R: Unleash Machine Learning Techniques E-Book

Raghav Bali

• Herausgeber: Packt Publishing
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 1440

Veröffentlichungsjahr: 2016

Beschreibung

Find out how to build smarter machine learning systems with R. Follow this three module course to become a more fluent machine learning practitioner.

About This Book

• Build your confidence with R and find out how to solve a huge range of data-related problems
• Get to grips with some of the most important machine learning techniques being used by data scientists and analysts across industries today
• Don't just learn – apply your knowledge by following featured practical projects covering everything from financial modeling to social media analysis

Who This Book Is For

Aimed for intermediate-to-advanced people (especially data scientist) who are already into the field of data science

What You Will Learn

• Get to grips with R techniques to clean and prepare your data for analysis, and visualize your results
• Implement R machine learning algorithms from scratch and be amazed to see the algorithms in action
• Solve interesting real-world problems using machine learning and R as the journey unfolds
• Write reusable code and build complete machine learning systems from the ground up
• Learn specialized machine learning techniques for text mining, social network data, big data, and more
• Discover the different types of machine learning models and learn which is best to meet your data needs and solve your analysis problems
• Evaluate and improve the performance of machine learning models
• Learn specialized machine learning techniques for text mining, social network data, big data, and more

In Detail

R is the established language of data analysts and statisticians around the world. And you shouldn't be afraid to use it...

This Learning Path will take you through the fundamentals of R and demonstrate how to use the language to solve a diverse range of challenges through machine learning. Accessible yet comprehensive, it provides you with everything you need to become more a more fluent data professional, and more confident with R.

In the first module you'll get to grips with the fundamentals of R. This means you'll be taking a look at some of the details of how the language works, before seeing how to put your knowledge into practice to build some simple machine learning projects that could prove useful for a range of real world problems.

For the following two modules we'll begin to investigate machine learning algorithms in more detail. To build upon the basics, you'll get to work on three different projects that will test your skills. Covering some of the most important algorithms and featuring some of the most popular R packages, they're all focused on solving real problems in different areas, ranging from finance to social media.

This Learning Path has been curated from three Packt products:

• R Machine Learning By Example By Raghav Bali, Dipanjan Sarkar
• Machine Learning with R Learning - Second Edition By Brett Lantz
• Mastering Machine Learning with R By Cory Lesmeister

Style and approach

This is an enticing learning path that starts from the very basics to gradually pick up pace as the story unfolds. Each concept is first defined in the larger context of things succinctly, followed by a detailed explanation of their application. Each topic is explained with the help of a project that solves a real-world problem involving hands-on work thus giving you a deep insight into the world of machine learning.

Leseprobe

Table of Contents

R: Unleash Machine Learning Techniques

R: Unleash Machine Learning Techniques

Credits

Preface

What this learning path covers

What you need for this learning path

Who this learning path is for

Reader feedback

Customer support

Downloading the example code

Errata

Piracy

Questions

I. Module 1

1. Getting Started with R and Machine Learning

Delving into the basics of R

Using R as a scientific calculator

Operating on vectors

Special values

Data structures in R

Vectors

Creating vectors

Indexing and naming vectors

Arrays and matrices

Creating arrays and matrices

Names and dimensions

Matrix operations

Lists

Creating and indexing lists

Combining and converting lists

Data frames

Creating data frames

Operating on data frames

Working with functions

Built-in functions

User-defined functions

Passing functions as arguments

Controlling code flow

Working with if, if-else, and ifelse

Working with switch

Loops

Advanced constructs

lapply and sapply

apply

tapply

mapply

Next steps with R

Getting help

Handling packages

Machine learning basics

Machine learning – what does it really mean?

Machine learning – how is it used in the world?

