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

Using R to manipulate data

This recipe will discuss how to use the built-in R functions to manipulate data. As data manipulation is the most time consuming part of most analysis procedures, you should gain knowledge of how to apply these functions on data.

Getting ready

Ensure you have completed the previous recipes by installing R on your operating system.

How to do it...

Perform the following steps to manipulate the data with R.

Subset the data using the bracelet notation:

  1. Load the dataset iris into the R session:

    > data(iris)

  2. To select values, you may use a bracket notation that designates the indices of the dataset. The first index is for the rows and the second for the columns:

    > iris[1,"Sepal.Length"]
[1] 5.1

  3. You can also select multiple columns using c():

    > Sepal.iris = iris[, c("Sepal.Length", "Sepal.Width")]

  4. You can then use str() to summarize and display the internal structure of Sepal.iris:

    > str(Sepal.iris)
'data.frame':  150 obs. of  2 variables:
 $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
 $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ..

  5. To subset data with the rows of given indices, you can specify the indices at the first index with the bracket notation. In this example, we show you how to subset data with the top five records with the Sepal.Length column and the Sepal.Width selected:

    > Five.Sepal.iris = iris[1:5, c("Sepal.Length", "Sepal.Width")]
> str(Five.Sepal.iris)
'data.frame':	5 obs. of  2 variables:
 $ Sepal.Length: num  5.1 4.9 4.7 4.6 5
 $ Sepal.Width : num  3.5 3 3.2 3.1 3.6

  6. It is also possible to set conditions to filter the data. For example, to filter returned records containing the setosa data with all five variables. In the following example, the first index specifies the returning criteria, and the second index specifies the range of indices of the variable returned:

    > setosa.data = iris[iris$Species=="setosa",1:5]
> str(setosa.data)
'data.frame':	50 obs. of  5 variables:
 $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
 $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
 $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
 $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
 $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

  7. Alternatively, the which function returns the indexes of satisfied data. The following example returns indices of the iris data containing species equal to setosa:

    > which(iris$Species=="setosa")
 [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18
[19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
[37] 37 38 39 40 41 42 43 44 45 46 47 48 49 50

  8. The indices returned by the operation can then be applied as the index to select the iris containing the setosa species. The following example returns the setosa with all five variables:

    > setosa.data = iris[which(iris$Species=="setosa"),1:5]
> str(setosa.data)
'data.frame':	50 obs. of  5 variables:
 $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
 $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
 $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
 $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
 $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

Subset data using the subset function:

  1. Besides using the bracket notation, R provides a subset function that enables users to subset the data frame by observations with a logical statement.

  2. First, subset species, sepal length, and sepal width out of the iris data. To select the sepal length and width out of the iris data, one should specify the column to be subset in the select argument:

    > Sepal.data = subset(iris, select=c("Sepal.Length", "Sepal.Width"))
> str(Sepal.data)
'data.frame': 150 obs. of  2 variables:
 $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
 $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...

This reveals that Sepal.data contains 150 objects with the Sepal.Length variable and Sepal.Width.

  1. On the other hand, you can use a subset argument to get subset data containing setosa only. In the second argument of the subset function, you can specify the subset criteria:

    > setosa.data = subset(iris, Species =="setosa")
> str(setosa.data)
'data.frame': 50 obs. of  5 variables:
 $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
 $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
 $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
 $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
 $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

  2. Most of the time, you may want to apply a union or intersect a condition while subsetting data. The OR and AND operations can be further employed for this purpose. For example, if you would like to retrieve data with Petal.Width >=0.2 and Petal.Length < = 1.4:

    > example.data= subset(iris, Petal.Length <=1.4 & Petal.Width >= 0.2, select=Species )
> str(example.data)
'data.frame': 21 obs. of  1 variable:
 $ Species: Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

Merging data: merging data involves joining two data frames into a merged data frame by a common column or row name. The following example shows how to merge the flower.type data frame and the first three rows of the iris with a common row name within the Species column:

> flower.type = data.frame(Species = "setosa", Flower = "iris")
> merge(flower.type, iris[1:3,], by ="Species")
  Species Flower Sepal.Length Sepal.Width Petal.Length Petal.Width
1  setosa   iris          5.1         3.5          1.4         0.2
2  setosa   iris          4.9         3.0          1.4         0.2
3  setosa   iris          4.7         3.2          1.3         0.2

Ordering data: the order function will return the index of a sorted data frame with a specified column. The following example shows the results from the first six records with the sepal length ordered (from big to small) iris data

> head(iris[order(iris$Sepal.Length, decreasing = TRUE),])
    Sepal.Length Sepal.Width Petal.Length Petal.Width   Species
132          7.9         3.8          6.4         2.0 virginica
118          7.7         3.8          6.7         2.2 virginica
119          7.7         2.6          6.9         2.3 virginica
123          7.7         2.8          6.7         2.0 virginica
136          7.7         3.0          6.1         2.3 virginica
106          7.6         3.0          6.6         2.1 virginica

How it works

Before conducting data analysis, it is important to organize collected data into a structured format. Therefore, we can simply use the R data frame to subset, merge, and order a dataset. This recipe first introduces two methods to subset data: one uses the bracket notation, while the other uses the subset function. You can use both methods to generate the subset data by selecting columns and filtering data with the given criteria. The recipe then introduces the merge function to merge data frames. Last, the recipe introduces how to use order to sort the data.

There's more...

The sub and gsub functions allow using regular expression to substitute a string. The sub and gsub functions perform the replacement of the first and all the other matches, respectively:

> sub("e", "q", names(iris))
[1] "Sqpal.Length" "Sqpal.Width"  "Pqtal.Length" "Pqtal.Width"  "Spqcies"     
> gsub("e", "q", names(iris))
[1] "Sqpal.Lqngth" "Sqpal.Width"  "Pqtal.Lqngth" "Pqtal.Width"  "Spqciqs"
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