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Machine Learning with R
Machine Learning with R

Machine Learning with R: R gives you access to the cutting-edge software you need to prepare data for machine learning. No previous knowledge required – this book will take you methodically through every stage of applying machine learning.

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Machine Learning with R

Chapter 2. Managing and Understanding Data

A key early component of any machine learning project involves managing and understanding the data you have collected. Although you may not find it as gratifying as building and deploying models—the stages in which you begin to see the fruits of your labor—you cannot ignore the preparatory work.

Any learning algorithm is only as good as its input data, and in many cases, input data is complex, messy, and spread across multiple sources and formats. Because of this complexity, the largest portion of effort invested in machine learning projects is spent on the data preparation and exploration process.

This chapter is divided into three main sections. The first section discusses the basic data structures R uses to store data. You will become very familiar with these structures as you create and manipulate datasets. The second section is practical, as it covers several functions that are useful for getting data in and out of R. In the third section, methods...

R data structures


There are numerous types of data structures across programming languages, each with strengths and weaknesses specific to particular tasks. Since R is a programming language used widely for statistical data analysis, the data structures it utilizes are designed to make it easy to manipulate data for this type of work. The R data structures used most frequently in machine learning are vectors, factors, lists, arrays, and data frames. Each of these data types is specialized for a specific data management task, which makes it important to understand how they will interact in your R project.

Vectors


The fundamental R data structure is the vector, which stores an ordered set of values called elements. A vector can contain any number of elements. However, all the elements must be of the same type; for instance, a vector cannot contain both numbers and text.

There are several vector types commonly used in machine learning: integer (numbers without decimals), numeric (numbers with decimals), character (text data), or logical (TRUE or FALSE values). There are also two special values: NULL, which is used to indicate the absence of any value, and NA, which indicates a missing value.

It is tedious to enter large amounts of data manually, but simple vectors can be created by using the combine function c(). The vector can also be given a name using the arrow <- operator, which is R's assignment operator, used in a similar way to the = assignment operator in many other programming languages.

For example, let's construct a set of vectors containing data on three medical patients. We'll...

Factors


If you recall from Chapter 1, Introducing Machine Learning, features that represent a characteristic with categories of values are known as nominal. Although it is possible to use a character vector to store nominal data, R provides a data structure known as a factor specifically for this purpose. A factor is a special case of vector that is solely used for representing nominal variables. In the medical dataset we are building, we might use a factor to represent gender, because it uses two categories: MALE and FEMALE.

Why not use character vectors? An advantage of using factors is that they are generally more efficient than character vectors because the category labels are stored only once. Rather than storing MALE, MALE, FEMALE, the computer may store 1, 1, 2. This can save memory. Additionally, certain machine learning algorithms use special routines to handle categorical variables. Coding categorical variables as factors ensures that the model will treat this data appropriately...

Managing data with R


One of the challenges faced when working with massive datasets involves gathering, preparing, and otherwise managing data from a variety of sources. This task is facilitated by R's tools for loading data from many common formats.

Saving and loading R data structures

When you have spent a lot of time getting a particular data frame into the format that you want, you shouldn't need to recreate your work each time you restart your R session. To save a particular data structure to a file that can be reloaded later or transferred to another system, you can use the save() function. The save() function writes R data structures to the location specified by the file parameter. R data files have the file extension .RData.

If we had three objects named x, y, and z, we could save them to a file mydata.RData using the following command:

> save(x, y, z, file = "mydata.RData")

Regardless of whether x, y, and z are vectors, factors, lists, or data frames, they will be saved to the file...

Exploring and understanding data


After collecting data and loading it into R data structures, the next step in the machine learning process involves examining the data in detail. It is during this step that you will begin to explore the data's features and examples, and realize the peculiarities that make your data unique. The better you understand your data, the better you will be able to match a machine learning model to your learning problem.

The best way to understand the process of data exploration is by example. In this section, we will explore the usedcars.csv dataset, which contains actual data about used cars recently advertised for sale on a popular U.S. website.

Tip

The usedcars.csv dataset is available for download on Packt's website. If you are following along with the examples, be sure that this file has been downloaded and saved to your R working directory.

Since the dataset is stored in CSV form, we can use the read.csv() function to load the data into an R data frame:

usedcars...

Summary


In this chapter, we learned about the basics of managing data in R. We started by taking an in-depth look at the structures used for storing various types of data. The foundational R data structure is the vector, which is extended and combined into more complex data types such as lists and data frames. The data frame is an R data structure that corresponds to the notion of a dataset, having both features and examples.

We also learned about how to get data into R from a variety of sources. R provides functions for reading from and saving to CSV files; SQL databases can be queried with the RODBC package.

Finally, we applied these skills while exploring a real-world dataset containing data on used car prices. We examined numeric variables using common summary statistics of center and spread, and visualized relationships between prices and odometer readings with a scatterplot. We examined nominal variables using tables. In examining the used car data, we followed an exploratory process...

