# 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

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