appyazh.R

library(shiny)
library(shinydashboard)
library(ggplot2)
library(lubridate)
library(DT)
library(jpeg)
library(grid)
library(leaflet)
library(scales)
library(tidyr)
library(plotly)
library(dplyr)
library(dashboardthemes)

#read all the files (combining all aqi_by_county files into one dataframe and aqs_sites in another dataframe)
temp = list.files(pattern="^an.*csv")
allData2 <- lapply(temp, read.csv)
weatherinfo <- do.call(rbind, allData2)
weathermap <- read.csv(file = "aqs_sites.csv",header = TRUE, sep = ",")

#converting the variable type of County, State and Year
weatherinfo$County <- as.character(weatherinfo$County)
weatherinfo$State <- as.character(weatherinfo$State)
weatherinfo$Year <- as.numeric(as.character(weatherinfo$Year))

#changing the names of the columns for better graph plotting
names(weatherinfo)[names(weatherinfo)=="Good.Days"] <- "Good"
names(weatherinfo)[names(weatherinfo)=="Moderate.Days"] <- "Moderate"
names(weatherinfo)[names(weatherinfo)=="Unhealthy.for.Sensitive.Groups.Days"] <- "Unhealthy(Sensitive)"
names(weatherinfo)[names(weatherinfo)=="Unhealthy.Days"] <- "Unhealthy"
names(weatherinfo)[names(weatherinfo)=="Very.Unhealthy.Days"] <- "V Unhealthy"
names(weatherinfo)[names(weatherinfo)=="Hazardous.Days"] <- "Hazardous"


#gather is used to create seperate column for the AQI Parameters, Pollutants and MaxQ levels
weatherinfoGather <- gather(weatherinfo, "AQI Parameters", "Days", 5:10)
weatherinfoGather2 <- gather(weatherinfo, "Pollutants", "Days", 14:19)
weatherinfoGather3 <- gather(weatherinfo,"Maxq", "Count", 11:13)

State1info <- weatherinfoGather2
State2info <- weatherinfoGather2
State3info <- weatherinfoGather2

weatherinfo1 <- weatherinfo
weatherinfo1$CO <- (weatherinfo1$Days.CO / weatherinfo1$Days.with.AQI)* 100
weatherinfo1$NO2 <- (weatherinfo1$Days.NO2 / weatherinfo1$Days.with.AQI) *100
weatherinfo1$Ozone <- (weatherinfo1$Days.Ozone / weatherinfo1$Days.with.AQI) *100
weatherinfo1$SO2 <- (weatherinfo1$Days.SO2 / weatherinfo1$Days.with.AQI) * 100
weatherinfo1$PM2_5 <- (weatherinfo1$Days.PM2.5 / weatherinfo1$Days.with.AQI) * 100
weatherinfo1$PM_10 <- (weatherinfo1$Days.PM10 / weatherinfo1$Days.with.AQI) * 100

weatherinfo1 <- weatherinfo1 %>% mutate_if(is.numeric, round, 2)
weatherinfo1Gather <- gather(weatherinfo1,"NewPollutants","Count",20:25)

#Creating new columns which has aggregate percentage of all AQI types
weatherinfo2 <-weatherinfo
weatherinfo2$Good <- (weatherinfo2$Good / weatherinfo2$Days.with.AQI)* 100
weatherinfo2$Moderate <- (weatherinfo2$Moderate / weatherinfo2$Days.with.AQI) *100
weatherinfo2$`Unhealthy(Sensitive)` <- (weatherinfo2$`Unhealthy(Sensitive)` / weatherinfo2$Days.with.AQI) *100
weatherinfo2$Unhealthy <- (weatherinfo2$Unhealthy / weatherinfo2$Days.with.AQI) * 100
weatherinfo2$`V Unhealthy` <- (weatherinfo2$`V Unhealthy` / weatherinfo2$Days.with.AQI) * 100
weatherinfo2$Hazardous <- (weatherinfo2$Hazardous / weatherinfo2$Days.with.AQI) * 100


weatherinfo2 <- weatherinfo2 %>% mutate_if(is.numeric, round, 2)
weatherinfo2Gather <- gather(weatherinfo2,"AQIP","Count",5:10)

