Introduction to Shiny Workshop

Shiny for R

• Housekeeping
• Introduction to Shiny
• Gapminder data
• Running example: visualising the gapminder data
• Deploying Shiny apps

Housekeeping

• Fire exit
• Toilets
• Coffee breaks and lunch
• Course timing
• Feedback (form and verbal)

Research IT

Described on the IT Services website

Announcements given via the Research IT blog and newsletter (sign up via the blog page) and on Twitter [@UoM_eResearch](http://twitter.com/uom_eresearch)

• Training courses teach computing skills for research
• Advice and guidance about research software
• Access to specialist support and consultancy e.g. code reviews
• Access to HPC systems - where you can run R.
• Full list of services on offer

Get in touch via the support centre

R user groups

R user groups are a great way of finding out more about R.

• University of Manchester R user group
  • Meets monthly
  • Email list for announcements, questions etc.
  • Has tea, coffee and biscuits
  • Separate beginners’ group
• ManchesterR
  • City-wide group (not affiliated with the university)
  • Meets quarterly
  • Typically 3-4 presentations; often a commercial focus.

Introduction to Shiny

• Shiny lets us easily and quickly build web apps using R
• Can use pretty much any output from R within our app
• And control it using widgets

  For example:

• Trafford Data Lab Ward Profiles
• Movie explorer
• Bus dashboard
• PK modelling
  

• Shiny gallery

Why (not?) use Shiny

• Fast to develop
• Use R’s libraries easily -including e.g. leaflet for mapping
• Many built in widgets
  • And packages for others

• Awkward to scale
• Non-free pro version needed for some server features

Gapminder

Workshop Aims

• To use Shiny to make an interactive plot
  • Select / animate the year
  • Only plot data for selected continents
  • Deploy the app to shinyapps.io
  • (Possible extensions included in “going further”)

Getting started

• The course builds on a single worked example.
• Download the zip file
• Decompress this to a directory within C:\work

• Each step is separate labelled folder, which is cross referenced in the notes, e.g. 01_defaultapp.

• The course website https://uomresearchit.github.io/r-shiny-course/ contains all the exercises, links to solutions and more detail than these slides.

Interactive exercises with git

If you’re happy using git:

git clone https://github.com/UoMResearchIT/r-shiny-course-materials.git

• Solutions are stored as tagged commits.
  • e.g 01_defaultapp
  • Link takes you to github’s commit page
  • Checkout versions with git checkout 01_defaultapp
• Make your own Shiny app in another directory

Getting started (2)

• Run rstudio:

rstudio

• We’ll make a new project for our app
  • Make this in C:/work and copy to your P drive at the end of the course
  • When we make a Shiny Web Application project, we get a simple Shiny app for free.

Getting started (3)

• Copy the following files from the zip file to your project directory:
  • gapminder.rds contains the gapminder data
  • workshopFunctions.R contains the functions we will use.
    • These are mostly to make it easier to make the plots, and to avoid this turning into a ggplot workshop.
  • codeExample.R shows how to use the plotting functions.
• Run App using the toolbar or Ctrl+Shift+Enter

01_defaultapp

Anatomy of a Shiny App - user interface

ui <- fluidPage(
    
    # Application title
    titlePanel("Old Faithful Geyser Data"),
    
    # Sidebar with a slider input for number of bins 
    sidebarLayout(
        sidebarPanel(
            sliderInput("bins", ...
        ),
        
        
        mainPanel(
           plotOutput("distPlot")
        )
    )
)

input$bins is a source

output$distPlot is an endpoint

Anatomy of a Shiny App - server

server <- function(input, output) {
    
    output$distPlot <- renderPlot({
        # generate bins based on input$bins from ui.R
        x    <- faithful[, 2] 
        bins <- seq(min(x), max(x), length.out = input$bins + 1)
        
        # draw the histogram with the specified number of bins
        hist(x, breaks = bins, col = 'darkgray', border = 'white')
    })
}

Shiny handles the dependencies between sources and endpoints, and only updates what is needed - Reactive programming.

Exercise - getting ready for the rest of the course.

• Copy the gapminder data (gapminder.rds) and workshopFunctions.R to your app directory

• Modify the app to load the ggplot2 and dplyr libraries, gapminder data, and workshopFunctions.R

• Check your app still works

02_loaddata

(Course notes: Getting started)

More widgets

• Many common widget types are built into Shiny see: https://shiny.rstudio.com/gallery/widget-gallery.html
• Can be extended using additional packages (e.g. https://github.com/dreamRs/shinyWidgets)
• or by writing your own.

Exercise

• Add a checkboxGroupInput() widget to select the continents we wish to display on the graph
• Change the title of the app in titlePanel() to something sensible

(This won’t do anything yet - we’ll connect it to the graph shortly)

03_continentwidget

(Course notes: User interface design)

Exercises - Putting it all together

• Replace the histogram with a gapminder plot, using produceGapminderPlot() 04_gapminderplot
  • See codeExample.R
• Replace the bins widget with a widget to select the year 05_yearwidget
• Use the outputs of the widgets to make the graph interactive 06_interactive

(Course notes: Putting it all together)

Deploying Shiny apps

• Run from R(Studio)

• https://shinyapps.io
  • Easy deployment from RStudio
  • Various pricing tiers (inc. free)
• Shiny server
  • Free and commercial versions
  • Authentication requires commercial version

(Course notes: Deploying Shiny apps)

Summary

• Define the UI and server functions
• Reactive programming handles the depencencies between elements
• Deploy apps using shinyapps.io or to a Shiny Server

Extra Slides

App layout

• The default apps sidebarLayout() is a good starting point.
• The fluidPage() layout gives us more control.

ui <- fluidPage(
  titlePanel("Gapminder visualisation"), # Will cover whole width
  plotOutput("gapminderPlot", click = "plotClick"), # Ditto
  fluidRow( 
    column(6, # column width
           sliderInput(inputId = "year", ....) # Things to include in the column
    ),
    column(6, # Next column
           checkboxGroupInput("continent", ....)# Things to include in second column
    )
  )
)

• The widths of each row of columns should sum to 12 15_layout