The highcharter package include highmaps libraries from highchartsJS to chart maps, choropleths and geojson.

Basics

The easiest way to chart a map with highcharter is using hcmap() function. Select a map (a url) from the highmaps collection https://code.highcharts.com/mapdata/. and use the url as a map in hcmap() function. This will download the map and create a highchart object using the info as a mapData argument.

Let’s try some maps:

library(highcharter)
hcmap("countries/nz/nz-all")
hcmap("custom/usa-and-canada", showInLegend = FALSE)
hcmap("countries/us/us-ca-all") %>%
  hc_title(text = "California") %>% 
  hc_subtitle(text = "You can use the same functions to modify your map!")

Note: The copyright information is added to the chart credits by default, but please be aware that you will have to display this information somewhere else if you choose to disable chart credits. Copyright information for each map can be found as properties in the GeoJSON and Javascript files.

Choropleths

What about add data to get a choropleth? Every map downloaded from the highchartsJS maps collection have keys to join data. There are 2 functions to help to know what are the regions coded to know how to join the map and data:

require(dplyr)

mapdata <- get_data_from_map(download_map_data("custom/usa-and-canada"))

glimpse(mapdata)
## Rows: 64
## Columns: 21
## $ `hc-group`    <chr> "admin1", "admin1", "admin1", "admin1", "admin1", "ad...
## $ `hc-middle-x` <dbl> 0.40, 0.50, 0.52, 0.59, 0.48, 0.50, 0.47, 0.48, 0.51,...
## $ `hc-middle-y` <dbl> 0.53, 0.51, 0.50, 0.50, 0.55, 0.49, 0.35, 0.50, 0.54,...
## $ `hc-key`      <chr> "us-ca", "us-or", "us-nd", "ca-sk", "us-mt", "us-az",...
## $ `hc-a2`       <chr> "CA", "OR", "ND", "SK", "MT", "AZ", "NV", "AL", "NM",...
## $ labelrank     <chr> "0", "0", "0", "2", "0", "0", "0", "0", "0", "0", "0"...
## $ hasc          <chr> "US.CA", "US.OR", "US.ND", "CA.SK", "US.MT", "US.AZ",...
## $ `alt-name`    <chr> "CA|Calif.", "OR|Ore.", "ND|N.D.", NA, "MT|Mont.", "A...
## $ `woe-id`      <chr> "2347563", "2347596", "2347593", "2344925", "2347585"...
## $ subregion     <chr> "Pacific", "Pacific", "West North Central", "Prairies...
## $ fips          <chr> "US06", "US41", "US38", "CA11", "US30", "US04", "US32...
## $ `postal-code` <chr> "CA", "OR", "ND", "SK", "MT", "AZ", "NV", "AL", "NM",...
## $ name          <chr> "California", "Oregon", "North Dakota", "Saskatchewan...
## $ country       <chr> "United States of America", "United States of America...
## $ `type-en`     <chr> "State", "State", "State", "Province", "State", "Stat...
## $ region        <chr> "West", "West", "Midwest", "Western Canada", "West", ...
## $ longitude     <chr> "-119.591", "-120.386", "-100.302", "-105.682", "-110...
## $ `woe-name`    <chr> "California", "Oregon", "North Dakota", "Saskatchewan...
## $ latitude      <chr> "36.7496", "43.8333", "47.4675", "54.4965", "46.9965"...
## $ `woe-label`   <chr> "California, US, United States", "Oregon, US, United ...
## $ type          <chr> "State", "State", "State", "Province", "State", "Stat...
data_fake <- mapdata %>%
  select(code = `hc-a2`) %>%
  mutate(value = 1e5 * abs(rt(nrow(.), df = 10)))

glimpse(data_fake)
## Rows: 64
## Columns: 2
## $ code  <chr> "CA", "OR", "ND", "SK", "MT", "AZ", "NV", "AL", "NM", "CO", "...
## $ value <dbl> 84148.5593, 74862.0062, 87383.4984, 33816.0180, 181697.2677, ...

