Package {DSIR}

Title:	Data Science Infrastructure for Global Health
Version:	0.7.0
Description:	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, 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/.
License:	MIT + file LICENSE
Language:	en-US
URL:	https://github.com/shanlong-who/DSIR, https://shanlong-who.github.io/DSIR/
BugReports:	https://github.com/shanlong-who/DSIR/issues
Encoding:	UTF-8
Depends:	R (≥ 4.1.0)
Imports:	cli, flextable (≥ 0.9.0), ggplot2 (≥ 3.4.0), httr2, rlang, tibble (≥ 3.0.0)
Suggests:	patchwork, officer, testthat (≥ 3.0.0), httptest2, knitr, rmarkdown, dplyr
Config/testthat/edition:	3
LazyData:	true
VignetteBuilder:	knitr
Config/roxygen2/version:	8.0.0
NeedsCompilation:	no
Packaged:	2026-05-21 15:53:06 UTC; User
Author:	Shanlong Ding [aut, cre]
Maintainer:	Shanlong Ding <dings@who.int>
Repository:	CRAN
Date/Publication:	2026-05-21 16:12:05 UTC

DSIR: Data Science Infrastructure for Global Health in R

Description

A small, opinionated toolkit for global health data analysis. It bundles a publication-ready ggplot2 theme (theme_dsi(), theme_dsi_facet()) with flush-axis bar-chart scales (scale_y_dsi_col(), scale_x_dsi_col()), sensible flextable defaults (dsi_flextable_defaults()), a quick pie chart helper (ggpie()), regional country-code datasets (wpro_cty) with WHO region and UN M49 lookups (iso3_to_region(), iso3_to_m49(), m49_to_iso3()), a geometric mean helper for indicator aggregation (geomean()), thin clients for the WHO Global Health Observatory API (gho_indicators(), gho_data(), gho_dimensions(), gho_has_data(), gho_count(), gho_coverage()) and the UN Sustainable Development Goals API (sdg_goals(), sdg_targets(), sdg_indicators(), sdg_areas(), sdg_data(), sdg_coverage()), plus a unified cleaning / binding pipeline (gho_clean(), sdg_clean(), bind_indicators()) that puts GHO and SDG output into the same 15-column schema.

Author(s)

Maintainer: Shanlong Ding dings@who.int (ORCID)

Authors:

• Shanlong Ding dings@who.int (ORCID)

See Also

Useful links:

• https://github.com/shanlong-who/DSIR

• https://shanlong-who.github.io/DSIR/

• Report bugs at https://github.com/shanlong-who/DSIR/issues

Bind Cleaned Indicator Tibbles

Description

Combines two or more tibbles produced by gho_clean() or sdg_clean() into a single tibble. Because both cleaners output the same 15-column schema, the result is a uniform table that can be filtered, joined, or visualised without source-specific code paths; use the source column to tell GHO rows apart from SDG rows.

Usage

bind_indicators(...)

Arguments

Details

Inputs do not need to be in any particular order. NULL inputs are silently dropped, which makes it ergonomic to write code like bind_indicators(maybe_gho, maybe_sdg) where some sources may not have been fetched.

Value

A single tibble with the unified cleaned- indicator schema (15 columns). Row order is c(input_1, input_2, ...), preserving within-input order.

See Also

gho_clean(), sdg_clean().

Examples

gho <- gho_data("NCDMORT3070", area = wpro_cty) |> gho_clean()
sdg <- sdg_data("3.4.1",        area = wpro_cty) |> sdg_clean()
bind_indicators(gho, sdg)

Set DSIR Flextable Defaults

Description

Applies a consistent set of flextable formatting defaults for publication-ready tables (booktabs theme, bold headers, modest padding). Pick any font you like — the default "" leaves the flextable default in place so the call is safe on systems where Cambria is not installed.

Usage

dsi_flextable_defaults(
  font_size = 12,
  font_family = "",
  font_color = "#333333",
  border_color = "black",
  padding = c(3, 3, 4, 4)
)

Arguments

Value

Invisibly returns NULL. Called for its side effect of mutating the flextable global defaults via flextable::set_flextable_defaults().

Examples

dsi_flextable_defaults()

Geometric Mean

Description

Computes the geometric mean of a numeric vector, with optional weights. Useful for aggregating multiplicative quantities such as ratio-based health indicators (e.g. UHC service-coverage tracers, where the composite index is the geometric mean of component coverage values).

