Getting Started

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Getting Started

Installation

Install from GitHub:

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

Your First Analysis: Infant Mortality Trends

Let’s walk through a complete workflow using infant mortality data—a key indicator of population health and health system performance.

Step 1: Load Packages

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

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

# Set global ggplot2 theme with proper margins to prevent text cutoff
theme_set(
  theme_minimal(base_size = 11) +
    theme(
      plot.margin = margin(10, 10, 10, 10),
      plot.title = element_text(size = 13, face = "bold"),
      plot.subtitle = element_text(size = 10, color = "grey40"),
      legend.text = element_text(size = 9),
      legend.title = element_text(size = 10)
    )
)

Step 2: Find the Right Table

Search for infant mortality data:

# Search by keyword
mortality_tables <- nso_itms_search("infant mortality")
mortality_tables |>
  select(tbl_id, tbl_eng_nm) |>
  head(5)

We’ll use DT_NSO_2100_015V1 - Infant Mortality Rate per 1,000 live births (Monthly).

Step 3: Explore Table Metadata

Before fetching data, check what dimensions are available:

# View table structure
meta <- nso_table_meta("DT_NSO_2100_015V1")
meta

# Check available months
time_vals <- nso_dim_values("DT_NSO_2100_015V1", "Month", labels = "en")
head(time_vals, 10)

Step 4: Fetch Data

Get national infant mortality rates for the past two decades:

# Get all month codes
months <- nso_dim_values("DT_NSO_2100_015V1", "Month", labels = "en")

imr_national <- nso_data(
  tbl_id = "DT_NSO_2100_015V1",
  selections = list(
    "Region" = "0", # National level
    "Month" = months$code
  ),
  labels = "en" # Get English labels
)

# Preview
imr_national |>
  head(10)

Step 5: Visualize the Trend

Create a publication-ready plot:

# Prepare the data for visualization
# Step 1: Convert month strings to proper dates for time series plotting
# Step 2: Filter to recent decade (2015-2024) for clear trend visibility

p <- imr_national |>
  mutate(date = as.Date(paste0(Month_en, "-01"))) |>  # convert "YYYY-MM" string to Date
  filter(date >= as.Date("2015-01-01") & date <= as.Date("2024-12-31")) |>
  ggplot(aes(x = date, y = value, group = 1)) +
  geom_line(color = "#2c3e50", linewidth = 1, alpha = 0.3) +  # dim raw data so trend stands out
  geom_point(color = "#e74c3c", size = 3, shape = 21, fill = "white", stroke = 1.5, alpha = 0.6) +
  geom_smooth(method = "loess", se = TRUE, color = "#3498db", fill = "#3498db", alpha = 0.2, linewidth = 1.5) +  # LOESS reveals trend through monthly noise
  scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
  labs(
    title = "Infant Mortality Rate in Mongolia (Monthly)",
    subtitle = "Deaths per 1,000 live births (National Trend)",
    x = NULL,
    y = "IMR (per 1,000)",
    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 clutter time series
  )

p  # print static ggplot

Regional Comparison

Compare infant mortality across different regions:

# Get all aimags for most recent year (2024)
# We'll take the average of monthly rates
months_2024 <- months |>
  filter(grepl("2024", label_en)) |>
  pull(code)

# Fetch IMR data for all regions in 2024
# We'll calculate the annual average from monthly data

imr_regional <- nso_data(
  tbl_id = "DT_NSO_2100_015V1",
  selections = list(
    "Region" = nso_dim_values("DT_NSO_2100_015V1", "Region")$code,
    "Month" = months_2024
  ),
  labels = "en"
) |>
  filter(nchar(Region) == 3) |> # Keep only Aimags and Ulaanbaatar (code length = 3)
  mutate(
    Region_en = trimws(Region_en),
    # Standardize region names to match geographic boundary data
    Region_en = dplyr::case_match(
      Region_en,
      "Bayan-Ulgii" ~ "Bayan-Ölgii",
      "Uvurkhangai" ~ "Övörkhangai",
      "Khuvsgul" ~ "Hovsgel",
      "Umnugovi" ~ "Ömnögovi",
      "Tuv" ~ "Töv",
      "Sukhbaatar" ~ "Sükhbaatar",
      .default = Region_en
    ),
    Type = ifelse(Region %in% c("1", "2", "3", "4"), "Region", "Aimag")
  ) |>
  # Calculate annual average IMR from monthly data
  group_by(Region_en, Type) |>
  summarise(value = mean(value, na.rm = TRUE), .groups = "drop")

