Maps

Integrated maps

Overview

library(plotly)

## Warning: package 'plotly' was built under R version 4.3.3

## Loading required package: ggplot2

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

Sys.setenv('MAPBOX_TOKEN' = 'pk.eyJ1IjoiY29sbGF6b3NuIiwiYSI6ImNsdWZud3gydDI3dWsycG8zcjdwdXRjcWEifQ.muLD8VXiLgaAiPmtiRpWyw')
base <- map_data("world", "canada") %>%
plot_mapbox(maps::canada.cities) %>%
  add_markers(
    x = ~long,
    y = ~lat,
    size = ~pop,
    color = ~country.etc,
    colors = "Accent",
    text = ~paste(name, pop),
    hoverinfo = "text"
  )

library(listviewer)

## Warning: package 'listviewer' was built under R version 4.3.3

styles <- schema()$layout$layoutAttributes$mapbox$style$values
styles

##  [1] "basic"             "streets"           "outdoors"         
##  [4] "light"             "dark"              "satellite"        
##  [7] "satellite-streets" "carto-darkmatter"  "carto-positron"   
## [10] "open-street-map"   "stamen-terrain"    "stamen-toner"     
## [13] "stamen-watercolor" "white-bg"

#> [1] "basic"                             "streets"
#> [3] "outdoors"                          "light"
#> [5] "dark"                              "satellite"
#> [7] "satellite-streets"                 "open-street-map"
#> [9] "white-bg"                          "carto-positron"
#> [11] "carto-darkmatter"                 "stamen-terrain"
#> [13] "stamen.toner"                     "stamen-watercolor"

layout(
  plot_mapbox(),
  mapbox = list(style = "satellite")
)

## No scattermapbox mode specifed:
##   Setting the mode to markers
##   Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode

style_buttons <- lapply(styles, function(s) {
  list(
    label = s,
    method = "relayout",
    args = list("mapbox.style", s)
  )
})
layout(
  plot_mapbox(),
  mapbox = list(style = "dark"),
  updatemenus = list(
    list(y = 0.8, buttons = style_buttons)
  )
)

## No scattermapbox mode specifed:
##   Setting the mode to markers
##   Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

#airport locations
air <- read.csv(
  'D:/3 semestre geo/Analisis estadistico/airport_locations.csv'
  )
#flights between airports
flights <- read.csv(
  'D:/3 semestre geo/Analisis estadistico/flight_paths.csv'
)
flights$id <- seq_len(nrow(flights))

# map projection
geo <- list(
  projection = list(
    type = 'orthographic',
    rotation = list(lon = -100, lat = 40, roll = 0)
  ),
  showland = TRUE,
  landcolor = toRGB("gray95"),
  countrycolor = toRGB("gray80")
)

plot_geo(color = I("red")) %>%
  add_markers(
    data = air, x = ~long, y = ~lat, text = ~airport,
    size = ~cnt, hoverinfo = "text", alpha = 0.5
  ) %>%
  add_segments(
    data = group_by(flights, id),
    x = ~start_lon, xend = ~end_lon,
    y = ~start_lat, yend = ~end_lat,
    alpha = 0.3, size = I(1), hoverinfo = "none"
  ) %>%
  layout(geo = geo, showlegend = FALSE)

## Warning: `line.width` does not currently support multiple values.

map1 <- plot_mapbox() %>% 
  add_segments(x = -100, xend = -50, y = 50, yend = 75) %>%
  layout(
    mapbox = list(
      zoom = 0,
      center = list(lat = 65, lon = -75)
    )
  )

map2 <- plot_geo() %>% 
  add_segments(x = -100, xend = -50, y = 50, yend = 75) %>%
  layout(geo = list(projection = list(type = "mercator")))

library(htmltools)
browsable(tagList(map1, map2))

