In this article, we demonstrate
how to set up a leafdown
map with three map levels and show
that it is an natural extension of the two map level case. (More than
two map levels is supported from leafdown
version 1.1.)
Let’s first load the libraries we are going to use for our app.
library(leafdown)
library(leaflet)
library(shiny)
library(dplyr)
library(shinycssloaders)
library(shinyjs)
library(dplyr)
library(raster)
(Note that the shinyjs
package is loaded for some
automatic warning messages that the leafdown
map can return
to the user of the shiny app.)
As usual, we first create a list of
SpatialPolygonsDataFrames
(spdfs
) for the
regions we want to display on our map. (Further details can be found in
the Introduction
article.)
list (spdfs_list)
│
└───spdf (spdf of first map level)
│
└───spdf (spdf of second map level)
│
└───spdf (spdf of third map level)
# Germany
ger0 <- raster::getData(country = "Germany", level = 0)
ger1 <- raster::getData(country = "Germany", level = 1)
ger2 <- raster::getData(country = "Germany", level = 2)
# Austria
a0 <- raster::getData(country = "Austria", level = 0)
a1 <- raster::getData(country = "Austria", level = 1)
a2 <- raster::getData(country = "Austria", level = 2)
# Switzerland
ch0 <- raster::getData(country = "Switzerland", level = 0)
ch1 <- raster::getData(country = "Switzerland", level = 1)
ch2 <- raster::getData(country = "Switzerland", level = 2)
For this app, we simply generate random target values
y
.
set.seed(20220106)
# Simulate data
data_sim_y_level_3 <- spdfs_list[[3]]@data
data_sim_y_level_3$y <- round(rnorm(nrow(data_sim_y_level_3), 1e2, sd = 5e2), 0)
data_sim_y_level_2 <- data_sim_y_level_3 %>% group_by(NAME_0, NAME_1) %>% summarise(y = sum(y))
data_sim_y_level_1 <- data_sim_y_level_2 %>% group_by(NAME_0) %>% summarise(y = sum(y))
# Assign map levels
data_sim_y_level_3$level <- 3
data_sim_y_level_2$level <- 2
data_sim_y_level_1$level <- 1
# Assign area names
data_sim_y_level_3$area <- data_sim_y_level_3$NAME_2
data_sim_y_level_2$area <- data_sim_y_level_2$NAME_1
data_sim_y_level_1$area <- data_sim_y_level_1$NAME_0
# Combine data of map levels
data_sim_y <- rbind(
data_sim_y_level_3[, c("area", "y", "level")],
data_sim_y_level_2[, c("area", "y", "level")],
data_sim_y_level_1[, c("area", "y", "level")]
)
head(data_sim_y)
#> area y level
#> 1 Eisenstadt 1086 3
#> 2 Eisenstadt Umgebung 404 3
#> 3 Güssing 502 3
#> 4 Jennersdorf 752 3
#> 5 Mattersburg 329 3
#> 6 Neusiedl am See 184 3
In order for a leafdown
map to know which shapes of a
lower map level belong to the shapes in the upper map level, we need to
specify how the data of the spdfs
of different map levels
need to be joined. This is defined by the
join_map_levels_by
argument, which is a named vector of
length length(spdfs_list) - 1
, whereby
The name of an element specifies the join column in the respective upper map level and the actual element the join column of the lower map level.
Let’s have a look at the data of our the spdfs
in
spdf_list
:
head(spdfs_list[[2]]@data[, c("GID_0", "NAME_0", "GID_1", "NAME_1")])
#> GID_0 NAME_0 GID_1 NAME_1
#> 1 AUT Austria AUT.1_1 Burgenland
#> 2 AUT Austria AUT.2_1 Kärnten
#> 3 AUT Austria AUT.3_1 Niederösterreich
#> 4 AUT Austria AUT.4_1 Oberösterreich
#> 5 AUT Austria AUT.5_1 Salzburg
#> 6 AUT Austria AUT.6_1 Steiermark
In this example, map levels 1 and 2 can be joined by their column “GID_0”. So the name of first element is “GID_0” (the respective column name of the first map level) and the first element is also “GID_0” (the respective column name of the first map level):
head(spdfs_list[[3]]@data[, c("GID_0", "NAME_0", "GID_1", "NAME_1", "GID_2", "NAME_2")])
#> GID_0 NAME_0 GID_1 NAME_1 GID_2 NAME_2
#> 1 AUT Austria AUT.1_1 Burgenland AUT.1.2_1 Eisenstadt
#> 2 AUT Austria AUT.1_1 Burgenland AUT.1.1_1 Eisenstadt Umgebung
#> 3 AUT Austria AUT.1_1 Burgenland AUT.1.3_1 Güssing
#> 4 AUT Austria AUT.1_1 Burgenland AUT.1.4_1 Jennersdorf
#> 5 AUT Austria AUT.1_1 Burgenland AUT.1.5_1 Mattersburg
#> 6 AUT Austria AUT.1_1 Burgenland AUT.1.6_1 Neusiedl am See
We see, that map levels 2 and 3 can be joined by their column “GID_1”.