# Create the menu items to select the different years and the different rooms
counties <- weatherinfo[,2]
years<-c(1980:2018)
states <- weatherinfo[,1]

counties1 <- weatherinfo[,2]
years1<-c(1980:2018)
states1 <- weatherinfo[,1]

counties2 <- weatherinfo[,2]
years2<-c(1980:2018)
states2 <- weatherinfo[,1]

counties3 <- weatherinfo[,2]
years3<-c(1980:2018)
states3 <- weatherinfo[,1]

df <- weatherinfo1Gather[(weatherinfo1Gather$State == "Illinois"  & weatherinfo1Gather$County == "Cook") | 
                           (weatherinfo1Gather$State == "Alabama"  & weatherinfo1Gather$County == "Autauga") | 
                           (weatherinfo1Gather$State == "Alabama"  & weatherinfo1Gather$County == "Colbert") & 
                           weatherinfo1Gather$Year == 2018,c(2,14,15) ]
# Dreate the shiny dashboard
ui <- dashboardPage(skin = "purple", 
                    dashboardHeader(title = "Just Breathe", titleWidth = 350),
                    dashboardSidebar(includeCSS("styles.css"), disable = FALSE, collapsed = FALSE,
                              selectInput("State", "Select the State to visualize", states, selected = "Illinois"),
                              selectInput("County", "Select the County to visualize", counties, selected = "Cook"),
                              selectInput("Year", "Select the year to visualize", years, selected = 2018),
                                    
                              sidebarMenu(
                                    menuItem("About", tabName = "about"),
                                    menuItem("AQI Air Quality",tabName = "aqiquality"),
                                    menuItem("Pollutants In the Air ",tabName = "Pollutant") ,
                                    menuItem("Map of USA", tabName = "mapofusa"),
                                    menuItem("Compare Data of 3 Counties", tabName = "comparedata"))),
                    
                    dashboardBody(
                      shinyDashboardThemes( theme = "grey_dark"),
                      tabItems(
                        tabItem("about", includeHTML("about.html")),
                        tabItem("aqiquality",
                                fluidRow(column(width =12,h1("AQI Over The Years"))),
                                fluidRow(column(12,fluidRow(box(width = 14,status = "info",dataTableOutput("AQIQualityTable"))))),
                                fluidRow(splitLayout(style = "border: 1px solid silver:", cellWidths = c(350,350,400), 
                                                     plotlyOutput("AQIPiePlot"), 
                                                     plotlyOutput("AQIBarGraph"),
                                                     plotlyOutput("AQImaxq")))
                                ),
                        tabItem("Pollutant", 
                                fluidRow(column(width =12,h1("Pollutant Over The Years"))), 
                                fluidRow(splitLayout(style = "border: 1px solid silver:", cellWidths = c(760,660), 
                                                     dataTableOutput("PollutantTable"), 
                                                     dataTableOutput("PollutantTypeTable"))),
                                fluidRow(splitLayout(style = "border: 1px solid silver:", cellWidths = c(300,350,350,300), 
                                                     plotlyOutput("Pollutantline"), 
                                                     plotlyOutput("PollutantPiePlot"),
                                                     plotlyOutput("PollutantBarGraph")
                                                     ))
                               ),
                        tabItem("mapofusa", width = 12, h1("Leaflet of USA"),leafletOutput("Map0", height = 400) ),
                        tabItem("comparedata", 
                                fluidRow(box(width = 4, selectInput("Year1", "Select the year to visualize", years1, selected = 2018, width="120px"),
                                             selectInput("State1", "Select the State to visualize", states1, selected = "Illinois", width="120px"),
                                             selectInput("County1", "Select the County to visualize", counties1, selected = "Cook", width="120px")),
                                         
                                         box(width = 4, selectInput("Year2", "Select the year to visualize", years2, selected = 2018, width="120px"),
                                             selectInput("State2", "Select the State to visualize", states2, selected = "Illinois", width="120px"),
                                             selectInput("County2", "Select the County to visualize", counties2, selected = "Cook",width="120px")),
                                         
                                         box(width = 4, selectInput("Year3", "Select the year to visualize", years3, selected = 2018, width="120px"),
                                             selectInput("State3", "Select the State to visualize", states3, selected = "Illinois", width="120px"),
                                             selectInput("County3", "Select the County to visualize", counties3, selected = "Cook",width="120px"))),
                                fluidRow(title = "Compare 3 Counties", box(splitLayout(style = "border: 1px solid silver:", cellWidths = c(350,350,350), 
                                                        plotlyOutput("comparepollutant_bar", height = "60vh"), 
                                                        plotlyOutput("comparepollutant_line"),
                                                        plotlyOutput("compareaqi_line", height = "60vh"),
                                                        plotlyOutput("compareaqi_bar")
                                                       ))
                        )
                      )
                    ))
)
server <- function(input,output,session){
  
