Playing With Mapview

Author

Arvind Venkatadri

Published

April 29, 2025

Abstract

Making Interactive maps in R, using the mapview package

Keywords

maps, mapview, interactive

Introduction

In this tutorial, we will learn to create interactive maps in R, using a package called mapview, which is a simpler way to access leaflet, which is a wellknown package to create interactive maps.

Leaflet is a JavaScript library for creating dynamic maps that support panning and zooming along with various annotations like markers, polygons, and popups.

Whereas leaflets code becomes lengthy fairly quickly, mapview allows full functionality of leaflet using sensible defaults. Type ?mapview in the console for more help.

More Information

More information on mapview is available at https://r-spatial.github.io/mapview/.

There are also two wonderful talks by Tim Appelhans, the creator of mapview that are available here:

library(tidyverse)
library(sf)
##
# Mapview and allied packages
library(mapview)
library(leaflet)
library(leafem) # Provides extensions for packages 'leaflet' & 'mapdeck', many of which are used by package 'mapview'.
library(leafgl) # High-Performance 'WebGl' Rendering for Package 'leaflet'
library(leafsync) # Create small multiples of several leaflet web maps with (optional) synchronised panning and zooming control.
##
library(slideview) # Create a side-by-side view of raster(image)s with an interactive slider to switch between regions of the images.
library(cubeview) # View 3D Raster Cubes Interactively
library(plainview) # Provides methods for plotting potentially large (raster) images interactively on a plain HTML canvas.

# Data
library(osmdata) # Import OSM Vector Data into R
# library(osmplotr) # Creating maps with OSM data in R. Package is no longer maintained, so not used.

Basic Maps using mapview

franconia , trails, and breweries are geospatial datasets of class sf from the mapview package. franconia contains MULTIPOLYGON, trails contains MULTILINESTRING, and breweries contains POINT geometries.

class(franconia)

[1] "sf"         "data.frame"

head(franconia, 1)

ABCDEFGHIJ0123456789

	NUTS_ID <chr>	SHAPE_AREA <dbl>	SHAPE_LEN <dbl>	CNTR_CODE <fct>	NAME_ASCI <fct>	geometry <s_MULTIP>	district <chr>
1	DE241	0.006736012	0.3926225	DE	Bamberg, Kreisfreie Stadt	<s_MULTIP>	Oberfranken

class(trails)

[1] "sf"         "data.frame"

head(trails, 1)

ABCDEFGHIJ0123456789

	FGN <chr>	FKN <fct>	district <chr>	geometry <s_MULTIL>
1	003756/Kunigundenweg	003756	Oberfranken	<s_MULTIL>

class(breweries)

[1] "sf"         "data.frame"

head(breweries, 1)

ABCDEFGHIJ0123456789

	brewery <chr>	address <chr>	zipcode <chr>	village <chr>	state <fct>	founded <dbl>	number.of.types <dbl>
1	Brauerei Rittmayer	Aischer Hauptstrasse 5	91325	Adelsdorf	Bayern	1422	2

Plotting these is a simple one-liner:

mapview(franconia)

mapview(breweries)

mapview(trails)

mapview has automagically added shapes to the map by detecting the geometry column in each sf dataframe. (rather like geom_sf in ggplot). The map is interactive and clicking on any of the shapes provides a popup containing all the remaining attribute information ( from the non-geometry columns)

Note that there are multiple basemaps available by default in mapview. The layers icon on the left allows the user to interactively choose the base map style. There are other basemaps that can be specified programmatically.

We can also plot these maps as overlays ( since they all pertain to the same geographical area.) Each of the maps can also be given a layer name:

# Single overlay plot with layer names
mapview(franconia, layer.name = "1-Franconia") +
  mapview(trails, layer.name = "2-Brewery Trails") +
  mapview(breweries, layer.name = "3-Breweries")

Add Colours to Shapes

mapview offers a simple way of adding colours to shapes, based on any of the other columns in the respective dataframe, by passing that column name(in quotes!) to the parameter zcol in mapview():

mapview(franconia,
  zcol = "district",
  col.regions = grDevices::hcl.colors
) + # set colour palette
  mapview(breweries, col.regions = "red")

Legends

Note that legends are created by default. They can be turned off with ,legend = FALSE inside the mapview() function. Note also the home button at the bottom right: that re-centres and resets the map.

