Shiny reactive

DESCRIPTION

@@ -13,17 +13,18 @@ Depends:
     R (>= 3.6.0)
 Imports: 
     colourvalues,
+    ggplot2,
     shiny (>= 1.3.2),
     leaflet (>= 2.0.2),
     sf (>= 0.7.6),
-    tmap (>= 2.3),
     mapdeck (>= 0.2.1)
+Suggests: 
+    knitr,
+    rmarkdown,
+    tmap
 Remotes:
     SymbolixAU/geojsonsf,
     SymbolixAU/spatialwidget,
     SymbolixAU/mapdeck
-RoxygenNote: 6.1.1
-Suggests: 
-    knitr,
-    rmarkdown
+RoxygenNote: 7.0.1
 VignetteBuilder: knitr

NAMESPACE

@@ -5,5 +5,4 @@ export(shinyAppServer)
 export(shinyAppUI)
 import(leaflet)
 import(shiny)
-import(tmap)
 importFrom(graphics,hist)

R/health-calculations.R

@@ -0,0 +1,112 @@
+# Average global mortality from WHO database 
+get_mortality = function() {
+    #x = read.csv("../who3/health-econ/who-mortality.csv")
+    #names(x) = c("country", "population", "deaths", "remove")
+    #x$remove = NULL
+    #x = x [!is.na(x$deaths), ]
+    #mortality = mean(x$deaths / x$population)
+    0.007425708
+}
+
+# Mode shift response based entirely on Accra walking statistics
+mode_shift_response = function(mode_incr = 0.01, city_pop, mortality) {
+    # Accra data for distance walked to market
+    d_market = c(0.5, 1.5, 2.5, 4.5, 8.5)
+    p_market = c(0.273, 0.212, 0.061, 0.424, 0.03)
+    d_market = sum(d_market * p_market)
+
+    # Accra data for distance walked to trotro
+    d_tro = c(0.25, 0.75, 1.5, 3.5, 7.5)
+    p_tro = c(0.832, 0.119, 0.023, 0.005, 0.022)
+    d_tro = sum(d_tro * p_tro)
+
+    # Estimate of distance walked to work based on relative frequencies of trips
+    # # to market and trotro
+    d_work = (0.474 * d_market + 0.19 * d_tro) / (0.474 + 0.19)
+
+    # Accra data for numbers of weekly walking trips
+    n_walk = c(5, 15.5, 25.5, 35.5, 50.5, 80.5)
+    p_walk = c(0.64, 0.204, 0.062, 0.029, 0.034, 0.007)
+    n_walk = 7 * sum(n_walk * p_walk)
+
+    # Reference weekly walking distance
+    d_walk_ref = (n_walk - 5) * d_tro + 2.5 * 0.474 * d_work + 2.5 * d_market
+
+    # Change in daily distance walked to work in response to mode_incr for
+    # walking
+    d_work = ((0.474 * (1 + mode_incr)) * d_market +
+        (0.19 * (1 + mode_incr)) * d_tro) / (0.474 + 0.19)
+    # Change in daily distance walked in general in response to mode_incr for
+    # walking
+    d_walk = (n_walk - 5) * (1 + mode_incr) * d_tro +
+        2.5 * 0.474 * (1 + mode_incr) * d_work +
+        2.5 * (1 + mode_incr) * d_market
+
+    # Overall risk ratio for change in distance walked:
+    rr = 0.114 * d_walk / d_walk_ref - 0.114
+    mortality_here = city_pop * mortality * rr
+    data.frame(mode_shift = mode_incr,
+               dist_ref = d_walk_ref,
+               dist = d_walk,
+               increase = d_walk / d_walk_ref - 1,
+               rr = rr,
+               d_mortality = mortality_here)
+}
+
+get_scenario_results = function(city = "Accra", has_tram = FALSE) {
+    nm = "scenario-results-table.csv"
+    f = system.file(nm, package = "upthat")
+    if(f == "") {
+        u = paste0 ("https://github.com/ATFutures/upthat/releases/",
+                     "download/0.0.2/", nm)
+        path = dirname(system.file("net.Rds", package = "upthat"))
+        utils::download.file(u, destfile = file.path(path, nm))
+        f = system.file(nm, package = "upthat")
+    }
+    mode_shift = utils::read.csv(f)
+    mode_shift [tolower(mode_shift$City) == tolower(city) &
+                mode_shift$has_tram == has_tram, ]
+}
+
+get_population = function(city) {
+    switch(city,
+           "Accra" = 2.27e6,
+           "Kathmandu" = 1.74e6)
+}
+
+
+calc_exposure = function(city = "Accra", has_tram = FALSE) {
+    # Assume fixed PM2.5 values as for Accra
+    pm25bg = 35
+    pm25max = 50
+
+    mode_shift = get_scenario_results(city = city, has_tram = has_tram)
+
+    # pm25bg modified by reduction in car usage due to mode shift:
+    car_red = mode_shift$car / 100
+    pm25bg_mod = pm25bg + car_red * (pm25max - pm25bg) / pm25bg
+    # presume average walking concentrations half way to max value:
+    pm25walk = (pm25max + pm25bg_mod) / 2
+
+    response = mode_shift_response(mode_incr = mode_shift$walking / 100,
+                                    city_pop = get_population(city),
+                                    mortality = get_mortality())
+
+    mode_shift = cbind(mode_shift, response [, -1])
+
+    # average weekly concentration for reference case:
+    walk_time = response$dist_ref / 5.3
+    non_walk_time = 24 * 7 - walk_time
+    pm25_ref = (pm25bg_mod * non_walk_time + pm25walk * walk_time) / (24 * 7)
+
+    # modified average weekly concentration for scenario
+    walk_time = response$dist / 5.3
+    non_walk_time = 24 * 7 - walk_time
+    pm25_scenario = (pm25bg_mod * non_walk_time + pm25walk * walk_time) / (24 * 7)
+    # capped at 50, but all well below here, so can be left as is
+
+    d_exposure = pm25_scenario - pm25_ref
+    mode_shift$exposure = get_population(city) *
+        get_mortality() * d_exposure * 0.07 / 10
+    return(mode_shift)
+}

