For proof of concept, I just pulled in data relating to the Isle of Wight NHS Trust, but it should be possible to build a more generic explorer: Isle of Wight NHS Sit Rep Explorer Demo.

Three files are used to crate the app – a script to define the user interface (ui.R), a script to define the server that responds to UI actions and displays the charts (server.R), and a supporting file that creates variables and functions that are globally available to bother the server and UI scripts (global.R).

##wightsitrep2/global.R

#Loading in CSV directly from https seems to cause problems but this workaround seems okay
floader=function(fn){
  temporaryFile <- tempfile()
  download.file(fn,destfile=temporaryFile, method="curl")
  read.csv(temporaryFile)
}

#This is the data source - a scraperwiki API call
#It would make sense to abstract this further, eg allowing the creation of the URL based around a passed in a select statement
u="https://api.scraperwiki.com/api/1.0/datastore/sqlite?format=csv&name=nhs_sit_reps&query=select%20SHA%2CName%2C%20fromDateStr%2CtoDateStr%2C%20tableName%2CfacetB%2Cvalue%20from%20fulltable%20%20where%20Name%20like%20'%25WIGH%25'"

#Load the data and do a bit typecasting, just in case...
d=floader(u)
d$fdate=as.Date(d$fromDateStr)
d$tdate=as.Date(d$toDateStr)
d$val=as.integer(d$value)

##wightsitrep2/ui.R

library(shiny)

tList=levels(d$tableName)
names(tList) = tList

# Define UI for application that plots random distributions 
shinyUI(pageWithSidebar(
  
  
  # Application title
  headerPanel("IW NHS Trust Sit Rep Explorer"),
  
  sidebarPanel(
    #Just a single selector here - which table do you want to view?
    selectInput("tbl", "Report:",tList),
    
    div("This demo provides a crude graphical view over data extracted from",
        a(href='http://transparency.dh.gov.uk/2012/10/26/winter-pressures-daily-situation-reports-2012-13/',
          "NHS Winter pressures daily situation reports"),
        "relating to the Isle of Wight NHS Trust."),
    div("The data is pulled in from a scraped version of the data stored on Scraperwiki",
        a(href="https://scraperwiki.com/scrapers/nhs_sit_reps/","NHS Sit Reps"),".")
    
 ),
  
  #The main panel is where the "results" charts are plotted
  mainPanel(
    plotOutput("testPlot"),
    tableOutput("view")
    
  )
))

##wightsitrep2/server.R

library(shiny)
library(ggplot2)

# Define server logic
shinyServer(function(input, output) {
  
  #Do a simple barchart of data in the selected table.
  #Where there are "subtables", display these using the faceted view
  output$testPlot = reactivePlot(function() {
    g=ggplot(subset(d,fdate>as.Date('2012-11-01') & tableName==input$tbl))
    g=g+geom_bar(aes(x=fdate,y=val),stat='identity')+facet_wrap(~tableName+facetB)
    g=g+theme(axis.text.x=element_text(angle=-90),legend.position="none")+labs(title="Isle of Wight NHS Trust")
    #g=g+scale_y_discrete(breaks=0:10)
    print(g)
  })
  
  #It would probable make sense to reshape the data presented in this table
  #For example, define columns based on facetB values, so we have one row per date range
  #I also need to sort the table by date
  output$view = reactiveTable(function() {
    head(subset(d,tableName==input$tbl,select=c('Name','fromDateStr','toDateStr','tableName','facetB','value')),n=100)
  })
  
})

I get the feeling that it shouldn’t be too hard to create quite complex Shiny apps relatively quickly, pulling on things like Scraperwiki as a remote data source. One thing I haven’t tried is to use googleVis components, which would support in the first instance at least a sortable table view… Hmmm…