Map Stack with All Attributes

One can get a stack of maps where the shapes are coloured by all variables simultaneously by using , burst = TRUE instead of zcol:

mapview(franconia, burst = TRUE)

Using mapview with external geospatial data

On to something more complex. We want to plot a known set of locations on a mapview map. mapview takes in geographical data in many ways and we will explore most of them.

Data Sources for mapview

Objects of the following spatial classes are supported in mapview:

sf

raster (Layer, Stack, Brick and SpatialPixels* / SpatialGridDataFrame)

stars

sp (Points, Polygons, Lines and their DataFrame version)

satellite

Which means we cannot give mapview simple vectors / matrices/ dataframes containing lon / lat information: they need to be converted into sf format first. (Leaflet could natively do this! Hmm…)

Let us read in the data set from data.world that gives us POINT locations of all airports in India in a data frame / tibble. The dataset is available at India Airports Locations.

You can either download it, save a copy, and read it in as usual, or use the URL itself to read it in directly from data.world. In the latter case, you will need the package data.world and also need to register your credentials for that page with RStudio. The (very simple!) instructions are available here at data.world

# library(devtools)
# devtools::install_github("datadotworld/data.world-r", build_vignettes = TRUE)

library(data.world)

india_airports <-
  read_csv(file = "https://query.data.world/s/ahtyvnm2ybylf65syp4rsb5tulxe6a") %>%
  slice(-1) %>% # Drop the first row which contains labels

  dplyr::mutate(
    id = as.integer(id),
    latitude_deg = as.numeric(latitude_deg),
    longitude_deg = as.numeric(longitude_deg),
    elevation_ft = as.integer(elevation_ft)
  ) %>%
  rename("lon" = longitude_deg, "lat" = latitude_deg) %>%
  # Remove four locations which seem to be in the African Atlantic
  filter(!id %in% c(330834, 330867, 325010, 331083)) %>%
  # Convert to `sf` dataframe
  st_as_sf(
    coords = c("lon", "lat"),
    remove = FALSE, # retain the original lon and lat columns
    sf_column_name = "geometry",
    crs = 4326 # specify Projection,else no basemap will be plotted
  )

india_airports %>% head()

ABCDEFGHIJ0123456789

id <int>	ident <chr>	type <chr>	name <chr>	lat <dbl>	lon <dbl>	elevation_ft <int>	continent <chr>
26555	VIDP	large_airport	Indira Gandhi International Airport	28.56650	77.1031	777	AS
26434	VABB	large_airport	Chhatrapati Shivaji International Airport	19.08870	72.8679	39	AS
35145	VOBL	large_airport	Kempegowda International Airport	13.19790	77.7063	3000	AS
26618	VOMM	large_airport	Chennai International Airport	12.99001	80.1693	52	AS
26444	VAGO	large_airport	Dabolim Airport	15.38080	73.8314	150	AS
26609	VOCI	large_airport	Cochin International Airport	10.15200	76.4019	30	AS

Let us plot this in `mapview`, using an ESRI National Geographic style map instead of the OSM Base Map. We will also place small circle markers for each airport.

# Change the order of basemaps in mapview
# Male OpenTopoMap the default
mapviewOptions(basemaps = c("OpenTopoMap", "CartoDB.Positron", "CartoDB.DarkMatter", "OpenStreetMap", "Esri.WorldImagery"))

mapview(india_airports,
  zcol = "type"
)

Using popups and labels

By default, mapview provides a mouseover label information (feature ID, or a zcol attribute if zcol has been set), and a popup table containing all attribute fields. This can be customized to show the user wants. There are various options for popups in mapview:

popup = popupTable() Text/table based popup
popup = popupImage() Images in popups
popup = popupGraph() a data visualization in the popup
popup = popupIframe() URL, Image, Video in a popup using iframe

We will download a small dataset of restaurants in say Malleswaram, Bangalore and plot them with mapview, adding popups and labels:

# library(osmdata)

bbox <- osmdata::getbb("Malleswaram, Bengaluru")
bbox

       min      max
x 77.55033 77.59033
y 12.98274 13.02274

restaurants <-
  osmdata::opq(bbox = bbox) %>%
  osmdata::add_osm_feature(
    key = "amenity",
    value = "restaurant"
  ) %>%
  osmdata_sf() %>% # Convert to Simple Features format
  purrr::pluck("osm_points") # Pull out the data frame of interest

restaurants <- restaurants %>%
  dplyr::filter(cuisine == "indian")
restaurants

ABCDEFGHIJ0123456789

	osm_id <chr>	name <chr>	addr:city <chr>	addr:country <chr>	addr:district <chr>
461539222	461539222	Adiga's	NA	NA	NA
598500940	598500940	Udupi Sri Krishnarajathadri	NA	NA	NA
673377213	673377213	Sana Di Ge	NA	NA	NA
673860152	673860152	New Shanthi Sagar	NA	NA	NA
1116484556	1116484556	Kabab Studio	NA	NA	NA
1448082496	1448082496	Sai Shakti	NA	NA	NA
2025970848	2025970848	Coastal Express	NA	NA	NA
2303827881	2303827881	Sattvam	NA	NA	NA
2631196051	2631196051	Maiyas	NA	NA	NA
4008859835	4008859835	Shristi Sagar	Bangalore	NA	NA

Let us add popups containing the restaurant name and cuisine; we need to add the R package leafpop to add popups

library(leafpop)
mapviewOptions(basemaps = "OpenStreetMap") # set basemap to OSM
mapview(
  restaurants,
  col.regions = "green", # Point Fill colour
  cex = 10, # Point Size
  color = "red", # Points Border
  popup = popupTable(restaurants, zcol = c("name", "cuisine"))
)

Using icons for markers

We can also change the icon for each airport. Let us try one of the several icon families that we can use with leaflet : glyphicons, ionicons, and fontawesome icons.

# Define popup message for each airport

# Based on data in india_airports

popup <- paste(
  "<strong>",
  india_airports$name,
  "</strong><br>",
  india_airports$iata_code,
  "<br>",
  india_airports$municipality,
  "<br>",
  "Elevation(feet)",
  india_airports$elevation_ft,
  "<br>",
  india_airports$wikipedia_link,
  "<br>"
)

iata_icon <- leaflet::makeIcon(

  "./images/iata-logo-transp.png", # Downloaded from www.iata.org
  iconWidth = 24,
  iconHeight = 24,
  iconAnchorX = 0,
  iconAnchorY = 0
)

# Create the mapview map

mapview(india_airports) %>%
  popupImage(
    img = iata_icon,
    embed = TRUE,
    popup = popup
  )

mapview(
  x = india_airports,
  popup = popupImage(
    img = iata_icon, embed = TRUE,
    popup = popup
  )
)

There are other icons we can use to mark the POINTs. leaflet allows the use of [ionicons](http://ionicons.com/), [glyphicons](https://icons.getbootstrap.com/#icons), and [FontAwesomeIcons](http://fontawesome.io/icons/)

It is possible to create a list of icons, so that different Markers can have different icons. Let us try to map the MNCs in the ITPL area of Bangalore: we use the ideas in [Using Leaflet Markers @JLA-Data.net](https://www.jla-data.net/eng/leaflet-markers-in-r/)

# Make a dataframe of addresses of Companies we wan to plot in ITPL

companies_itpl <-
  data.frame(
    ticker = c(
      "MBRDI",
      "DTICI",
      "IBM",
      "Exxon",
      "Mindtree",
      "FIS Global",
      "Sasken",
      "LTI"
    ),
    lat = c(
      12.986178620989264,
      12.984160906190121,
      12.983659088566357,
      12.985112265986636,
      12.983794997606187,
      12.980658616215155,
      12.982080447350246,
      12.981338168875348
    ),
    lon = c(
      77.7270652183105,
      77.72808445774321,
      77.73103488768001,
      77.72935046040699,
      77.7227844126931,
      77.72685064158782,
      77.72545589289041,
      77.72287024338216
    )
  ) %>% sf::st_as_sf(coords = c("lon", "lat"), crs = 4326)

# Vanilla leaflet map

leaflet(companies_itpl) %>%
  addTiles() %>%
  addMarkers()

Let us make a list of logos of the Companies and use them as markers!

