• Package from RStudio (http://shiny.rstudio.com/)
• Interactive analysis of data
• Stand-alone applications and dashboards

if (!require("devtools"))
  install.packages("devtools")
devtools::install_github("rstudio/shiny")

• Two files

1. User-Interface file (ui.R) - inputs and display of output in a customizable layout
2. Server (server.R) - the work-horse that takes inputs, processes them, and creates outputs to display in the user-interface

• One-file option exists using shiny markdown… My preference is for two separate files.

Sepal length and width and Petal length and width of 50 flowers from each of 3 species of iris - setosa, versicolor, and virginica

1. View data (or a part of it)
2. Summarize data
3. 2 graphs of data - a scatter plot and a box plot

head(iris,5)

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa

# tail(iris,n) 
# iris

summary(iris)

##   Sepal.Length    Sepal.Width     Petal.Length    Petal.Width   
##  Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
##  1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
##  Median :5.800   Median :3.000   Median :4.350   Median :1.300  
##  Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199  
##  3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  
##  Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  
##        Species  
##  setosa    :50  
##  versicolor:50  
##  virginica :50  
##                 
##                 
## 

library(ggplot2)
ggplot(iris,aes(x=Sepal.Length,y=Sepal.Width,color=Species))+geom_point()

ggplot(iris,aes(x=Species,y=Sepal.Length))+geom_boxplot()

1. View data (or a section of it) (using head, tail, or just the entire data)
2. Summarize data (using summary)
3. 2 graphs of data - a scatter plot (x= Sepal.Length, y= Sepal.Width) and a box plot (how distribution of Sepal.Length varied across the three Species)

1. What if one wanted the ability to specify any variable for x and y axes of the scatter plot?
2. What if one wanted to alter the variable whose distribution one wanted to plot across the three species in the box plot?
3. What if one wanted to select a subset of the data (any range of any of the 4 numeric variables) and wanted to see how the data, summary, and the two plots changed?

Off-the-shelf widgets for inputs: http://shiny.rstudio.com/gallery/widget-gallery.html

• One "Select box" each for the x-variable and y-variable (for scatter plot), and one "Select box" for the dvariable (for distribution to study in box plot) - The options for each of the 4 select boxes will be the same, the four numeric variables
• One "Slider range" for each of the four numeric variables - the minimum and maximum for each "Slider range" should be the minimum and maximum of the variable they are representing. Default state of each: The entire range (complete dataset)

# The 5th variable in the iris dataset is not numeric - Species - Not a choice option 
selectInput("xvar", "x-variable:", choices=names(iris[,-5]))
selectInput("yvar", "y-variable:", choices=names(iris[,-5]),selected = names(iris[2]))
selectInput("dvar", "Distribution of which variable for box plot?", choices=names(iris[,-5]), selected = names(iris[3]))

sliderInput("Sepal.Length", label = "Sepal.Length", min = min(iris$Sepal.Length), max = max(iris$Sepal.Length), 
            value=c(min(iris$Sepal.Length),max(iris$Sepal.Length)))

sliderInput("Sepal.Width", label = "Sepal.Width", min = min(iris$Sepal.Width), 
                       max = max(iris$Sepal.Width), value = c(min(iris$Sepal.Width),max(iris$Sepal.Width)))

sliderInput("Petal.Length", label = "Petal.Length", min = min(iris$Petal.Length), 
                       max = max(iris$Petal.Length), value = c(min(iris$Petal.Length),max(iris$Petal.Length)))
           
sliderInput("Petal.Width", label = "Petal.Width", min = min(iris$Petal.Width), 
                       max = max(iris$Petal.Width), value = c(min(iris$Petal.Width),max(iris$Petal.Width)))

Source: http://shiny.rstudio.com/tutorial/written-tutorial/lesson4/

dataTableOutput(outputId="subsetdata")

To present an interactive table of the entire data using the jQuery library DataTables

verbatimTextOutput("summary")

For a verbatim textOutput of the summary function

plotOutput("scatterplot")

plotOutput("boxplot")

For each of the two plots - the scatter plot and the box plot

Source: http://shiny.rstudio.com/tutorial/written-tutorial/lesson2/

Code of this intermediate app: https://github.com/patilv/shinyapptutorial/tree/master/shinyUI

• Take inputs from sliders and subset the data
• Display resulting subset using datatable (name: "subsetdata" in ui.r)
• Compute summary of subsetted data and ship it off to "summary" in ui.r
• Take inputs for x and y variable
• Create scatter plot using the ggplot2 package and ship this off to "scatterplot" in ui.r
• Take input for dvar (for distribution variable) and create box plot
• Create box plot using ggplot2 and ship this last piece off to "boxplot" in ui.r

Don't forget, the input variable names in the server should be the same ones the ui.r is sending to it.

• Creating dataset that is reactive to the inputs from the 4 sliders
• Each of those sliders returns two values, the lower and upper bounds of the range

dataset=reactive(iris[(iris$Sepal.Length>=input$Sepal.Length[1] & iris$Sepal.Length<=input$Sepal.Length[2]&
                            iris$Sepal.Width>=input$Sepal.Width[1] & iris$Sepal.Width<=input$Sepal.Width[2]&
                               iris$Petal.Length>=input$Petal.Length[1] & iris$Petal.Length<=input$Petal.Length[2]&
                                  iris$Petal.Width>=input$Petal.Width[1] & iris$Petal.Width<=input$Petal.Width[2]),])

More information on "reactivity" can be found here: http://shiny.rstudio.com/articles/reactivity-overview.html

Source: http://shiny.rstudio.com/tutorial/written-tutorial/lesson4/

output$subsetdata <- renderDataTable(dataset(),options=list(pageLength=10)) # for Data table
  
output$summary <- renderPrint(summary(dataset()))
  
output$scatterplot=renderPlot(ggplot(dataset(),aes_string(x=input$xvar, y=input$yvar,color="Species"))+ geom_point()+ggtitle("Scatter Plot"))
  
output$boxplot=renderPlot(ggplot(dataset(),aes_string(x="Species",y=input$dvar))+ geom_boxplot()+ ggtitle("Box Plot"))

• Note how the reactive dataset is referred to as dataset()
• Since the inputs from "Select box" show up as strings, we use aes_string instead of aes in our ggplot creations

Code for final app: https://github.com/patilv/shinyapptutorial/tree/master/shinyApp

App in new window: https://patilv.shinyapps.io/shinyApp/

Source: http://shiny.rstudio.com/tutorial/lesson7/

if {R and shiny available on computer} {

• Two R files (or one, if a shiny markdown file)
• shiny::runURL (from any web link to a zip file comprising the two R files; Try: shiny::runUrl("http://patilv.com/shinyapptutorial/shinyApp.zip"))
• runGist (Try: shiny::runGist("0e8fee078f0fab4e2ead185d39dc362c"))
• The Gist can be found at: https://gist.github.com/patilv/0e8fee078f0fab4e2ead185d39dc362c
• runGitHub (Try: shiny::runGitHub( "shinyApp", "patilv"))

} else {

• http://www.shinyapps.io/

• Shiny Server - Open Source or Professional }

• http://shiny.rstudio.com/
• Sample apps I have worked on: http://patilv.com/tags/#Shiny
• https://rstudio.github.io/leaflet/
• Search on R-Bloggers: http://www.r-bloggers.com
• Dashboards can be created using shinydashboard (https://rstudio.github.io/shinydashboard/)