Usage

geomean(x, w = NULL, na.rm = TRUE)

Arguments

Details

Pass w to compute a weighted geometric mean, defined as exp(weighted.mean(log(x), w)).

Value

A numeric scalar. Returns NA_real_ when the input is empty, when it is entirely NA, or when na.rm = FALSE and any element is NA. Returns NaN with a warning when x contains negative values, or when all weights are zero.

Examples

# Unweighted
geomean(c(1, 4, 16))                # 4
geomean(c(0.6, 0.8, 0.95))          # ~0.772 — typical UHC tracer aggregation
geomean(c(1, NA, 4))                # 2
geomean(c(1, NA, 4), na.rm = FALSE) # NA_real_
geomean(c(1, 0, 4))                 # 0

# Weighted
geomean(c(1, 4, 16), w = c(1, 1, 1))     # 4 (equal weights = unweighted)
geomean(c(1, 4, 16), w = c(1, 2, 1))     # weighted toward 4
geomean(c(0.6, 0.8, 0.95), w = c(2, 1, 1))

Create a Pie Chart with ggplot2

Description

Builds a pie chart from a data frame using one categorical column and one numeric column. Slices are labeled with the category name and percentage share.

Usage

ggpie(
  df,
  .x,
  .y,
  .offset = 1,
  .color = "white",
  .legend = FALSE,
  .label = TRUE,
  .label_size = 3.5
)

Arguments

Value

A ggplot object.

Examples

df <- data.frame(
  category = c("A", "B", "C"),
  value = c(40, 35, 25)
)
ggpie(df, "category", "value")

Tidy a GHO Data Frame

Description

Selects, renames, and type-casts the most useful columns from a GHO observation table returned by gho_data(), producing a compact tibble in the unified DSIR cleaned-indicator schema — the same schema produced by sdg_clean(), so the two outputs can be combined directly with bind_indicators().

Usage

gho_clean(df)

Arguments

Details

The mapping (GHO source → unified column) is:

• IndicatorCode → id

• IndicatorCode resolved against the GHO indicator catalog → indicator (the human-readable name; cached at session level after the first call)

• SpatialDim → location; also iso3 when it matches a WHO Member State, otherwise iso3 = NA

• TimeDim → year (integer)

• Value → value (character; raw)

• NumericValue → value_num (numeric)

• Low, High → low, high (numeric)

• Dim1, Dim2, Dim3 → dim1, dim2, dim3 (character)

The series column is always NA for GHO output (it is an SDG-only concept). The location_name column is populated by looking up location (an ISO3 code or a WHO region code) against the who_countries dataset and a hardcoded set of WHO regional names; locations that match neither (e.g. non-Member State areas) are left as NA.

Source columns absent from df (e.g. Low / High for indicators without confidence intervals) are filled with typed NA, so the output always has the same 15 columns with the same column types.

The GHO data endpoint (⁠/api/{IndicatorCode}⁠) does not return IndicatorName; that field lives on the catalog endpoint queried by gho_indicators(). On the first call within an R session, gho_clean() fetches the catalog once and caches it for the rest of the session, so the indicator column carries the full human-readable indicator name. If the catalog cannot be fetched (e.g. no network), gho_indicators() emits a warning and the indicator column falls back to NA.

Value

A tibble with 15 columns: source (always "gho"), id, indicator, location, iso3, location_name, year, value, value_num, low, high, series (NA), dim1, dim2, dim3. Sorted by location then year. Empty input returns an empty tibble with the same columns and types.

See Also

gho_data(), sdg_clean(), bind_indicators().

Examples

gho_data("NCDMORT3070", spatial_type = "country") |>
  gho_clean()

Count Observations for a GHO Indicator Filter

Description

Sends a ⁠$top=0&$count=true⁠ request to the WHO GHO OData API, which returns the matching row count without transferring any observations. Useful for sizing a download before issuing it.

Usage

gho_count(
  indicator,
  spatial_type = NULL,
  area = NULL,
  year_from = NULL,
  year_to = NULL
)

Arguments

Value

An integer scalar — the number of observations the server would return for the same filter via gho_data(). Returns NA_integer_ (with a warning) if the request fails.

See Also

gho_data(), gho_has_data(), gho_coverage().

Examples

# How many rows would gho_data() pull for France?
gho_count("WHOSIS_000001", area = "FRA")

# Compare coverage across regions
gho_count("NCDMORT3070", spatial_type = "country")
gho_count("NCDMORT3070", spatial_type = "region")

Summarise Per-Location Data Coverage of a GHO Indicator

Description

Fetches only the SpatialDim and TimeDim columns for a GHO indicator (much lighter than gho_data()) and summarises the year range and observation count per location. Useful for answering "which countries have data, and for what years?" before committing to a full download.

