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Murach’s R for Data Analysis
List price: $59.50
Teaching professional data analysis skills has never been easier! Murach’s R for Data Analysis covers everything your students need to hit the ground running with R and RStudio, even if they’ve never programmed before. Then, it presents a thorough course in data analysis. And it includes three real-world case studies that tie all the coursework together.
Now available as a Canvas course!
The Canvas course file contains all the objectives, quizzes, assignments, and slides that you need to run an effective course. It only takes a few clicks to import it into the Canvas LMS. Then, you can customize it for your course. Learn more.
Praise for Python for Data Analysis
I really appreciated the case studies. They were a big help for my students as they illustrated all phases of data analysis and visualization.”
J. Jasperson – Texas A&M University
- About this Book
- Table of Contents
- Courseware
- FAQs
- Corrections
What courses this book can be used for
As we see it, this is the best primary text for any course in which the focus is on using R for data analysis. But it is also the ideal supplementary text for a general course on data analysis because it shows how to use R to apply the concepts and statistical methods to real-world data sets.
Why your students will learn faster and better with our book
Like all our books, this book is designed to make it as easy as possible for your students to learn new skills faster and retain them better. Here are a few of those features:
- All of the information is presented in paired pages, with the essential syntax, guidelines, and examples on the right page and clear explanations on the left page. This helps your students learn faster by reading less.
- The paired-pages format is ideal for reference when your students need to refresh their memories about how to do something.
- The three analyses presented in section 3 use real-world data sets.
- The hundreds of short examples present usable code for tasks that your students are likely to need for their own analyses.
- The exercises at the end of each chapter provide a way for your students to gain valuable hands-on experience without any extra busywork.
Book description
To present the essential R and data analysis skills in a manageable progression and at the right pace, this book is divided into 5 sections.
Section 1: Get off to a fast start
This section gets your students off to a fast start. First, they’ll learn how to use RStudio, a popular program for coding in R. Then, they’ll learn the parts of the R language that they’ll need to analyze data. Next, they’ll learn how to use R with the tidyverse package to create their first analysis.
Section 2: The essential skills for descriptive analysis
Most analysis is descriptive analysis, in which you analyze data to better understand it. That’s why section 2 of this book presents the critical descriptive analysis skills that your students need. That includes how to:
- Create powerful visualizations that can guide an analysis
- Get data from CSV files, Excel files, JSON files, and databases
- Clean data by dropping unneeded rows and columns, by using the correct data types, and by finding and fixing missing values and outliers
- Prepare data by adding columns, modifying the data in columns, applying functions and lambda expressions, grouping and aggregating data, and more
- Enhance data visualizations to make them ready for professional presentation
Section 3: The case studies
This section presents three complete analyses that show your students how the skills presented in the first two sections can be applied to real-world data sets:
- Polling data for the 2016 US presidential election
- Wildfire data from the US Forest Service
- Basketball shot data from the NBA (National Basketball Association)
These in-depth analyses make sure that your students master the professional skills they’re going to need.
Section 4: Get started with predictive analysis
Predictive analysis uses statistical models to predict unknown or future values. Although predictive analysis is a large topic that could be an entire course of its own, this section presents the concepts your students need to get started with it. More specifically, it shows your students how to use linear regression models to predict continuous numeric values and how to use classification models to predict categorical values.
Section 5: Presentation skills
Section 5 shows how to present an analysis. To do that, your students can use R Markdown to convert an analysis into an HTML document, PDF file, or PowerPoint slideshow. This is an important skill because the value of an analysis comes from being able to present the insights gained from it to a target audience.
The course prerequisites
The only prerequisite for this book is basic computer literacy. That’s because chapters 1 and 2 present the parts of the R language that your students need to start using R for data analysis. However, it’s helpful for your students to have some background in statistics.
What software your students need
To analyze data with R as shown in this book, your students just need to download and install the RStudio program and the R language. Both are available for free. Then, they can install some R packages for data analysis that are also freely available. For information about how to do this, they can consult appendix A for Windows or appendix B for macOS.
What people say about Murach's Python for Data Analysis
“I really appreciated the four case studies. They were a big help for my students as they illustrated all phases of data analysis and visualization.”
— J. Jasperson – Texas A&M University
“In his first at-bat, Scott McCoy smashes this one out of the park! This book is not just informative, it is exciting.”
— Scott Spurlock, Software Engineer, Georgia
“Unlike some other books on data analysis with Python, the explanations of how to perform data analysis are thorough rather than terse or with no explanations.”
— Posted at an online bookseller
What people say about Murach books
“I really like the paired-pages format of detailed information on the left and quick notes on the right. This helps me to quickly find the information I’m looking for.”
— Roxanne T., Student, Washington
“Another awesome book from Murach. Their format makes learning new material easier, and their code examples WORK.”
— Posted at an online bookseller
“I can’t praise this book highly enough. The clarity used in picking what to include, when to introduce it, and how to do so is remarkable.”
— Charles Ferguson, Software Developer, Australia
“This book is very well-organized and easy to follow. It covers the perfect amount of description, and it does not make you bored by providing unnecessary details.”