  #changing options for County depending on the State chosen
  observe({
    x <- input$State
    # Can use character(0) to remove all choices
    if (is.null(x))
      x <- character(0)
    # Can also set the label and select items
    updateSelectInput(session, "County",
                      choices = weatherinfo$County[weatherinfo$State==x]
    )
  })
  observe({
    y <- input$State1
    # Can use character(0) to remove all choices
    if (is.null(y))
      y <- character(0)
    # Can also set the label and select items
    updateSelectInput(session, "County1",
                      choices = State1info$County[State1info$State==y])
   # updateSelectInput(session, "State2", choices = State2info$State[State2info$State != y]
  #  )
  })
  observe({
    z <- input$State2
    # Can use character(0) to remove all choices
    if (is.null(z))
      z <- character(0)
    # Can also set the label and select items
    updateSelectInput(session, "County2",
                      choices = State2info$County[State2info$State==z]
    )
  })
  observe({
    a <- input$State3
    # Can use character(0) to remove all choices
    if (is.null(a))
      a <- character(0)
    # Can also set the label and select items
    updateSelectInput(session, "County3",
                      choices = State3info$County[State3info$State==a]
    )
  })


  output$AQIQualityTable <-
    renderDataTable(weatherinfo[weatherinfo$County == input$County & weatherinfo$State == input$State & weatherinfo$Year == input$Year ,c(1,2,3,5,6,7,8,9,10) ], options = list(columnDefs = list(list(
      targets = 1:9 , className = "right"
    )), pageLength = 5))
  
  
  output$AQIBarGraph  <- renderPlotly ({
    
    plot_ly(data = weatherinfoGather[weatherinfoGather$State == input$State  & weatherinfoGather$County == input$County & weatherinfoGather$Year == input$Year, ], x = reorder(weatherinfoGather[weatherinfoGather$State == input$State  & weatherinfoGather$County == input$County & weatherinfoGather$Year == input$Year, 14],-weatherinfoGather[weatherinfoGather$State == input$State  & weatherinfoGather$County == input$County & weatherinfoGather$Year == input$Year,15 ]), y = weatherinfoGather[weatherinfoGather$State == input$State  & weatherinfoGather$County == input$County & weatherinfoGather$Year == input$Year,15 ],
            color = (reorder(weatherinfoGather[weatherinfoGather$State == input$State  & weatherinfoGather$County == input$County & weatherinfoGather$Year == input$Year, 14],-weatherinfoGather[weatherinfoGather$State == input$State  & weatherinfoGather$County == input$County & weatherinfoGather$Year == input$Year,15 ])) , 
            type = 'bar', colors = c("#2E8B57","#00CED1","#FF8C00","#000080","#C6590F","#8B008B")) %>%
      layout(title = paste("AQI types for", input$Year))
    
  })
  
  output$AQIPiePlot <- 
    renderPlotly({
      colors <- c("#2E8B57","#00CED1","#FF8C00","#000080","#C6590F","#8B008B")
      plot_ly(weatherinfoGather[weatherinfoGather$State == input$State  & weatherinfoGather$County == input$County & weatherinfoGather$Year == input$Year, ], labels = ~ weatherinfoGather[weatherinfoGather$State == input$State  & weatherinfoGather$County == input$County & weatherinfoGather$Year == input$Year, 14], values = ~ weatherinfoGather[weatherinfoGather$State == input$State  & weatherinfoGather$County == input$County & weatherinfoGather$Year == input$Year, 15], type = 'pie',
              textposition = 'inside',
              textinfo = 'label+percent',
              insidetextfont = list(color = '#FFFFFF'),
              marker = list(colors = colors,
                            line = list(color = '#FFFFFF', width = 1)), showlegend = FALSE) %>%
        layout(title = paste('AQI Quality for year ', input$Year),
               xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
               yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
      
    })
  
  output$AQImaxq <- renderPlotly({ 
    
    plot_ly(data = weatherinfoGather3[weatherinfoGather3$State == input$State & weatherinfoGather3$County == input$County,], 
            x = weatherinfoGather3[weatherinfoGather3$State == input$State & weatherinfoGather3$County == input$County,3],
            y = weatherinfoGather3[weatherinfoGather3$State == input$State & weatherinfoGather3$County == input$County,18], 
            color = weatherinfoGather3[weatherinfoGather3$State == input$State & weatherinfoGather3$County == input$County,17],
            colors =c("#2E8B57","#00CED1","#FF8C00"),type="scatter",mode="lines+markers") %>%
      layout(title = "AQI over the years")
    
