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 R.
• Tidy Data: Analysis-ready tibbles with English or Mongolian labels.
• Stable Periods: Years and months come back as labels such as "2024" or "2024-03", and can be selected in any NSO period format.
• Resilient: Tables that NSO moves within its catalogue are found automatically, and large requests are split to stay within the server’s limits.
• Mapping Ready: Results join by NSO code to the maps in the companion package mongolmaps.

Installation

You can install mongolstats from CRAN with:

install.packages("mongolstats")

Or install the development version, with mongolmaps for maps, from GitHub with:

# install.packages("pak")
pak::pak(c("temuulene/mongolstats", "temuulene/mongolmaps"))

Quick Start

1. The Economic Pulse: GDP Trends

Find a table, then fetch it with selections written as codes or labels:

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

nso_search("gross domestic product, by production approach") |>
  select(tbl_id, tbl_eng_nm)
#> # A tibble: 7 × 2
#>   tbl_id              tbl_eng_nm                                                
#>   <chr>               <chr>                                                     
#> 1 DT_NSO_0500_001V4   GROSS DOMESTIC PRODUCT, by production approach, by econom…
#> 2 DT_NSO_0500_001V6   GROSS DOMESTIC PRODUCT, by production approach, by econom…
#> 3 DT_NSO_0500_004V1   GROSS DOMESTIC PRODUCT, by production approach, by econom…
#> 4 DT_NSO_0500_022V1   GROSS DOMESTIC PRODUCT, by production approach, by econom…
#> 5 DT_NSO_0500_022V1-1 GROSS DOMESTIC PRODUCT, by production approach, by econom…
#> 6 DT_NSO_0500_001V1   GROSS DOMESTIC PRODUCT, by production approach, by econom…
#> 7 DT_NSO_0500_001V3   GROSS DOMESTIC PRODUCT, by production approach, by econom…

gdp <- nso_data(
  tbl_id = "DT_NSO_0500_001V1",
  selections = list(
    "Statistical indicator" = "GDP, at current prices",
    "Economic activity" = "Total",
    Year = nso_period_seq("2010", "2025")
  )
)

# Years come back as labels, newest first; "2025*" marks a preliminary figure
head(gdp, 3)
#> # A tibble: 3 × 4
#>   `Statistical indicator` `Economic activity` Year      value
#>   <chr>                   <chr>               <chr>     <dbl>
#> 1 0                       0                   2025* 89937128.
#> 2 0                       0                   2024  80663051.
#> 3 0                       0                   2023  70441516.

gdp |>
  ggplot(aes(x = nso_period_date(Year), y = value / 1e6)) +
  geom_area(fill = "#42b883", alpha = 0.6) +
  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")) +
  labs(
    title = "Mongolia's GDP, 2010-2025",
    subtitle = "Gross domestic product at current prices (trillion MNT)",
    x = NULL,
    y = NULL,
    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()
  )

2. Mapping Regional Population

NSO tables identify places by code. mongolmaps boundaries carry the same codes, so mn_join() attaches a table to the map in one step, keeping the aimags and dropping the national and regional totals:

library(mongolmaps)

pop_tbl <- "DT_NSO_0300_002V1"
latest <- nso_latest_periods(pop_tbl)

pop <- nso_data(pop_tbl, selections = list(Year = latest))

pop |>
  mn_join(by = "Region", level = "aimag") |>
  mn_map(fill = value, trans = "log10", title = paste("Population by aimag,", latest)) +
  labs(fill = "Population\n(log scale)")

Documentation

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

• Getting Started - Your first epidemiological analysis
• Discovery Guide - Find and explore tables
• Mapping Guide - Map NSO data with mongolmaps

Contributing

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

License

MIT