Types of machine learning algorithms

Supervised machine learning algorithms

Unsupervised machine learning algorithms

Popular machine learning packages in R

Summary

2. Let's Help Machines Learn

Understanding machine learning

Algorithms in machine learning

Perceptron

Families of algorithms

Supervised learning algorithms

Linear regression

K-Nearest Neighbors (KNN)

Collecting and exploring data

Normalizing data

Creating training and test data sets

Learning from data/training the model

Evaluating the model

Unsupervised learning algorithms

Apriori algorithm

K-Means

Summary

3. Predicting Customer Shopping Trends with Market Basket Analysis

Detecting and predicting trends

Market basket analysis

What does market basket analysis actually mean?

Core concepts and definitions

Techniques used for analysis

Making data driven decisions

Evaluating a product contingency matrix

Getting the data

Analyzing and visualizing the data

Global recommendations

Advanced contingency matrices

Frequent itemset generation

Getting started

Data retrieval and transformation

Building an itemset association matrix

Creating a frequent itemsets generation workflow

Detecting shopping trends

Association rule mining

Loading dependencies and data

Exploratory analysis

Detecting and predicting shopping trends

Visualizing association rules

Summary

4. Building a Product Recommendation System

Understanding recommendation systems

Issues with recommendation systems

Collaborative filters

Core concepts and definitions

The collaborative filtering algorithm

Predictions

Recommendations

Similarity

Building a recommender engine

Matrix factorization

Implementation

Result interpretation

Production ready recommender engines

Extract, transform, and analyze

Model preparation and prediction

Model evaluation

Summary

5. Credit Risk Detection and Prediction – Descriptive Analytics

Types of analytics

Our next challenge

What is credit risk?

Getting the data

Data preprocessing

Dealing with missing values

Datatype conversions

Data analysis and transformation

Building analysis utilities

Analyzing the dataset

Saving the transformed dataset

Next steps

Feature sets

Machine learning algorithms

Summary

6. Credit Risk Detection and Prediction – Predictive Analytics

Predictive analytics

How to predict credit risk

Important concepts in predictive modeling

Preparing the data

Building predictive models

Evaluating predictive models

Getting the data

Data preprocessing

Feature selection

Modeling using logistic regression

Modeling using support vector machines

Modeling using decision trees

Modeling using random forests

Modeling using neural networks

Model comparison and selection

Summary

7. Social Media Analysis – Analyzing Twitter Data

Social networks (Twitter)

Data mining @social networks

Mining social network data

Data and visualization

Word clouds

Treemaps

Pixel-oriented maps

Other visualizations

Getting started with Twitter APIs

Overview

Registering the application

Connect/authenticate

Extracting sample tweets

Twitter data mining

Frequent words and associations

Popular devices

Hierarchical clustering

Topic modeling

Challenges with social network data mining

References

Summary

8. Sentiment Analysis of Twitter Data

Understanding Sentiment Analysis

Key concepts of sentiment analysis

Subjectivity

Sentiment polarity

Opinion summarization

Feature extraction

Approaches

Applications

Challenges

Sentiment analysis upon Tweets

Polarity analysis

Classification-based algorithms

Labeled dataset

Support Vector Machines

Ensemble methods

Boosting

Cross-validation

Summary

II. Module 2

1. Introducing Machine Learning

The origins of machine learning

Uses and abuses of machine learning

Machine learning successes

The limits of machine learning

Machine learning ethics

How machines learn

Data storage

Abstraction

Generalization

Evaluation

Machine learning in practice

Types of input data

Types of machine learning algorithms

Matching input data to algorithms

Machine learning with R

Installing R packages

Loading and unloading R packages

Summary

2. Managing and Understanding Data

R data structures

Vectors

Factors

Lists

Data frames

Matrixes and arrays

Managing data with R

Saving, loading, and removing R data structures

Importing and saving data from CSV files

Exploring and understanding data

Exploring the structure of data

Exploring numeric variables

Measuring the central tendency – mean and median

Measuring spread – quartiles and the five-number summary

Visualizing numeric variables – boxplots

Visualizing numeric variables – histograms

Understanding numeric data – uniform and normal distributions

Measuring spread – variance and standard deviation

Exploring categorical variables

Measuring the central tendency – the mode

Exploring relationships between variables

Visualizing relationships – scatterplots

Examining relationships – two-way cross-tabulations

Summary

3. Lazy Learning – Classification Using Nearest Neighbors

Understanding nearest neighbor classification

The k-NN algorithm

Measuring similarity with distance

Choosing an appropriate k

Preparing data for use with k-NN

Why is the k-NN algorithm lazy?

