Campbell Learn RStudio IDE

Quick, Effective, and Productive Data Science
1. Auflage 2019
ISBN: 978-1-4842-4511-8
Verlag: APRESS
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 153 Seiten, eBook

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.
What YouWill Learn
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
Who This Book Is For
Programmers who want to start doing data science, but don’t know what tools to focus on to get up to speed quickly.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Campbell, Matthew

Inhaltsverzeichnis

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.

Über Autor(innen)

Matthew Campbell has worked on data visualization and dashboards with a data science team using RStudio. He got his start with technology after college when he learned SAS to do statistical programming at the Educational Testing Service (ETS). Learning this programming language kicked off a lifelong obsession with technology.