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app.r
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library(shiny)
library(leaflet)
# library(mapview)
library(shinyWidgets)
library(terra)
library(sf)
library(exactextractr)
library(dplyr)
library(ggplot2)
library(DT)
library(plotly)
library(htmlTable)
source("R/functions.r")
# backend code
# countries_df <- st_read("www/GADM_adm1.gpkg",
# query = "select GID_0, NAME_0 from 'GADM_adm1'", quiet = TRUE) |>
# unique()
sub_s_full <- st_read("www/aggregated_data_adm0.gpkg",
query = paste(
"select * from 'aggregated_data_adm0'",
"where NAME_0='region_avg'")) |>
st_drop_geometry()
sub_s_full$vulnerability_index <- NA # dummy to allow binding
countries_df <- st_read("www/aggregated_data_adm1.gpkg",
query = "select GID_0, NAME_0 from 'aggregated_data_adm1'", quiet = TRUE) |>
unique()
# filters_df <- data.frame( # In half dev - need to complete
# name = c("population", "poverty"),
# path = c("www/AfriPop-total.tiff", "www/grdi_r1r3r2_filled.tif")
# )
dict_df <- read.csv("www/file_dict.csv")
AC_df <- dict_df[dict_df$group == "ad_cap", ]
hazard_df <- dict_df[dict_df$group == "hazard", ]
future_dict_df <- read.csv("www/future_file_dict.csv")
fut_AC_df <- future_dict_df[future_dict_df$group == "ad_cap", ]
# fut_hazard_df <- future_dict_df[future_dict_df$group == "hazard", ]
# AC_df <- read.csv("www/cloud_file_dict.csv")
# AC_df$path <- paste0("/vsicurl/", AC_df$path)
ac_weightings <- read.csv("www/ac_weights.csv")
# pop <- rast("www/AfriPop-total.tiff")
# UI
ui <- fluidPage(
navbarPage("Vulnerability Prototype",
### The UI section for baseline ac -----
tabPanel("Baseline",
sidebarLayout(
sidebarPanel(
pickerInput("Country", "Select Country",
choices = countries_df$NAME_0,
selected = countries_df$NAME_0[5],
options = list(
`actions-box` = TRUE,
`live-search` = TRUE),
multiple = TRUE
),
conditionalPanel(
condition = "input.Country.length == 1",
pickerInput("Admin", "Admin Region", choices = NULL,
options = list(
`actions-box` = TRUE,
`live-search` = TRUE),
multiple = TRUE)
),
pickerInput("ac_weight", "Vulnerability Index Calculation Method",
choices = c('Mean', 'Geometric Mean'),
#choices = names(ac_weightings[-c(1, 2)])
),
checkboxInput("explore_dims",
label = "Explore Individual Vulnerability Dimensions?",
FALSE
),
conditionalPanel(
condition = "input.explore_dims == 1",
pickerInput("AC_dim", "Select Vulnerability Dimension",
choices = AC_df$name,
selected = NULL,
options = list(`actions-box` = TRUE),
multiple = TRUE
),
),
# pickerInput("filterVar", "Filter by:", choices = filters_df$name,
# selected = NULL),
# conditionalPanel(
# condition = paste0("input.filterVar == '", filters_df$name[1], "'"),
# sliderInput("pop_range", paste(filters_df$name[1], "Range:"),
# min = 0, max = 200000, # minmax(pop)[2],
# value = c(0, 50000))
# ),
# sliderInput("pop_range", "Population Range:",
# min = 0, max = 200000, # minmax(pop)[2],
# value = c(0, 50000)),
hr(),
#actionButton(inputId = "crop", label = "Crop"),
downloadButton("download", "Download Data"),
radioButtons("downloadType", "Download Type",
choices = c("CSV" = ".csv",
"GeoTIFF" = ".tif",
"Geopackage" = ".gpkg",
"Shapefile" = ".shp"),
)
),
mainPanel(
leafletOutput("map"),
tabsetPanel(type = "tabs",
tabPanel("Summary Table",
DTOutput("init_Table")),
tabPanel("Regional Comparison",
plotlyOutput("bar_plot")),
tabPanel("Distribution Comparison",
plotlyOutput("box_plot")),
tabPanel("Correlation",
textOutput("cor_plot_warning"),
plotOutput("cor_plot"))
),
