Data Science Infrastructure for Global Health

Supports global health data analysis, including a publication-ready 'ggplot2' theme, a 'flextable' defaults helper, a thin pie chart wrapper, built-in regional country-code datasets with a WHO region lookup helper, a geometric mean function for indicator aggregation, an average annual rate of reduction function for indicator progress tracking, direct age standardization against the bundled WHO World Standard Population, period life-table construction, a snapshot helper for reproducible data pulls, and convenience clients for the World Health Organization Global Health Observatory (GHO) OData API < https://ghoapi.azureedge.net/api/> and the United Nations Sustainable Development Goals (SDG) API < https://unstats.un.org/SDGAPI/swagger/>.

DSIR

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/

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" NA

iso3_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).

dsi_flextable_defaults()

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.

geomean(c(0.6, 0.8, 0.95))                      # ~0.772
geomean(c(0.6, 0.8, 0.95), w = c(2, 1, 1))      # weighted version

aarr() — average annual rate of reduction, the standard WHO / UNICEF metric for tracking progress in declining indicators (maternal / child mortality, premature NCD mortality, stunting). Fits an OLS line to log(value) against year (the UNICEF-standard method; an endpoint-only method is also available). Positive values mean the indicator is declining; multiply by 100 to compare with published tables.

years  <- 2000:2015
values <- 100 * (1 - 0.024) ^ (years - 2000)
aarr(years, values)                             # 0.024 = 2.4%/yr decline
aarr(years, values, method = "endpoint")        # first/last years only

# Slots into a grouped summarise() on cleaned indicator data
gho_data("NCDMORT3070", area = wpro_cty, dim1 = "SEX_BTSX") |>
  gho_clean() |>
  dplyr::summarise(aarr = aarr(year, value_num), .by = iso3)

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    66

Fetch 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
)

# Filter breakdown dimensions server-side — e.g. keep only the
# both-sexes rows instead of downloading every stratum. Use
# gho_dimensions() to discover the values available for an indicator.
gho_data("NCDMORT3070", spatial_type = "country", dim1 = "SEX_BTSX")

# Tidy the raw GHO response
raw <- gho_data("NCDMORT3070", spatial_type = "country", area = wpro_cty)
gho_clean(raw)

By default, 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.

To show what each GHO dimension means, set keep_dimensions = TRUE. The additional dim1_type, dim2_type, and dim3_type columns come directly from each observation. Their meaning can vary between rows: for financial hardship, dim1_type can be WEALTHQUINTILE, FINANCIALHARDSHIPCOMPONENT, or DEMOGRAPHIC, among others.

raw <- gho_data("FINANCIALHARDSHIP_PROPORTIONOFPOP", area = "PHL")
gho_detailed <- gho_clean(raw, keep_dimensions = TRUE, keep_metadata = TRUE)
gho_detailed |>
  dplyr::select(iso3, year, value_num, dim1_type, dim1, dim2_type, dim2)

keep_metadata = TRUE also preserves the observation identifier, source code and type, spatial/time types, parent location, update timestamp, time interval, and comments. Comments are stored in the footnotes list-column. No dimension type or unit is inferred from an indicator name or code prefix. Both options default to FALSE.

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)

By default, 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).

Preserving SDG dimensions. sdg_data() retains named breakdowns inside raw$dimensions. They do not map to GHO's three dimension positions. Use keep_dimensions = TRUE to append all observed SDG dimensions as character columns such as dim_age, dim_sex, and dim_quantile. Missing values remain NA. The default 15-column output omits these breakdowns, so select the required strata first when using the compact format. Use keep_metadata = TRUE to also retain units, nature, and other attributes as attr_* columns.

raw <- sdg_data("3.8.2", area = "PHL")
unique(raw$dimensions) # Inspect actual dimension names and codes
financial_hardship <- sdg_clean(
  raw, keep_dimensions = TRUE, keep_metadata = TRUE
)

# National total: use the exact UN names and codes, not GHO codes.
# Series and dimension filters run locally after all pages are fetched.
national <- sdg_data(
  "3.8.2", area = "PHL",
  series = "SH_OOP_XPD_EARNNET40",
  dimensions = list(
    Age = "ALLAGE", Location = "ALLAREA", Sex = "BOTHSEX",
    Quantile = "_T", Type_of_household = "_T"
  )
) |>
  sdg_clean(keep_dimensions = TRUE)

Discovering dimension codes and labels. This reads official series metadata without downloading the observation table:

codebook <- sdg_dimensions("3.8.2", include_attributes = TRUE)
codebook |>
  dplyr::select(series, kind, dimension, code, label, sdmx)

The codebook keeps series separate and distinguishes dimensions from attributes such as units. Use code, not the alternative sdmx code, in dimensions filters. A listed category does not guarantee that it occurs in every country's observations.

Keeping source context. With keep_metadata = TRUE, the SDG cleaner appends attributes (e.g. attr_units, attr_nature), data_source, time detail/coverage, base period, value type, and the geographic information URL. All footnotes and linked indicator, goal, and target codes are preserved in list-columns. This makes no additional network requests. For example:

financial_hardship |>
  dplyr::select(dplyr::any_of(c(
    "iso3", "year", "value_num", "attr_units", "attr_nature", "data_source"
  )))
head(financial_hardship$footnotes, 1L) # Every note for the first observation

Use saveRDS() (or snapshot()) to preserve these list-columns when saving results. CSV requires an explicit choice of how to represent multiple notes or links in one cell.

Complete downloads. sdg_data() checks declared page numbers, page counts, and total row counts before applying local filters. Inconsistent counts, an unexpected empty page, or a later request failure produce a warning and no rows; partial observations are not returned. Counts not supplied by the API cannot be checked. sdg_coverage() retains these warnings and accepts the same series and dimensions filters. Without filters, its counts include all population strata within each location and series.

For SDG 3.8.2, the UN catalogue checked on 2026-09-30 publishes the 2025-definition series SH_OOP_XPD_EARNNET40. GHO's FINANCIALHARDSHIP_PROPORTIONOFPOP also contains total, large, and impoverishing expenditure components in Dim1; these components are not separate series in that UN catalogue. Do not treat every GHO row as the SDG national total or infer missing UN component rows. See the WHO definition and components and the UN series catalogue.

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)

Additional columns, including the named dimensions retained by keep_dimensions = TRUE, are preserved by bind_indicators(). Inputs without a given column receive typed missing values. Binding does not harmonise dimension codes or indicator definitions across the two sources.

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    10

GHO-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.