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mongolstats

mongolstats is your gateway to the National Statistics Office of Mongolia (NSO). Access official data, analyze economic trends, and map regional statistics—all from within R.

Why mongolstats?

  • Instant Access: Query thousands of official datasets directly from
  • Tidy Data: Analysis-ready tibble format compatible with dplyr and ggplot2.
  • Mapping Ready: Built-in administrative boundaries for effortless geospatial analysis.
  • Reliable: Smart caching and robust error handling for smooth workflows.

Installation

You can install the development version of mongolstats from GitHub with:

# install.packages("devtools")
devtools::install_github("temuulene/mongolstats")

Quick Start

Visualize Mongolia’s economic growth in seconds.

library(mongolstats)
library(dplyr)
library(ggplot2)

# Set language to English
nso_options(mongolstats.lang = "en")

# Fetch GDP data - using labels for clarity
gdp <- nso_data(
  tbl_id = "DT_NSO_0500_001V1",
  selections = list(
    "Indicator" = "GDP, at current prices",
    "Economic activity" = "Total",
    "Year" = c(
      "2010", "2011", "2012", "2013", "2014",
      "2015", "2016", "2017", "2018", "2019",
      "2020", "2021", "2022", "2023", "2024"
    )
  ),
  labels = "en" # Get English labels
)

# Visualize the GDP trend as a static plot
p <- gdp |>
  ggplot(aes(x = as.integer(Year_en), y = value / 1e6, group = 1)) +
  geom_area(fill = "#42b883", alpha = 0.6) +  # shaded area emphasizes cumulative growth
  geom_line(color = "#2c3e50", linewidth = 1.2) +
  geom_point(color = "#2c3e50", size = 3, shape = 21, fill = "white", stroke = 1.5) +
  scale_y_continuous(labels = scales::label_number(suffix = "T")) +  # "T" suffix for trillions
  scale_x_continuous(breaks = function(x) seq(ceiling(min(x)), floor(max(x)), by = 1)) +
  labs(
    title = "Mongolia's GDP Growth (2010-2024)",
    subtitle = "Gross Domestic Product (in Trillions MNT)",
    x = NULL,
    y = "GDP (Trillions MNT)",
    caption = "Source: NSO Mongolia via mongolstats"
  ) +
  theme_minimal(base_size = 12) +
  theme(
    plot.title = element_text(face = "bold", size = 16),
    plot.subtitle = element_text(color = "grey40"),
    panel.grid.minor = element_blank(),
    panel.grid.major.x = element_blank()  # vertical gridlines removed for cleaner look
  )

p  # print static ggplot

2. Mapping Regional Population

Discover how population is distributed across the country.

library(sf)

# 1. Fetch Population by Aimag
# Get all region codes first
regions <- nso_dim_values("DT_NSO_0300_002V1", "Region")$code

pop <- nso_data(
  tbl_id = "DT_NSO_0300_002V1",
  selections = list(
    "Region" = regions,
    "Year" = "2024" # Use the year label
  ),
  labels = "en" # Get English labels for joining
) |>
  filter(!Region %in% c("0", "1", "2", "3", "4", "511")) |> # Exclude Total, Regions, and duplicate UB
  mutate(
    Region_en = trimws(Region_en),
    Region_en = dplyr::case_match(
      Region_en,
      "Bayan-Ulgii" ~ "Bayan-Ölgii",
      "Uvurkhangai" ~ "Övörkhangai",
      "Khuvsgul" ~ "Hovsgel",
      "Umnugovi" ~ "Ömnögovi",
      "Tuv" ~ "Töv",
      "Sukhbaatar" ~ "Sükhbaatar",
      .default = Region_en
    )
  )

# 2. Get Administrative Boundaries
map <- mn_boundaries(level = "ADM1")

# 3. Join and Map
pop_map <- map |>
  left_join(pop, by = c("shapeName" = "Region_en"))

p <- ggplot(pop_map) +
  geom_sf(aes(fill = value), color = "white", size = 0.2) +
  # Log scale because population spans 3 orders of magnitude (20k to 1.5M)
  scale_fill_viridis_c(
    option = "magma",
    direction = -1,
    trans = "log10",
    breaks = c(20000, 50000, 100000, 500000, 1500000),
    labels = scales::label_number(scale_cut = scales::cut_short_scale()),
    name = "Population\n(Log Scale)"
  ) +
  labs(
    title = "Population Distribution (2024)",
    subtitle = "Mid-year resident population by Aimag (Log Scale)",
    caption = "Source: NSO Mongolia"
  ) +
  theme_void() +
  theme(
    plot.title = element_text(face = "bold", size = 16),
    plot.subtitle = element_text(color = "grey40"),
    legend.position = "bottom",
    legend.key.width = unit(1.5, "cm")
  )

p  # print static ggplot

Documentation

Full documentation is available at temuulene.github.io/mongolstats.

Contributing

We welcome contributions! Please see the Contributing Guidelines for details.

License

MIT