Getting started

The leaflet equivalent of the ggplot function is leaflet:

leaflet()

leaflet() %>% addTiles()

leaflet() %>% setView(lng = -79.442778, lat = 37.783889, zoom = 12) %>% addTiles()

For example, NASA’s earth at night map:

leaflet() %>% 
  setView(lng = -79.442778, lat = 37.783889, zoom = 8) %>% 
  addProviderTiles(provider = providers$NASAGIBS.ViirsEarthAtNight2012)

Or CartoDB’s “Positron” map (nice if you want the map elements to be “light”):

leaflet() %>% 
  setView(lng = -79.442778, lat = 37.783889, zoom = 12) %>% 
  addProviderTiles(providers$CartoDB.Positron)

leaflet() %>% 
  addTiles() %>% 
  setView(lng = -79.442778, lat = 37.783889, zoom = 8) %>%
  addWMSTiles(
    "http://mesonet.agron.iastate.edu/cgi-bin/wms/nexrad/n0r.cgi",
    layers = "nexrad-n0r-900913",
    options = WMSTileOptions(format = "image/png", transparent = TRUE),
    attribution = "Weather data © 2012 IEM Nexrad"
  )

Custom data and markers

Here are the rules for your table:

• Latitudes should be in column named “lat” or “latitude”
• Longitudes should be in a column named “lon”, “lng”, “long” or “longitude”

lex <- data.frame( lat    = c(37.789444 , 37.787673  ,  37.785624  )
                 , lon    = c(-79.441725, -79.443623 , -79.441544  )
                 , place  = c("Lab"     , "Classroom", "Thai food!")
                 , rating = c(7         , 5          , 10          )
                 , stringsAsFactors = FALSE
                 )

We can make a simple map as before, but this time we specify the data argument in our call to leaflet:

leaflet(data = lex) %>% 
  setView(lng = -79.442778, lat = 37.783889, zoom = 12) %>%
  addTiles()

Nothing’s changed; but now we can use the additional columns to add annotations onto our map, like markers:

leaflet(data = lex) %>% 
  addTiles() %>% 
  addMarkers(popup = ~place)

Assuming "lon" and "lat" are longitude and latitude, respectively

If you have a lot of markers in one place the clustering feature can be useful (note the lower zoom level):

leaflet(data = lex) %>% 
  setView(lng = -79.442778, lat = 37.783889, zoom = 10) %>% 
  addTiles() %>% 
  addMarkers(label = ~place, clusterOptions = markerClusterOptions())

Assuming "lon" and "lat" are longitude and latitude, respectively

You can also add two different kinds of “circles” onto your maps to visualize quantitative data. The first is addCircleMarkers:

leaflet(data = lex) %>% 
  addTiles() %>%
  addCircleMarkers(radius = ~rating, label = ~place)

Assuming "lon" and "lat" are longitude and latitude, respectively

The second addCircles:

leaflet(data = lex) %>% addTiles() %>%
  addCircles(radius = ~rating, label = ~place)

Assuming "lon" and "lat" are longitude and latitude, respectively

The difference between the two is that the quantitative variable being used for the radius different things:

• In addCircleMarkers it specifies the size in pixels (image-land); no matter the zoom the circles are always the same size
• In addCircles it specifies the size in meters (real-world); so they get bigger or smaller when you zoom.

Leaflet with Shiny

Here’s an example:

ui.R

library(shiny)
library(leaflet)

fluidPage(
  # map output
  leafletOutput("worldMap"),
  
  # line break (puts some space between map and button)
  br(),
  
  # a button
  actionButton("newButton", "New place!")
)

server.R

library(shiny)
library(leaflet)

function(input, output, session) {
  
  lats <- -90:90
  lons <- -180:180
  
  output$worldMap <- renderLeaflet({
    
    # note the dummy use of the action button input
    btn <- input$newButton
    
    leaflet() %>% 
      setView(lng = sample(lons, 1), lat = sample(lats, 1), zoom = 5) %>% 
      addTiles()
  })
  
}

server.R

function(input, output, session) {
  
  lats <- -90:90
  lons <- -180:180
  
  output$worldMap <- renderLeaflet({
    leaflet() %>% 
      setView(lng = -79.442778, lat = 37.783889, zoom = 5) %>% 
      addTiles()
  })
  
  observe({
    # note the dummy use of the action button input
    btn <- input$newButton
    
    leafletProxy("worldMap") %>%
      setView(lng = sample(lons, 1), lat = sample(lats, 1), zoom = 5)
  })
  
}

function(input, output, session) {
  
  lats <- -90:90
  lons <- -180:180
  
  output$worldMap <- renderLeaflet({
    leaflet() %>% 
      setView(lng = -79.442778, lat = 37.783889, zoom = 5) %>% 
      addTiles()
  })
  
  observe({
    # note the dummy use of the action button input
    btn <- input$newButton
    
    leafletProxy("worldMap") %>%
      setView(lng = sample(lons, 1), lat = sample(lats, 1), zoom = 5)
  })
  
}
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