  })
  
  
  
  output$PollutantTable <-
    renderDataTable(weatherinfo[weatherinfo$County == input$County & weatherinfo$State == input$State & weatherinfo$Year == input$Year ,c(1,2,3,14,15,16,17,18,19) ], 
                    options = list(columnDefs = list(list(
                      targets = 1:9 , className = "right")), pageLength = 5))
  
  
  
  output$PollutantTypeTable <-
    renderDataTable(weatherinfo1[weatherinfo1$County == input$County & weatherinfo1$State == input$State ,c(1,2,3,20,21,22,23,24,25) ], 
                    options = list(columnDefs = list(list(targets = 1:9 , className = "right")), pageLength = 5))
  
  
  
  output$PollutantBarGraph  <- renderPlotly ({
    
    plot_ly(data = weatherinfoGather2[weatherinfoGather2$State == input$State  & weatherinfoGather2$County == input$County & weatherinfoGather2$Year == input$Year, ], x = reorder(weatherinfoGather2[weatherinfoGather2$State == input$State  & weatherinfoGather2$County == input$County & weatherinfoGather2$Year == input$Year, 14],-weatherinfoGather2[weatherinfoGather2$State == input$State  & weatherinfoGather2$County == input$County & weatherinfoGather2$Year == input$Year, 15]), y = weatherinfoGather2[weatherinfoGather2$State == input$State  & weatherinfoGather2$County == input$County & weatherinfoGather2$Year == input$Year,15 ],
            color = reorder(weatherinfoGather2[weatherinfoGather2$State == input$State  & weatherinfoGather2$County == input$County & weatherinfoGather2$Year == input$Year, 14],-weatherinfoGather2[weatherinfoGather2$State == input$State  & weatherinfoGather2$County == input$County & weatherinfoGather2$Year == input$Year, 15]) , 
            type = 'bar', colors = c("#2E8B57","#00CED1","#FF8C00","#000080","#C6590F","#8B008B")) %>%
      layout(title = paste("Pollutant types for", input$Year))
    
    
  })
  
  output$PollutantPiePlot <- 
    renderPlotly({
      colors <- c("#2E8B57","#00CED1","#FF8C00","#000080","#C6590F","#8B008B")
      
      plot_ly(weatherinfoGather2[weatherinfoGather2$State == input$State  & weatherinfoGather2$County == input$County & 
                                   weatherinfoGather2$Year == input$Year, ],
              labels = ~ weatherinfoGather2[weatherinfoGather2$State == input$State  & 
                                              weatherinfoGather2$County == input$County & 
                                              weatherinfoGather2$Year == input$Year, 14], 
              values = ~ weatherinfoGather2[weatherinfoGather2$State == input$State  &
                                              weatherinfoGather2$County == input$County & weatherinfoGather2$Year == input$Year, 15], 
              type = 'pie',
              textposition = 'inside',
              textinfo = 'label+percent',
              insidetextfont = list(color = '#FFFFFF'),
              marker = list(colors = colors,
                            line = list(color = '#FFFFFF', width = 1)),        showlegend = FALSE) %>%
        layout(title = paste('Pollutants for year ', input$Year) ,
               xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
               yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
      
    })
  output$Pollutantline <- renderPlotly({ 
    
    plot_ly(data = weatherinfo1Gather[weatherinfo1Gather$State == input$State & weatherinfo1Gather$County == input$County,], 
            x = weatherinfo1Gather[weatherinfo1Gather$State == input$State & weatherinfo1Gather$County == input$County,3],
            y = weatherinfo1Gather[weatherinfo1Gather$State == input$State & weatherinfo1Gather$County == input$County,21], 
            color = weatherinfo1Gather[weatherinfo1Gather$State == input$State & weatherinfo1Gather$County == input$County,20],
            colors = c("#2E8B57","#00CED1","#FF8C00","#000080","#C6590F","#8B008B"),type="scatter",mode="lines+markers") %>%
      layout(title = "Pollutants % over the years")
    
  })
  