Usage

gho_coverage(
  indicator,
  spatial_type = "country",
  area = NULL,
  year_from = NULL,
  year_to = NULL
)

Arguments

Value

A tibble with one row per location and columns:

• location (chr) — the SpatialDim value (typically ISO3).

• year_min (int) — earliest year with data.

• year_max (int) — latest year with data.

• n_obs (int) — number of observations.

  Sorted by location. Empty input or service failure returns an empty tibble with the same four columns.

See Also

gho_data(), gho_has_data(), gho_count().

Examples

# Year coverage of life expectancy for three countries
gho_coverage("WHOSIS_000001", area = c("FRA", "DEU", "JPN"))

# All countries with any life-expectancy data, since 2010
gho_coverage("WHOSIS_000001", year_from = 2010)

Fetch GHO Data

Description

Retrieves observations for a specific indicator from the WHO GHO OData API, with optional filters by spatial level, country / region and year range.

Usage

gho_data(
  indicator,
  spatial_type = NULL,
  area = NULL,
  year_from = NULL,
  year_to = NULL
)

Arguments

Value

A tibble of indicator observations, or an empty tibble when the service is unreachable.

See Also

gho_indicators(), gho_dimensions().

Examples

# Country-level data for one indicator
gho_data("NCDMORT3070", spatial_type = "country")

# Specific countries and years
gho_data("WHOSIS_000001", area = c("FRA", "DEU"), year_from = 2015)

List Dimensions of a GHO Indicator

Description

Returns the unique values of a given dimension across all observations of a GHO indicator. Useful for discovering which ages, sexes, regions, or other breakdowns are available before calling gho_data().

Usage

gho_dimensions(indicator, dimension = "SpatialDimType")

Arguments

Value

A character vector of unique, sorted dimension values, or an empty character vector when the service is unreachable or the dimension is missing.

See Also

gho_data(), gho_indicators().

Examples

gho_dimensions("NCDMORT3070")
gho_dimensions("NCDMORT3070", dimension = "Dim1")

Check Whether a GHO Indicator Has Data for a Filter

Description

Sends a minimal request (⁠$top=1&$select=Id⁠) to the WHO GHO OData API to find out whether any observations exist for the given indicator and filter combination, without downloading the full result set. Useful as a quick precheck before gho_data().

Usage

gho_has_data(
  indicator,
  spatial_type = NULL,
  area = NULL,
  year_from = NULL,
  year_to = NULL
)

Arguments

Value

A logical scalar:

• TRUE if at least one observation exists for the filter.

• FALSE if the server returns an empty result.

• NA if the request fails (network failure, unreachable host, or the indicator code does not exist and the server returns an HTTP error). A warning is emitted in the failure case.

See Also

gho_data(), gho_count(), gho_coverage().

Examples

# Does WHO have life-expectancy data for France?
gho_has_data("WHOSIS_000001", area = "FRA")

# Quickly screen a list of indicators before downloading any data
inds <- c("WHOSIS_000001", "NCDMORT3070")
vapply(inds, gho_has_data, logical(1), area = "FRA")

List GHO Indicators

Description

Fetches the catalog of indicators from the WHO Global Health Observatory (GHO) OData API.

Usage

gho_indicators(search = NULL)

Arguments

Value

A tibble with columns IndicatorCode, IndicatorName and Language. Returns an empty tibble (with a message) when the service is unreachable.

See Also

gho_data(), gho_dimensions().

Examples

# All indicators
inds <- gho_indicators()

# Single keyword
gho_indicators("mortality")

# Multiple keywords from one string (AND): both terms must appear
gho_indicators("child mortality")

# Or pass terms as a vector
gho_indicators(c("child", "mortality"))

Convert ISO3 Codes to UN M49 Numeric Codes

Description

Maps ISO 3166-1 alpha-3 country codes to UN M49 numeric area codes using the who_countries dataset shipped with DSIR. Useful when moving from data sources keyed by ISO3 (e.g. the WHO GHO API) to sources keyed by M49 (e.g. the UN SDG API).

Usage

iso3_to_m49(iso3)

Arguments

Details

Codes that do not correspond to a WHO Member State return NA. This includes Associate Members (e.g. Puerto Rico) and other non-Member areas that some indicator data sets cover.