— Posted at an online bookseller
“You folks make the hard stuff seem easy.”
— Thomas Finn, Sr. Software Developer, Illinois
“This is my first exposure to Murach’s books, and I love them. I like the organization of the content, the consistent approach in each book, and the accuracy of the material.”
— Bob L., Michigan
“Another thing I like is the exercises at the end of each chapter. They’re a great way to reinforce the main points of each chapter and force you to get your hands dirty.”
— Hien Luu, SD Forum/Java SIG
“Your book was indispensable to me. The answers were right there at every turn. All the examples made sense, and they all worked!”
— Alan Vogt, ETL Consultant, Massachusetts
“Your books shine out from the rest—the quality of writing and presentation of information is topnotch, and the consistency of quality across books is impressive.”
— Nolan Tamashiro, Developer
View the table of contents for this book in a PDF: Table of Contents (PDF)
Click on any chapter title to display or hide its content.
Section 1 Get started fast
Chapter 1 Introduction to RStudio and R
Introduction to data analysis
What data analysis is
The five phases of data analysis
How to get started with RStudio
Introduction to RStudio
How to run code in the Console pane
How to run code in the Source pane
How to view variables in the Environment pane
How to get started with R
How to create variables
How to work with variables
How to code arithmetic expressions
How to use arithmetic expressions in statements
How to interpret error messages
Chapter 2 More skills for working with R
How to use functions
How to call functions
How to use functions to work with strings
How to use functions to work with numbers
How to work with data structures
How to work with vectors
How to work with data frames
How to work with lists
How to add values to data structures
How to code Boolean expressions
How to use the relational operators
How to use the logical operators
How to work with control structures
How to code if statements
How to code nested if statements
How to code for loops
How to define functions
Chapter 3 How to code your first analysis
An introduction to the analysis
The child mortality data
How to set the working directory
How to work with packages
How to get and examine the data
How to read the data into a tibble
How to select the top and bottom rows
How to view summary statistics
How to prepare and analyze the data
How to melt the data
How to add, modify, and rename columns
How to save a tibble as an RDS file
How to calculate summary columns
How to create a line plot
Section 2 The essential skills for data analysis
Chapter 4 How to visualize data
How to get some data to plot
How to use the datasets package
How to get the irises data
How to get the chicks data
How to select rows based on a condition
An introduction to ggplot2
How to create a base plot
Functions for common plot types
How to create relational plots
How to create a line plot
How to create a scatter plot
How to create categorical plots
How to create a bar plot
How to create a box plot
How to create distribution plots
How to create a histogram
How to create a KDE plot
How to create an ECDF plot
How to create a 2D KDE plot
More skills for working with plots
How to combine plots
How to create a grid of plots
How to view documentation
How to save a plot
Chapter 5 How to get data
Basic skills for getting data
How to find the data you want
How to read data from CSV and Excel files
How to download data
How to work with a zip file
How to read data from a database
How to connect to a database
How to list the tables in a database
How to list the columns of a table
How to code a query
How to use a query to read data
How to read data from a JSON file
How to read a JSON file into a list
How to get the index for a list
How to get data from a list
How to build a tibble from the data in a list
Chapter 6 How to clean data
Introduction to cleaning data
A general plan for cleaning data
How to display column names and data types
How to examine the unique values for a single column
How to display the unique values for all columns
How to count the unique values for all columns
How to display the value counts
How to sort the data
How to simplify a data set
How to filter and drop rows
How to drop columns
How to rename columns
How to work with missing values
How to find missing values
How to fix missing values
How to work with data types
How to select columns by data type
How to convert strings to numbers
How to convert strings to dates and times
How to work with the factor type
How to work with outliers
How to assess outliers
How to calculate quartiles and quantiles
How to calculate the fences for the box plot
How to fix the outliers
Chapter 7 How to prepare data
How to add and modify columns
How to work with date columns
How to use stringr to work with strings
How to work with string and numeric columns
How to group and bin data
How to use statistical functions
How to summarize data
How to group and summarize data
Another way to group and summarize data
How to rank rows
How to add a cumulative sum
How to bin data
How to apply functions and lambda expressions
How to define functions that operate on rows
How to define functions that operate on columns
How to use lambda expressions instead of functions
How to reshape tibbles
How to add columns by joining tibbles
How to add rows
Chapter 8 More skills for data visualization
More skills for working with plots
Get the data
More skills for working with scatter plots
More skills for working with bar plots
How to add an error bar to a bar plot
More skills for working with line plots
How to create a smooth line plot
How to add labels to plots
How to work with shapes
How to plot shapes
How to plot a baseball field
How to return plot components from a function
How to plot hits on a baseball field
How to work with maps
How to plot maps
How to add data to a map
How to tune plots
How to zoom in on part of a plot
How to adjust the limits of a plot
How to work with the plot title and axes labels
How to change the position of the legend
How to edit the legend
How to hide the text and ticks for each axis
How to set the colors for the plot
How to change the theme of the plot
How to work with a grid of plots
How to create a pairwise grid of scatter plots
How to use other plot types in the grid
Section 3 Three case studies
Chapter 9 The Polling analysis
Get and examine the data