# tabsetPanel(type = "tabs",
# tabPanel("Interactive Map",
# leafletOutput("map"),
# # tableOutput("init_Table")
# ),
# tabPanel("Variable Maps",
# plotOutput("variable_plot")
# ),
# tabPanel("Summary Table",
# DTOutput("init_Table")
# ),
# )
)
),
),
### The UI section for the future projections of ac -----
tabPanel("Future",
sidebarLayout(
sidebarPanel(
pickerInput("fut_Country", "Select Country",
choices = countries_df$NAME_0,
selected = NULL,
options = list(
`actions-box` = TRUE,
`live-search` = TRUE),
multiple = TRUE
),
conditionalPanel(
condition = "input.Country.length == 1",
pickerInput("fut_Admin", "Admin Region", choices = NULL,
options = list(
`actions-box` = TRUE,
`live-search` = TRUE),
multiple = TRUE)
),
pickerInput("proj_year", "Projection Year",
choices = unique(future_dict_df$year)),
pickerInput("scenario", "Select Scenario",
choices = unique(future_dict_df$projection)),
pickerInput("fut_ac_weight", "Vulnerability Index Calculation Method",
choices = c("Mean", "Geometric Mean"),
),
checkboxInput("fut_explore_dims",
label = "Explore Individual Vulnerability Dimensions?",
FALSE
),
conditionalPanel(
condition = "input.fut_explore_dims == 1",
pickerInput("fut_AC_dim", "Future Vulnerability Dimension",
choices = NULL,
selected = NULL,
options = list(`actions-box` = TRUE),
multiple = TRUE
),
),
hr(),
downloadButton("fut_download", "Download Data"),
radioButtons("fut_downloadType", "Download Type",
choices = c("CSV" = ".csv",
"GeoTIFF" = ".tif",
"Geopackage" = ".gpkg",
"Shapefile" = ".shp"),
)
),
mainPanel(
leafletOutput("fut_map"),
tabsetPanel(type = "tabs",
tabPanel("Summary Table",
DTOutput("fut_init_Table")),
tabPanel("Regional Comparison",
plotlyOutput("fut_bar_plot")),
tabPanel("Distribution Comparison",
plotlyOutput("fut_box_plot")),
tabPanel("Correlation",
textOutput("fut_cor_plot_warning"),
plotOutput("fut_cor_plot"))
)
)
)
),
### The UI section for bi-variate mapping -----
tabPanel("Hazard Interaction",
sidebarLayout(
sidebarPanel(
pickerInput("bivar_x", "Select Vulnerability Variable",
choices = c(AC_df$name, 'Vulnerability Index'),
selected = "gender"),
pickerInput("bivar_y", "Select Hazard Variable",
choices = hazard_df$name,
selected = "heat_stress"),
checkboxInput("addZ", "Plot a 3rd variable?", FALSE),
conditionalPanel(
condition = "input.addZ == 1",
pickerInput("bivar_z", "Select 3rd Variable", choices = AC_df$name,
selected = NULL)
),
hr(),
downloadButton("download_bivar", "Download Bivariate analysis")
),
mainPanel(
leafletOutput("bivar_map"),
absolutePanel(id = "bivar_leg", class = "panel panel-default",
fixed = TRUE, draggable = FALSE, top = 80, left = "auto",
right = 30, bottom = "auto", width = 100, height = "auto",
style = "opacity: 0.8;",
# h2(),
plotOutput("bivar_legend", height = 100),
)
)
)
)
)
)
server <- function(input, output, session) {
region_selection <- reactive({
req(input$Country)
gids <- countries_df$GID_0[match(input$Country, countries_df$NAME_0)]
if (length(input$Country) == 1) {
st_read("www/aggregated_data_adm1.gpkg",
query = paste(
"select * from 'aggregated_data_adm1'",
"where GID_0 in",
paste0("(",
paste(paste0("'", gids, "'"), collapse = ", "),
")")
), quiet = TRUE
)
} else if (length(input$Country) > 1) {
st_read("www/aggregated_data_adm0.gpkg",
query = paste(
"select * from 'aggregated_data_adm0'",
"where GID_0 in",
paste0("(",
paste(paste0("'", gids, "'"), collapse = ", "),
")")
), quiet = TRUE
)
}
})
region_selection <- debounce(region_selection, 2000)
observeEvent(region_selection(), {
if (length(input$Country) == 1) {
admn_1 <- unique(region_selection()$NAME_1)
