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Learn RStudio IDEquick, effective, a...

Campbell, Matthew.

Learn RStudio IDEquick, effective, and productive data science /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Learn RStudio IDEby Matthew Campbell.
Reminder of title:	quick, effective, and productive data science /
Author:	Campbell, Matthew.
Published:	Berkeley, CA :Apress :2019.
Description:	ix, 153 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Data mining.
Online resource:	https://doi.org/10.1007/978-1-4842-4511-8
ISBN:	9781484245118$q(electronic bk.)

Learn RStudio IDEquick, 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$q(electronic bk.)

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

184440
Data mining.

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

Dewey Class. No.: 006.312

Learn RStudio IDEquick, effective, and productive data science /
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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.
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