Example – diagnosing breast cancer with the k-NN algorithm

Step 1 – collecting data

Step 2 – exploring and preparing the data

Transformation – normalizing numeric data

Data preparation – creating training and test datasets

Step 3 – training a model on the data

Step 4 – evaluating model performance

Step 5 – improving model performance

Transformation – z-score standardization

Testing alternative values of k

Summary

4. Probabilistic Learning – Classification Using Naive Bayes

Understanding Naive Bayes

Basic concepts of Bayesian methods

Understanding probability

Understanding joint probability

Computing conditional probability with Bayes' theorem

The Naive Bayes algorithm

Classification with Naive Bayes

The Laplace estimator

Using numeric features with Naive Bayes

Example – filtering mobile phone spam with the Naive Bayes algorithm

Step 1 – collecting data

Step 2 – exploring and preparing the data

Data preparation – cleaning and standardizing text data

Data preparation – splitting text documents into words

Data preparation – creating training and test datasets

Visualizing text data – word clouds

Data preparation – creating indicator features for frequent words

Step 3 – training a model on the data

Step 4 – evaluating model performance

Step 5 – improving model performance

Summary

5. Divide and Conquer – Classification Using Decision Trees and Rules

Understanding decision trees

Divide and conquer

The C5.0 decision tree algorithm

Choosing the best split

Pruning the decision tree

Example – identifying risky bank loans using C5.0 decision trees

Step 1 – collecting data

Step 2 – exploring and preparing the data

Data preparation – creating random training and test datasets

Step 3 – training a model on the data

Step 4 – evaluating model performance

Step 5 – improving model performance

Boosting the accuracy of decision trees

Making mistakes more costlier than others

Understanding classification rules

Separate and conquer

The 1R algorithm

The RIPPER algorithm

Rules from decision trees

What makes trees and rules greedy?