updatePickerInput(session = session, inputId = "Admin", choices = admn_1,
selected = NULL)
} else {
updatePickerInput(session = session, inputId = "Admin", choices = NULL)
}
})
adm_region_select <- reactive({
req(input$Admin)
region_selection()[region_selection()$NAME_1 %in% input$Admin, ]
})
final_region <- reactive({
if (length(input$Country) > 1) {
region_selection()
} else if (isTruthy(input$Admin)) {
adm_region_select()
} else {
region_selection()
}
})
ac_index <- reactive({
req(input$ac_weight)
weights <- ac_weightings[[input$ac_weight]]
ac_stack <- rast(AC_df$path) |>
crop(final_region(), mask = TRUE)
names(ac_stack) <- AC_df$name
# Round the pov layer atm to fix floating point errors
ac_stack[["poverty"]] <- round(ac_stack[["poverty"]], 10) # fix data instead
need_inv <- AC_df[AC_df$inverse, "name"]
ac_stack[[need_inv]] <- (
minmax(ac_stack[[need_inv]])[2, ]
- ac_stack[[need_inv]]
+ minmax(ac_stack[[need_inv]])[1, ]
)
if (input$ac_weight == "Mean") {
ac_index <- indexer(ac_stack, fun = "mean")
} else if (input$ac_weight == "Geometric Mean") {
ac_index <- indexer(ac_stack, fun = "geometric_mean")
}
names(ac_index) <- 'vulnerabiltiy_index'
return(ac_index)
})
region_filled <- reactive({
req_cols <- c("GID_0", "NAME_0", "population", "vop")
if ("NAME_1" %in% names(final_region())) {
req_cols <- c("NAME_1", req_cols)
}
if (isTruthy(input$AC_dim)) {
col_clean_names <- gsub(".*\\.", "", names(final_region()))
usr_cols <- names(final_region()[col_clean_names %in% input$AC_dim])
selected_region_data <- final_region()[c(req_cols, usr_cols)]
} else {
selected_region_data <- final_region()[req_cols]
}
selected_region_data$vulnerability_index <- exact_extract(ac_index(),
final_region(),
fun = "mean")
selected_region_data[["population"]] <- as.numeric(selected_region_data[["population"]])
pop <- round(selected_region_data[["population"]], 0)
selected_region_data$population <- pop
# pop_col <- which(names(selected_region_data) == "population")
# selected_region_data <- selected_region_data[-pop_col]
return(selected_region_data)
})
output$map <- renderLeaflet({
req(ac_index(), final_region())
ac_pal <- colorNumeric("OrRd", values(ac_index()), na.color = "transparent")
leaflet() |>
addTiles() |>
addRasterImage(ac_index(), colors = ac_pal, group = 'ac_index') |>
addLegend(pal = ac_pal, values = values(ac_index()), group = 'ac_index') |>
addCircles(lng = 20, lat = -6, group = "ADM1", # added for group delete
fill = FALSE, weight = 0) |>
addCircles(lng = 20, lat = -6, group = "ADM0", fill = FALSE, weight = 0)
})
# output$ac_index_plot <- renderPlot({
# plot(ac_index())
# })
observe({
req(region_selection())
leafletProxy("map") |>
clearGroup(group = "ADM0") |>
addPolygons(data = region_selection(), weight = .6,
fillColor = "grey10", color = "black", group = "ADM0")
})
observe({
req(input$Admin)
leafletProxy("map") |>
clearGroup(group = "ADM1") |>
addPolygons(data = adm_region_select(), group = "ADM1",
color = "blue", weight = .7, fillOpacity = 0.2)
})
# pop_mask <- eventReactive(input$crop, {
# req(final_region())
# crop(pop, final_region(), mask = TRUE) |>
# clamp(lower = input$pop_range[1], upper = input$pop_range[2],
# values = FALSE)
# })
# filter_rast <- eventReactive(input$crop, {
# req(input$filter_var)
# filter_paths <- filters_df$path[match(input$filter_var, filters_df$name)]
# rast(filter_paths)
# })
cropped_rasters <- reactive({
req(input$AC_dim)
paths <- AC_df$path[match(input$AC_dim, AC_df$name)]
ac_names <- AC_df$name[match(input$AC_dim, AC_df$name)]
ac_dims <- rast(paths) |>
crop(final_region(), mask = TRUE)
