# Load Midwest
data("midwest", package = "ggplot2")
midwest <- data.frame(midwest)

# Select data of interest for analysis
table <- select(midwest, county, state, poptotal, popdensity, percollege, percprof,
                percpovertyknown, inmetro)
table <- table %>%
  mutate(inmetro = gsub("0", "Not Available", inmetro),
         inmetro = gsub("1", "Available", inmetro))

# Prep data for widget
min_density <- min(table$popdensity)
max_density <- max(table$popdensity)
min_percprof <- min(table$percprof)
max_percprof <- max(table$percprof)

# Create ui page
ui_page <- navbarPage(
  tabPanel(
    "Urbanization",
    titlePanel("Urbanization vs. Poverty Rate"),
    sidebarLayout(
      sidebarPanel(
        # Slider (Widget1)
        h4("Select Population Density Range of Interest"),
        sliderInput("popdensity",
                    label = h4("Population Density"),
                    min = min_density,
                    max = max_density,
                    value = c(min_density, max_density)
        ),

        # Check Box (Widget2)
        h4("Select State(s) of Interest"),
        checkboxGroupInput("States",
                           label = h3("Select the State(s)"),
                           choices = list("IL" = "IL", "IN" = "IN", "MI" = "MI", "OH" = "OH", "WI" = "WI"),
                           selected = NULL),
        hr(),
        fluidRow
      ),
      mainPanel(
        plotOutput("bar_chart")
      )
    )
  ),

  tabPanel(
    "Profession",
    titlePanel("Professional Occupation vs. Poverty Rate"),
    sidebarLayout(
      sidebarPanel(
        h4("Select Percent Population with Professional Occupation Range of Interest"),
        sliderInput("percprof",
                    label = h4("Percent Population with Professional Occupation"),
                    min = min_percprof,
                    max = max_percprof,
                    value = c(min_percprof, max_percprof)
                    ),
        titlePanel("States"),
        h4("Input your", strong("State"), ("of interest")),
        h6(em("Please enter the abbreviation in all caps.")),
        textInput("state", label = "State of Interest")
        #textOutput("state_name")
      ),
      mainPanel(
        plotOutput("scatter_plot")
      )
    )
  )
)

# Slider of Population Density and Checkbox (PAGE 1)
  filter1 <- reactive({
    filtered_data <- filter(table, popdensity >= input$popdensity[[1]], poptotal <= input$popdensity[[2]])
    if (input$States != "") {
      filtered_table <- table %>%
        filter(popdensity >= input$popdensity[[1]], popdensity <= input$popdensity[[2]]) %>%
        filter(state %in% input$States) %>%
        group_by(inmetro) %>%
        summarize(avg_poverty = mean(percpovertyknown))
    }
    return(filtered_data)
  })

  # Bar Chart Output
  output$bar_chart <- renderPlot({
    bar_chart <- ggplot(data = filter1(), aes(x = inmetro, y = avg_poverty)) +
      geom_bar(stat = "identity", fill = "steelblue") +
      theme_minimal() +
      labs(title = "Urbanization vs. Poverty Rate",
           x = "Metro Availability",
           y = "Average Poverty Rate")
    bar_chart
  })

  # Slider of Percent Profession and Text Input (PAGE 2)
  filter2 <- reactive({
    filtered <- filter(table, percprof >= input$percprof[[1]], percprof <= input$percprof[[2]])
    if (input$state != "") {
      filtered <- table %>%
        filter(percprof >= input$percprof[[1]], percprof <= input$percprof[[2]]) %>%
        filter(state == input$state)
    }
    return(filtered)
  })

  # Scatter Plot
  output$scatter_plot <- renderPlot({
    scatter_plot <- ggplot(data = filter2(), mapping = aes(x = percprof, y = percpovertyknown)) +
      geom_point(aes(col = state), size = 3) +
      geom_smooth(method = "lm", col = "firebrick", size = 2) +
      coord_cartesian(xlim = c(0, max_percprof), ylim = c(0, 100)) +
      labs(title = "Percent with Professional Occupation vs. Poverty Rate",
           x = "Percent of Population with Professional Occupation",
           y = "Poverty Rate")
    scatter_plot
  })
}```