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

In this handy, practical book you will cover each concept concisely, with many illustrative examples. You'll be introduced to several R data science packages, with examples of how to use each of them. In this book, you’ll learn about the following APIs and packages that deal specifically with data science applications: readr, dibble, forecasts, lubridate, stringr, tidyr, magnittr, dplyr, purrr, ggplot2, modelr, and more.After using this handy quick reference guide, 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. **What You Will Learn** * 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. In this handy, practical book you will cover each concept concisely, with many illustrative examples. You'll be introduced to several R data science packages, with examples of how to use each of them. In this book, you'll learn about the following APIs and packages that deal specifically with data science applications: readr, dibble, forecasts, lubridate, stringr, tidyr, magnittr, dplyr, purrr, ggplot2, modelr, and more. After using this handy quick reference guide, 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. What You Will Learn 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. In this handy, practical book you will cover each concept concisely, with many illustrative examples. You'll be introduced to several R data science packages, with examples of how to use each of them. In this book, you'll learn about the following APIs and packages that deal specifically with data science applications: readr, tibble, forcates, lubridate, stringr, tidyr, magnittr, dplyr, purrr, ggplot2, modelr, broom, knitr, shiny, and more. After using this handy quick reference guide, 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. You will: Get started with RMarkdown and notebooks 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 ggplot 2 and data fit for models using modelr and broom Report results with markdown, knitr, shiny, and more In this handy, practical book you will cover each concept concisely, with many illustrative examples. You'll be introduced to several R data science packages, with examples of how to use each of them. You'll learn about the following APIs and packages that deal specifically with data science applications: readr, dibble, forecasts, lubridate, stringr, tidyr, magnittr, dplyr, purrr, ggplot2, modelr and more. After completing this quick reference guide, 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. You will: 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 work with fitted models using broom and modelr Front Matter ....Pages i-ix Introduction (Thomas Mailund)....Pages 1-3 Importing Data: readr (Thomas Mailund)....Pages 5-31 Representing Tables: tibble (Thomas Mailund)....Pages 33-43 Reformatting Tables: tidyr (Thomas Mailund)....Pages 45-69 Pipelines: magrittr (Thomas Mailund)....Pages 71-81 Functional Programming: purrr (Thomas Mailund)....Pages 83-107 Manipulating Data Frames: dplyr (Thomas Mailund)....Pages 109-160 Working with Strings: stringr (Thomas Mailund)....Pages 161-180 Working with Factors: forcats (Thomas Mailund)....Pages 181-193 Working with Dates: lubridate (Thomas Mailund)....Pages 195-203 Working with Models: broom and modelr (Thomas Mailund)....Pages 205-218 Plotting: ggplot2 (Thomas Mailund)....Pages 219-238 Conclusions (Thomas Mailund)....Pages 239-239 Back Matter ....Pages 241-246