# |>
# crop(pop_mask(), mask = TRUE)
names(ac_dims) <- ac_names
# ac_stack <- c(ac_dims, ac_index())
# names(ac_stack) <- c(names(ac_dims), "ac_index")
# return(ac_stack)
return(ac_dims)
})
output$init_Table <- renderDT(
# expr = ({
# table_df <- st_drop_geometry(region_filled())
# if (length(input$Country) > 1) {
# sub_s_full <- sub_s_full[names(sub_s_full) %in% names(table_df)]
# rbind(table_df, sub_s_full)
# } else {
# table_df
# }
# }),
expr = ({
region_df <- st_drop_geometry(region_filled())
rapply(region_df, round, classes = "numeric", how = "replace", digits = 2)
}),
options = list(paging = TRUE,
pageLength = 5)
)
output$bar_plot <- renderPlotly({
req(region_filled())
region_df <- st_drop_geometry(region_filled())
if ("NAME_1" %in% names(region_df)) {
region_df$region <- region_df$NAME_1
} else {
region_df$region <- region_df$NAME_0
}
if (is.null(input$AC_dim)){
gplot <- ggplot(region_df) +
geom_bar(aes(x = region, y = vulnerability_index, fill = region),
stat = "identity", position = "dodge") +
labs(fill = "") +
theme(
axis.title.y = element_blank(),
axis.title.x = element_blank(),
legend.title = element_blank(),
legend.text = element_text(size = 10)) +
coord_flip()
} else {
if (length(input$Country) > 1) {
sub_s_full <- sub_s_full[names(sub_s_full) %in% names(region_df)]
sub_s_full$region <- sub_s_full$NAME_0
region_df <- rbind(region_df, sub_s_full)
}
mean_cols <- grep("mean\\.", names(region_df))
long_region <- tidyr::pivot_longer(region_df,
cols = mean_cols, names_to = "Varible", values_to = "value")
print(long_region$value)
long_region$value <- round(as.numeric(long_region$value), 3)
gplot <- ggplot(long_region) +
geom_bar(aes(x = region, y = value, fill = region),
stat = "identity", position = "dodge") +
labs(fill = "") +
facet_wrap(~ Varible, scales = "free_x") +
theme(
axis.title.y = element_blank(),
axis.title.x = element_blank(),
legend.title = element_blank(),
legend.text = element_text(size = 10)) +
coord_flip()
}
ggplotly(gplot, tooltip = c('fill', 'y'))
})
output$box_plot <- renderPlotly({
req(final_region(), ac_index())
if (is.null(input$AC_dim)) {
vals <- extract(ac_index(), final_region())
if ("NAME_1" %in% names(final_region())) {
vals$region <- st_drop_geometry(final_region())[vals[["ID"]], "NAME_1"]
} else {
vals$region <- st_drop_geometry(final_region())[vals[["ID"]], "NAME_0"]
}
vals$region <- as.factor(vals$region)
ggbox <- ggplot(vals) +
geom_boxplot(aes(x = region, y = vulnerabiltiy_index,
group = region, fill = region)) +
theme(
axis.title.y = element_blank(),
axis.title.x = element_blank(),
legend.title = element_blank(),
legend.text = element_text(size = 10)) +
coord_flip()
} else {
vals <- extract(cropped_rasters(), final_region())
if ("NAME_1" %in% names(final_region())) {
vals$region <- st_drop_geometry(final_region())[vals[["ID"]], "NAME_1"]
} else {
vals$region <- st_drop_geometry(final_region())[vals[["ID"]], "NAME_0"]
}
vals$region <- as.factor(vals$region)
long_vals <- tidyr::pivot_longer(vals, cols = names(cropped_rasters()),
names_to = "layer", values_to = 'values')
ggbox <- ggplot(long_vals) +
geom_boxplot(aes(x = region, y = round(values, 3),
group = region, fill = region)) +
facet_wrap(~layer, scales = "free_x") +
theme(
axis.title.y = element_blank(),
axis.title.x = element_blank(),
legend.title = element_blank(),
legend.text = element_text(size = 10)) +
coord_flip()
}
ggplotly(ggbox, tooltip = c('x', 'y', 'fill'))
})
output$cor_plot_warning <- renderText({
if (!isTruthy(input$AC_dim)) {
print("At least one vulnerability dimension must be selected.")
