Merge remote-tracking branch 'origin/main'

easternshore-dashboard/esva-atlas-prototype/app-leaflet-test.R

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+# https://walkerke.shinyapps.io/neighborhood_diversity/
+# https://github.com/walkerke/neighborhood_diversity/blob/master/neighborhood_diversity.Rmd
+
+library(shiny)
+library(tidyverse)
+library(readxl)
+library(sf)
+library(leaflet)
+library(plotly)
+library(RColorBrewer)
+library(rmapshaper)
+library(tigris)
+options(tigris_use_cache = TRUE)
+library(highcharter)
+
+library(qs)
+
+source("functions/utils.R")
+
+es_blkgrp <- readRDS("www/flood_composite_blkgrp.RDS")
+blkgrp_names <- read_excel("www/tract_names.xlsx", sheet = "blkgrp2020")
+pop <- read_csv("www/population_blkgrp.csv")
+# data <- read_csv("blkgrp_pop_data.csv")
+
+# make 10 percent columns
+flood_blkgrp <- es_blkgrp %>% 
+  mutate(across(starts_with("sum"), ~round(.x/cells *100, 2) , .names = "per_{.col}"))
+
+counties_geo <- counties(state = 'VA', year = 2022, cb = TRUE) # from tigris / used 2021 bc 2022 caused error
+counties_geo <- counties_geo %>% subset(COUNTYFP %in% c("001", "131"))
+counties_geo <- st_transform(counties_geo, 4326)
+
+# below variables for leaflet map boundary settings
+bbox <- st_bbox(counties_geo) %>% as.vector()
+
+blkgrp_names <- blkgrp_names %>% 
+  mutate(localityfips = str_pad(localityfips, width = 3, side = "left", pad = "0"),
+         tract = str_pad(tract, width = 6, side = "left", pad = "0"),
+         GEOID = paste0("51",localityfips,tract,blkgrp))
+
+flood_blkgrp <- flood_blkgrp %>% 
+  left_join(blkgrp_names)
+
+input_choices <- flood_blkgrp %>% 
+  select(starts_with("per_sum"))%>% 
+  st_drop_geometry %>% 
+  colnames() 
+
+# view(input_choices)
+
+flood_blkgrp <- st_transform(flood_blkgrp, 4326)
+# es_blkgrp <- rmapshaper::ms_simplify(es_blkgrp)
+
+full_tracts <- flood_blkgrp %>%
+  mutate(tract_id = GEOID) %>% 
+  dplyr::select(GEOID, tract_id, names, locality, geometry,
+                starts_with("per_sum")
+                # mean_SeaLevelRise, mean_FEMAFloodZones, mean_StormSurge, mean_HazardNumber,
+                # mean_HighTideFlooding
+                ) 
+
+
+## add demographic/population/housing data ----
+pop <- pop %>% 
+  select(GEOID, totpop_est,
+         whiteper_est, blackper_est, ltnxper_est, remainper_est) %>% 
+  mutate(GEOID = as.character(GEOID),
+         whiteper_est = round(whiteper_est,0),
+         blackper_est = round(blackper_est,0),
+         ltnxper_est = round(ltnxper_est,0),
+         remainper_est = round(remainper_est,0))
+
+es_blkgrp_pop <- full_tracts %>% 
+  left_join(pop)
+
+# per_sum_slr1.ft
+# per_sum_slr2.ft
+# per_sum_slr3.ft
+# per_sum_vzone.1percent
+# per_sum_azone.1percent
+# per_sum_azone.2percent
+# per_sum_ss.cat1
+# per_sum_ss.cat2
+# per_sum_ss.cat3
+# per_sum_HighTideFlooding
+
+per_sum_slr1.ft <- es_blkgrp_pop %>% 
+  mutate(entropy = per_sum_slr1.ft,
+         group = "SeaLevelRise") %>% 
+  select(tract_id, names, locality, geometry,group,
+         entropy, totpop_est,
+         whiteper_est, blackper_est, ltnxper_est, remainper_est)
+
+per_sum_slr2.ft <- es_blkgrp_pop %>% 
+  mutate(entropy = per_sum_slr2.ft,
+         group = "SeaLevelRise") %>% 
+  select(tract_id, names, locality, geometry, group,
+         entropy, totpop_est,
