There are two ways to do this: 1) use the RStudio button (See Figure 8.2) or 2) type the function runApp() in the RStudio console.
Copy the code in “app.R version 1” into app.R. In the “ShinyPractice” directory, create a blank R script called app.R. Create a new directory called “ShinyPractice”. Follow the subsequent steps to create and run this Shiny app (note these steps are already done for you in the “ShinyPractice” directory in “shiny_chapter_code.zip”, but you might want to do it yourself): The script file that defines this basic application is called app.R and is provided in the code block below called “version 1”. We begin by defining a very basic Shiny application script upon which we can build the desired functionality. Given the namesDF data, our goal over the next few sections is to develop a Shiny application that allows users to explore age distributions for males and females. 
$ Last : chr "Poole" "Dowd" "Wilkins" "Murray". $ First: chr "Sarah" "Boris" "Jessica" "Diane".
NamesDF <- read.csv( "", stringsAsFactors = FALSE) str(namesDF) 'ame': 200 obs.
11.6 A Summary of Useful graphics Functions and Arguments. 8.4.2 Michigan Campgrounds Server Logic. 8.4 More Advanced Shiny App: Michigan Campgrounds. 7.2 Programming: Conditional Statements. 6.2 Reading Data with Missing Observations. 4.7.2 Logical Subsetting and Data Frames. 4.7.1 Modifying or Creating Objects via Subsetting. 4.6.1 Accessing Specific Elements of Lists. 4.5.1 Accessing Specific Elements of Data Frames. 4.1.2 Accessing Specific Elements of Vectors. 3.2.1 Creating and processing R Markdown documents. 2.5 Workspace, Working Directory, and Keeping Organized. 2.3.2 Basic descriptive statistics and graphics in R. 1.6 How to learn (The most important section in this book!).
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