Example – identifying poisonous mushrooms with rule learners

Step 1 – collecting data

Step 2 – exploring and preparing the data

Step 3 – training a model on the data

Step 4 – evaluating model performance

Step 5 – improving model performance

Summary

6. Forecasting Numeric Data – Regression Methods

Understanding regression

Simple linear regression

Ordinary least squares estimation

Correlations

Multiple linear regression

Example – predicting medical expenses using linear regression

Step 1 – collecting data

Step 2 – exploring and preparing the data

Exploring relationships among features – the correlation matrix

Visualizing relationships among features – the scatterplot matrix

Step 3 – training a model on the data

Step 4 – evaluating model performance

Step 5 – improving model performance

Model specification – adding non-linear relationships

Transformation – converting a numeric variable to a binary indicator

Model specification – adding interaction effects

Putting it all together – an improved regression model

Understanding regression trees and model trees

Adding regression to trees

Example – estimating the quality of wines with regression trees and model trees

Step 1 – collecting data

Step 2 – exploring and preparing the data

Step 3 – training a model on the data

Visualizing decision trees

Step 4 – evaluating model performance

Measuring performance with the mean absolute error

Step 5 – improving model performance

Summary

7. Black Box Methods – Neural Networks and Support Vector Machines

Understanding neural networks

From biological to artificial neurons

Activation functions

Network topology

The number of layers

The direction of information travel

The number of nodes in each layer

Training neural networks with backpropagation

Example – Modeling the strength of concrete with ANNs

Step 1 – collecting data

Step 2 – exploring and preparing the data

Step 3 – training a model on the data

Step 4 – evaluating model performance

Step 5 – improving model performance

Understanding Support Vector Machines

Classification with hyperplanes

The case of linearly separable data

The case of nonlinearly separable data

Using kernels for non-linear spaces

Example – performing OCR with SVMs

Step 1 – collecting data

Step 2 – exploring and preparing the data

Step 3 – training a model on the data

Step 4 – evaluating model performance

Step 5 – improving model performance

Summary

8. Finding Patterns – Market Basket Analysis Using Association Rules

Understanding association rules

The Apriori algorithm for association rule learning

Measuring rule interest – support and confidence

Building a set of rules with the Apriori principle

Example – identifying frequently purchased groceries with association rules

Step 1 – collecting data

Step 2 – exploring and preparing the data

Data preparation – creating a sparse matrix for transaction data

Visualizing item support – item frequency plots

Visualizing the transaction data – plotting the sparse matrix

Step 3 – training a model on the data

Step 4 – evaluating model performance

Step 5 – improving model performance

Sorting the set of association rules

Taking subsets of association rules

Saving association rules to a file or data frame

Summary

9. Finding Groups of Data – Clustering with k-means

Understanding clustering

Clustering as a machine learning task

The k-means clustering algorithm

Using distance to assign and update clusters

Choosing the appropriate number of clusters

Example – finding teen market segments using k-means clustering

Step 1 – collecting data

Step 2 – exploring and preparing the data

Data preparation – dummy coding missing values

Data preparation – imputing the missing values

Step 3 – training a model on the data

Step 4 – evaluating model performance

Step 5 – improving model performance

Summary

10. Evaluating Model Performance

Measuring performance for classification

Working with classification prediction data in R

A closer look at confusion matrices

Using confusion matrices to measure performance

Beyond accuracy – other measures of performance

The kappa statistic

Sensitivity and specificity

Precision and recall

The F-measure

Visualizing performance trade-offs

ROC curves

Estimating future performance

The holdout method

Cross-validation

Bootstrap sampling

Summary

11. Improving Model Performance

Tuning stock models for better performance

Using caret for automated parameter tuning

Creating a simple tuned model

Customizing the tuning process

Improving model performance with meta-learning

Understanding ensembles

Bagging

Boosting

Random forests

Training random forests

Evaluating random forest performance

Summary

12. Specialized Machine Learning Topics

Working with proprietary files and databases

Reading from and writing to Microsoft Excel, SAS, SPSS, and Stata files

Querying data in SQL databases

Working with online data and services

Downloading the complete text of web pages

Scraping data from web pages

Parsing XML documents

Parsing JSON from web APIs

Working with domain-specific data

Analyzing bioinformatics data

Analyzing and visualizing network data

Improving the performance of R

Managing very large datasets

Generalizing tabular data structures with dplyr

Making data frames faster with data.table

Creating disk-based data frames with ff

Using massive matrices with bigmemory

Learning faster with parallel computing

Measuring execution time

Working in parallel with multicore and snow

Taking advantage of parallel with foreach and doParallel

Parallel cloud computing with MapReduce and Hadoop

GPU computing

Deploying optimized learning algorithms