+         whiteper_est, blackper_est, ltnxper_est, remainper_est)
+
+per_sum_slr3.ft <- es_blkgrp_pop %>% 
+  mutate(entropy = per_sum_slr3.ft,
+         group = "SeaLevelRise") %>% 
+  select(tract_id, names, locality, geometry, group,
+         entropy, totpop_est,
+         whiteper_est, blackper_est, ltnxper_est, remainper_est)
+
+per_sum_ss.cat1 <- es_blkgrp_pop %>% 
+  mutate(entropy = per_sum_ss.cat1,
+         group = "StormSurge") %>% 
+  select(tract_id, names, locality, geometry, group,
+         entropy, totpop_est,
+         whiteper_est, blackper_est, ltnxper_est, remainper_est)
+
+per_sum_ss.cat2 <- es_blkgrp_pop %>% 
+  mutate(entropy = per_sum_ss.cat2,
+         group = "StormSurge") %>% 
+  select(tract_id, names, locality, geometry, group,
+         entropy, totpop_est,
+         whiteper_est, blackper_est, ltnxper_est, remainper_est)
+
+data_test <- list(per_sum_slr1.ft=per_sum_slr1.ft, per_sum_slr2.ft=per_sum_slr2.ft, per_sum_slr3.ft=per_sum_slr3.ft, per_sum_ss.cat1=per_sum_ss.cat1, per_sum_ss.cat2=per_sum_ss.cat2)
+
+
+## reshape for heatmap ----
+flood_blkgrp_long <- flood_blkgrp %>% 
+  select(GEOID, COUNTYFP, names, starts_with("per")) %>% 
+  pivot_longer(starts_with("per"), names_to = "variable", values_to = "risk") %>% 
+  mutate(group = case_when(str_detect(variable, "HighTide") ~ "HighTideFlooding",
+                           str_detect(variable, "ss.cat") ~ "StormSurge",
+                           str_detect(variable, "slr") ~ "SeaLevelRise",
+                           str_detect(variable, "vzone|azone") ~ "FEMACat"),
+    # variable = str_remove(variable, "per_sum_"),
+         variable = factor(variable, levels = c("per_sum_HighTideFlooding", "per_sum_vzone.1percent", "per_sum_azone.1percent", "per_sum_azone.2percent",
+                                                "per_sum_ss.cat1", "per_sum_ss.cat2", "per_sum_ss.cat3", "per_sum_slr1.ft", "per_sum_slr2.ft", "per_sum_slr3.ft"),
+                           labels = c("HighTideFlooding", "vzone.1percent", "azone.1percent", "azone.2percent",
+                                      "ss.cat1", "ss.cat2", "ss.cat3", "slr1.ft", "slr2.ft", "slr3.ft")
+                           # labels = c("High Tide", "FEMA V (1%)", "FEMA A (1%)", "FEMA A (2%)", 
+                           #            "Surge Category 1", "Surge Category 2", "Surge Category 3", "Sea Level 1 Foot",
+                           #            "Sea Level 2 Foot", "Sea Level 3 Foot")
+                           )
+
+         )
+
+ui <- fluidPage(
+  fluidRow(
+    column(2, 
+           prettyRadioButtons(
+             inputId = "metro_name",
+             label = NULL,
+             selected = NULL,
+             status = "primary",
+             shape = c("round"),
+             width = NULL,
+             choices = c("per_sum_slr1.ft", "per_sum_slr2.ft", "per_sum_slr3.ft", "per_sum_ss.cat1","per_sum_ss.cat2")
+           )
+           # radioButtons("metro_name", "Datasets", choices = c("mean_SeaLevelRise", "mean_FEMAFloodZones", "mean_StormSurge", "mean_HighTideFlooding"), selected = "mean_SeaLevelRise")
+           ),
+    column(3, plotlyOutput('heatmap', height = '600px')),
+    column(5,leafletOutput('map', height = '550px')),
+    # column(5, highchartOutput('raceplot'))
+
+  ),
+  fluidRow(
+    column(4, highchartOutput('raceplot')),