  # Render Leaflet Map for Location Data
  output$Map0 <- renderLeaflet({
    map <- leaflet()
    map <- addTiles(map)
    map <- setView(map, lng = weathermap[weathermap$County.Name == input$County & weathermap$State.Name == input$State,5][1], lat = weathermap[weathermap$County.Name == "Cook" & weathermap$State.Name == "Illinois",4][1], zoom = 4)
    map <- addMarkers(map, lng = weathermap[weathermap$County.Name == input$County & weathermap$State.Name == input$State,5][1], lat = weathermap[weathermap$County.Name == input$County & weathermap$State.Name == input$State,4][1], popup = input$County)
    map
  } )
  
  compare1 <- reactive({
    
    data1 <- weatherinfoGather[(weatherinfoGather$State == input$State1  & weatherinfoGather$County == input$County1 & weatherinfoGather$Year == input$Year1) | 
                                 (weatherinfoGather$State == input$State2  & weatherinfoGather$County == input$County2 & weatherinfoGather$Year == input$Year2) | 
                                 (weatherinfoGather$State == input$State3  & weatherinfoGather$County == input$County3 & weatherinfoGather$Year == input$Year3) ,c(2,14,15) ]
    
    data1 <- data1[order(data1$County),]
    
    data1
  })
  
  output$compareaqi_bar <- renderPlotly({
    
    
    ggplot(compare1(), aes(y=Days, x=`AQI Parameters`, color=`AQI Parameters`, fill=`AQI Parameters`)) + 
      geom_bar( stat="identity") +  facet_wrap(~County) + labs( y = "Days",title = paste("AQI types for", input$Year))+
      theme(axis.title.x=element_blank(),axis.text.x=element_blank(),axis.ticks.x=element_blank(),legend.title =element_blank())
    
  })
  
  compare2 <- reactive({
    
    data2 <- weatherinfo2Gather[(weatherinfo2Gather$State == input$State1  & weatherinfo2Gather$County == input$County1 ) | 
                                  (weatherinfo2Gather$State == input$State2  & weatherinfo2Gather$County == input$County2) | 
                                  (weatherinfo2Gather$State == input$State3  & weatherinfo2Gather$County == input$County3) ,c(2,3,14,15)]
    data2 <- data2[order(data2$County),]
    
    data2
  })
  
  output$compareaqi_line <- renderPlotly({
    
    ggplot(compare2(), aes(y=Count, x=Year, color=AQIP, group =AQIP)) + 
      geom_line() +  facet_wrap(~County) + labs( y = "Days",title = paste("AQI types over the Years"))+
      theme(axis.title.x=element_blank(),legend.title =element_blank())
  })
  
  compare3 <- reactive({
    
    data3 <- weatherinfoGather2[(weatherinfo2Gather$State == input$State1  & weatherinfo2Gather$County == input$County1 & weatherinfo2Gather$Year == input$Year1) | 
                                  (weatherinfo2Gather$State == input$State2  & weatherinfo2Gather$County == input$County2 & weatherinfo2Gather$Year == input$Year2) | 
                                  (weatherinfo2Gather$State == input$State3  & weatherinfo2Gather$County == input$County3 & weatherinfo2Gather$Year == input$Year3) ,c(2,14,15) ]
    
    data3 <- data3[order(data3$County),]
    
    data3
  })
  
  output$comparepollutant_bar <- renderPlotly({
    
    ggplot(compare3(), aes(y=Days, x=Pollutants, color=Pollutants, fill=Pollutants)) + 
      geom_bar( stat="identity") +  facet_wrap(~County) + labs(y = "Days",title = paste("Pollutant types for", input$Year))+
      theme(axis.title.x=element_blank(),axis.text.x=element_blank(),axis.ticks.x=element_blank(),legend.title =element_blank())
    
    
  })
  
  compare4 <- reactive({
    
    data4 <- weatherinfo1Gather[(weatherinfo1Gather$State == input$State1  & weatherinfo1Gather$County == input$County1) | 
                                  (weatherinfo1Gather$State == input$State2  & weatherinfo1Gather$County == input$County2) | 
                                  (weatherinfo1Gather$State == input$State3  & weatherinfo1Gather$County == input$County3) ,c(2,3,20,21)]
    
    data4 <- data4[order(data4$County),]
    
    data4
  })
  output$comparepollutant_line <- renderPlotly({
    
    ggplot(compare4(), aes(y=Count, x=Year, color=NewPollutants, group =NewPollutants )) + 
      geom_line() +  facet_wrap(~County) + labs(y = "Days",title = paste("Pollutant types % over the Years"))+
      theme(axis.title.x=element_blank(),legend.title =element_blank())
    

  })
  
}

shinyApp(server = server, ui = ui)