Building bigger regression models with biglm

Growing bigger and faster random forests with bigrf

Training and evaluating models in parallel with caret

Summary

III. Module 3

1. A Process for Success

The process

Business understanding

Identify the business objective

Assess the situation

Determine the analytical goals

Produce a project plan

Data understanding

Data preparation

Modeling

Evaluation

Deployment

Algorithm flowchart

Summary

2. Linear Regression – The Blocking and Tackling of Machine Learning

Univariate linear regression

Business understanding

Multivariate linear regression

Business understanding

Data understanding and preparation

Modeling and evaluation

Other linear model considerations

Qualitative feature

Interaction term

Summary

3. Logistic Regression and Discriminant Analysis

Classification methods and linear regression

Logistic regression

Business understanding

Data understanding and preparation

Modeling and evaluation

The logistic regression model

Logistic regression with cross-validation

Discriminant analysis overview

Discriminant analysis application

Model selection

Summary

4. Advanced Feature Selection in Linear Models

Regularization in a nutshell

Ridge regression

LASSO

Elastic net

Business case

Business understanding

Data understanding and preparation

Modeling and evaluation

Best subsets

Ridge regression

LASSO

Elastic net

Cross-validation with glmnet

Model selection

Summary

5. More Classification Techniques – K-Nearest Neighbors and Support Vector Machines

K-Nearest Neighbors

Support Vector Machines

Business case

Business understanding

Data understanding and preparation

Modeling and evaluation

KNN modeling

SVM modeling

Model selection

Feature selection for SVMs

Summary

6. Classification and Regression Trees

Introduction

An overview of the techniques

Regression trees

Classification trees

Random forest

Gradient boosting

Business case

Modeling and evaluation

Regression tree

Classification tree

Random forest regression

Random forest classification

Gradient boosting regression

Gradient boosting classification

Model selection

Summary

7. Neural Networks

Neural network

Deep learning, a not-so-deep overview

Business understanding

Data understanding and preparation

Modeling and evaluation

An example of deep learning

H2O background

Data preparation and uploading it to H2O

Create train and test datasets

Modeling

Summary

8. Cluster Analysis

Hierarchical clustering

Distance calculations

K-means clustering

Gower and partitioning around medoids

Gower

PAM

Business understanding

Data understanding and preparation

Modeling and evaluation

Hierarchical clustering

K-means clustering

Clustering with mixed data

Summary

9. Principal Components Analysis

An overview of the principal components

Rotation

Business understanding

Data understanding and preparation

Modeling and evaluation

Component extraction

Orthogonal rotation and interpretation

Creating factor scores from the components

Regression analysis

Summary

10. Market Basket Analysis and Recommendation Engines

An overview of a market basket analysis

Business understanding

Data understanding and preparation

Modeling and evaluation

An overview of a recommendation engine

User-based collaborative filtering

Item-based collaborative filtering

Singular value decomposition and principal components analysis

Business understanding and recommendations

Data understanding, preparation, and recommendations

Modeling, evaluation, and recommendations

Summary

11. Time Series and Causality

Univariate time series analysis

Bivariate regression

Granger causality

Business understanding

Data understanding and preparation

Modeling and evaluation

Univariate time series forecasting

Time series regression

Examining the causality

Summary

12. Text Mining

Text mining framework and methods

Topic models

Other quantitative analyses

Business understanding

Data understanding and preparation

Modeling and evaluation

Word frequency and topic models

Additional quantitative analysis

Summary

A. R Fundamentals

Introduction

Getting R up and running

Using R

Data frames and matrices

Summary stats

Installing and loading the R packages

Summary

A. Bibliography

Index

R: Unleash Machine Learning Techniques

R: Unleash Machine Learning Techniques

Find out how to build smarter machine learning systems with R. Follow this three module course to become a more fluent machine learning practitioner

A course in three modules

BIRMINGHAM - MUMBAI

R: Unleash Machine Learning Techniques

Copyright © 2016 Packt Publishing

All rights reserved. No part of this course may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this course to ensure the accuracy of the information presented. However, the information contained in this course is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this course.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this course by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

Published on: September 2016

Published by Packt Publishing Ltd.

Livery Place

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Birmingham B3 2PB, UK.

ISBN 978-1-78712-734-0

www.packtpub.com

Credits

Authors

Raghav Bali

Dipanjan Sarkar

Brett Lantz

Cory Lesmeister

Reviewers

Alexey Grigorev

Vijayakumar Nattamai Jawaharlal

Kent S. Johnson

Mzabalazo Z. Ngwenya

Anuj Saxena

Vikram Dhillon

Miro Kopecky

Pavan Narayanan

Doug Ortiz

Shivani Rao, PhD

Content Development Editor

Parshva Sheth

Graphics

Abhinash Sahu

Production Coordinator

Melwyn Dsa

Preface

 

"He who defends everything, defends nothing."