R/server.R

@@ -4,8 +4,6 @@
 #' @param output provided by shiny
 #' @param session The shiny session
 
-#' @import leaflet
-#' @import tmap
 #' @export
 # Define server logic required to draw a histogram
 shinyAppServer = function(input, output, session) {
@@ -38,6 +36,9 @@ shinyAppServer = function(input, output, session) {
     "Found these Rds files: ",
     paste0(rds_files_available, collapse = ", ")
   )
+
+  ggplot2::theme_set(ggplot2::theme_minimal())
+
   output$mymap = mapdeck::renderMapdeck({
     mapdeck::mapdeck(style = "mapbox://styles/mapbox/light-v10")
   })
@@ -59,7 +60,7 @@ shinyAppServer = function(input, output, session) {
       message("Reading this matching file: ", matching_file)
       net <<- readRDS(matching_file)
       net$layer = net$flow
-      plot_layer(net, input$layer, update_view = TRUE)
+      plot_map(net, input$layer, update_view = TRUE)
     }
   )
 
@@ -77,7 +78,7 @@ shinyAppServer = function(input, output, session) {
           net$layer = net$flow
         }
       }
-      plot_layer(net, input$layer, update_view = FALSE)
+      plot_map(net, input$layer, update_view = FALSE)
     }
   )
 
@@ -93,14 +94,23 @@ shinyAppServer = function(input, output, session) {
           net$layer = net$flow
         }
       }
-      plot_layer(net, input$layer, update_view = TRUE)
+      plot_map(net, input$layer, update_view = TRUE)
     }
     )
+
+  x = reactive({
+    g = plot_chart(city = input$city_sc)
+    return(g)
+  })
+  output$plot = renderPlot({
+    print(x())
+  })
+
 }
 
