| Description |
ix, 246 pages : illustrations ; 24 cm |
| Note |
"For professionals by professionals." |
| Bibliography |
Includes bibliographical references and index |
| Contents |
Introduction -- Importing data : readr -- Representing Tables: tibble -- Reformatting Tables: tidyr -- Pipelines: magrittr -- Functional programming: purrr -- Manipulating data frames: dplyr -- Working with strings: stringr -- Working with factors: forcats -- Working with dates: lubridate -- Working with models: broom and modelr -- Plotting: ggplot2 -- Conclusions |
| Summary |
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 |
| Subject |
R (Computer program language)
|
|
Application program interfaces (Computer software)
|
| Added Title |
Pocket guide to APIs, libraries, and packages |
| ISBN |
9781484248935 |
|
1484248937 |
|
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