  --Frederick the Great

Machine learning is a very broad topic. The following quote sums it up nicely: The first problem facing you is the bewildering variety of learning algorithms available. Which one to use? There are literally thousands available, and hundreds more are published each year. (Domingo, P., 2012.) It would therefore be irresponsible to try and cover everything in the chapters that follow because, to paraphrase Frederick the Great, we would achieve nothing. With this constraint in mind, we hope to provide a solid foundation of algorithms and business considerations that will allow the reader to walk away and, first of all, take on any machine learning tasks with complete confidence, and secondly, be able to help themselves in figuring out other algorithms and topics. Essentially, if this course significantly helps you to help yourself, then I would consider this a victory. Don't think of this course as a destination but rather, as a path to self-discovery.

What this learning path covers

Module 1, R Machine Learning By Example, Data science and machine learning are some of the top buzzwords in the technical world today. From retail stores to Fortune 500 companies, everyone is working hard to make machine learning give them data-driven insights to grow their businesses. With powerful data manipulation features, machine learning packages, and an active developer community, R empowers users to build sophisticated machine learning systems to solve real-world data problems. This module takes you on a data-driven journey that starts with the very basics of R and machine learning and gradually builds upon the concepts to work on projects that tackle real-world problems.

Module 2, Machine Learning with R, Machine learning, at its core, is concerned with the algorithms that transform information into actionable intelligence. This fact makes machine learning well-suited to the present-day era of big data. Without machine learning, it would be nearly impossible to keep up with the massive stream of information. Given the growing prominence of R—a cross-platform, zero-cost statistical programming environment—there has never been a better time to start using machine learning. R offers a powerful but easy-to-learn set of tools that canassist you with finding data insights. By combining hands-on case studies with the essential theory that you need to understand how things work under the hood, this book provides all the knowledge that you will need to start applying machine learning to your own projects.

Module 3 Mastering Machine Learning with R, The world of R can be as bewildering as the world of machine learning! There is seemingly an endless number of R packages with a plethora of blogs, websites, discussions, and papers of various quality and complexity from the community that supports R. This is a great reservoir of information and probably R's greatest strength, but I've always believed that an entity's greatest strength can also be its greatest weakness. R's vast community of knowledge can quickly overwhelm and/or sidetrack you and your efforts. Show me a problem and give me ten different R programmers and I'll show you ten different ways the code is written to solve the problem. As I've written each chapter, I've endeavored to capture the critical elements that can assist you in using R to understand, prepare, and model the data. I am no R programming expert by any stretch of the imagination, but again, I like to think that I can provide a solid foundation herein. Another thing that lit a fire under me to write this book was an incident that happened in the hallways of a former employer a couple of years ago. My team had an IT contractor to support the management of our databases. As we were walking and chatting about big data and the like, he mentioned that he had bought a book about machine learning with R and another about machine learning with Python. He stated that he could do all the programming, but all of the statistics made absolutely no sense to him. I have always kept this conversation at the back of my mind throughout the writing process. It has been a very challenging task to balance the technical and theoretical with the practical. One could, and probably someone has, turned the theory of each chapter to its own book. I used a heuristic of sorts to aid me in deciding whether a formula or technical aspect was in the scope, which was would this help me or the readers in the discussions with team members and business leaders? If I felt it might help, I would strive to provide the necessary details. I also made a conscious effort to keep the datasets used in the practical exercises large enough to be interesting but small enough to allow you to gain insight without becoming overwhelmed.

This book is not about big data, but make no mistake about it, the methods and concepts that we will discuss can be scaled to big data. In short, this module will appeal to a broad group of individuals, from IT experts seeking to understand and interpret machine learning algorithms to statistical gurus desiring to incorporate the power of R into their analysis. However, even those that are well-versed in both IT and statistics—experts if you will—should be able to pick up quite a few tips and tricks to assist them in their efforts.