+    column(8, plotlyOutput('scatter', width = "100%"))
+  )
+  # selectInput('metro_name', label = 'Select flood risk', choices = input_choices, selected = "mean_HighTideFlooding"),
+
+
+)
+
+server <- function(input, output, session){
+
+  metro <- reactive({
+
+    data_test[[input$metro_name]]
+
+  })
+
+
+  # Draw the map without selected tracts
+
+  output$map <- renderLeaflet({
+
+    m <- metro()
+
+    pal <- colorNumeric('OrRd', c(0,100))
+
+    map <- leaflet(m) %>%
+      addProviderTiles('CartoDB.Positron') %>%
+      # fitBounds(bbox[1], bbox[2], bbox[3], bbox[4]) %>% 
+      clearShapes() %>%
+      addPolygons(weight = 1,
+                  color = "#FFFFFF",
+                  # stroke = FALSE, 
+                  smoothFactor = 0.2, 
+                  fillColor = ~pal(entropy), fillOpacity = 0.7, 
+                  layerId = ~tract_id) %>%
+      addLegend(position = 'bottomright', pal = pal, 
+                # values = m$entropy, 
+                values = c(0,100),
+                labFormat = labelFormat(suffix = "%"),
+                title = input$metro_name)
+
+    map
+
+  })
+
+  # Click event for the map (will use to generate chart)
+  click_tract <- eventReactive(input$map_shape_click, {
+
+    x <- input$map_shape_click
+
+    y <- x$id
+
+    return(y)
+
+  })
+
+  tract_ids <- reactive({
+    eventdata <- event_data("plotly_selected", source = "source")
+    if (is.null(eventdata)) {
+
+      return(NULL) # do nothing
+
+    } else {
+
+      tracts <- eventdata$key
+
+      return(tracts)
+    }
+  })
+
+
+
+  observe({
+
+    req(tract_ids())
+
+    proxy <- leafletProxy('map')
+
+    sub <- filter(metro(), tract_id %in% tract_ids())
+
+    box <- st_bbox(sub) %>% as.vector()
+
+    # Clear old selection on map, and add new selection
+    proxy %>%
+      clearGroup(group = 'sub') %>%
+      addPolygons(data = sub, fill = FALSE, color = '#FFFF00',
+                  opacity = 1, group = 'sub', weight = 1.5) 
+    # %>%
+    #   fitBounds(lng1 = box[1],
+    #             lat1 = box[2],
+    #             lng2 = box[3],
+    #             lat2 = box[4])
+
+  })
+
+
+  observeEvent(click_tract(), {
+
+    # Add the clicked tract to the map in aqua, and remove when a new one is clicked
+    map <- leafletProxy('map') %>%
+      removeShape('htract') %>%
+      addPolygons(data = filter(metro(), tract_id == click_tract()), fill = FALSE,
+                  color = '#00FFFF', opacity = 1, layerId = 'htract', 
+                  weight = 1.6)
+
+  })
+
+
+
+  tract_data <- reactive({
+
+    # Fetch data for the clicked tract
+    return(filter(metro(), tract_id == click_tract()))
+
+  })
+
+  # Here, we draw the diversity gradient with ggplotly
+  output$scatter <- renderPlotly({
+
+    m <- metro() %>%
+      mutate(text = glue::glue(
+        "Area Name: {names}<br>Population: {round(totpop_est, 0)}<br>Value: {round(entropy, 2)}"
+      ))
+
+    p1a <- ggplot(m) + 
+      geom_point(aes(totpop_est, entropy, key = tract_id, 
+                                  text = text, color = entropy),
+                 alpha = 0.8) + 
+      scale_color_distiller(palette = "OrRd",
+                            limits = c(0,100),
+                            direction = 1) +
+      theme_minimal(base_size = 14) + 
+      # stat_smooth(aes(totpop_est, entropy), color = 'red', method = 'loess', span = input$span, se = FALSE) +
