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)
#> # A tibble: 5 × 2
#>   tbl_id            tbl_eng_nm                                                  
#>   <chr>             <chr>                                                       
#> 1 DT_NSO_2100_014V1 NUMBER OF INFANT MORTALITY, aimags and the Capital and by m…
#> 2 DT_NSO_2100_014V2 INFANT MORTALITY RATE, per 1000 live births, aimags and the…
#> 3 DT_NSO_2100_014V4 INFANT MORTALITY, by sex, by soum, and by year              
#> 4 DT_NSO_2100_014V5 INFANT MORTALITY RATE,  per 1000 live births, by sex, by so…
#> 5 DT_NSO_2100_015V1 INFANT MORTALITY RATE, per 1000 live births, aimags and the…

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
#> # A tibble: 2 × 5
#>   dim    code  is_time n_values codes             
#>   <chr>  <chr> <lgl>      <int> <list>            
#> 1 Region Бүс   FALSE         28 <tibble [28 × 3]> 
#> 2 Month  Сар   FALSE        119 <tibble [119 × 3]>

# Check available months
time_vals <- nso_dim_values("DT_NSO_2100_015V1", "Month", labels = "en")
head(time_vals, 10)
#> # A tibble: 10 × 2
#>    code  label_en
#>    <chr> <chr>   
#>  1 0     2025-11 
#>  2 1     2025-10 
#>  3 2     2025-09 
#>  4 3     2025-08 
#>  5 4     2016-01 
#>  6 5     2016-02 
#>  7 6     2016-03 
#>  8 7     2016-04 
#>  9 8     2016-05 
#> 10 9     2016-06

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)
#> # A tibble: 10 × 5
#>    Region Month value Region_en Month_en
#>    <chr>  <chr> <dbl> <chr>     <chr>   
#>  1 0      0        13 Total     2025-11 
#>  2 0      1        11 Total     2025-10 
#>  3 0      2        14 Total     2025-09 
#>  4 0      3        16 Total     2025-08 
#>  5 0      4        12 Total     2016-01 
#>  6 0      5        13 Total     2016-02 
#>  7 0      6        14 Total     2016-03 
#>  8 0      7        15 Total     2016-04 
#>  9 0      8        15 Total     2016-05 
#> 10 0      9        14 Total     2016-06

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

Line plot showing decline in infant mortality rate from 2010 to 2015

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)
#> # A tibble: 10 × 2
#>    Region_en    value
#>    <chr>        <dbl>
#>  1 Hovsgel       27.2
#>  2 Arkhangai     24.8
#>  3 Övörkhangai   23.9
#>  4 Bayankhongor  21.6
#>  5 Ömnögovi      19.9
#>  6 Uvs           19.8
#>  7 Sükhbaatar    17.9
#>  8 Bayan-Ölgii   17.7
#>  9 Zavkhan       17.5
#> 10 Khovd         16.8

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

Bar chart comparing infant mortality rates across Mongolia's aimags

Adding Geographic Context

Combine with mapping for spatial analysis:

library(sf)

# Graceful degradation if external API is unreachable
# CRAN policy and CI robustness require handling network failures
try({
  # 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
    )
  
  print(p)  # print static ggplot
}, silent = TRUE)

Choropleth map of infant mortality rates across Mongolia

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

Indicator	Table_ID
Infant Mortality	DT_NSO_2100_015V1
Maternal Mortality	DT_NSO_2100_050V1
Under-5 Mortality	DT_NSO_2100_030V2
Cancer Incidence	DT_NSO_2100_012V1
TB Incidence	DT_NSO_2800_026V1
Communicable Diseases	DT_NSO_2100_020V2

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.