This translates into the following join_map_levels_by
argument:
library(leafdown)
library(leaflet)
library(shiny)
library(dplyr)
library(shinycssloaders)
library(shinyjs)
library(dplyr)
library(raster)
# Germany
ger0 <- raster::getData(country = "Germany", level = 0)
ger1 <- raster::getData(country = "Germany", level = 1)
ger2 <- raster::getData(country = "Germany", level = 2)
# Austria
a0 <- raster::getData(country = "Austria", level = 0)
a1 <- raster::getData(country = "Austria", level = 1)
a2 <- raster::getData(country = "Austria", level = 2)
# Switzerland
ch0 <- raster::getData(country = "Switzerland", level = 0)
ch1 <- raster::getData(country = "Switzerland", level = 1)
ch2 <- raster::getData(country = "Switzerland", level = 2)
# load the shapes for the three levels
spdfs_list <- list(rbind(aut0, ch0, ger0), rbind(aut1, ch1, ger1), rbind(aut2, ch2, ger2))
# Simulate some data
set.seed(20220106)
data_sim_y_level_3 <- spdfs_list[[3]]@data
data_sim_y_level_3$y <- rnorm(nrow(data_sim_y_level_3), 1e2, sd = 5e2)
data_sim_y_level_2 <- data_sim_y_level_3 %>% group_by(NAME_0, NAME_1) %>% summarise(y = sum(y))
data_sim_y_level_1 <- data_sim_y_level_2 %>% group_by(NAME_0) %>% summarise(y = sum(y))
data_sim_y_level_3$level <- 3
data_sim_y_level_2$level <- 2
data_sim_y_level_1$level <- 1
data_sim_y_level_3$area <- data_sim_y_level_3$NAME_2
data_sim_y_level_2$area <- data_sim_y_level_2$NAME_1
data_sim_y_level_1$area <- data_sim_y_level_1$NAME_0
data_sim_y <- rbind(
data_sim_y_level_3[, c("area", "y", "level")],
data_sim_y_level_2[, c("area", "y", "level")],
data_sim_y_level_1[, c("area", "y", "level")]
)
data_sim_y$y <- round(data_sim_y$y, 0)
ui <- fluidPage(
mainPanel(
# set the background of the map-container to be white
tags$head(
tags$style(HTML(".leaflet-container { background: #fff; height: 100%}")),
# workaround for the NA in leaflet legend see https://github.com/rstudio/leaflet/issues/615
tags$style(HTML(".leaflet-control div:last-child {clear: both;}"))
),
# we need shinyjs for the leafdown map
useShinyjs(),
fluidRow(
# the two buttons used for drilling
actionButton("drill_down", "Drill Down"),
actionButton("drill_up", "Drill Up"),
# the actual map element
withSpinner(leafletOutput("leafdown", height = 800), type = 8)
)
)
)
# Little helper function for hover labels
create_labels <- function(data, map_level) {
labels <- sprintf(
"<strong>%s</strong><br/>%g</sup>",
data[, paste0("NAME_", map_level - 1)], data$y
)
labels %>% lapply(htmltools::HTML)
}
server <- function(input, output) {
# create leafdown object
my_leafdown <- Leafdown$new(
spdfs_list, "leafdown", input, join_map_levels_by = c("GID_0" = "GID_0", "GID_1" = "GID_1")
)
rv <- reactiveValues()
rv$update_leafdown <- 0
# observers for the drilling buttons
observeEvent(input$drill_down, {
my_leafdown$drill_down()
rv$update_leafdown <- rv$update_leafdown + 1
})
observeEvent(input$drill_up, {
my_leafdown$drill_up()
rv$update_leafdown <- rv$update_leafdown + 1
})
data <- reactive({
req(rv$update_leafdown)
meta_data <- my_leafdown$curr_data
curr_map_level <- my_leafdown$curr_map_level
data_curr_map_level <- data_sim_y[data_sim_y$level == curr_map_level, ]
join_col_lhs <- paste0("NAME_", curr_map_level - 1)
data <- meta_data %>% left_join(data_curr_map_level, by = setNames("area", join_col_lhs))
# add the data back to the leafdown object
my_leafdown$add_data(data)
data
})
# this is where the leafdown magic happens
output$leafdown <- renderLeaflet({
req(spdfs_list)
req(data)
data <- data()
labels <- create_labels(data, my_leafdown$curr_map_level)
# draw the leafdown object
my_leafdown$draw_leafdown(
fillColor = ~leaflet::colorNumeric("Greens", data$y)(data$y),
weight = 3, fillOpacity = 1, color = "grey", label = labels
)
})
}