README

# from CRAN
install.packages("DSIR")

# or the development version from GitHub
# install.packages("remotes")
remotes::install_github("shanlong-who/DSIR")

What’s new in 0.13.0 (GitHub)

Added in 0.12.0 (GitHub)

What’s new in 0.11.0

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). flextable (>= 0.9.0) is optional; install it with install.packages("flextable") before using this helper.

dsi_flextable_defaults()

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

data_snapshot() / compare_snapshots() (development) — save complete GHO observations and query context to a local RDS file, then compare two extracts offline. Matching uses the full geographical, temporal and dimension key. Incompatible backends, query scopes or unit/measure definitions stop the comparison; value changes and metadata-only changes are reported separately.

# Repeat exactly the same query later, using the same backend.
old_data <- gho_data("WHS4_100", area = "PHL", backend = "xmart")
data_snapshot(old_data, file = "snapshots/gho_old.rds")

new_data <- gho_data("WHS4_100", area = "PHL", backend = "xmart")
data_snapshot(new_data, file = "snapshots/gho_new.rds")

result <- compare_snapshots("snapshots/gho_old.rds", "snapshots/gho_new.rds")
result$summary
result$modified

Existing files are protected by default. For locally filtered or imported data, declare the complete scope; raw GHO data are preferred to the default 15-column cleaned output. A provider timestamp, retrieval time, UTC snapshot creation time and latest observation year are different concepts. Differences between extracts do not establish when or why WHO revised data. See the offline snapshot tutorial for a reproducible simulation and the full comparison contract. The existing snapshot() expression cache remains available.

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. The public GHO directory contains many 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"))

Check an indicator’s timestamp (experimental). gho_last_updated() requests the latest non-missing observation timestamp across all locations, years and dimensions. It follows the same backend selection rules as gho_data(): an explicit backend overrides DSIR.who_backend, with "auto" as the default when neither is set. Missing timestamps or failed requests do not trigger fallback; automatic selection checks observation availability first.

gho_last_updated("WHS4_100")
gho_last_updated("WHS4_100", backend = "xmart")
gho_last_updated("WHS4_100", backend = "xmart", diagnostics = TRUE)

# Optional sequential queries for a small set of indicators.
codes <- c("WHS4_100", "NCDMORT3070")
updates <- lapply(codes, gho_last_updated, backend = "legacy")
do.call(rbind, updates)

The updated character column preserves the source timestamp, including any offset or unspecified time zone. timestamp_field identifies Date (legacy) or Sys_CommitDateUtc (xMart); neither is a formal publication date or the latest observation year. Each result has one row, with status = "ok", "not_available" (no timestamp supplied), or "failed" (lookup, request or response validation failed). checked_at is the UTC query start time. Whole-catalog queries can take considerable time because each indicator needs a date request and transient failures are retried.

diagnostics = TRUE appends reason, stage, http_status, timestamp_kind, provider_route and provider_indicator; the default eight columns stay unchanged. directory_not_listed means a successfully read directory does not list the code, rather than proving it invalid. http_error and request_failed identify HTTP and connection failures; timestamp_field_missing and timestamp_missing identify a missing field and a response with no non-missing timestamp. Diagnostics make no extra requests and do not suppress warnings or switch backends.

xMart’s Sys_CommitDateUtc records a system commit that can reflect data processing. DSIR has not established that legacy Date records an official modification or publication date. Shared timestamps do not prove that several indicators’ values changed together. checked_at is the UTC check start, not a source modification or observation retrieval time. Use data_snapshot() and compare_snapshots() to detect actual differences between retrieved observations.

gho_indicators() searches xMart when backend = "auto". Keyword searches cannot establish that an individual indicator is absent, so they do not switch catalogs. Select backend = "legacy" for legacy-only codes. If a legacy GHO request fails, it searches the bundled legacy catalog and warns with its collection date. attr(result, "catalog_snapshot") records the snapshot’s source and UTC collection time. The snapshot may omit new codes or contain outdated names; it does not confirm that observations are available. Successful empty searches do not trigger fallback. xMart never uses the legacy snapshot. Search matches names literally: use "UHC service coverage", not the abbreviation "UHC SCI", to find names containing those words.

# 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",
                backend = "legacy")
gho_detailed <- gho_clean(raw, keep_dimensions = TRUE, keep_metadata = TRUE)
gho_detailed |>
  dplyr::select(provider, iso3, year, value_num, dim1_type, dim1, dim2_type, dim2)

As verified on 2026-10-06, this financial-hardship code is absent from the public xMart directory. The example fixes the legacy provider explicitly. The UN SDG workflow below remains separate.

keep_metadata = TRUE also preserves the retrieval provider, 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

sdg_last_updated()

It returns the same columns and status values as gho_last_updated(), with scope = "database" and missing indicator and backend fields. The UN endpoint accepts no indicator or series filter, so this date does not mean that every indicator changed then. Series release codes and metadata-document update dates are not data update timestamps. Source time zones are preserved; an unspecified time zone is not assumed to be UTC.

# 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.

License

WHO backend selection (0.13.0)

GHO defaults to backend = "auto", preferring the public xMart API at https://xmart-api-public.who.int. Before filtering, automatic indicator queries read its directory and make a bounded availability request (at most one row) for the entire indicator. The same selection rules apply to data, counts, availability, coverage, dimension discovery and update timestamps:

xMart result	Automatic behavior
Exact code absent from a successfully read, non-empty directory	Try legacy, with a warning
Verified zero observations for the entire indicator	Try legacy, with a warning
Indicator has observations, but requested filters match none	Keep the valid empty xMart result
HTTP, transport, parsing, schema or route failure	Report failure; do not switch
Only some countries or years are available	Return xMart coverage; do not fill gaps from legacy

Fallback preserves known WHO ISO3/region codes and year filters. Named dimensions, positional dim1-dim3 filters and M49/native geography codes block fallback because equivalent filters have not been established. Dimension discovery can fall back only for SpatialDim, SpatialDimType, TimeDim and TimeDimType. Blocked fallback warns and returns empty/NA without dropping filters. Use an explicit backend with its native conventions in these cases.

