R 4 Data Science Quick Reference: A Pocket Guide to APIs, Libraries, and Packages

✍ Scribed by Thomas Mailund

• Publisher: Apress
• Year: 2022
• Tongue: English
• Leaves: 231
• Category: Library

✦ Synopsis

In this handy, quick reference book you'll be introduced to several R data science packages, with examples of how to use each of them. All concepts will be covered concisely, with many illustrative examples using the following APIs: readr, dibble, forecasts, lubridate, stringr, tidyr, magnittr, dplyr, purrr, ggplot2, modelr, and more.
With R 4 Data Science Quick Reference, you'll have the code, APIs, and insights to write data science-based applications in the R programming language. You'll also be able to carry out data analysis. All source code used in the book is freely available on GitHub..
What You'll Learn

• Implement applicable R 4 programming language specification features
• Import data with readr
• Work with categories using forcats, time and dates with lubridate, and strings with stringr
• Format data using tidyr and then transform that data using magrittr and dplyr
• Write functions with R for data science, data mining, and analytics-based applications
• Visualize data with ggplot2 and fit data to models using modelr

Who This Book Is For
Programmers new to R's data science, data mining, and analytics packages. Some prior coding experience with R in general is recommended.

✦ Table of Contents

Table of Contents
About the Author
About the Technical Reviewer
Chapter 1: Introduction
Chapter 2: Importing Data: readr
Functions for Reading Data
File Headers
Column Types
String-Based Column Type Specification
Function-Based Column Type Specification
Parsing Time and Dates
Space-Separated Columns
Functions for Writing Data
Chapter 3: Representing Tables: tibble
Creating Tibbles
Indexing Tibbles
Chapter 4: Tidy Select
Ranges
Complements
Unions and Intersections
Select Columns Based on Name
Everything
Indexing from the Last Column
Selecting from Strings
Selecting Columns Based on Their Content
It Is a Growing Language, so Check for Changes
Chapter 5: Reformatting Tables: tidyr
Tidy Data
Pivoting
Complex Column Encodings
Expanding, Crossing, and Completing
Missing Values
Nesting Data
Chapter 6: Pipelines: magrittr
The Problem with Pipelines
Pipeline Notation
Pipelines and Function Arguments
Function Composition
Other Pipe Operations
Chapter 7: Functional Programming: purrr
General Features of purrr Functions
Filtering
Mapping
Reduce and Accumulate
Partial Evaluation and Function Composition
Lambda Expressions
Chapter 8: Manipulating Data Frames: dplyr
Selecting Columns
Filter
Sorting
Modifying Data Frames
Grouping and Summarizing
Joining Tables
Income in Fictional Countries
Chapter 9: Working with Strings: stringr
Counting String Patterns
Splitting Strings
Capitalizing Strings
Wrapping, Padding, and Trimming
Detecting Substrings
Extracting Substrings
Transforming Strings
Chapter 10: Working with Factors: forcats
Creating Factors
Concatenation
Projection
Adding Levels
Reorder Levels
Chapter 11: Working with Dates: lubridate
Time Points
Time Zones
Time Intervals
Chapter 12: Working with Models: broom and modelr
broom
modelr
Chapter 13: Plotting: ggplot2
The Basic Plotting Components in ggplot2
Adding Components to a Plot Object
Adding Data
Adding Aesthetics
Adding Geometries
Facets
Adding Coordinates
Modifying Scales
Chapter 14: Conclusions
Index