+      labs(x = "Population",
+           y = "")+
+      theme(legend.position = "none")
+
+    g <- ggplotly(p1a, source = 'source', tooltip = "text") %>% 
+      layout(dragmode = 'lasso', 
+             # clickmode = "event+select",
+             yaxis = list(title = input$metro_name), 
+             margin = list(l = 100), 
+             font = list(family = 'Open Sans', size = 16)) %>%
+      event_register("plotly_selecting")
+
+  })  
+
+
+  ### Race/ethnicity, selected tract (click on the map to show chart)
+  output$raceplot <- renderHighchart({
+
+    td <- tract_data()
+
+    # lookup <- tigris::fips_codes
+    # state <- unique(filter(lookup, state_code == str_sub(td$tract_id, 1, 2))$state_name)
+    # county <- unique(filter(lookup, state_code == str_sub(td$tract_id, 1, 2), 
+    #                         county_code == str_sub(td$tract_id, 3, 5))$county)
+
+    chart <- highchart() %>%
+      hc_chart(type = 'column') %>%
+      hc_legend(enabled = FALSE) %>%
+      hc_xAxis(categories = c('White', 'Black', 'Hispanic', 'All Others'), title = list(text = 'Race/ethnicity')) %>%
+      hc_yAxis(title = list(text = 'Percent of Population')) %>%
+      hc_plotOptions(series = list(dataLabels = list(enabled = TRUE))) %>%
+      hc_add_series(name = 'Population, 2020', data = c(td$whiteper_est,
+                                                        td$blackper_est,
+                                                        td$ltnxper_est,
+                                                        td$remainper_est)) %>%
+      hc_title(text = paste0(td$names, ' (Block group ', td$tract_id, '), ', td$locality, ', Virginia'),
+               align = 'left') %>%
+      hc_subtitle(text = paste0(input$metro_name, ': ', as.character(round(td$entropy, 2)), '<br>Total Population:', as.character(td$totpop_est)),
+                  align = 'left') %>%
+      hc_add_theme(hc_theme_smpl()) %>%
+      hc_colors(c('#d01010', '#d01010')) %>%
+      hc_tooltip(enabled = FALSE)
+
+
+    chart
+
+  })
+
+
+  output$heatmap <- renderPlotly({
+
+    sel <- input$metro_name
+
+    vars <- if(str_detect(sel, "ss.cat")){
+      flood_blkgrp_long %>% filter(group == "StormSurge")
+    } else if (str_detect(sel, "slr")) {
+      flood_blkgrp_long %>% filter(group == "SeaLevelRise")
+    } else { flood_blkgrp_long }
+
+    p <- ggplot(vars, aes(x=variable,y=names, key = GEOID)) + 
+      geom_tile(aes(fill = risk)) +
+      scale_fill_distiller(palette = "OrRd", direction = 1)+
+      theme(legend.position="none",
+            axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
+
+    ggplotly(p) %>% 
+      layout(xaxis = list(side = "top", title=""), 
+                      yaxis = list(title = ""),
+                      showlegend = FALSE) %>% 
+      config(displayModeBar = FALSE)
+
+    g <- ggplotly(p, source = 'source2', tooltip = "text") %>% 
+      layout(clickmode = 'event+select', 
+             xaxis = list(side = "top", title=""), 
+             yaxis = list(title = ""),
+             showlegend = FALSE,
+             margin = list(l = 100), 
+             font = list(family = 'Open Sans', size = 16)) %>% 
+      config(displayModeBar = FALSE) %>%
+      event_register("plotly_selecting")
+
+ } )
+
+
+}
+
+shinyApp(ui, server)