server.R

 # libraries used. install as necessary

  library(shiny)
  library(RJSONIO) # acquiring and parsing data
  library(ggplot2) # graphs
  library(plyr)  # manipulating data
  library(lubridate) #dates
  library(stringr)

trim.leading <- function (x)  sub("^\\s+", "", x)

shinyServer(function(input, output) {
  
  
  data <- reactive(function() {  
   
    # create a set of months to be analyzed
    dates <-seq(Sys.time()-months(input$obs[2]),Sys.time()-months(input$obs[1]), by = "month")
   
    # create blank dataframe to hold three fields
        allData <- data.frame(count=numeric(),date=character(),name=character())
    # seperate each variable of the subject vector
    
        subject <- str_split(input$subjects, ",")[[1]]
    
    
    # loop through subjects and months
    for(k in 1:length(subject)) {
      
      # handle remote problems related to strings
      target <- trim.leading(subject[k])
      target <- str_replace(target," ","_")
      
      
      # create dataframe for individual records
      df <- data.frame(count=numeric()) 
      
      for (i in 1:length(dates)) {
        yr <- year(dates[i])
      
        mth <- month(dates[i]) 
        if (str_length(mth)==1) {
          mth<-paste0("0",as.character(mth))
        }
       
        # obtain and process daily count data by month by target
        url <- paste0("http://stats.grok.se/json/en/",yr,mth,"/",target)
          raw.data <- readLines(url, warn="F") 
          rd  <- fromJSON(raw.data)
          rd.views <- rd$daily_views 
          
          df <- rbind(df,as.data.frame(rd.views))
        }
      
      
       #create the dataframe with all targets search counts by day
      df$date <-  as.Date(rownames(df))
      df$name <- subject[k]
      colnames(df) <- c("count","date","name")
      df <- arrange(df,date)
      allData <- rbind(allData,df)
      
   }
 
   return(allData)
  })
  
  
  # Create a heading based on range of dates selected for printing as a caption
  output$caption <- reactiveText(function() {
    
    endDate <- Sys.time()-months(input$obs[1])
    startDate <- Sys.time()-months(input$obs[2])
    if (input$obs[2]==0){
    paste("Daily rates for",month(Sys.time(), label = TRUE, abbr = TRUE),year(Sys.time()),sep=" ")
    } else if ((input$obs[2]!=0)&(year(endDate)==year(startDate))) {
      paste("Daily rates from",month(startDate, label = TRUE, abbr = TRUE),"to",month(endDate, label = TRUE, abbr = TRUE), year(endDate),sep=" ")   
  } else  {
      paste("Daily rates from",month(startDate, label = TRUE, abbr = TRUE),year(startDate),"to",month(endDate, label = TRUE, abbr = TRUE), year(endDate),sep=" ")   
    }
  })
  
 
  # create plot for linear and log scales
  output$plot <- reactivePlot(function() {
    if (input$log) {
    print(ggplot(data(), aes(x=date,y=log10(count),group=name,colour=name))+
            geom_line()+ylab("log10")+xlab("")+theme_bw() + 
            theme(legend.position="top",legend.title=element_blank(),legend.text = element_text(colour="blue", size = 14, face = "bold")))
    } else {
      print(ggplot(data(), aes(x=date,y=count,group=name,colour=name))+
              geom_line()+ylab("")+xlab("") +theme_bw() + 
              theme(legend.position="top",legend.title=element_blank(),legend.text = element_text(colour="blue", size = 14, face = "bold")))
    }
    })
  
 
  # create summary data for each subject 
  output$view <- reactiveTable(function() {
    
    myTable <- data()
    myTable$count <- as.integer(myTable$count)
   
    myTable$date <- as.character(myTable$date) 
    mySummary <- ddply(subset(myTable,count>0),.(name), summarize, mean=mean(count),median=median(count),min=min(count),max=max(count),maxdate=date[which.max(count)])
    mySummary$showMax <- paste0(day(mySummary$maxdate)," ",month(mySummary$maxdate, label = TRUE, abbr = TRUE),", ",year(mySummary$maxdate))
    mySummary$maxdate <- NULL
    mySummary <- arrange(mySummary,desc(mean))
    colnames(mySummary) <- c("","Mean","Median","Min","Max","Max Date")
    mySummary                 
  })
  
  # make data downloadable

  output$downloadData <- downloadHandler(
   # filename = function() { paste(input$data, '.csv', sep='') }, in tutorial to distinguish files trickier with my work
    filename = function() { paste('results.csv', sep='') },
    content = function(file) {
      write.csv(data(), file)
    }
  )
  
})

ui.R

shinyUI(pageWithSidebar(
  
  # Application title
  headerPanel("Wikipedia Search Rates"),
  
  # Sidebar with controls to select the subjects and time span
  sidebarPanel(
      helpText(p(
      "The graph represents the daily number of Wikipedia searches for
      any subject(s) - animal, vegetable or mineral - over recent months."),
    p("The data is available from December 2007
      to the present day. Adjust the slider to amend the time covered."),
    p("Increasing the number of subjects and
      extending the time period will impact processing time")),
    
   wellPanel(
     p(strong("Enter Subject(s), correctly spelt, seperated by commas")),
     textInput(inputId = "subjects", label = " ", value = "Selena Gomez, Justin Bieber"),
       p("For ambiguous names use wiki nomenclature     e.g. Andrew Clark (priest)"), 
          p(strong("Date range (months back from present);")),
      sliderInput(inputId = "obs",
                  label=" ",
                  min = 0, max = 60, step = 1, value = c(0,2))
     
  ), 
      
    div(class="span6", submitButton("Get Graph")),
    div(class="span6", checkboxInput(inputId = "log", label = "log10 scale", value = FALSE)),
      helpText("Use log scale if compared searches are significantly different"),
      downloadButton('downloadData', 'Download Output as csv')
    
  ),
  
  # Show the caption a line graph of the dauly rate and summary of results 
  mainPanel(
    h3(textOutput("caption")),
    
    plotOutput("plot"), 
    tableOutput("view")
    
  )
))