlink all its formulae to homebrew/science:

    $ brew tap homebrew/science

  5. Install gfortran:

    $ brew install gfortran

  6. Install R:

    $ brew install R

For Linux users, there are precompiled binaries for Debian, Red Hat, SUSE, and Ubuntu. Alternatively, you can install R from a source code. Besides downloading precompiled binaries, you can install R for Linux through a package manager. Here are the installation steps for CentOS and Ubuntu.

Downloading and installing R on Ubuntu:

  1. Add the entry to the /etc/apt/sources.list file:

    $ sudo sh -c "echo 'deb http:// cran.stat.ucla.edu/bin/linux/ubuntu precise/' >> /etc/apt/sources.list"

  2. Then, update the repository:

    $ sudo apt-get update

  3. Install R with the following command:

    $ sudo apt-get install r-base

  4. Start R in the command line:

    $ R

Downloading and installing R on CentOS 5:

  1. Get rpm CentOS5 RHEL EPEL repository of CentOS5:

    $ wget http://dl.fedoraproject.org/pub/epel/5/x86_64/epel-release-5-4.noarch.rpm

  2. Install CentOS5 RHEL EPEL repository:

    $ sudo rpm -Uvh epel-release-5-4.noarch.rpm

  3. Update the installed packages:

    $ sudo yum update

  4. Install R through the repository:

    $ sudo yum install R

  5. Start R in the command line:

    $ R

Downloading and installing R on CentOS 6:

  1. Get rpm CentOS5 RHEL EPEL repository of CentOS6:

    $ wget http://dl.fedoraproject.org/pub/epel/6/x86_64/epel-release-6-8.noarch.rpm

  2. Install the CentOS5 RHEL EPEL repository:

    $ sudo rpm -Uvh epel-release-6-8.noarch.rpm

  3. Update the installed packages:

    $ sudo yum update

  4. Install R through the repository:

    $ sudo yum install R

  5. Start R in the command line:

    $ R

How it works...

CRAN provides precompiled binaries for Linux, Mac OS X, and Windows. For Mac and Windows users, the installation procedures are straightforward. You can generally follow onscreen instructions to complete the installation. For Linux users, you can use the package manager provided for each platform to install R or build R from the source code.

See also

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