What you need for this learning path

This software applies to all the chapters of the book:

Windows / Mac OS X / LinuxR 3.2.0 (or higher)RStudio Desktop 0.99 (or higher)

For hardware, there are no specific requirements, since R can run on any PC that has Mac, Linux, or Windows, but a physical memory of minimum 4 GB is preferred to run some of the iterative algorithms smoothly.

Who this learning path is for

Aimed for intermediate-to-advanced people (especially data scientist) who are already into the field of data science

Reader feedback

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Questions

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Part I. Module 1

R Machine Learning By Example

Understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated real-world problems successfully

Chapter 1. Getting Started with R and Machine Learning

This introductory chapter will get you started with the basics of R which include various constructs, useful data structures, loops and vectorization. If you are already an R wizard, you can skim through these sections and dive right into the next part which talks about what machine learning actually represents as a domain and the main areas it encompasses. We will also talk about different machine learning techniques and algorithms used in each area. Finally, we will conclude by looking at some of the most popular machine learning packages in R, some of which we will be using in the subsequent chapters.

If you are a data or machine learning enthusiast, surely you would have heard by now that being a data scientist is referred to as the sexiest job of the 21st century by Harvard Business Review.

Note

Reference: https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century/

There is a huge demand in the current market for data scientists, primarily because their main job is to gather crucial insights and information from both unstructured and structured data to help their business and organization grow strategically.

Some of you might be wondering how machine learning or R relate to all this! Well, to be a successful data scientist, one of the major tools you need in your toolbox is a powerful language capable of performing complex statistical calculations and working with various types of data and building models which help you get previously unknown insights and R is the perfect language for that! Machine learning forms the foundation of the skills you need to build to become a data analyst or data scientist, this includes using various techniques to build models to get insights from data.

This book will provide you with some of the essential tools you need to be well versed with both R and machine learning by not only looking at concepts but also applying those concepts in real-world examples. Enough talk; now let's get started on our journey into the world of machine learning with R!

In this chapter, we will cover the following aspects:

Delving into the basics of RUnderstanding the data structures in RWorking with functionsControlling code flowTaking further steps with RUnderstanding machine learning basicsFamiliarizing yourself with popular machine learning packages in R

Delving into the basics of R

It is assumed here that you are at least familiar with the basics of R or have worked with R before. Hence, we won't be talking much about downloading and installations. There are plenty of resources on the web which provide a lot of information on this. I recommend that you use RStudio which is an Integrated Development Environment (IDE), which is much better than the base R Graphical User Interface (GUI). You can visit https://www.rstudio.com/ to get more information about it.

Note

For details about the R project, you can visit https://www.r-project.org/ to get an overview of the language. Besides this, R has a vast arsenal of wonderful packages at its disposal and you can view everything related to R and its packages at https://cran.r-project.org/ which contains all the archives.

You must already be familiar with the R interactive interpreter, often called a Read-Evaluate-Print Loop (REPL). This interpreter acts like any command line interface which asks for input and starts with a > character, which indicates that R is waiting for your input. If your input spans multiple lines, like when you are writing a function, you will see a + prompt in each subsequent line, which means that you didn't finish typing the complete expression and R is asking you to provide the rest of the expression.

It is also possible for R to read and execute complete files containing commands and functions which are saved in files with an .R extension. Usually, any big application consists of several .R files. Each file has its own role in the application and is often called as a module. We will be exploring some of the main features and capabilities of R in the following sections.

Using R as a scientific calculator

The most basic constructs in R include variables and arithmetic operators which can be used to perform simple mathematical operations like a calculator or even complex statistical calculations.

> 5 + 6[1] 11> 3 * 2[1] 6> 1 / 0[1] Inf

Remember that everything in R is a vector. Even the output results indicated in the previous code snippet. They have a leading [1] symbol indicating it is a vector of size 1.