If we compare this 2 data frames the hc-key is same code that code. So we’ll use these columns as keys:

hcmap(
  "custom/usa-and-canada",
  data = data_fake,
  value = "value",
  joinBy = c("hc-a2", "code"),
  name = "Fake data",
  dataLabels = list(enabled = TRUE, format = "{point.name}"),
  borderColor = "#FAFAFA",
  borderWidth = 0.1,
  tooltip = list(
    valueDecimals = 2,
    valuePrefix = "$",
    valueSuffix = "USD"
  )
)

Categorized areas

data <- tibble(
  country = 
    c("PT", "IE", "GB", "IS",
      
      "NO", "SE", "DK", "DE", "NL", "BE", "LU", "ES", "FR", "PL", "CZ", "AT",
      "CH", "LI", "SK", "HU", "SI", "IT", "SM", "HR", "BA", "YF", "ME", "AL", "MK",
      
      "FI", "EE", "LV", "LT", "BY", "UA", "MD", "RO", "BG", "GR", "TR", "CY",
      
      "RU"),  
  tz = c(rep("UTC", 4), rep("UTC + 1",25), rep("UCT + 2",12), "UTC + 3")
  )

# auxiliar variable
data <- data %>% 
  mutate(value = cumsum(!duplicated(tz)))


# now we'll create the dataClasses
dta_clss <- data %>% 
  mutate(value = cumsum(!duplicated(tz))) %>% 
  group_by(tz) %>% 
  summarise(value = unique(value)) %>% 
  arrange(value) %>% 
  rename(name = tz, from = value) %>% 
  mutate(to = from + 1) %>% 
  list_parse()

hcmap(
  map = "custom/europe",
  data = data, 
  joinBy = c("iso-a2","country"),
  name = "Time zone",
  value = "value",
  tooltip = list(pointFormat = "{point.name} {point.tz}"),
  dataLabels = list(enabled = TRUE, format = "{point.country}")
  ) %>%
  hc_colorAxis(
    dataClassColor = "category",
    dataClasses = dta_clss
    ) %>% 
  hc_title(text = "Europe Time Zones")

Example from https://www.highcharts.com/demo/maps/category-map.

Adding Points

With highcharter is possible add data as points or bubbles. For this it is necessary a data frame with lat, lon columns, and name, z are optional:

cities <- data.frame(
  name = c("London", "Birmingham", "Glasgow", "Liverpool"),
  lat = c(51.507222, 52.483056, 55.858, 53.4),
  lon = c(-0.1275, -1.893611, -4.259, -3),
  z = c(1, 2, 3, 2)
)

hcmap("countries/gb/gb-all", showInLegend = FALSE) %>%
  hc_add_series(
    data = cities, 
    type = "mappoint",
    name = "Cities", 
    minSize = "1%",
    maxSize = "5%"
    ) %>%
  hc_mapNavigation(enabled = TRUE)
hcmap("countries/gb/gb-all", showInLegend = FALSE) %>%
  hc_add_series(
    data = cities, 
    type = "mapbubble",
    name = "Cities", 
    minSize = "1%",
    maxSize = "5%"
    ) %>%
  hc_mapNavigation(enabled = TRUE)

Another example:

library(dplyr)

airports <- read.csv(
  "https://raw.githubusercontent.com/ajdapretnar/datasets/master/data/global_airports.csv",
  stringsAsFactors = FALSE
  )

south_america_countries <- c(
    "Brazil", "Ecuador", "Venezuela",
    "Chile", "Argentina", "Peru",
    "Uruguay", "Paraguay", "Bolivia", 
    "Suriname", "Guyana", "Colombia"
  ) 

airports <- airports %>%
  filter(country %in% south_america_countries) %>%
  rename(lat = latitude, lon = longitude) %>%
  filter(lon < -30)

hcmap(
  "custom/south-america",
  name = "South America",
  showInLegend = FALSE
) %>%
  hc_add_series(
    data = airports,
    type = "mappoint",
    name = "Airports",
    color = hex_to_rgba("darkred", alpha = 0.3),
    maxSize = "10",
    tooltip = list(
      pointFormat = "{point.name}: {point.altitude:,.0f} feets <br>
      ({point.lat:,.2f}, {point.lon:,.2f})"ltitude:,.0f} feets 
({point.lat:,.2f}, {point.lon:,.2f})" ) ) %>% hc_chart(zoomType = "xy")