# Top 10 highest IMR regions
imr_regional |>
  arrange(desc(value)) |>
  select(Region_en, value) |>
  head(10)

Visualize Regional Disparities

# Calculate national aimag average for reference line
aimag_mean <- mean(imr_regional$value[imr_regional$Type == "Aimag"], na.rm = TRUE)

p <- imr_regional |>
  filter(!is.na(value)) |>
  arrange(desc(value)) |>
  mutate(Region_en = forcats::fct_reorder(Region_en, value)) |>  # order bars by value, not alphabet
  ggplot(aes(x = value, y = Region_en)) +
  # Aimags get gradient fill to show relative severity
  geom_col(data = ~ subset(., Type == "Aimag"), aes(fill = value), width = 0.7) +
  # Regions (aggregates) get distinct dark color to differentiate
  geom_col(data = ~ subset(., Type == "Region"), fill = "#2c3e50", width = 0.7) +
  geom_text(aes(label = round(value, 1)), hjust = -0.2, color = "grey30", size = 3.5) +  # inline labels replace tooltips
  scale_fill_gradient2(
    low = "#27ae60",   # green = low mortality (good)
    mid = "#f39c12",   # yellow = average
    high = "#e74c3c",  # red = high mortality (concerning)
    midpoint = aimag_mean
  ) +
  geom_vline(xintercept = aimag_mean, linetype = "dashed", color = "grey50", linewidth = 0.5) +  # national average reference
  scale_x_continuous(expand = expansion(mult = c(0, 0.15))) +  # extra space for labels
  labs(
    title = "Infant Mortality by Aimag (2024 Average)",
    subtitle = "Dark bars represent Regional Averages; dashed line = national aimag average",
    x = "Deaths per 1,000 live births",
    y = NULL
  ) +
  theme_minimal(base_size = 12) +
  theme(
    plot.title = element_text(face = "bold", size = 14),
    panel.grid.major.y = element_blank(),  # horizontal gridlines clutter bar charts
    panel.grid.minor = element_blank(),
    axis.text.y = element_text(color = "black"),
    legend.position = "none"  # gradient is self-explanatory
  )

p  # print static ggplot

Adding Geographic Context

Combine with mapping for spatial analysis:

library(sf)

# Get aimag boundaries
aimags <- mn_boundaries(level = "ADM1")

# Join IMR data to map
imr_map <- aimags |>
  left_join(imr_regional, by = c("shapeName" = "Region_en"))

# Create choropleth map
p <- imr_map |>
  ggplot() +
  geom_sf(aes(fill = value), color = "white", size = 0.2) +
  scale_fill_viridis_c(
    option = "magma",
    direction = -1,  # dark = high values (high mortality), light = low
    name = "IMR\n(per 1,000)",
    labels = scales::label_number()
  ) +
  labs(
    title = "Infant Mortality Geography (2024 Average)",
    subtitle = "Spatial distribution of mortality rates",
    caption = "Source: NSO Mongolia"
  ) +
  theme_void() +  # remove axes for cleaner map appearance
  theme(
    plot.title = element_text(face = "bold", size = 16),
    plot.subtitle = element_text(color = "grey40"),
    legend.position = "bottom",          # bottom legend maximizes map width
    legend.title = element_text(size = 10, face = "bold"),
    legend.key.width = unit(1.5, "cm")   # wider legend key for continuous scale
  )

p  # print static ggplot

Key Functions Summary

Function	Purpose	Example
`nso_itms_search()`	Find tables by keyword	`nso_itms_search("mortality")`
`nso_table_meta()`	Get table dimensions	`nso_table_meta("DT_NSO_...")`
`nso_dim_values()`	List dimension values	`nso_dim_values(tbl, "Region")`
`nso_table_periods()`	Check time coverage	`nso_table_periods(tbl)`
`nso_data()`	Fetch data	`nso_data(tbl, selections, labels)`
`mn_boundaries()`	Get geographic boundaries	`mn_boundaries(level = "ADM1")`

Best Practices

Always use labels: Set labels = "en" in nso_data() for readable output
Check metadata first: Use nso_table_meta() to understand dimensions before fetching
Use appropriate selections: Specify dimensions by their English labels (e.g., "Total" not "0")
Filter carefully: Exclude total rows (usually code "0") when analyzing subgroups
Clean labels: Use trimws() to remove leading/trailing spaces from region names before joining

Common Workflows

Time Series Analysis

Search for table → Check periods → Fetch years → Plot trend

Regional Comparison

Search table → Get all regions → Fetch latest year → Compare rates

Spatial Epidemiology

Fetch regional data → Get boundaries → Join → Create choropleth

Next Steps

Discover More Data: See the Discovery Guide for advanced search techniques
Create Maps: Learn spatial analysis in the Mapping Guide
Reference: Browse all functions in the Reference

Quick Reference: Common Health Tables

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.