Leaflet Shiny Integration slow - r

Question

I am trying to build an interactive Choropleth in Shiny using leaflet. However, the load time and recreate time is really slow. Any way to speed it up.
Here is a link to the entire app folder along with the data:
https://www.dropbox.com/home/Leaflet_Shiny_app
global.R
library(shinydashboard)
library(tidyverse)
library(ggvis)
library(leaflet)
library(WDI)
library(sp)
ui.R
header <- dashboardHeader(
title = "Greenhouse gas (GHG) emissions"
)
## Sidebar content
sidebar <- dashboardSidebar(
sidebarMenu(
menuItem("Interactive Choropleth", tabName = "choropleth")
)
)
## Body content
body <- dashboardBody(
# First tab content
tabItem("choropleth",
fluidRow(
column(width = 9,
box(width = NULL, solidHeader = TRUE,
title = "Greenhouse gas emissions (kt of CO2 equivalent)",
leafletOutput("choropleth_ghg", height = 500)
)
),
column(width = 3,
box(width = NULL, status = "warning",
selectInput("year", "Year",
choices = seq(1970, 2012, 1),
selected = 2012)
)
)
)
)
)
dashboardPage(
header,
sidebar,
body
)
server.R
# Read the dataset for choropleth
# From http://data.okfn.org/data/core/geo-countries#data
countries <- geojsonio::geojson_read("json/countries.geojson", what = "sp")
# Download the requested data by using the World Bank's API,
# parse the resulting JSON file, and format it in long country-year format.
load("who_ghg.RData")
function(input, output, session) {
# Interactive Choropleth map.........................................................
# Reactive expression for the data subsetted to what the user selected
countries_plus_ghg <- reactive({
# Filter the data to select for the year user selected
who_ghg_subset <- filter(who_ghg, year == input$year)
# Merge a Spatial object having a data.frame for Choropleth map
sp::merge(countries, who_ghg_subset,
by.x = "ISO_A3", by.y = "iso3c")
})
# Create the map
output$choropleth_ghg <- renderLeaflet({
leaflet(countries) %>%
setView(0, 20, zoom = 1) %>%
addTiles()
})
# Observer to change the color of countries, labels and legends
# based on the year user selects in the UI
observe({
dat <- countries_plus_ghg()
# Define numeric vector bins to add some color
bins <- ggplot2:::breaks(c(min(dat$EN.ATM.GHGT.KT.CE, na.rm = TRUE)
,max(dat$EN.ATM.GHGT.KT.CE, na.rm = TRUE)),
"width",n = 5)
# Call colorBin to generate a palette function that maps the RColorBrewer
#"YlOrRd" colors to our bins.
pal <- colorBin("YlOrRd",
domain = dat$EN.ATM.GHGT.KT.CE,
bins = bins)
# Generate the labels with some HTML
labels <- sprintf(
"<strong>%s</strong><br/>%g",
dat$country, dat$EN.ATM.GHGT.KT.CE
) %>% lapply(htmltools::HTML)
leafletProxy("choropleth_ghg", data = dat) %>%
addPolygons(
fillColor = ~pal(EN.ATM.GHGT.KT.CE),
weight = 1,
opacity = 1,
color = "white",
fillOpacity = 0.7,
highlight = highlightOptions(
weight = 2,
color = "#666",
dashArray = "",
fillOpacity = 0.7,
bringToFront = TRUE),
label = labels,
labelOptions = labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px"),
textsize = "15px",
direction = "auto")) %>%
clearControls() %>%
addLegend(pal = pal, values = ~EN.ATM.GHGT.KT.CE, opacity = 0.7, title = NULL,
position = "bottomleft")
})
}

Answer 1

Simplifying geometries using rmapshaper::ms_simplify helped make it a lot faster.
This is what I did-
# Topologically-aware geometry simplification using rmapshaper package,
# keep = proportion of points to retain
countries_simple <- rmapshaper::ms_simplify(countries, keep = 0.05, keep_shapes = TRUE)
I used countries_simple instead of countries in the code then.