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R data science quick reference = a p...

Mailund, Thomas.

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R data science quick reference = a pocket guide to APIs, libraries, and packages /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	R data science quick reference/ by Thomas Mailund.
其他題名:	a pocket guide to APIs, libraries, and packages /
作者:	Mailund, Thomas.
出版者:	Berkeley, CA :Apress : : 2019.,
面頁冊數:	ix, 246 p. :ill., digital ;24 cm.
內容註:	1. Introduction -- 2. Importing Data: readr -- 3. Representing Tables: tibble -- 4. Reformatting Tables: tidyr -- 5. Pipelines: magrittr -- 6. Functional Programming: purrr -- 7. Manipulating Data Frames: dplyr -- 8. Working with Strings: stringr -- 9. Working with Factors: forcats -- 10. Working with Dates: lubridate -- 11. Working with Models: broom and modelr -- 12. Plotting: ggplot2 -- 13. Conclusions.
Contained By:	Springer eBooks
標題:	R (Computer program language) -
電子資源:	https://doi.org/10.1007/978-1-4842-4894-2
ISBN:	9781484248942

R data science quick reference = a pocket guide to APIs, libraries, and packages /
Mailund, Thomas.

R data science quick referencea pocket guide to APIs, libraries, and packages /[electronic resource] :by Thomas Mailund. - Berkeley, CA :Apress :2019. - ix, 246 p. :ill., digital ;24 cm.

1. Introduction -- 2. Importing Data: readr -- 3. Representing Tables: tibble -- 4. Reformatting Tables: tidyr -- 5. Pipelines: magrittr -- 6. Functional Programming: purrr -- 7. Manipulating Data Frames: dplyr -- 8. Working with Strings: stringr -- 9. Working with Factors: forcats -- 10. Working with Dates: lubridate -- 11. Working with Models: broom and modelr -- 12. Plotting: ggplot2 -- 13. Conclusions.

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.

ISBN: 9781484248942

Standard No.: 10.1007/978-1-4842-4894-2doiSubjects--Topical Terms:

784593
R (Computer program language)

LC Class. No.: QA276.45.R3 / M35 2019

Dewey Class. No.: 005.133

R data science quick reference = a pocket guide to APIs, libraries, and packages /
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505 0 $a 1. Introduction -- 2. Importing Data: readr -- 3. Representing Tables: tibble -- 4. Reformatting Tables: tidyr -- 5. Pipelines: magrittr -- 6. Functional Programming: purrr -- 7. Manipulating Data Frames: dplyr -- 8. Working with Strings: stringr -- 9. Working with Factors: forcats -- 10. Working with Dates: lubridate -- 11. Working with Models: broom and modelr -- 12. Plotting: ggplot2 -- 13. Conclusions.
520 $a 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.
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