}
})
output$cor_plot <- renderPlot({
req(cropped_rasters())
cor_stack <- c(cropped_rasters(), ac_index())
cor_mat <- layerCor(cor_stack, "pearson", na.rm = T)[[1]] |>
as.data.frame()
row.names(cor_mat) <- names(cor_stack)
names(cor_mat) <- names(cor_stack)
long_cormat <- as.table(as.matrix(cor_mat)) |>
as.data.frame()
pal <- RColorBrewer::brewer.pal(10, "Spectral")
pal <- paste0(pal, 'A6')
ggplot(long_cormat, aes(Var1, Var2, fill = Freq)) +
geom_tile() +
geom_text(aes(label = round(Freq, 3))) +
scale_fill_gradientn(colours = pal,
breaks = c(-1, 0, 1),
limits = c(-1, 1)) +
labs(fill = "Correlation") +
theme_minimal() +
theme(
axis.title = element_blank(),
axis.text = element_text(size = 20),
legend.title = element_text(size = 20),
legend.text = element_text(size = 20)
)
})
observe({ #input$AC_dim
req(input$AC_dim)
leafletProxy("map") |>
clearImages() |>
clearControls()
ac_dims <- c(cropped_rasters(), ac_index())
for (layer in 1:nlyr(ac_dims)) {
pal <- colorNumeric(c("#0C2C84", "#41B6C4", "#FFFFCC"),
values(ac_dims[[layer]]), na.color = "transparent")
lyr_name <- names(ac_dims[[layer]])
units <- AC_df[AC_df$name == lyr_name, "units"]
leafletProxy("map") |>
# clearShapes() |>
clearControls() |>
addRasterImage(ac_dims[[layer]], colors = pal, opacity = 0.8,
group = lyr_name) |>
addLegend(position = "bottomleft", pal = pal,
values = values(ac_dims[[layer]]),
title = paste(lyr_name, units),
group = lyr_name) |>
addLayersControl(
position = "bottomright",
overlayGroups = c(names(ac_dims)),
options = layersControlOptions(collapsed = FALSE)) |>
hideGroup(names(cropped_rasters())) # |>
# addPolygons(data = region_filled(), fillColor = "transparent",
# color = "black", weight = .5
# )
}
leafletProxy("map") |>
addPolygons(data = region_filled(), fillColor = "transparent",
color = "black", weight = .5)
})
output$download <- downloadHandler(
filename = function() {
paste0("Vulnerability_data", input$downloadType)
},
content = function(file) {
if (input$downloadType == ".csv") {
st_drop_geometry(region_filled()) |>
data.frame() |>
write.csv(file)
} else if (input$downloadType == ".tif") {
if (isTruthy(input$AC_dim)) {
c(ac_index(), cropped_rasters()) |>
writeRaster(file, overwrite = TRUE)
}
writeRaster(ac_index(), file, overwrite = TRUE)
} else if (input$downloadType == ".gpkg") {
write_sf(region_filled(), file, append = FALSE)
} else if (input$downloadType == ".shp") {
write_sf(region_filled(), file, append = FALSE)
}
}
)
# Future/projections page -----
# Update the Selections based on baseline, if user has selected
observe({
updateSelectInput(inputId = "fut_Country", selected = input$Country)
})
observe({
updateSelectInput(inputId = "fut_ac_weight", selected = input$ac_weight)
})
fut_region_selection <- reactive({
req(input$fut_Country)
gids <- countries_df$GID_0[match(input$fut_Country, countries_df$NAME_0)]
if (length(input$fut_Country) == 1) {
st_read("www/aggregated_data_adm1.gpkg",
query = paste(
"select * from 'aggregated_data_adm1'",
"where GID_0 in",
paste0("(",
paste(paste0("'", gids, "'"), collapse = ", "),
")")
), quiet = TRUE
)
} else if (length(input$fut_Country) > 1) {
st_read("www/aggregated_data_adm0.gpkg",
query = paste(
"select * from 'aggregated_data_adm0'",
"where GID_0 in",
paste0("(",
paste(paste0("'", gids, "'"), collapse = ", "),
")")
), quiet = TRUE
)
}
})
fut_region_selection <- debounce(fut_region_selection, 2000)
observeEvent(fut_region_selection(), {