-plot_layer = function(net, leg_title, update_view = FALSE) {
+plot_map = function(net, leg_title, update_view = FALSE) {
   net$width = 100 * net$layer / max(net$layer, na.rm = TRUE)
-  cols = rgb(colourvalues::get_palette("inferno"), maxColorValue = 255)
+  cols = grDevices::rgb(colourvalues::get_palette("inferno"), maxColorValue = 255)
   variables = seq(min(net$layer), max(net$layer), length.out = 5)
   if (variables [1] < 1e-6) {
     variables [1] = 0
@@ -126,3 +136,16 @@ plot_layer = function(net, leg_title, update_view = FALSE) {
       layer_id = "mylayer"
     )
 }
+
+plot_chart = function(city) {
+    x = calc_exposure(city = city, has_tram = FALSE)
+    x$mortality_reduction = x$d_mortality - x$exposure
+    # suppress no visible binding notes:
+    bus_stops_per_1000 = mortality_reduction = NULL
+    ggplot2::ggplot(x, ggplot2::aes(x = bus_stops_per_1000,
+                                    y = mortality_reduction)) +
+        ggplot2::geom_point() + 
+        ggplot2::geom_line() +
+        ggplot2::geom_smooth(method = "lm") +
+        ggplot2::theme(axis.title.y = ggplot2::element_text(angle = 90))
+}

R/ui.R

@@ -4,19 +4,28 @@
 #' @import shiny
 #' @import leaflet
 #' @export
-shinyAppUI = fluidPage(
-  shiny::titlePanel("Urban Planning and Transport Health Assessment Tool (upthat)"),
-  column(12, shiny::htmlOutput("app_info")),
-  column(width = 3,
-         selectInput("city", label = "City",  choices = c("Accra", "Kathmandu", "Bristol", "NYC")),
-         selectInput("mode", label = "Mode of transport",  choices = c("Walk", "Cycle", "Ebike", "Escooter", "Fly")),
-         selectInput("layer", label = "Layer",  choices = c("pedestrian flow", "exposure")),
-         sliderInput("bus", "Number of bus stops added per 1,000", min = 0, max = 10, value = 1),
-         sliderInput("obs", "Investment (US $ millions):", min = 0, max = 50, value = 0.5, step = 0.1)
+shinyAppUI = navbarPage("Urban Planning and Transport Health Assessment Tool (upthat)",
+  tabPanel("Maps",
+    column(12, shiny::htmlOutput("app_info")),
+    column(width = 3,
+           selectInput("city", label = "City",  choices = c("Accra", "Kathmandu", "Bristol", "NYC")),
+           selectInput("mode", label = "Mode of transport",  choices = c("Walk", "Cycle", "Ebike", "Escooter", "Fly")),
+           selectInput("layer", label = "Layer",  choices = c("pedestrian flow", "exposure")),
+           sliderInput("bus", "Number of bus stops added per 1,000", min = 0, max = 10, value = 1),
+           sliderInput("obs", "Investment (US $ millions):", min = 0, max = 50, value = 0.5, step = 0.1)
+    ),
+    column(width = 9,
+           mapdeck::mapdeckOutput ("mymap")
+    ),
+    p(),
+    actionButton("recalc", "Zoom to city extent")
   ),
-  column(width = 9,
-         mapdeck::mapdeckOutput ("mymap")
-  ),
-  p(),
-  actionButton("recalc", "Zoom to city extent")
+  tabPanel("Health Impacts",
+    column(width=3,
+           selectInput("city_sc", label = "City",  choices = c("Accra", "Kathmandu", "Bristol", "NYC"))
+    ),
+    column(width=9,
+           plotOutput("plot")
+    )
+  )
 )