Load the packages
Get the data
Examine the data
Clean the data
Select and rename the columns
Sort the rows
Select the rows
Improve some columns
Prepare the data
Add columns
Pivot the data
Analyze the data
Plot the national polls
Plot the polls for swing states
Analyze the polls by voter type
More preparation and analysis
Plot the gap for the last week of the election
Plot the weekly gap over time
Chapter 10 The Wildfires analysis
Get the data
Load the packages for this analysis
Unzip the database file
Read the data from the database
Clean the data
Improve column names and data types
Drop duplicate rows
Select rows for large fires
Examine NA values
Prepare the data
Add, modify, and select columns
Sort the rows
Analyze the data
Plot the largest fire per year in California
Plot the mean and median acres burned in California
Plot the fires per month in California
Plot the total acres burned for the top 10 states
Plot the acres burned per year for the top 4 states
Plot the data on a map
Plot the 20 largest fires in California
Plot all fires in California larger than 500 acres
Plot all fires in the U.S. larger than 100,000 acres
Chapter 11 The Basketball Shots analysis
Get the data
Load the packages
Read the data
Build the tibble
Clean the data
Examine the unique values
Select and rename the columns
Improve the data types for two columns
Prepare the data
Add a Season column
Add a Points column
Add some summary columns
Analyze the shot statistics
Plot shots made per game by season
Plot shots attempted vs. made per game
Plot shots made per game for all seasons
Plot shot statistics by season
Plot shooting percentages per season
Analyze the shot locations
Plot shot locations for two games
Define a function for drawing the court
Plot shot locations for two games on a court
Plot shots by zone for one season
Plot shot count by zone
Plot shooting percentage by zone
Plot shot density
Compare shot locations and density for two seasons
Section 4 An introduction to data modeling
Chapter 12 How to work with simple regression models
Introduction to predictive analysis
Types of predictive models
Introduction to regression analysis
The diamonds data set
How to get the data
How to examine and clean the data
How to find correlations
How to interpret correlation coefficients
How to identify correlations with r-values
How to identify correlations visually
How to create a model that uses a straight line
A procedure for working with a regression model
How to split the data
How to drop outliers from the training data set
How to create a model
How to use a model to make predictions
How to work with formulas
How to plot an equation
How to plot an equation on a scatter plot
How to code formulas
How to plot a formula on a scatter plot
How to create a model for a curved line
Chapter 13 How to work with multiple regression models
How to work with multiple variables
How to create and fit the model
How to judge the model by its R2 value
How to judge the model by its residuals
How to work with variable interactions
More formula operators
How to create and fit the model
How to view the model’s terms
How to remove insignificant terms
How to plot regression coefficients
How to work with nonlinear patterns
Five common nonlinear patterns
How to transform variables
How to create, fit, and judge the model
How to work with ordinal variables
How to examine ordinal variables
How to create, fit, and judge the model
Chapter 14 How to work with classification models
Introduction to classification models
Introduction to classification analysis
How to get the data for this chapter
How to visually investigate the data
How to create and judge a classification model
How to create a decision tree
How to plot a decision tree
How to judge a model with a confusion matrix
How to tune a classification model
How to use variable importance to select variables
How to adjust the hyperparameters
How to compare decision trees
How to cross validate a model
How to tune hyperparameters with a grid search
Section 5 Presentation skills
Chapter 15 How to use R Markdown to present an analysis
How to get started with R Markdown files
How to create an R Markdown file
How to render an R Markdown file
How to work with R Markdown documents
How to code the YAML header
How to add headings and paragraphs
How to add chunks of code
How to run chunks of code
How to format text
How to create dynamic documents
How to specify multiple output formats
The Wildfires document
The HTML document displayed in a browser
The PDF and Word documents for the same markdown
The R Markdown
How to work with R Markdown presentations
How to start a presentation
The first two slides of a presentation
Appendixes
Appendix A How to set up Windows for this book
How to install R
How to install RStudio
How to install the files for this book
How to install the packages for this book
Appendix B How to set up macOS for this book
How to install R
How to install RStudio
How to install the files for this book
How to install the packages for this book
To learn about the supporting courseware that we provide for our books, please visit About our Courseware.
End-of-chapter resources
- Summary bullets
- Step-by-step exercises
Student download
This download includes files for:
- The R scripts for all examples and analyses presented in this book
- The starting points for the exercises presented at the end of each chapter
- The solutions to those exercises
- The data for all of the examples, analyses, and exercises
Appendixes A and B show how to install these files on Windows and macOS.
Instructor’s materials
- Instructional objectives by chapter
- Test banks in multiple formats
- Student projects that can be assigned at the end of certain chapters
- Case studies that can be assigned at the end of each section or as a final assignment
- Accessible PowerPoint slides
For a more detailed description of the courseware for this book, please read the Instructor’s Summary.
On this page, we’ll be posting answers to the questions that come up most often about this book. So if you have any questions that you haven’t found answered here at our site, please email us. Thanks!
To view the corrections for this book in a PDF, just click on this link: View the corrections
Then, if you find any other errors, please email us so we can correct them in the next printing of the book. Thank you!
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