if (length(input$fut_Country) == 1) {
admn_1 <- unique(fut_region_selection()$NAME_1)
updatePickerInput(session = session, inputId = "fut_Admin",
choices = admn_1, selected = NULL)
} else {
updatePickerInput(session = session, inputId = "fut_Admin", choices = NULL)
}
})
fut_adm_region_select <- reactive({
req(input$fut_Admin)
fut_region_selection()[fut_region_selection()$NAME_1 %in% input$fut_Admin, ]
})
fut_final_region <- reactive({
if (length(input$fut_Country) > 1) {
fut_region_selection()
} else if (isTruthy(input$fut_Admin)) {
fut_adm_region_select()
} else {
fut_region_selection()
}
})
usr_fut_AC_df <- reactive({
req(input$proj_year, input$scenario)
subset(fut_AC_df,
year == input$proj_year &
projection == input$scenario)
})
fut_ac_index <- reactive({
req(usr_fut_AC_df())
ac_stack <- rast(usr_fut_AC_df()$path) |>
crop(fut_final_region(), mask = TRUE)
names(ac_stack) <- usr_fut_AC_df()$name
# Round the pov layer atm to fix floating point errors
# ac_stack[["poverty"]] <- round(ac_stack[["poverty"]], 10)
# need_inv <- usr_fut_AC_df()[usr_fut_AC_df()$inverse, "name"]
# ac_stack[[need_inv]] <- (
# minmax(ac_stack[[need_inv]])[2, ]
# - ac_stack[[need_inv]]
# + minmax(ac_stack[[need_inv]])[1, ]
# )
if (input$fut_ac_weight == "Mean") {
ac_index <- indexer(ac_stack, fun = "mean")
} else if (input$fut_ac_weight == "Geometric Mean") {
ac_index <- indexer(ac_stack, fun = "geometric_mean")
}
names(ac_index) <- "vulnerabiltiy_index"
return(ac_index)
})
observeEvent(c(input$proj_year, input$scenario), {
updatePickerInput(session = session, inputId = "fut_AC_dim",
choices = usr_fut_AC_df()$name)
})
fut_region_filled <- reactive({
req_cols <- c("GID_0", "NAME_0")
if ("NAME_1" %in% names(fut_final_region())) {
req_cols <- c("NAME_1", req_cols)
}
if (isTruthy(input$fut_AC_dim)) {
col_clean_names <- gsub(".*\\.", "", names(fut_final_region()))
usr_cols <- names(fut_final_region()[col_clean_names %in% input$fut_AC_dim])
selected_region_data <- fut_final_region()[c(req_cols, usr_cols)]
} else {
selected_region_data <- fut_final_region()[req_cols]
}
selected_region_data$vulnerability_index <- exact_extract(fut_ac_index(),
fut_final_region(),
fun = "mean")
return(selected_region_data)
})
output$fut_map <- renderLeaflet({
req(fut_ac_index(), fut_final_region())
ac_pal <- colorNumeric("OrRd", values(fut_ac_index()),
na.color = "transparent")
leaflet() |>
addTiles() |>
addRasterImage(fut_ac_index(), colors = ac_pal, group = 'ac_index') |>
addLegend(pal = ac_pal, values = values(fut_ac_index()),
group = 'ac_index') |>
addCircles(lng = 20, lat = -6, group = "ADM1", # added for group delete
fill = FALSE, weight = 0) |>
addCircles(lng = 20, lat = -6, group = "ADM0", fill = FALSE, weight = 0)
})
# output$ac_index_plot <- renderPlot({
# plot(ac_index())
# })
observe({
req(fut_region_selection())
leafletProxy("fut_map") |>
clearGroup(group = "ADM0") |>
addPolygons(data = fut_region_selection(), weight = .6,
fillColor = "grey10", color = "black", group = "ADM0")
})
observe({
req(input$Admin)
leafletProxy("fut_map") |>
clearGroup(group = "ADM1") |>
addPolygons(data = fut_adm_region_select(), group = "ADM1",
color = "blue", weight = .7, fillOpacity = 0.2)
})
# pop_mask <- eventReactive(input$crop, {
# req(final_region())
# crop(pop, final_region(), mask = TRUE) |>
# clamp(lower = input$pop_range[1], upper = input$pop_range[2],
# values = FALSE)
# })
fut_cropped_rasters <- reactive({
req(input$fut_AC_dim)