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Key benefits

  • Harness the power of R for statistical computing and data science
  • Use R to apply common machine learning algorithms with real-world applications
  • Prepare, examine, and visualize data for analysis
  • Understand how to choose between machine learning models
  • Packed with clear instructions to explore, forecast, and classify data

Description

Machine learning, at its core, is concerned with transforming data into actionable knowledge. This fact makes machine learning well-suited to the present-day era of "big data" and "data science". Given the growing prominence of R—a cross-platform, zero-cost statistical programming environment—there has never been a better time to start applying machine learning. Whether you are new to data science or a veteran, machine learning with R offers a powerful set of methods for quickly and easily gaining insight from your data. "Machine Learning with R" is a practical tutorial that uses hands-on examples to step through real-world application of machine learning. Without shying away from the technical details, we will explore Machine Learning with R using clear and practical examples. Well-suited to machine learning beginners or those with experience. Explore R to find the answer to all of your questions. How can we use machine learning to transform data into action? Using practical examples, we will explore how to prepare data for analysis, choose a machine learning method, and measure the success of the process. We will learn how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering. By applying the most effective machine learning methods to real-world problems, you will gain hands-on experience that will transform the way you think about data. "Machine Learning with R" will provide you with the analytical tools you need to quickly gain insight from complex data.

Who is this book for?

Intended for those who want to learn how to use R's machine learning capabilities and gain insight from your data. Perhaps you already know a bit about machine learning, but have never used R; or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required.

What you will learn

  • Understand the basic terminology of machine learning and how to differentiate among various machine learning approaches
  • Use R to prepare data for machine learning
  • Explore and visualize data with R
  • Classify data using nearest neighbor methods
  • Learn about Bayesian methods for classifying data
  • Predict values using decision trees, rules, and support vector machines
  • Forecast numeric values using linear regression
  • Model data using neural networks
  • Find patterns in data using association rules for market basket analysis
  • Group data into clusters for segmentation
  • Evaluate and improve the performance of machine learning models
  • Learn specialized machine learning techniques for text mining, social network data, and ‚Äúbig‚Äù data
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Table of Contents

12 Chapters
Introducing Machine Learning Chevron down icon Chevron up icon
Managing and Understanding Data Chevron down icon Chevron up icon
Lazy Learning – Classification Using Nearest Neighbors Chevron down icon Chevron up icon
Probabilistic Learning – Classification Using Naive Bayes Chevron down icon Chevron up icon
Divide and Conquer – Classification Using Decision Trees and Rules Chevron down icon Chevron up icon
Forecasting Numeric Data – Regression Methods Chevron down icon Chevron up icon
Black Box Methods – Neural Networks and Support Vector Machines Chevron down icon Chevron up icon
Finding Patterns – Market Basket Analysis Using Association Rules Chevron down icon Chevron up icon
Finding Groups of Data – Clustering with k-means Chevron down icon Chevron up icon
Evaluating Model Performance Chevron down icon Chevron up icon
Improving Model Performance Chevron down icon Chevron up icon
Specialized Machine Learning Topics Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.4
(73 Ratings)
5 star 71.2%
4 star 17.8%
3 star 1.4%
2 star 2.7%
1 star 6.8%
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Leif C. Ulstrup Nov 16, 2014
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I have a large collection of books on programming, R, and machine learning and I am constantly looking for new material on state of the art practices related to data science. I think this is one of the best in terms of readability, straightforward and practical examples that demonstrate the key concepts in real-world terms, and up-to-date information about the use of advanced R packages for parallel processing and very large datasets. Unlike many other books on the subject, author Brett Lantz presents the material in a crisp and clear manner and does an excellent job integrating the machine learning concepts, underlying statistical foundations, R programming constructs, and practical examples. I think this will be a constant reference in my work. I look forward to future publications from this author and hope he has a blog or other means to keep up with his ideas and insights.
Amazon Verified review Amazon
Leon Shernoff Jul 10, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
A well-designed book which is organized as a tour of different kinds of machine learning. The order in which the learners are treated is a little unusual, but it does a good job of bringing up the more abstract issues in a way that makes them easy to understand.
Amazon Verified review Amazon
Amazon Customer Feb 16, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Really good application in R for beginners. You can get understand it even if you know nothing about ML or R language. But if you want to get more detailed information about the methods, like algorithms etc. This is not a good choice.
Amazon Verified review Amazon
Kurt J. Aug 19, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I really liked this book. Very well written and instructive. I had a decent knowledge of R and got A's in classes at college, but I learned more about it with this book. It taught me a good bit about the topic, all the things I really wanted to know that weren't covered in my statistics master's program. I took a week off work and covered it by spending about 2-4 hours a day. I feel like I added a skill. I'm ready to go back and see where I can apply supervised and unsupervised learning algorithms to my job.
Amazon Verified review Amazon
John L. Whiteman Sep 14, 2014
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I would recommend this book for those who are new to machine learning and want to learn R. Each chapter starts with a core machine learning concept that Lantz presents in a very readable manner. The exercises that follow put these concepts into practice with just the right amount of R. I am currently enrolled in a machine learning course at a university. As students we could choose any language, so I decided to go with R. This book has helped me to feel good about my decision.
Amazon Verified review Amazon
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