# a named list of rescaled icons with links to images

favicons <- iconList(
  "MBRDI" = makeIcon(
    iconUrl = "https://www.mercedes-benz.com/etc/designs/brandhub/frontend/static-assets/header/logo.svg%22",
    iconWidth = 25, iconHeight = 25
  ),
  "DTICI" = makeIcon(
    iconUrl = "https://media-exp1.licdn.com/dms/image/C4D0BAQGzOep26lC03w/company-logo_200_200/0/1638298367374?e=2147483647&v=beta&t=mPyF4gvNhNFvd-tedbqNzJofq4q9qcw6A9z9jQeLAwc%22",
    iconWidth = 45, iconHeight = 45
  ),
  "IBM" = makeIcon(
    iconUrl = "https://www.ibm.com/favicon.ico%22",
    iconWidth = 25, iconHeight = 25
  ),
  "Exxon" = makeIcon(
    iconUrl = "https://corporate.exxonmobil.com/-/media/Global/Icons/logos/ExxonMobilLogoColor2x.png%22",
    iconWidth = 45, iconHeight = 25
  ),
  "Mindtree" = makeIcon(
    iconUrl = "https://www.mindtree.com/themes/custom/mindtree_theme/mindtree-lnt-logo-png.png%22",
    iconWidth = 75, iconHeight = 25
  ),
  "FIS Global" = makeIcon(
    iconUrl = "https://1000logos.net/wp-content/uploads/2021/09/FIS-Logo-768x432.png%22",
    iconWidth = 25, iconHeight = 25
  ),
  "Sasken" = makeIcon(
    iconUrl = "https://www.sasken.com/sites/all/themes/sasken_website/logo.png%22",
    iconWidth = 35, iconHeight = 35,
  ),
  "LTI" = makeIcon(
    iconUrl = "https://www.lntinfotech.com/wp-content/uploads/2021/09/LTI-logo.svg%22",
    iconWidth = 25, iconHeight = 25
  )
)

# Create the Leaflet map

leaflet(companies_itpl) %>%
  addMarkers(
    icon = ~ favicons[ticker], # lookup based on ticker

    label = ~ companies_itpl$ticker,
    labelOptions = labelOptions(noHide = F, offset = c(15, -25))
  ) %>%
  addProviderTiles("CartoDB.Positron")

Points using `sf` objects

We will use data from an sf data object. This differs from the earlier situation where we had a simple data frame with lon and lat columns. In sf, the lon and lat info is embedded in the geometry column of the sf data frame.

The tmap package has a data set of all World metro cities, titled metro. We will plot these on the map and also scale the markers in proportion to one of the feature attributes, pop2030. The popup will be the name of the metro city. We will also use the CartoDB.Positron base map.

Note that the metro data set has a POINT geometry, as needed!

data(metro, package = "tmap")

metro

ABCDEFGHIJ0123456789

	name <chr>	name_long <chr>	iso_a3 <chr>	pop1950 <dbl>	pop1960 <dbl>	pop1970 <dbl>	pop1980 <dbl>	pop1990 <dbl>	pop2000 <dbl>
2	Kabul	Kabul	AFG	170784	285352	471891	977824	1549320	2401109
8	Algiers	El Djazair (Algiers)	DZA	516450	871636	1281127	1621442	1797068	2140577
13	Luanda	Luanda	AGO	138413	219427	459225	771349	1390240	2591388
16	Buenos Aires	Buenos Aires	ARG	5097612	6597634	8104621	9422362	10513284	12406780
17	Cordoba	Cordoba	ARG	429249	605309	809794	1009521	1200168	1347561
25	Rosario	Rosario	ARG	554483	671349	816230	953491	1083819	1152387
32	Yerevan	Yerevan	ARM	341432	537759	778158	1041587	1174524	1111301
33	Adelaide	Adelaide	AUS	429277	571822	850168	971856	1081618	1141623
34	Brisbane	Brisbane	AUS	441718	602999	904777	1134833	1381306	1666203
37	Melbourne	Melbourne	AUS	1331966	1851220	2499109	2839019	3154314	3460541

leaflet(data = metro) %>%
  setView(lat = 18, lng = 77, zoom = 4) %>%
  # Add CartoDB.Positron

  addProviderTiles(providers$CartoDB.Positron) %>% # CartoDB Basemap

  # Add Markers for each airport

  addCircleMarkers(
    radius = ~ sqrt(pop2030) / 350,
    color = "red",
    popup = paste(
      "Name: ", metro$name, "<br>",
      "Population 2030: ", metro$pop2030
    )
  )