paths <- usr_fut_AC_df()$path[match(input$fut_AC_dim, usr_fut_AC_df()$name)]
ac_names <- usr_fut_AC_df()$name[match(input$fut_AC_dim, usr_fut_AC_df()$name)]
ac_dims <- rast(paths) |>
crop(fut_final_region(), mask = TRUE)
# |>
# crop(pop_mask(), mask = TRUE)
names(ac_dims) <- ac_names
# ac_stack <- c(ac_dims, ac_index())
# names(ac_stack) <- c(names(ac_dims), "ac_index")
# return(ac_stack)
return(ac_dims)
})
output$fut_init_Table <- renderDT(
# expr = ({
# table_df <- st_drop_geometry(region_filled())
# if (length(input$Country) > 1) {
# sub_s_full <- sub_s_full[names(sub_s_full) %in% names(table_df)]
# rbind(table_df, sub_s_full)
# } else {
# table_df
# }
# }),
st_drop_geometry(fut_region_filled()),
options = list(paging = TRUE,
pageLength = 5)
)
output$fut_bar_plot <- renderPlotly({
req(fut_region_filled())
region_df <- st_drop_geometry(fut_region_filled())
if ("NAME_1" %in% names(region_df)) {
region_df$region <- region_df$NAME_1
} else {
region_df$region <- region_df$NAME_0
}
if (is.null(input$fut_AC_dim)){
gbarplot <- ggplot(region_df) +
geom_bar(aes(x = region, y = vulnerability_index, fill = region),
stat = "identity", position = "dodge") +
labs(fill = "") +
theme(
axis.title.y = element_blank(),
legend.title = element_blank(),
legend.text = element_text(size = 10)) +
coord_flip()
} else {
sd.vars <- grep("stdev\\.", names(region_df))
region_df <- region_df[-sd.vars]
if (length(input$fut_Country) > 1) {
sub_s_full <- sub_s_full[names(sub_s_full) %in% names(region_df)]
sub_s_full$region <- sub_s_full$NAME_0
region_df <- rbind(region_df, sub_s_full)
}
mean_cols <- grep("mean\\.", names(region_df))
long_region <- tidyr::pivot_longer(region_df,
cols = mean_cols, names_to = "Varible", values_to = "value")
gbarplot <- ggplot(long_region) +
geom_bar(aes(x = region, y = value, fill = region),
stat = "identity", position = "dodge") +
labs(fill = '') +
theme(
axis.title.y = element_blank(),
legend.title = element_blank(),
legend.text = element_text(size = 10)) +
facet_wrap(~Varible, scales = "free_x") +
coord_flip()
}
ggplotly(gbarplot, tooltip = c('fill', 'y'))
})
output$fut_box_plot <- renderPlotly({
req(fut_final_region(), fut_ac_index())
if (is.null(input$fut_AC_dim)) {
vals <- extract(fut_ac_index(), fut_final_region())
if ("NAME_1" %in% names(fut_final_region())) {
vals$region <- st_drop_geometry(fut_final_region())[vals[["ID"]], "NAME_1"]
} else {
vals$region <- st_drop_geometry(fut_final_region())[vals[["ID"]], "NAME_0"]
}
vals$region <- as.factor(vals$region)
ggbox <- ggplot(vals) +
geom_boxplot(aes(x = region, y = vulnerabiltiy_index,
group = region, fill = region)) +
coord_flip()
} else {
vals <- extract(fut_cropped_rasters(), fut_final_region())
if ("NAME_1" %in% names(fut_final_region())) {
vals$region <- st_drop_geometry(fut_final_region())[vals[["ID"]], "NAME_1"]
} else {
vals$region <- st_drop_geometry(fut_final_region())[vals[["ID"]], "NAME_0"]
}
vals$region <- as.factor(vals$region)
long_vals <- tidyr::pivot_longer(vals, cols = names(fut_cropped_rasters()),
names_to = "layer", values_to = 'values')
ggbox <- ggplot(long_vals) +
geom_boxplot(aes(x = region, y = values,
group = region, fill = region)) +
facet_wrap(~layer, scales = "free_y") +
coord_flip()
}
ggplotly(ggbox, tooltip = c("x", "y", "fill"))
})
output$fut_cor_plot_warning <- renderText({
if (!isTruthy(input$fut_AC_dim)) {
print("At least one vulnerability dimension must be selected.")