Cloropleths

density <- state.x77[, "Population"] / state.x77[, "Area"]

g <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  lakecolor = toRGB('white')
)

plot_geo() %>%
  add_trace(
    z = ~density, text = state.name, span = I(0),
    locations = state.abb, locationmode = 'USA-states'
  ) %>%
  layout(geo = g)

plot_ly() %>%
  add_trace(
    type = "choroplethmapbox",
    # See how this GeoJSON URL was generated at
    # https://plotly-r.com/data-raw/us-states.R
    geojson = paste(c(
      "https://gist.githubusercontent.com/cpsievert/",
      "7cdcb444fb2670bd2767d349379ae886/raw/",
      "cf5631bfd2e385891bb0a9788a179d7f023bf6c8/", 
      "us-states.json"
    ), collapse = ""),
    locations = row.names(state.x77),
    z = state.x77[, "Population"] / state.x77[, "Area"],
    span = I(0)
  ) %>%
  layout(
    mapbox = list(
      style = "light",
      zoom = 4,
      center = list(lon = -98.58, lat = 39.82)
    )
  ) %>%
  config(
    mapboxAccessToken = Sys.getenv("MAPBOX_TOKEN"),
    # Workaround to make sure image download uses full container
    # size https://github.com/plotly/plotly.js/pull/3746
    toImageButtonOptions = list(
      format = "svg", 
      width = NULL, 
      height = NULL
    )
  )

library(rnaturalearth)

## Warning: package 'rnaturalearth' was built under R version 4.3.3

world <- ne_countries(returnclass = "sf")
class(world)

## [1] "sf"         "data.frame"

#> [1] "sf"    "data.frame"
plot_ly(world, color = I("gray90"), stroke = I("black"), span = I(1))

## No trace type specified:
##   Based on info supplied, a 'scatter' trace seems appropriate.
##   Read more about this trace type -> https://plotly.com/r/reference/#scatter

library(sf)

## Warning: package 'sf' was built under R version 4.3.3

## Linking to GEOS 3.11.2, GDAL 3.8.2, PROJ 9.3.1; sf_use_s2() is TRUE

world %>%
  select(name) %>%
  print(n = 4)

## Simple feature collection with 177 features and 1 field
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -180 ymin: -90 xmax: 180 ymax: 83.64513
## Geodetic CRS:  WGS 84
## First 4 features:
##        name                       geometry
## 1      Fiji MULTIPOLYGON (((180 -16.067...
## 2  Tanzania MULTIPOLYGON (((33.90371 -0...
## 3 W. Sahara MULTIPOLYGON (((-8.66559 27...
## 4    Canada MULTIPOLYGON (((-122.84 49,...

canada <- ne_states(country = "Canada", returnclass = "sf")
plot_ly(canada, split = ~name, color = ~provnum_ne)

## No trace type specified:
##   Based on info supplied, a 'scatter' trace seems appropriate.
##   Read more about this trace type -> https://plotly.com/r/reference/#scatter

plot_ly(
  canada, 
  split = ~name, 
  color = I("gray90"), 
  text = ~paste(name, "is \n province number", provnum_ne),
  hoveron = "fills",
  hoverinfo = "text",
  showlegend = FALSE
)

## No trace type specified:
##   Based on info supplied, a 'scatter' trace seems appropriate.
##   Read more about this trace type -> https://plotly.com/r/reference/#scatter

# filter the world sf object down to canada
canada <- filter(world, name == "Canada")
# coerce cities lat/long data to an official sf object
cities <- st_as_sf(
  maps::canada.cities, 
  coords = c("long", "lat"),
  crs = 4326
)

# A PROJ4 projection designed for Canada
# http://spatialreference.org/ref/sr-org/7/
# http://spatialreference.org/ref/sr-org/7/proj4/
moll_proj <- "+proj=moll +lon_0=0 +x_0=0 +y_0=0 +ellps=WGS84 
+units=m +no_defs"

# perform the projections
canada <- st_transform(canada, moll_proj)
cities <- st_transform(cities, moll_proj)

# plot with geom_sf()
p <- ggplot() + 
  geom_sf(data = canada) +
  geom_sf(data = cities, aes(size = pop), color = "red", alpha = 0.3)
ggplotly(p)