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This function creates a Leaflet map widget using htmlwidgets. The widget can be rendered on HTML pages generated from R Markdown, Shiny, or other applications.
leaflet(
data = NULL,
width = NULL,
height = NULL,
padding = 0,
options = leafletOptions(),
elementId = NULL,
sizingPolicy = leafletSizingPolicy(padding = padding)
)leafletOptions(
minZoom = NULL,
maxZoom = NULL,
crs = leafletCRS(),
worldCopyJump = NULL,
preferCanvas = NULL,
...
)
leafletCRS(
crsClass = "L.CRS.EPSG3857",
code = NULL,
proj4def = NULL,
projectedBounds = NULL,
origin = NULL,
transformation = NULL,
scales = NULL,
resolutions = NULL,
bounds = NULL,
tileSize = NULL
)
A HTML widget object, on which we can add graphics layers using
%>% (see examples).
a data object. Currently supported objects are matrix, data
frame, spatial data from the sf package,
SpatVector from the terra package, and the Spatial*
objects from the sp package that represent points, lines, or polygons.
the width of the map
the height of the map
the padding of the map
the map options
Use an explicit element ID for the widget (rather than an automatically generated one).
htmlwidgets sizing policy object. Defaults to leafletSizingPolicy()
Minimum zoom level of the map. Overrides any minZoom set on map layers.
Maximum zoom level of the map. This overrides any maxZoom set on map layers.
Coordinate Reference System to use. Don't change this if you're not sure what it means.
With this option enabled, the map tracks when you pan to another "copy" of the world and seamlessly jumps to the original one so that all overlays like markers and vector layers are still visible.
Whether leaflet.js Paths should be rendered on a Canvas renderer.
other options used for leaflet.js map creation.
One of L.CRS.EPSG3857, L.CRS.EPSG4326, L.CRS.EPSG3395, L.CRS.Simple, L.Proj.CRS
CRS identifier
Proj4 string
DEPRECATED! Use the bounds argument.
Origin in projected coordinates, if set overrides transformation option.
to use when transforming projected coordinates into pixel coordinates
Scale factors (pixels per projection unit, for example pixels/meter) for zoom levels; specify either scales or resolutions, not both
factors (projection units per pixel, for example meters/pixel) for zoom levels; specify either scales or resolutions, not both
Bounds of the CRS, in projected coordinates; if defined, Proj4Leaflet will use this in the getSize method, otherwise defaulting to Leaflet's default CRS size
DEPRECATED! Specify the tilesize in the tileOptions() argument.
leafletOptions(): Options for map creation
leafletCRS(): class to create a custom CRS
The data argument is only needed if you are going to reference
variables in this object later in map layers. For example, data can be
a data frame containing columns latitude and longtitude, then
we may add a circle layer to the map by leaflet(data) %>%
addCircles(lat = ~latitude, lng = ~longtitude), where the variables in the
formulae will be evaluated in the data.
leafletCRS for creating a custom CRS.
See https://web.archive.org/web/20220702182250/https://leafletjs.com/reference-1.3.4.html#map-option for details and more options.
# !formatR
library(leaflet)
m <- leaflet() %>% addTiles()
m # a map with the default OSM tile layer
# \donttest{
# set bounds
m %>% fitBounds(0, 40, 10, 50)
# move the center to Snedecor Hall
m <- m %>% setView(-93.65, 42.0285, zoom = 17)
m
# popup
m %>% addPopups(-93.65, 42.0285, "Here is the Department of Statistics, ISU")
rand_lng <- function(n = 10) rnorm(n, -93.65, .01)
rand_lat <- function(n = 10) rnorm(n, 42.0285, .01)
# use automatic bounds derived from lng/lat data
m <- m %>% clearBounds()
# popup
m %>% addPopups(rand_lng(), rand_lat(), "Random popups")
# marker
m %>% addMarkers(rand_lng(), rand_lat())
m %>% addMarkers(
rand_lng(), rand_lat(), popup = paste("A random letter", sample(LETTERS, 10))
)
Rlogo <- file.path(R.home("doc"), "html", "logo.jpg")
m %>% addMarkers(
174.7690922, -36.8523071, icon = list(
iconUrl = Rlogo, iconSize = c(100, 76)
), popup = "R was born here!"
