國立虎尾科技大學 |

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

紀錄類型:	書目-語言資料,印刷品 : 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.
面頁冊數:	IX, 246 p. 11 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Programming languages (Electronic computers). -
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. - 1st ed. 2019. - IX, 246 p. 11 illus.online resource.

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:

1127615
Programming languages (Electronic computers).

LC Class. No.: QA76.7-76.73

Dewey Class. No.: 005.13

R Data Science Quick Reference = A Pocket Guide to APIs, Libraries, and Packages /
LDR:02894nam a22004095i 4500 001 1003887
003 DE-He213
005 20200702200013.0
007 cr nn 008mamaa
008 210106s2019 xxu| s |||| 0|eng d
020 $a 9781484248942 $9 978-1-4842-4894-2
024 7 $a 10.1007/978-1-4842-4894-2 $2 doi
035 $a 978-1-4842-4894-2
050 4 $a QA76.7-76.73
050 4 $a QA76.76.C65
072 7 $a UMX $2 bicssc
072 7 $a COM051010 $2 bisacsh
072 7 $a UMX $2 thema
072 7 $a UMC $2 thema
082 0 4 $a 005.13 $2 23
100 1 $a Mailund, Thomas. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1140758
245 1 0 $a R Data Science Quick Reference $h [electronic resource] : $b A Pocket Guide to APIs, Libraries, and Packages / $c by Thomas Mailund.
250 $a 1st ed. 2019.
264 1 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2019.
300 $a IX, 246 p. 11 illus. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
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.
650 0 $a Programming languages (Electronic computers). $3 1127615
650 0 $a Computer programming. $3 527822
650 0 $a Big data. $3 981821
650 0 $a Data mining. $3 528622
650 1 4 $a Programming Languages, Compilers, Interpreters. $3 669782
650 2 4 $a Programming Techniques. $3 669781
650 2 4 $a Big Data. $3 1017136
650 2 4 $a Data Mining and Knowledge Discovery. $3 677765
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9781484248935
776 0 8 $i Printed edition: $z 9781484248959
856 4 0 $u https://doi.org/10.1007/978-1-4842-4894-2
912 $a ZDB-2-CWD
912 $a ZDB-2-SXPC
950 $a Professional and Applied Computing (SpringerNature-12059)
950 $a Professional and Applied Computing (R0) (SpringerNature-43716)