東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Learn RStudio IDE = quick, effective...

Campbell, Matthew.

FindBook

Google Book

Amazon

博客來

Learn RStudio IDE = quick, effective, and productive data science /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Learn RStudio IDE/ by Matthew Campbell.
其他題名:	quick, effective, and productive data science /
作者:	Campbell, Matthew.
出版者:	Berkeley, CA :Apress : : 2019.,
面頁冊數:	ix, 153 p. :ill., digital ;24 cm.
內容註:	1. Installing RStudio -- 2. Hello World -- 3. RStudio Views -- 4. RStudio Projects -- 5. Repeatable Analysis -- 6. Essential R Packages: Tidyverse -- 7. Data Visualization -- 8. R Markdown -- 9. Shiny R Dashboards -- 10. Custom R Packages -- 11. Code Tools -- 12. R Programming.
Contained By:	Springer eBooks
標題:	Data mining. -
電子資源:	https://doi.org/10.1007/978-1-4842-4511-8
ISBN:	9781484245118

Learn RStudio IDE = quick, effective, and productive data science /
Campbell, Matthew.

Learn RStudio IDEquick, effective, and productive data science /[electronic resource] :by Matthew Campbell. - Berkeley, CA :Apress :2019. - ix, 153 p. :ill., digital ;24 cm.

1. Installing RStudio -- 2. Hello World -- 3. RStudio Views -- 4. RStudio Projects -- 5. Repeatable Analysis -- 6. Essential R Packages: Tidyverse -- 7. Data Visualization -- 8. R Markdown -- 9. Shiny R Dashboards -- 10. Custom R Packages -- 11. Code Tools -- 12. R Programming.

Discover how to use the popular RStudio IDE as a professional tool that includes code refactoring support, debugging, and Git version control integration. This book gives you a tour of RStudio and shows you how it helps you do exploratory data analysis; build data visualizations with ggplot; and create custom R packages and web-based interactive visualizations with Shiny. In addition, you will cover common data analysis tasks including importing data from diverse sources such as SAS files, CSV files, and JSON. You will map out the features in RStudio so that you will be able to customize RStudio to fit your own style of coding. Finally, you will see how to save a ton of time by adopting best practices and using packages to extend RStudio. Learn RStudio IDE is a quick, no-nonsense tutorial of RStudio that will give you a head start to develop the insights you need in your data science projects. You will: Quickly, effectively, and productively use RStudio IDE for building data science applications Install RStudio and program your first Hello World application Adopt the RStudio workflow Make your code reusable using RStudio Use RStudio and Shiny for data visualization projects Debug your code with RStudio Import CSV, SPSS, SAS, JSON, and other data.

ISBN: 9781484245118

Standard No.: 10.1007/978-1-4842-4511-8doiSubjects--Topical Terms:

562972
Data mining.

LC Class. No.: QA76.9.D343 / C36 2019

Dewey Class. No.: 006.312

Learn RStudio IDE = quick, effective, and productive data science /
LDR:02578nmm a2200337 a 4500 001 2190682
003 DE-He213
005 20190417151727.0
006 m d
007 cr nn 008maaau
008 200501s2019 cau s 0 eng d
020 $a 9781484245118 $q (electronic bk.)
020 $a 9781484245101 $q (paper)
024 7 $a 10.1007/978-1-4842-4511-8 $2 doi
035 $a 978-1-4842-4511-8
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.D343 $b C36 2019
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 006.312 $2 23
090 $a QA76.9.D343 $b C189 2019
100 1 $a Campbell, Matthew. $3 1899554
245 1 0 $a Learn RStudio IDE $h [electronic resource] : $b quick, effective, and productive data science / $c by Matthew Campbell.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2019.
300 $a ix, 153 p. : $b ill., digital ; $c 24 cm.
505 0 $a 1. Installing RStudio -- 2. Hello World -- 3. RStudio Views -- 4. RStudio Projects -- 5. Repeatable Analysis -- 6. Essential R Packages: Tidyverse -- 7. Data Visualization -- 8. R Markdown -- 9. Shiny R Dashboards -- 10. Custom R Packages -- 11. Code Tools -- 12. R Programming.
520 $a Discover how to use the popular RStudio IDE as a professional tool that includes code refactoring support, debugging, and Git version control integration. This book gives you a tour of RStudio and shows you how it helps you do exploratory data analysis; build data visualizations with ggplot; and create custom R packages and web-based interactive visualizations with Shiny. In addition, you will cover common data analysis tasks including importing data from diverse sources such as SAS files, CSV files, and JSON. You will map out the features in RStudio so that you will be able to customize RStudio to fit your own style of coding. Finally, you will see how to save a ton of time by adopting best practices and using packages to extend RStudio. Learn RStudio IDE is a quick, no-nonsense tutorial of RStudio that will give you a head start to develop the insights you need in your data science projects. You will: Quickly, effectively, and productively use RStudio IDE for building data science applications Install RStudio and program your first Hello World application Adopt the RStudio workflow Make your code reusable using RStudio Use RStudio and Shiny for data visualization projects Debug your code with RStudio Import CSV, SPSS, SAS, JSON, and other data.
650 0 $a Data mining. $3 562972
650 1 4 $a Programming Languages, Compilers, Interpreters. $3 891123
650 2 4 $a Programming Techniques. $3 892496
650 2 4 $a Data Engineering. $3 3409361
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Probability and Statistics in Computer Science. $3 891072
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
856 4 0 $u https://doi.org/10.1007/978-1-4842-4511-8
950 $a Professional and Applied Computing (Springer-12059)