}
})
output$fut_cor_plot <- renderPlot({
req(fut_cropped_rasters())
cor_stack <- c(fut_cropped_rasters(), fut_ac_index())
cor_mat <- layerCor(cor_stack, "pearson", na.rm = T)[[1]] |>
as.data.frame()
row.names(cor_mat) <- names(cor_stack)
names(cor_mat) <- names(cor_stack)
long_cormat <- as.table(as.matrix(cor_mat)) |>
as.data.frame()
pal <- RColorBrewer::brewer.pal(10, "Spectral")
pal <- paste0(pal, 'A6')
ggplot(long_cormat, aes(Var1, Var2, fill = Freq)) +
geom_tile() +
geom_text(aes(label = round(Freq, 3))) +
scale_fill_gradientn(colours = pal,
breaks = c(-1, 0, 1),
limits = c(-1, 1)) +
labs(fill = "Correlation") +
theme_minimal() +
theme(
axis.title = element_blank(),
axis.text = element_text(size = 20),
legend.title = element_text(size = 20),
legend.text = element_text(size = 20)
)
})
observeEvent(input$fut_AC_dim, {
req(input$fut_AC_dim)
leafletProxy("fut_map") |>
clearImages() |>
clearControls()
# top_lyr <- cropped_rasters()[[grep(input$AC_dim, names(cropped_rasters()))]]
# pal <- colorNumeric(c("#0C2C84", "#41B6C4", "#FFFFCC"),
# values(top_lyr), na.color = "transparent")
# units <- AC_df[AC_df$name == names(top_lyr), 'units']
# leafletProxy("map") |>
# clearShapes() |>
# clearControls() |>
# clearImages() |>
# addRasterImage(top_lyr, colors = pal, opacity = 0.8) |>
# addLegend(position = "bottomright", pal = pal,
# values = values(top_lyr),
# title = paste(names(top_lyr), units))
ac_dims <- c(fut_cropped_rasters(), fut_ac_index())
for (layer in 1:nlyr(ac_dims)) {
pal <- colorNumeric(c("#0C2C84", "#41B6C4", "#FFFFCC"),
values(ac_dims[[layer]]), na.color = "transparent")
lyr_name <- names(ac_dims[[layer]])
print(lyr_name)
print(names(fut_cropped_rasters()))
units <- AC_df[AC_df$name == lyr_name, 'units']
leafletProxy("fut_map") |>
# clearShapes() |>
# clearControls() |>
addRasterImage(ac_dims[[layer]], colors = pal, opacity = 0.8,
group = lyr_name) |>
addLegend(position = "bottomleft", pal = pal,
values = values(ac_dims[[layer]]),
title = paste(lyr_name, units),
group = lyr_name) |>
addLayersControl(
position = "bottomright",
overlayGroups = c(names(ac_dims)),
options = layersControlOptions(collapsed = FALSE)) |>
hideGroup(names(fut_cropped_rasters())) # |>
# addPolygons(data = region_filled(), fillColor = "transparent",
# color = "black", weight = .5
# )
}
leafletProxy("fut_map") |>
addPolygons(data = fut_region_filled(), fillColor = "transparent",
color = "black", weight = .5)
})
output$fut_download <- downloadHandler(
filename = function() {
paste0("Future_Vulnerability","-", input$scenario, "_", input$proj_year,
input$fut_downloadType)
},
content = function(file) {
if (input$fut_downloadType == ".csv") {
st_drop_geometry(fut_region_filled()) |>
data.frame() |>
write.csv(file)
} else if (input$fut_downloadType == ".tif") {
if (isTruthy(input$fut_AC_dim)) {
c(fut_ac_index(), fut_cropped_rasters()) |>
writeRaster(file, overwrite = TRUE)
}
writeRaster(fut_ac_index(), file, overwrite = TRUE)
} else if (input$fut_downloadType == ".gpkg") {
write_sf(fut_region_filled(), file, append = FALSE)
} else if (input$fut_downloadType == ".shp") {
write_sf(fut_region_filled(), file, append = FALSE)
}
}
)
# Bivariate Mapping
output$Bivar_messages <- renderPrint({
req(input$bivar_x, input$bivar_y)
if (input$bivar_x == input$bivar_y) {
print("X and Y variables cannot be the same.")
}
})
bi_vars <- reactive({
req(final_region(), input$bivar_x, input$bivar_y)
if (input$bivar_x == "Vulnerability Index") {
ac_rast <- ac_index()
} else {
ac_rast <- rast(AC_df$path[match(input$bivar_x, AC_df$name)]) |>
crop(final_region(), mask = TRUE)
}
haz_rast <- rast(hazard_df$path[match(input$bivar_y, hazard_df$name)]) |>
crop(final_region(), mask = TRUE) |>
resample(ac_rast)
var_stack <- c(ac_rast, haz_rast)
return(var_stack)
# if (input$bivar_x != input$bivar_y) {
# paths <- AC_df$path[match(c(input$bivar_x, input$bivar_y), AC_df$name)]
# var_stack <- rast(paths) |>
# crop(final_region(), mask = TRUE)
# }
})
bivar_data <- reactive({
req(bi_vars())
make_bivariate_data(bi_vars())
})
output$bivar_legend <- renderPlot({
req(final_region(), bivar_data())
bivar_legend(input$bivar_x, input$bivar_y)
})
output$bivar_map <- renderLeaflet({
pal1 <- colorNumeric(c("#0C2C84", "#41B6C4", "#FFFFCC"),
values(bi_vars()[[1]]), na.color = "transparent")
pal2 <- colorNumeric(c("#0C2C84", "#41B6C4", "#FFFFCC"),
values(bi_vars()[[2]]), na.color = "transparent")
bi_pal <- c("#d3d3d3", "#97c5c5", "#52b6b6", "#cd9b88",