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TitleLearn RStudio IDE [electronic resource] : Quick, Effective, and Productive Data Science / by Matthew Campbell
ImprintBerkeley, CA : Apress : Imprint: Apress, 2019
Edition 1st ed. 2019
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Descript IX, 153 p. 88 illus., 6 illus. in color. online resource


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


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

Computer science Engineering—Data processing Data mining Programming Languages Compilers Interpreters. Programming Techniques. Data Engineering. Data Mining and Knowledge Discovery. Probability and Statistics in Computer Science.


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