Project2.Rmd

---
title: "Wellbeing in the UK"
output: 
 flexdashboard::flex_dashboard:
   orientation: rows
   vertical_layout: scroll
   source_code: embed
   theme: simplex
runtime: shiny
---

```{r context="setup", include=FALSE}
library(shiny)
library(ggplot2)
library(dplyr)
library(flexdashboard)
library(plotly)
require(rgdal)
require(leaflet)
require(readxl)
library(jsonlite) # fromJSON
library(utils) # URLencode functions
require(stringr)
require(leaflet.extras)
```

```{r, data_load, results='hide'}
railway_data   <- readOGR("./GBR_rails.shp", GDAL1_integer64_policy=TRUE)  # for lines map
admin_data     <- read.csv("GBR_adm2.csv")                                 # for poly map
admin.load     <- readOGR("./GBR_adm2.shp",  GDAL1_integer64_policy=TRUE)  # for poly map  
happiness_data <- read.csv("HappinessData.csv")    
```

```{r, cleaning}
admin_data        <- admin_data[ -c(2:5, 7, 9, 11:12) ]
names(admin_data) <- c("OBJECTID", "Country", "Area", "Type")
happiness_data    <- subset(happiness_data, happiness_data$Area!="City of London")

# change necessary columns to numeric data for plotting
happiness_data$Happiness        = as.numeric(happiness_data$Happiness)
happiness_data$NoReligion       = as.numeric(happiness_data$NoReligion)
happiness_data$NoSport          = as.numeric(happiness_data$NoSport)
happiness_data$ChildhoodObesity = as.numeric(happiness_data$ChildhoodObesity)
happiness_data$SuicideRAtes     = as.numeric(happiness_data$SuicideRates)
happiness_data$CrimeRates       = as.numeric(happiness_data$CrimeRates)


# merge happiness dataset with administrative areas dataset
admin_data <-left_join(x = admin_data, y = happiness_data, by = "Area")

# merge above dataset with the shape file
admin      <- admin.load[admin.load$ID_2 %in% admin_data$OBJECTID,]
admin@data <- merge(admin@data, admin_data, sort=FALSE, by.x='ID_2', by.y='OBJECTID') %>%
  distinct(Area, .keep_all=TRUE)  # don't use area duplicates when merging
```


Inputs {.sidebar}
=======================================================================

```{r context="render"}
radioButtons(inputId = "countrySelect", 
                   label = "Country Filter:",
                   choices = c('England'          = 'England',
                               'Northern Ireland' = 'Northern Ireland',
                               'Scotland'         = 'Scotland',
                               'Wales'            = 'Wales'),
                   selected = ('England'))
```

```{r context="render"}
checkboxGroupInput(inputId = 'Year',
                   label = "Select Year(s):",
                   choices = c('2011' = '2011',
                               '2012' = '2012',
                               '2013' = '2013',
                               '2014' = '2014',
                               '2015' = '2015',
                               '2016' = '2016'),
                   selected = ('2016'))
```

```{r}
admin_subset <- reactive({
  admin_data %>%
    # filter for year in happiness data
    # but keep location information when ther eis no happiness data
    filter(Year %in% input$Year | is.na(admin_data$Year))
})
```

```{r context="render"}
selectInput(inputId = 'x_input',
            label = "Plotting against Happiness:",
            choices = c('No Religion'       = 'NoReligion',
                        'No Sport'          = 'NoSport',
                        'Childhood Obesity' = 'ChildhoodObesity',
                        'Suicide Rates'     = 'SuicideRates',
                        'Crime Rates'       = 'CrimeRates',
                        selected = 'NoSport'))
```

```{r context="render"}
output$downloadData <- downloadHandler(
  filename = function() {
    paste('data-', Sys.Date(), '.csv', sep='')
  },
    content = function(file) {
      write.csv(admin_subset(), file)
  }
)

downloadLink(outputId = "downloadData", label = "Click Here to Download Data")
```


Main
=======================================================================


Row {}
-----------------------------------------------------------------------

### United Kingdom Counties and Railroads

```{r context="server"}
countryInputs <- reactive({
  admin2 <- subset(admin, Country == input$countrySelect)
  return(admin2)
  })

output$leaflet <- renderLeaflet({
  leaflet() %>%
    
    # Determine base maps
    addProviderTiles("OpenStreetMap.HOT", group = "Street Map") %>%
    addProviderTiles("Esri.WorldTerrain", group = "World Terrain Map") %>%
    
    setView(lng = -5, lat = 55, zoom = 5)  %>%
    
    # Add in layers control
    addLayersControl(
      baseGroups = c("Street Map", "World Terrain Map"),
      options = layersControlOptions(collapsed=FALSE))
   })