)
m %>% addMarkers(rnorm(30, 175), rnorm(30, -37), icon = list(
iconUrl = Rlogo, iconSize = c(25, 19)
))
# circle (units in metres)
m %>% addCircles(rand_lng(50), rand_lat(50), radius = runif(50, 50, 150))
# circle marker (units in pixels)
m %>% addCircleMarkers(rand_lng(50), rand_lat(50), color = "#ff0000")
m %>% addCircleMarkers(rand_lng(100), rand_lat(100), radius = runif(100, 5, 15))
# rectangle
m %>% addRectangles(
rand_lng(), rand_lat(), rand_lng(), rand_lat(),
color = "red", fill = FALSE, dashArray = "5,5", weight = 3
)
# polyline
m %>% addPolylines(rand_lng(50), rand_lat(50))
# polygon
m %>% addPolygons(rand_lng(), rand_lat(), layerId = "foo")
# geoJSON
seattle_geojson <- list(
type = "Feature",
geometry = list(
type = "MultiPolygon",
coordinates = list(list(list(
c(-122.36075812146, 47.6759920119894),
c(-122.360781646764, 47.6668890126755),
c(-122.360782108665, 47.6614990696722),
c(-122.366199035722, 47.6614990696722),
c(-122.366199035722, 47.6592874248973),
c(-122.364582509469, 47.6576254522105),
c(-122.363887331445, 47.6569107302038),
c(-122.360865528129, 47.6538418253251),
c(-122.360866157644, 47.6535254473167),
c(-122.360866581103, 47.6533126275176),
c(-122.362526540691, 47.6541872926348),
c(-122.364442114483, 47.6551892850798),
c(-122.366077719797, 47.6560733960606),
c(-122.368818463838, 47.6579742346694),
c(-122.370115159943, 47.6588730808334),
c(-122.372295967029, 47.6604350102328),
c(-122.37381369088, 47.660582362063),
c(-122.375522972109, 47.6606413027949),
c(-122.376079703095, 47.6608793094619),
c(-122.376206315662, 47.6609242364243),
c(-122.377610811371, 47.6606160735197),
c(-122.379857378879, 47.6610306942278),
c(-122.382454873022, 47.6627496239169),
c(-122.385357955057, 47.6638573778241),
c(-122.386007328104, 47.6640865692306),
c(-122.387186331506, 47.6654326177161),
c(-122.387802656231, 47.6661492860294),
c(-122.388108244121, 47.6664548739202),
c(-122.389177800763, 47.6663784774359),
c(-122.390582858689, 47.6665072251861),
c(-122.390793942299, 47.6659699214511),
c(-122.391507906234, 47.6659200946229),
c(-122.392883050767, 47.6664166747017),
c(-122.392847210144, 47.6678696739431),
c(-122.392904778401, 47.6709016021624),
c(-122.39296705153, 47.6732047491624),
c(-122.393000803496, 47.6759322346303),
c(-122.37666945305, 47.6759896300663),
c(-122.376486363943, 47.6759891899754),
c(-122.366078869215, 47.6759641734893),
c(-122.36075812146, 47.6759920119894)
)))
),
properties = list(
name = "Ballard",
population = 48000,
# You can inline styles if you want
style = list(
fillColor = "yellow",
weight = 2,
color = "#000000"
)
),
id = "ballard"
)
m %>% setView(-122.36075812146, 47.6759920119894, zoom = 13) %>% addGeoJSON(seattle_geojson)
# use the Dark Matter layer from CartoDB
leaflet() %>% addTiles("https://{s}.basemaps.cartocdn.com/dark_all/{z}/{x}/{y}.png",
attribution = paste(
"© OpenStreetMap contributors",
"© CartoDB"
)
) %>% setView(-122.36, 47.67, zoom = 10)
# provide a data frame to leaflet()
categories <- LETTERS[1:10]
df <- data.frame(
lat = rand_lat(100), lng = rand_lng(100), size = runif(100, 5, 20),
category = factor(sample(categories, 100, replace = TRUE), levels = categories),
value = rnorm(100)
)
m <- leaflet(df) %>% addTiles()
m %>% addCircleMarkers(~lng, ~lat, radius = ~size)
m %>% addCircleMarkers(~lng, ~lat, radius = runif(100, 4, 10), color = c("red"))
# Discrete colors using the "RdYlBu" colorbrewer palette, mapped to categories
RdYlBu <- colorFactor("RdYlBu", domain = categories)
m %>% addCircleMarkers(~lng, ~lat, radius = ~size,
color = ~RdYlBu(category), fillOpacity = 0.5)
# Continuous colors using the "Greens" colorbrewer palette, mapped to value
greens <- colorNumeric("Greens", domain = NULL)
m %>% addCircleMarkers(~lng, ~lat, radius = ~size,
color = ~greens(value), fillOpacity = 0.5)
# }
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