Data Science Infrastructure for Global Health
An R package for global-health data work. Bundles country metadata and lightweight clients for the WHO Global Health Observatory and UN Sustainable Development Goals APIs, plus reusable WHO-style themes for ggplot2 and flextable so that charts and tables produced from this data look consistent across reports.
Documentation: https://shanlong-who.github.io/DSIR/
⚠️ Note about CRAN version
The current CRAN release (0.2.0) has a known issue where gho_data() returns an HTTP 400 error when called with the full wpro_cty vector (28 countries) or other long region vectors. This has been fixed in the GitHub development version (0.7.0).
If you are using DSIR for regional analysis with all WPR / EUR / AFR / AMR countries, please install from GitHub for now:
remotes::install_github("shanlong-who/DSIR")CRAN release 0.7.0 is planned for mid 2026.
Installation
# from CRAN
install.packages("DSIR")
# or the development version from GitHub
# install.packages("remotes")
remotes::install_github("shanlong-who/DSIR")Features
Country metadata
who_countries — a tibble of all 194 WHO Member States with ISO3, ISO2, UN M49 codes, official and short names, WHO region, and a is_pic flag for Pacific Island Countries.
library(DSIR)
library(dplyr)
who_countries
# Filter Member States in the Western Pacific Region
who_countries |>
filter(who_region == "WPR") |>
select(iso3, name_short, is_pic)Regional ISO3 vectors — convenience character vectors for each WHO region, derived from who_countries:
wpro_cty # 28 Western Pacific Member States (since May 2025)
afro_cty # 47 African Region Member States
amro_cty # 35 Region of the Americas Member States
searo_cty # 10 South-East Asia Region Member States
euro_cty # 53 European Region Member States
emro_cty # 21 Eastern Mediterranean Region Member States
pic_cty # 14 Pacific Island Country Member States (subset of WPR)iso3_to_region() — quick lookup from ISO3 codes to WHO regions. Vectorized; returns NA for codes not matching a Member State.
iso3_to_region("PHL") # "WPR"
iso3_to_region(c("PHL", "FRA", "ZAF", "XYZ")) # "WPR" "EUR" "AFR" NAiso3_to_m49() / m49_to_iso3() — convert between ISO3 codes and UN M49 numeric codes in either direction. Useful for moving between GHO (ISO3) and SDG (M49) workflows. Case-insensitive on the ISO3 side; both accept zero-padded or bare M49 input on the M49 side; non-Member areas return NA.
iso3_to_m49("PHL") # "608"
iso3_to_m49(c("PHL", "FRA", "JPN")) # "608" "250" "392"
iso3_to_m49(c("PRI", "PHL")) # NA "608"
m49_to_iso3(c("608", "250", "392")) # "PHL" "FRA" "JPN"
m49_to_iso3(c("076", "76")) # "BRA" "BRA"
m49_to_iso3("900") # NA (world aggregate)In practice you rarely need to call these directly: sdg_data() and sdg_coverage() accept ISO3 codes for their area argument and convert via iso3_to_m49() internally, and sdg_clean() populates its iso3 column via m49_to_iso3().
Visualization
theme_dsi() and theme_dsi_facet() — publication-ready ggplot2 themes. Use theme_dsi() for single-panel charts and theme_dsi_facet() for faceted plots; the facet variant adds panel borders, light strip backgrounds, and panel spacing tuned for multi-panel layouts.
library(ggplot2)
library(dplyr)
# Single panel — theme_dsi()
who_countries |>
count(who_region) |>
ggplot(aes(reorder(who_region, n), n)) +
geom_col(fill = "#0093D5") +
coord_flip() +
scale_y_dsi_col() +
theme_dsi() +
labs(title = "WHO Member States by region", x = NULL, y = NULL)
# Faceted — theme_dsi_facet()
who_countries |>
count(who_region, is_pic) |>
ggplot(aes(reorder(who_region, n), n, fill = is_pic)) +
geom_col() +
coord_flip() +
scale_y_dsi_col() +
facet_wrap(~ is_pic, labeller = as_labeller(
c(`TRUE` = "Pacific Island Countries", `FALSE` = "Other Member States")
)) +
scale_fill_manual(values = c(`TRUE` = "#0093D5", `FALSE` = "grey70"),
guide = "none") +
theme_dsi_facet() +
labs(title = "WHO Member States by region and PIC status",
x = NULL, y = NULL)scale_y_dsi_col() and scale_x_dsi_col() — drop-in replacements for scale_y_continuous() and scale_x_continuous() that remove the default lower expansion, so columns in bar charts sit flush with the axis instead of floating above it. Pick the one that matches where you mapped the value: scale_y_dsi_col() when value is the y aesthetic (including horizontal bars made with coord_flip() — the aesthetic is still y), and scale_x_dsi_col() when value is the x aesthetic (e.g. geom_col(aes(value, category))). Both accept any argument that scale_*_continuous() accepts.
