Interactive version of the dashboard can be accessed here

For this project, I created a dashboard that uses Google’s COVID-19 Community Mobility Reports of the Netherlands and visualizes the data so that the viewer can explore trends over time, differences between area categories, and provinces.

All credit for the instructions and data go to the Tillburg science hub.

Code can be found below.

library(shiny)
library(plotly)
library(DT)
library(rsconnect)
mobility <- read.csv("C:/Users/shasu/OneDrive/Desktop/R Files/mobility_data.csv", sep = ';')
mobility$Date <- as.Date(mobility$Date)
mobility$Province <- as.factor(mobility$Province)


ui <- fluidPage(
  sidebarLayout(
    sidebarPanel(
      h2("COVID-19 Mobility Data"),
      selectInput(inputId = "dv", label = "Category",
                  choices = c("Retail_Recreation", "Grocery_Pharmarcy", "Parks", "Transit_Stations", "Workplaces", "Residential"),
                  selected = "Grocery_Pharmarcy"),
      selectInput(inputId = "provinces", "Province(s)",
                  choices = levels(mobility$Province),
                  multiple = TRUE,
                  selected = c("Utrecht", "Friesland", "Zeeland")),
      dateRangeInput(inputId = "date", "Date range",
                     start = min(mobility$Date),
                     end   = max(mobility$Date)),
      downloadButton(outputId = "download_data", label = "Download"),
    ),
    mainPanel(
      plotlyOutput(outputId = "plot"), br(),
      em("Postive and negative percentages indicate an increase and decrease from the baseline period (median value between January 3 and February 6, 2020) respectively."),
      br(), br(), br(),
      DT::dataTableOutput(outputId = "table")
    )
  )
)

server <- function(input, output) {
  filtered_data <- reactive({
    subset(mobility,
           Province %in% input$provinces &
             Date >= input$date[1] & Date <= input$date[2])})
  
  output$plot <- renderPlotly({
    ggplotly({
      p <- ggplot(filtered_data(), aes_string(x="Date", y=input$dv, color="Province")) +
        geom_point(alpha=0.5) + theme(legend.position = "none") +
        ylab("% change from baseline")
      
      p
    })
  })
  
  output$table <- DT::renderDataTable({
    filtered_data()
  })
  
  output$download_data <- downloadHandler(
    filename = "download_data.csv",
    content = function(file) {
      data <- filtered_data()
      write.csv(data, file, row.names = FALSE)
    }
  )
  
}

shinyApp(ui = ui, server = server)