```

```{r context="render"}
# leaflet proxy updating layer
observe({
  proxy <- leafletProxy("leaflet")
  
  proxy %>%
    
    clearShapes() %>%
    
    addPolygons(data = countryInputs(),color = "tomato", fillOpacity = 0.2, weight=2,
                highlightOptions = highlightOptions(color="White", bringToFront=TRUE),
                popup = ~paste0(countryInputs()$Area)) %>%
    
    addPolylines(data = railway_data, color = "black", weight=1, opacity=1)
  })
   
leafletOutput("leaflet")
   
```

Row {data-height=100}
-----------------------------------------------------------------------

This map represents administrative areas (such as counties) of the United Kingdom along with lines indicating the presence of railroads.  Multiple converging railroads signifies a "hub" of activity, such as a city like London.  This mape and the graphs below give you the opportunity to look at data related to overall wellbeing and view the area geographically--for example, Ealing, England had the highest suicide rates of 2016 and it is located near London with multiple train lines running through the small county.

Row {data-height=400}
-----------------------------------------------------------------------

### Relationship between Input and Happiness

```{r context="server"}
output$InputPoint <- renderPlotly({
  ggplotly(
        
    # subset out any rows that do not have happiness data
    ggplot(subset(admin_subset(), !is.na(admin_subset()$Year)), 
           aes_string(x=input$x_input, y='Happiness', color='Happiness')) + 
      scale_color_gradient(low = 'red', high = 'lightyellow') +
      geom_point() +
      theme(legend.position="none"),
    tooltip = c('x', 'y')
  )
})
```

```{r context="render"}
plotlyOutput('InputPoint')
```

Row {data-height=250}
-----------------------------------------------------------------------

```{r context="server"}
output$InputBar <- renderPlotly({
  ggplotly(
    
    # subset out any rows that do not have happiness data
    # those rows were needed for mapping but not plotting
    ggplot(subset(admin_subset(), !is.na(admin_subset()$Year)), 
           aes_string(x=paste0('reorder(Area, desc(Happiness))'), y=input$x_input, fill='Happiness')) + 
      scale_fill_gradient(low = 'red', high = 'lightyellow') +
      geom_bar(stat="identity", position=position_dodge()) + 
      theme(axis.title.y=element_blank(), axis.text.x=element_blank(),legend.position="none") + 
      coord_flip(),
    tooltip = c('y')
  )
})
```

```{r context="render"}
plotlyOutput('InputBar')
```


```{r context="server"}
output$HappBar <- renderPlotly({
  ggplotly(
        
    # subset out any rows that do not have happiness data
    ggplot(subset(admin_subset(), !is.na(admin_subset()$Year)), 
           aes(x=reorder(Area, desc(Happiness)), y=Happiness, fill=Happiness)) +
      scale_fill_gradient(low = 'red', high = 'lightyellow') +
      geom_bar(stat="identity", position=position_dodge()) +
      theme(axis.title.y=element_blank(), axis.text.x=element_blank(), legend.position="none") + 
      coord_flip(),
    tooltip = c('y')
  )
})
```

```{r context="render"}
plotlyOutput('HappBar')
```


Data Table
=======================================================================

Row {}
-----------------------------------------------------------------------

### Avg. Happiness

```{r}
renderValueBox({
  avg_Happ <- mean(admin_subset()$Happiness, na.rm = TRUE)
  valueBox(avg_Happ)
})
```

### Avg. Not Religious

```{r}
renderValueBox({
  avg_Religion <- mean(admin_subset()$NoReligion, na.rm = TRUE)
  valueBox(round(avg_Religion, 2))
})
```

### Avg. No Sport

```{r}
renderValueBox({
  avg_Sport <- mean(admin_subset()$NoSport, na.rm = TRUE)
  valueBox(round(avg_Sport, 2))
})
```

Row {}
-----------------------------------------------------------------------

### Avg. Childhood Obesity

```{r}
renderValueBox({
  avg_Ob <- mean(admin_subset()$ChildhoodObesity, na.rm = TRUE)
  valueBox(round(avg_Ob, 2))
})
```

### Avg. Suicide Rates

```{r}
renderValueBox({
  avg_SuRa <- mean(admin_subset()$SuicideRates, na.rm = TRUE)
  valueBox(round(avg_SuRa, 2))
})
```

### Avg. Crime Rates

```{r}
renderValueBox({
  avg_Crime <- mean(admin_subset()$CrimeRates, na.rm = TRUE)
  valueBox(round(avg_Crime, 2))
})
```


Row {data-height=250}
-----------------------------------------------------------------------

### Happiness Dataset

```{r context="server"}
output$DataTable <- DT::renderDataTable({
  DT::datatable(admin_subset(), 
                options=list(pageLength=10), 
                rownames=FALSE)
  })
```

```{r context="render"}
DT::dataTableOutput('DataTable')
```