You can also assign values to variables and operate on them just like any other programming language.

> num <- 6> num ^ 2[1] 36> num[1] 6 # a variable changes value only on re-assignment> num <- num ^ 2 * 5 + 10 / 3> num[1] 183.3333

Operating on vectors

The most basic data structure in R is a vector. Basically, anything in R is a vector, even if it is a single number just like we saw in the earlier example! A vector is basically a sequence or a set of values. We can create vectors using the : operator or the c function which concatenates the values to create a vector.

> x <- 1:5> x[1] 1 2 3 4 5> y <- c(6, 7, 8 ,9, 10)> y[1] 6 7 8 9 10> z <- x + y> z[1] 7 9 11 13 15

You can clearly in the previous code snippet, that we just added two vectors together without using any loop, using just the + operator. This is known as vectorization and we will be discussing more about this later on. Some more operations on vectors are shown next:

> c(1,3,5,7,9) * 2[1] 2 6 10 14 18> c(1,3,5,7,9) * c(2, 4)[1] 2 12 10 28 18 # here the second vector gets recycled

Output:

> factorial(1:5)[1] 1 2 6 24 120> exp(2:10) # exponential function[1] 7.389056 20.085537 54.598150 148.413159 403.428793 1096.633158[7] 2980.957987 8103.083928 22026.465795> cos(c(0, pi/4)) # cosine function[1] 1.0000000 0.7071068> sqrt(c(1, 4, 9, 16))[1] 1 2 3 4> sum(1:10)[1] 55

You might be confused with the second operation where we tried to multiply a smaller vector with a bigger vector but we still got a result! If you look closely, R threw a warning also. What happened in this case is, since the two vectors were not equal in size, the smaller vector in this case c(2, 4) got recycled or repeated to become c(2, 4, 2, 4, 2) and then it got multiplied with the first vector c(1, 3, 5, 7 ,9) to give the final result vector, c(2, 12, 10, 28, 18). The other functions mentioned here are standard functions available in base R along with several other functions.

Tip

Downloading the example code

You can download the example code files for this book from your account at http://www.packtpub.com. If you purchased this book elsewhere, you can visit http://www.packtpub.com/support and register to have the files e-mailed directly to you.

You can download the code files by following these steps:

Log in or register to our website using your e-mail address and password.Hover the mouse pointer on the SUPPORT tab at the topClick on Code Downloads & ErrataEnter the name of the book in the Search boxSelect the book for which you're looking to download the code filesChoose from the drop-down menu where you purchased this book fromClick on Code Download

Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:

WinRAR / 7-Zip for WindowsZipeg / iZip / UnRarX for Mac7-Zip / PeaZip for Linux

Special values

Since you will be dealing with a lot of messy and dirty data in data analysis and machine learning, it is important to remember some of the special values in R so that you don't get too surprised later on if one of them pops up.

> 1 / 0[1] Inf> 0 / 0[1] NaN> Inf / NaN[1] NaN> Inf / Inf[1] NaN> log(Inf)[1] Inf> Inf + NA[1] NA

The main values which should concern you here are Inf which stands for Infinity, NaN which is Not a Number, and NA which indicates a value that is missing or Not Available. The following code snippet shows some logical tests on these special values and their results. Do remember that TRUE and FALSE are logical data type values, similar to other programming languages.

> vec <- c(0, Inf, NaN, NA)> is.finite(vec)[1] TRUE FALSE FALSE FALSE> is.nan(vec)[1] FALSE FALSE TRUE FALSE> is.na(vec)[1] FALSE FALSE TRUE TRUE> is.infinite(vec)[1] FALSE TRUE FALSE FALSE

The functions are pretty self-explanatory from their names. They clearly indicate which values are finite, which are finite and checks for NaN and NA values respectively. Some of these functions are very useful when cleaning dirty data.