# Vertical bars
ggplot(mtcars, aes(factor(cyl))) +
geom_bar(fill = "#0093D5") +
scale_y_dsi_col() +
theme_dsi() +
labs(title = "Cars by cylinder count", x = "Cylinders", y = NULL)dsi_flextable_defaults() — one-line setup for flextable formatting (booktabs style, bold headers, paddings).
ggpie() — quick pie charts with automatic percentage labels.
df <- data.frame(
region = c("AFR", "AMR", "EUR", "WPR", "SEAR", "EMR"),
countries = c(47, 35, 53, 28, 10, 21)
)
ggpie(df, "region", "countries", .offset = 1.2)Utilities
geomean() — geometric mean, with optional weights. Useful for aggregating ratio-based health indicators where the composite is multiplicative — e.g. UHC service-coverage tracers.
WHO GHO API
Check availability before downloading. GHO has thousands of indicators but any one of them may not cover the countries or years you need. Three lightweight helpers ask the server what is available without transferring observations:
# Yes / no for a given indicator + filter (TRUE / FALSE / NA on failure)
gho_has_data("WHOSIS_000001", area = "FRA")
# Row count the same filter would return — useful for sizing a download
gho_count("WHOSIS_000001", area = wpro_cty)
# Per-country year coverage and observation counts
gho_coverage("WHOSIS_000001", area = c("FRA", "DEU", "JPN"))
#> location year_min year_max n_obs
#> 1 DEU 2000 2021 66
#> 2 FRA 2000 2021 66
#> 3 JPN 2000 2021 66Fetch and clean. The typical workflow is search → fetch → clean:
# Search indicators by keyword
gho_indicators("mortality")
# Discover available dimensions for an indicator
gho_dimensions("NCDMORT3070")
gho_dimensions("NCDMORT3070", dimension = "Dim1")
# Fetch country-level data
gho_data("NCDMORT3070", spatial_type = "country")
# Fetch with area and year filters
gho_data(
indicator = "WHOSIS_000001",
area = wpro_cty,
year_from = 2015
)
# Tidy the raw GHO response
raw <- gho_data("NCDMORT3070", spatial_type = "country", area = wpro_cty)
gho_clean(raw)gho_clean() produces the unified DSIR cleaned-indicator schema — the same 15-column shape produced by sdg_clean(), so GHO and SDG output can be combined directly with bind_indicators() (see below). Output columns: source, id, indicator, location, iso3, location_name, year, value, value_num, low, high, series, dim1–dim3. Source columns missing from the raw response (e.g. Low / High for indicators without confidence intervals) are filled with typed NA.
UN SDG API
Same pattern as GHO: search → fetch → clean.
# Browse goals, targets, indicators, and geographic areas
sdg_goals()
sdg_targets()
sdg_indicators()
sdg_areas()
# Search indicators by keyword — AND semantics, case-insensitive
# substring match on the indicator description (client-side filter)
sdg_indicators("mortality")
sdg_indicators("mortality cancer")
sdg_indicators(c("maternal", "mortality"))
# Fetch indicator data — `area` accepts ISO3 codes (converted internally)
# or UN M49 numeric codes. ISO3 lets DSIR's regional vectors be passed
# directly, the same way they work with the GHO client.
sdg_data("3.2.1", area = "PHL", year_from = 2015, year_to = 2023)
sdg_data("3.4.1", area = wpro_cty)
# M49 also works (e.g. when copy-pasting codes from sdg_areas())
sdg_data("3.2.1", area = "608", year_from = 2015, year_to = 2023)
# Tidy the SDG response
raw <- sdg_data("3.2.1", area = "PHL")
sdg_clean(raw)sdg_clean() produces the same unified 15-column schema as gho_clean(): source, id, indicator, location, iso3, location_name, year, value, value_num, low, high, series, dim1–dim3. SDG-side fields populate id (the indicator code, e.g. "3.4.1"), indicator (the human-readable series description), location (UN M49 numeric code), iso3 (via m49_to_iso3() — NA for region / world aggregates and non-Member areas), location_name, and series. The GHO-only dim1–dim3 columns are NA for SDG rows. value is kept as character to preserve non-numeric entries ("<0.1", aggregate notes); value_num is the numeric coercion (NA where coercion fails).
Combining GHO and SDG output
Because both cleaners produce the same schema, you can stack their output with bind_indicators() and use the source column to keep track of where each row came from:
gho <- gho_data("NCDMORT3070", area = wpro_cty) |> gho_clean()
sdg <- sdg_data("3.4.1", area = wpro_cty) |> sdg_clean()
bind_indicators(gho, sdg)Exploring series. A single SDG indicator often contains several series — for example different vaccines, sex strata, or causes of death — each with its own country / year coverage. sdg_coverage() summarises the year range and observation count per (location, series) so you can see what is available before deciding which series to analyse.
# 3.b.1 (vaccine coverage) is published as four separate series
sdg_coverage("3.b.1", area = c("156", "608"))
#> location series year_min year_max n_obs
#> 1 156 SH_ACS_DTP3 2000 2023 24
#> 2 156 SH_ACS_HPV 2018 2023 6
#> 3 156 SH_ACS_MCV2 2000 2023 24
#> 4 156 SH_ACS_PCV3 2017 2023 7
#> 5 608 SH_ACS_DTP3 2000 2023 24
#> 6 608 SH_ACS_HPV 2017 2023 7
#> 7 608 SH_ACS_MCV2 2000 2023 24
#> 8 608 SH_ACS_PCV3 2014 2023 10GHO-style has_data() / count() helpers are intentionally not provided for SDG because SDG data is generally complete enough that pre-flight checks add little value.