We can also try downloading an sf data frame with POINT geometry from say OSM datahttps://osm. Let us get hold of restaurants data in Malleswaram, Bangalore from OSM data:

bbox <- osmdata::getbb("Malleswaram, Bengaluru")
bbox

       min      max
x 77.55033 77.59033
y 12.98274 13.02274

locations <-
  osmdata::opq(bbox = bbox) %>%
  osmdata::add_osm_feature(key = "amenity", value = "restaurant") %>%
  osmdata_sf() %>%
  purrr::pluck("osm_points") %>%
  dplyr::select(name, cuisine, geometry) %>%
  dplyr::filter(cuisine == "indian")

locations %>% head()

ABCDEFGHIJ0123456789

	name <chr>	cuisine <chr>	geometry <sf_POINT>
461539222	Adiga's	indian	<sf_POINT>
598500940	Udupi Sri Krishnarajathadri	indian	<sf_POINT>
673377213	Sana Di Ge	indian	<sf_POINT>
673860152	New Shanthi Sagar	indian	<sf_POINT>
1116484556	Kabab Studio	indian	<sf_POINT>
1448082496	Sai Shakti	indian	<sf_POINT>

# Fontawesome icons seem to work in `leaflet` only up to FontAwesome V4.7.0.
# The Fontawesome V4.7.0 Cheatsheet is here: <https://fontawesome.com/v4/cheatsheet/>


leaflet(
  data = locations,
  options = leafletOptions(minZoom = 12)
) %>%
  addProviderTiles(providers$CartoDB.Voyager) %>%
  # Regular `leaflet` code
  addAwesomeMarkers(
    icon = awesomeIcons(
      icon = "fa-coffee",
      library = "fa",
      markerColor = "blue",
      iconColor = "black",
      iconRotate = TRUE
    ),
    popup = paste(
      "Name: ", locations$name, "<br>",
      "Food: ", locations$cuisine
    )
  )

Fontawesome Workaround

For more later versions of Fontawesome, here below is a workaround from https://github.com/rstudio/leaflet/issues/691. Despite this some fontawesome icons simply do not seem to show up. ;-()

library(fontawesome)

coffee <- makeAwesomeIcon(
  text = fa("mug-hot"), # mug-hot was introduced in fa version 5

  iconColor = "black",
  markerColor = "blue",
  library = "fa"
)

leaflet(data = locations) %>%
  addProviderTiles(providers$CartoDB.Voyager) %>%
  # Workaround code

  addAwesomeMarkers(
    icon = coffee,
    popup = paste(
      "Name: ", locations$name, "<br>",
      "Food: ", locations$cuisine, "<br>"
    )
  )

Note that leaflet automatically detects the lon/lat columns from within the POINT geometry column of the sf data frame.

Points using Two-Column Matrices

We can now quickly try providing lon and lat info in a two column matrix.This can be useful to plot a bunch of points recorded on a mobile phone app.

mysore5 <- matrix(
  c(
    runif(5, 76.652985 - 0.01, 76.652985 + 0.01),
    runif(5, 12.311827 - 0.01, 12.311827 + 0.01)
  ),
  nrow = 5
)

mysore5

         [,1]     [,2]
[1,] 76.64376 12.31612
[2,] 76.65193 12.31728
[3,] 76.64627 12.31413
[4,] 76.64405 12.31439
[5,] 76.65144 12.31021

leaflet(data = mysore5) %>%
  addProviderTiles(providers$OpenStreetMap) %>%
  # Pick an icon from <https://www.w3schools.com/bootstrap/bootstrap_ref_comp_glyphs.asp>

  addAwesomeMarkers(
    icon = awesomeIcons(
      icon = "music",
      iconColor = "black",
      library = "glyphicon"
    ),
    popup = "Carnatic Music !!"
  )

Polygons, Lines, and Polylines Data Sources for `leaflet`

We have seen how to get POINT data into leaflet.

