An Introduction to R and Python for Data Analysis: A Side-By-Side Approach

Length: 249 pages
Edition: 1
Language: English
Publisher: Chapman and Hall/CRC
Publication Date: 2023-06-28
ISBN-10: 1032203250
ISBN-13: 9781032203256
Sales Rank: #316863 (See Top 100 Books)

An Introduction to R and Python for Data Analysis helps teach students to code in both R and Python simultaneously. As both R and Python can be used in similar manners, it is useful and efficient to learn both at the same time, helping lecturers and students to teach and learn more, save time, whilst reinforcing the shared concepts and differences of the systems. This tandem learning is highly useful for students, helping them to become literate in both languages, and develop skills which will be handy after their studies. This book presumes no prior experience with computing, and is intended to be used by students from a variety of backgrounds. The side-by-side formatting of this book helps introductory graduate students quickly grasp the basics of R and Python, with the exercises providing helping them to teach themselves the skills they will need upon the completion of their course, as employers now ask for competency in both R and Python. Teachers and lecturers will also find this book useful in their teaching, providing a singular work to help ensure their students are well trained in both computer languages. All data for exercises can be found here: https://github.com/tbrown122387/r_and_python_book/tree/master/data. Instructors can access the solutions manual via the book’s website.

Key features:

– Teaches R and Python in a “side-by-side” way.

– Examples are tailored to aspiring data scientists and statisticians, not software engineers.

– Designed for introductory graduate students.

– Does not assume any mathematical background.

Cover
Half Title
Title Page
Copyright Page
Dedication
Contents
List of Figures
Welcome
Preface
I. Introducing the Basics
	1. Introduction
		1.1. Hello World in R
		1.2. Hello World in Python
		1.3. Getting Help
			1.3.1. Reading Documentation
			1.3.2. Understanding File Paths
	2. Basic Types
		2.1. Basic Types in Python
			2.1.1. Type Conversions in Python
		2.2. Basic Types in R
			2.2.1. Type Conversions in R
			2.2.2. R’s Simplification
		2.3. Exercises
			2.3.1. R Questions
			2.3.2. Python Questions
	3. R Vectors versus Numpy Arrays and Pandas’ Series
		3.1. Overview of R
		3.2. Overview of Python
		3.3. Vectorization in R
		3.4. Vectorization in Python
		3.5. Indexing Vectors in R
		3.6. Indexing Numpy arrays
		3.7. Indexing Pandas’ Series
		3.8. Some Gotchas
			3.8.1. Shallow versus Deep Copies
			3.8.2. How R and Python Handle Missing Values
		3.9. An Introduction to Regular Expressions
			3.9.1. Literal Characters versus Metacharacters
			3.9.2. The Trouble with Backslashes: Escape Sequences
			3.9.3. More Examples of Using Regular Expressions
		3.10. Exercises
			3.10.1. R Questions
			3.10.2. Python Questions
	4. Numpy ndarrays versus R’s Matrix and Array Types
		4.1. Numpy ndarrays in Python
		4.2. The Matrix and Array Classes in R
		4.3. Exercises
			4.3.1. R Questions
			4.3.2. Python Questions
	5. R’s lists versus Python’s lists and dicts
		5.1. lists in R
		5.2. lists in Python
		5.3. Dictionaries in Python
		5.4. Exercises
			5.4.1. R Questions
			5.4.2. Python Questions
	6. Functions
		6.1. Defining R Functions
		6.2. Defining Python Functions
		6.3. More Details on R’s User-Defined Functions
		6.4. More Details on Python’s User-Defined Functions
		6.5. Function Scope in R
		6.6. Function Scope in Python
		6.7. Modifying a Function’s Arguments
			6.7.1. Passing by Value in R
			6.7.2. Passing by Assignment in Python
		6.8. Accessing and Modifying Captured Variables
			6.8.1. Accessing Captured Variables in R
			6.8.2. Accessing Captured Variables in Python
			6.8.3. Modifying Captured Variables in R
			6.8.4. Modifying Captured Variables in Python
		6.9. Exercises
			6.9.1. R Questions
			6.9.2. Python Questions
	7. Categorical Data
		7.1. factors in R
		7.2. Two Options for Categorical Data in Pandas
		7.3. Exercises
			7.3.1. R Questions
			7.3.2. Python Questions
	8. Data Frames
		8.1. Data Frames in R
		8.2. Data Frames in Python
		8.3. Exercises
			8.3.1. R Questions
			8.3.2. Python Questions
II. Common Tasks and Patterns
	9. Input and Output
		9.1. General Input Considerations
		9.2. Reading in Text Files with R
		9.3. Reading in Text Files with Pandas
		9.4. Saving Data in R
			9.4.1. Writing Out Tabular Plain Text Data in R
			9.4.2. Serialization in R
		9.5. Saving Data in Python
			9.5.1. Writing Out Tabular Plain Text Data in Python
			9.5.2. Serialization in Python
		9.6. Exercises
			9.6.1. R Questions
			9.6.2. Python Questions
	10. Using Third-Party Code
		10.1. Installing Packages in R
		10.2. Installing Packages in Python
		10.3. Loading Packages in R
		10.4. Loading Packages in Python
			10.4.1. Importing Examples
		10.5. Exercises
	11. Control Flow
		11.1. Conditional Logic
		11.2. Loops
		11.3. Exercises
			11.3.1. R Questions
			11.3.2. Python Questions
	12. Reshaping and Combining Data Sets
		12.1. Ordering and Sorting Data
		12.2. Stacking Data Sets and Placing Them Shoulder to Shoulder
		12.3. Merging or Joining Data Sets
		12.4. Long versus Wide Data
			12.4.1. Long versus Wide in R
			12.4.2. Long versus Wide in Python
		12.5. Exercises
			12.5.1. R Questions
			12.5.2. Python Questions
	13. Visualization
		13.1. Base R Plotting
		13.2. Plotting with ggplot2
		13.3. Plotting with Matplotlib
		13.4. Plotting with Pandas
		13.5. Exercises
			13.5.1. R Questions
			13.5.2. Python Questions
III. Programming Styles
	14. An Introduction to Object-Oriented Programming
		14.1. OOP in Python
			14.1.1. Overview
			14.1.2. A First Example
			14.1.3. Adding Inheritance
			14.1.4. Adding in Composition
		14.2. OOP in R
			14.2.1. S3 Objects: The Big Picture
			14.2.2. Using S3 Objects
			14.2.3. Creating S3 Objects
			14.2.4. S4 Objects: The Big Picture
			14.2.5. Using S4 Objects
			14.2.6. Creating S4 Objects
			14.2.7. Reference Classes: The Big Picture
			14.2.8. Creating Reference Classes
			14.2.9. Creating R6 Classes
		14.3. Exercises
			14.3.1. Python Questions
			14.3.2. R Questions
	15. An Introduction to Functional Programming
		15.1. Functions as Function Inputs in R
			15.1.1. sapply() and vapply()
			15.1.2. lapply()
			15.1.3. apply()
			15.1.4. tapply()
			15.1.5. mapply()
			15.1.6. Reduce() and do.call()
		15.2. Functions as Function Inputs in Python
			15.2.1. Functions as Function Inputs in Base Python
			15.2.2. Functions as Function Inputs in Numpy
			15.2.3. Functional Methods in Pandas
		15.3. Functions as Function Outputs in R
		15.4. Functions as Function Outputs in Python
			15.4.1. Writing Our Own Decorators
		15.5. Exercises
			15.5.1. Python Questions
			15.5.2. R Questions
Bibliography
Index