Putting your data on a map

In mongolmaps: Maps and Administrative Boundaries of Mongolia and Ulaanbaatar

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 4.2,
  # Sharp figures on the website; small ones in the CRAN package.
  dpi = if (identical(Sys.getenv("IN_PKGDOWN"), "true")) 200 else 96,
  eval = rlang::is_installed("ggplot2")
)

library(mongolmaps)

The usual workflow has three steps: get a table with one row per place, join it to boundaries with mn_join(), and draw it with mn_map().

A small table

Place names can be written any common way, in English or Cyrillic:

cases <- data.frame(
  aimag = c("Khovsgol", "Hovd", "\u0423\u0432\u0441", "Ulan Bator", "Dornogobi"),
  cases = c(12, 30, 7, 140, 9)
)
cases_map <- mn_join(cases, by = aimag)
cases_map[c("name", "cases")]

Every aimag stays in the result, so places without data show up grey:

mn_map(cases_map, fill = cases)

NSO tables

Tables from the National Statistics Office list several levels in one column (the national total, regions, aimags, soums ...). Join them by the code column, not the label column: labels such as "Ulaanbaatar" name both a region and an aimag. mn_join() keeps the level you ask for and drops the rest with a message.

head(mn_example_population)

pop_2025 <- mn_example_population[mn_example_population$Year == 2025, ]
aimag_pop <- mn_join(pop_2025, by = "Region", level = "aimag")
mn_map(aimag_pop, fill = value / area_km2, title = "People per km2, 2025")

The same table has soum figures:

soum_pop <- mn_join(pop_2025, by = "Region", level = "soum")
mn_map(soum_pop, fill = log10(value), title = "Soum population (log10), 2025")

Several rows per place

Rows for several years give several copies of each polygon, ready for facets:

pop_years <- mn_join(mn_example_population, by = "Region", level = "aimag")
mn_map(pop_years, fill = value / 1000) + ggplot2::facet_wrap(~Year, ncol = 2)

Repeated soum names

Many soums share a name. Give each row's aimag with by_parent:

soums <- data.frame(
  aimag = c("Dornod", "Govi-Altai", "Khentii"),
  soum = c("Bayan-Uul", "Bayan-Uul", "Bayan-Adarga"),
  herders = c(820, 640, 910)
)
joined <- mn_join(soums, by = "soum", level = "soum", by_parent = "aimag")
joined[!is.na(joined$herders), c("name", "aimag_pcode", "herders")]

Fetching NSO data with mongolstats

The mongolstats package downloads any NSO table. Its Region codes work directly with mn_join():

library(mongolstats)
tbl <- "DT_NSO_0300_002V4"
regions <- nso_dim_values(tbl, "Region")$code
years <- nso_dim_values(tbl, "Year", labels = "en")
latest <- years$code[1]
pop <- nso_data(tbl, selections = list(Region = regions, Year = latest), labels = "en")
mn_map(mn_join(pop, by = "Region", level = "aimag"), fill = value)

Checking the matches

mn_match() shows what each value matches, and warns about anything ambiguous or unmatched:

mn_match(c("Khovd", "Hovd", "Kobdo", "Jargalant"), to = "name_en")

mongolmaps documentation built on Oct. 10, 2026, 5:08 p.m.