Line and polygon data can come from a variety of sources:

SpatialPolygons, SpatialPolygonsDataFrame, Polygons, and Polygon objects (from the sp package)
SpatialLines, SpatialLinesDataFrame, Lines, and Line objects (from the sp package)
MULTIPOLYGON, POLYGON, MULTILINESTRING, and LINESTRING objects (from the sf package)
map objects (from the maps package’s map() function); use map(fill = TRUE) for polygons, FALSE for polylines
Two-column numeric matrix; the first column is longitude and the second is latitude. Polygons are separated by rows of (NA, NA). It is not possible to represent multi-polygons nor polygons with holes using this method; use SpatialPolygons instead.

We will concentrate on using sf data into leaflet. We may explore maps() objects at a later date.

Polygons/MultiPolygons and LineString/MultiLineString using `sf` data frames

Let us download College buildings, parks, and the cycling lanes in Amsterdam, Netherlands, and plot these in leaflet.

library(osmdata)
# Option 1
# Gives too large a bbox
bbox <- osmdata::getbb("Amsterdam, Netherlands")
# bbox

# Setting bbox manually is better
amsterdam_coords <- matrix(c(4.85, 4.95, 52.325, 52.375),
  byrow = TRUE,
  nrow = 2, ncol = 2,
  dimnames = list(c("x", "y"), c("min", "max"))
)
amsterdam_coords

     min    max
x  4.850  4.950
y 52.325 52.375

colleges <- amsterdam_coords %>%
  osmdata::opq() %>%
  osmdata::add_osm_feature(
    key = "amenity",
    value = "college"
  ) %>%
  osmdata_sf() %>%
  purrr::pluck("osm_polygons")

parks <- amsterdam_coords %>%
  osmdata::opq() %>%
  osmdata::add_osm_feature(key = "landuse", value = "grass") %>%
  osmdata_sf() %>%
  purrr::pluck("osm_polygons")

roads <- amsterdam_coords %>%
  osmdata::opq() %>%
  osmdata::add_osm_feature(
    key = "highway",
    value = "primary"
  ) %>%
  osmdata_sf() %>%
  purrr::pluck("osm_lines")

cyclelanes <- amsterdam_coords %>%
  osmdata::opq() %>%
  osmdata::add_osm_feature(key = "cycleway") %>%
  osmdata_sf() %>%
  purrr::pluck("osm_lines")

We have 12 colleges in our data and 3290 parks in our data.

leaflet() %>%
  addTiles() %>%
  addPolygons(data = colleges, popup = ~ colleges$name) %>%
  addPolygons(data = parks, color = "green", popup = parks$name) %>%
  addPolylines(data = roads, color = "red") %>%
  addPolylines(data = cyclelanes, color = "purple")

Chapter 3: Using Raster Data in `leaflet`

So far all the geospatial data we have plotted in leaflet has been vector data. We will now explore how to plot raster data using leaflet. Raster data are used to depict continuous variables across space, such as vegitation, salinity, forest cover etc. Satellite imagery is frequently available as raster data.

Importing Raster Data [Work in Progress!]

Raster data can be imported into R in many ways:

using the maptiles package
using the OpenStreetMap package

library(terra)

library(maptiles)

# library(OpenStreetMap) # causes RStudio to crash...

Bells and Whistles in `leaflet`: layers, groups, legends, and graticules

Adding Legends[Work in Progress!]

## Generate some random lat lon data around Bangalore

df <- data.frame(
  lat = runif(20, min = 11.97, max = 13.07),
  lng = runif(20, min = 77.48, max = 77.68),
  col = sample(c("red", "blue", "green"), 20,
    replace = TRUE
  ),
  stringsAsFactors = FALSE
)

df %>%
  leaflet() %>%
  addTiles() %>%
  addCircleMarkers(color = df$col) %>%
  addLegend(values = df$col, labels = LETTERS[1:3], colors = c("blue", "red", "green"))