Each automatic result records the actual backend, requested_backend = "auto" and any fallback_reason in attr(result, "who_provenance"); this also applies to scalar counts and availability results. Raw observations retain Provider. No result combines providers. Decisions are checked afresh on subsequent calls. Pin a backend for repeated analysis, since provider coverage and published estimates may change. The 15-column default of gho_clean() is unchanged.

gho_indicators('mortality', backend = 'xmart')
gho_dimensions('NCDMORT3070', 'DIM_SEX', backend = 'xmart')
ncd <- gho_data('NCDMORT3070', area = 'PHL', year_from = 2020,
                year_to = 2021, dimensions = list(DIM_SEX = 'TOTAL'),
                backend = 'xmart')
gho_clean(ncd, keep_dimensions = TRUE, keep_metadata = TRUE)

Named dimensions carry their meaning directly (dim_sex, dim_age, etc.). For wide xMart tables, Dim1-Dim3 follow the source table dimension schema, ordered by sex, age, then other names. This may differ from old positional breakdowns. The legacy sex codes and AGEGROUP_ age namespace remain supported in positional filters. Named filters retain exact native source codes. Prefer named filters when updating analyses.

Migration checks on 2026-10-06 matched 18 observations per indicator across France, Japan and the Philippines in 2020-2021. NCDMORT3070 values and bounds agreed exactly. WHOSIS_000001 and MDG_0000000001 values and bounds differed after matching dimension codes; rounding alone did not explain all differences. DSIR preserves each provider’s published estimates. Record the provider and retrieval date when comparing historical analyses. Raw observations have a Provider column and a who_provenance attribute. gho_clean(keep_metadata = TRUE) retains row-level origin as provider, which survives bind_indicators(). Unknown imported origins remain NA. Missing indicator labels are resolved with the recorded backend, even when the session option has changed since retrieval.

Failures warn and return empty results without switching providers. Reference lookups use a ten-minute memory cache. No keys, startup requests or disk cache are required. Selection follows: explicit backend argument, then the DSIR.who_backend option, then "auto". An argument never changes the session option. To choose a session default once, use options(DSIR.who_backend = "xmart"). Other advanced settings are DSIR.who_base_url (xMart HTTPS origin), DSIR.who_page_size and DSIR.who_max_rows. Use snapshot() to save a reproducible pull explicitly.

An absent xMart directory code produces a warning. For legacy HTTP 404 responses, DSIR checks the exact code in its directory before reporting absence. Successful observation queries with no matching rows give an informational message: gho_has_data() returns FALSE, gho_count() returns 0L, and data/coverage queries return empty tables. Failed requests or malformed responses warn; availability and count queries return NA, never FALSE or zero.

If xMart redirects a query to an HTML error page, DSIR retries within its three-attempt limit and warns if the service still does not return JSON. An empty result accompanied by this warning is a failed request, not evidence that no matching indicators or observations exist. Retry later. To use the separate legacy GHO service explicitly for a comparison or temporary workaround:

mortality_indicators <- gho_indicators('mortality', backend = 'legacy')

The legacy catalog and estimates can differ from xMart. Backend selection applies to GHO functions; GHE continues to use its xMart source.

Global Health Estimates

Explore exact codes, then select a country, year, sex, age, cause and measure. GHE is a separate module backed by DEX_CMS/GHE_FULL. It returns long data with one row per measure. A download needs at least one filter and is limited to one million source rows by default.

ghe_dimensions('sex')
ghe_dimensions('age')
ghe_dimensions('measure')
ghe_causes('diabetes')
ghe_causes('stroke')

# All published ages, sexes and causes for one country/year
phl_all_causes <- ghe_data(area = 'PHL', year = 2023, measure = 'deaths')

# Philippine all-age, both-sex deaths, all causes, 2023
phl_deaths <- ghe_data(area = 'PHL', year = 2023, sex = 'TOTAL',
                       age = 'TOTAL', cause = 0, measure = 'deaths')

# Age-specific death rates by sex
phl_rates <- ghe_data(area = 'PHL', year = 2023,
                      sex = c('FEMALE', 'MALE'), age = 'Y40T44',
                      cause = 0, measure = 'death_rate')
ghe_coverage(area = 'PHL', sex = 'TOTAL', age = 'TOTAL', cause = 0)
ghe_clean(phl_rates, keep_dimensions = TRUE)

Counts are persons or years; rates are per 100,000; shares are percentages. Count uncertainty bounds are retained. Bounds for rates and shares are absent in the provider and remain missing. All-age rates are crude rates. Do not sum overlapping ages or cause-hierarchy totals. cause_group is a published group, not an invented parent code. Discovery lists describe the latest release; verify a selection with ghe_coverage(). New releases can revise earlier years.

WHO 2026 standard population

Beginning with DSIR 0.11.0, who_std_pop uses the WHO 2026 Standard Population. Its 18 groups end at 85+. WHO’s rounded Table 1 percentages sum to 100.02; DSIR normalizes them to 100 and derives a numeric standard million summing to 1,000,000. No finer split of 0-4 or 85+ is fabricated.

who_std_pop
count <- seq_len(nrow(who_std_pop))
population <- rep(10000, nrow(who_std_pop))
age_standardize(count, population, who_std_pop$weight)

See the WHO technical report, Table 1, page 6 (September 2026). The former 21-row object is replaced; align observed age groups explicitly before computing a standardized rate.

DSIR

Installation