The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
library(mongolstats)
library(sf)
library(dplyr)
library(ggplot2)
nso_options(mongolstats.lang = "en")
# Global 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)
)
)Geographic analysis is essential for understanding health disparities and targeting interventions. This guide demonstrates spatial epidemiology using Mongolia’s aimag-level (provincial) health data.
Mongolia’s administrative boundaries are available at three levels:
# ADM0: National boundary
country <- mn_boundaries(level = "ADM0")
# ADM1: Aimags (21 provinces + Ulaanbaatar)
aimags <- mn_boundaries(level = "ADM1")
# ADM2: Soums (districts)
soums <- mn_boundaries(level = "ADM2")
# Quick preview
aimags |>
ggplot() +
geom_sf(fill = "white", color = "grey30", size = 0.3) +
theme_void() +
labs(title = "Mongolia's 21 Aimags + Ulaanbaatar")
Maternal mortality is a critical indicator of health system performance and equity.
# Fetch maternal mortality data for all aimags (2020-2024)
# We'll calculate a 5-year average to smooth out year-to-year variability
# This is important because small populations can have unstable rates
mmr_data <- nso_data(
tbl_id = "DT_NSO_2100_050V1", # MMR per 100,000 live births
selections = list(
"Region" = nso_dim_values("DT_NSO_2100_050V1", "Region")$code,
"Year" = as.character(2020:2024)
),
labels = "en"
) |>
filter(!Region %in% c("0", "1", "2", "3", "4", "511")) |> # Exclude Total, Regions, and duplicate UB
mutate(
Region_en = trimws(Region_en),
# Standardize region names to match the geographic boundaries
Region_en = dplyr::case_match(
Region_en,
"Bayan-Ulgii" ~ "Bayan-Ölgii",
"Uvurkhangai" ~ "Övörkhangai",
"Khuvsgul" ~ "Hovsgel",
"Umnugovi" ~ "Ömnögovi",
"Tuv" ~ "Töv",
"Sukhbaatar" ~ "Sükhbaatar",
.default = Region_en
)
) |>
# Calculate 5-year average to reduce random variation
group_by(Region_en) |>
summarise(value = mean(value, na.rm = TRUE), .groups = "drop")
# Preview data
mmr_data |>
arrange(desc(value)) |>
select(Region_en, value) |>
head(10)
#> # A tibble: 10 × 2
#> Region_en value
#> <chr> <dbl>
#> 1 Khovd 70.4
#> 2 Arkhangai 65.9
#> 3 Selenge 60.7
#> 4 Sükhbaatar 54.0
#> 5 Hovsgel 48.4
#> 6 Dornogovi 45.3
#> 7 Töv 41.9
#> 8 Ulaanbaatar 40.4
#> 9 Bayan-Ölgii 39.1
#> 10 Bayankhongor 37.9# Join health data to geographic boundaries for spatial analysis
mmr_map <- aimags |>
left_join(mmr_data, by = c("shapeName" = "Region_en"))
# Create choropleth map
p <- mmr_map |>
ggplot() +
geom_sf(aes(fill = value), color = "white", size = 0.2) +
scale_fill_viridis_c(
option = "rocket",
direction = -1, # dark = high mortality (concerning)
name = "MMR\n(per 100k)",
labels = scales::label_number()
) +
labs(
title = "5-Year Average Maternal Mortality Ratio (2020-2024)",
subtitle = "Deaths per 100,000 live births (Mean)",
caption = "Source: NSO Mongolia"
) +
theme_void() + # removes axes for clean 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
# Get infant mortality rates
imr_tbl <- "DT_NSO_2100_015V1" # IMR per 1,000 live births (Monthly)
# Get metadata
months <- nso_dim_values(imr_tbl, "Month", labels = "en")
months_2024 <- months |>
filter(grepl("2024", label_en)) |>
pull(code)
imr_data <- nso_data(
tbl_id = imr_tbl,
selections = list(
"Region" = nso_dim_values(imr_tbl, "Region")$code,
"Month" = months_2024
),
labels = "en"
) |>
filter(nchar(Region) == 3) |> # Keep only Aimags and Ulaanbaatar
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
)
) |>
# Calculate annual average
group_by(Region_en) |>
summarise(value = mean(value, na.rm = TRUE), .groups = "drop") |>
mutate(
# Classify risk levels
risk_category = case_when(
value < 10 ~ "Low (<10)",
value < 20 ~ "Medium (10-20)",
value < 30 ~ "High (20-30)",
TRUE ~ "Very High (≥30)"
),
risk_category = factor(
risk_category,
levels = c("Low (<10)", "Medium (10-20)", "High (20-30)", "Very High (≥30)")
)
)
# Create risk category map
p <- aimags |>
left_join(imr_data, by = c("shapeName" = "Region_en")) |>
ggplot() +
geom_sf(aes(fill = risk_category), color = "white", size = 0.2) +
scale_fill_manual(
values = c(
"Low (<10)" = "#27ae60", # green = good outcome
"Medium (10-20)" = "#f1c40f", # yellow = caution
"High (20-30)" = "#e67e22", # orange = concerning
"Very High (≥30)" = "#c0392b" # red = critical
),
na.value = "grey90", # missing data shown in light grey
name = "Risk Level\n(IMR)",
drop = FALSE # show all levels even if not present in data
) +
labs(
title = "Infant Mortality Risk Categories (2024 Average)",
subtitle = "Deaths per 1,000 live births",
caption = "Source: NSO Mongolia"
) +
theme_void() +
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")
)
p # print static ggplot
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.