Most users will not need to call this function directly: sdg_data() and sdg_coverage() accept ISO3 codes for their area argument and convert internally. This helper is exported for cases where you want to inspect or manipulate the conversion yourself.

Value

A character vector the same length as iso3, with M49 codes in the same format as who_countries$m49_code (three- character zero-padded strings, e.g. "076"). Non-Member areas return NA.

See Also

who_countries, iso3_to_region(), sdg_data().

Examples

iso3_to_m49(c("PHL", "FRA", "JPN"))
# "608" "250" "392"

# Case-insensitive
iso3_to_m49("phl")
# "608"

# Non-Member areas return NA
iso3_to_m49(c("PRI", "PHL"))
# NA "608"

Look Up the WHO Region for ISO3 Codes

Description

Maps ISO 3166-1 alpha-3 country codes to WHO region codes using the who_countries dataset shipped with DSIR. Stays in sync with WHO governance changes reflected in DSIR — for example, Indonesia's reassignment from SEAR to WPR following EB156 (May 2025).

Usage

iso3_to_region(iso3, long = FALSE)

Arguments

Details

Codes that do not correspond to a WHO Member State return NA. This includes Associate Members (Puerto Rico, Tokelau) and other non-Member areas that some indicator data sets cover.

Value

A character vector the same length as iso3.

See Also

who_countries, wpro_cty.

Examples

iso3_to_region(c("PHL", "FRA", "USA", "COK"))
# "WPR" "EUR" "AMR" "WPR"

iso3_to_region(c("IDN", "JPN"), long = TRUE)
# "Western Pacific" "Western Pacific"  (Indonesia in WPR since May 2025)

# Non-Member areas return NA
iso3_to_region(c("PRI", "TKL", "PHL"))
# NA NA "WPR"

Convert UN M49 Numeric Codes to ISO3 Codes

Description

Maps UN M49 numeric area codes to ISO 3166-1 alpha-3 country codes using the who_countries dataset shipped with DSIR. Counterpart to iso3_to_m49() and used internally by sdg_clean() to populate the iso3 column on SDG output.

Usage

m49_to_iso3(m49)

Arguments

Details

M49 codes that do not correspond to a WHO Member State return NA. This includes region / world aggregates (e.g. "900" for World, "001" for World, "419" for Latin America and the Caribbean) and codes for non-Member areas (e.g. Puerto Rico, Tokelau).

Input accepts either the zero-padded form ("076") or the bare form ("76"); both are normalised before lookup. Non-numeric input returns NA (with a single warning from the underlying as.integer() coercion).

Value

A character vector the same length as m49. Non-Member codes (region aggregates, non-Member areas) return NA.

See Also

who_countries, iso3_to_m49(), sdg_clean().

Examples

m49_to_iso3(c("608", "250", "392"))
# "PHL" "FRA" "JPN"

# Zero-padded and bare forms both accepted
m49_to_iso3(c("076", "76"))
# "BRA" "BRA"

# Non-Member areas / aggregates return NA
m49_to_iso3(c("900", "608"))
# NA "PHL"

Pacific Island Country ISO3 codes

Description

Character vector of ISO3 codes for the 14 WHO Member States classified as Pacific Island Countries (PICs). Sorted alphabetically.

Usage

pic_cty

Format

A character vector of 14 ISO 3166-1 alpha-3 codes.

Details

All 14 PICs are within the Western Pacific Region, so all(pic_cty %in% wpro_cty) is TRUE. Non-Member Pacific areas (e.g. New Caledonia, French Polynesia, American Samoa, Tokelau) are not included.

The 14 PIC Member States are: Cook Islands, Fiji, Kiribati, Marshall Islands, Micronesia (Federated States of), Nauru, Niue, Palau, Papua New Guinea, Samoa, Solomon Islands, Tonga, Tuvalu, and Vanuatu.

See Also

who_countries, wpro_cty.

Examples

length(pic_cty)              # 14
all(pic_cty %in% wpro_cty)   # TRUE

Continuous Scales for DSIR Bar / Column Charts

Description

Thin wrappers around ggplot2::scale_y_continuous() and ggplot2::scale_x_continuous() that remove the default lower expansion so that columns sit flush with the axis — the convention for WHO and most publication-style bar charts. The upper expansion is preserved at 5% so the tallest column has breathing room above (or to the right of) it.

Usage

scale_y_dsi_col(...)

scale_x_dsi_col(...)

Arguments

Details

Use scale_y_dsi_col() for vertical bars (geom_col() / geom_bar()) and scale_x_dsi_col() when bars are horizontal (via coord_flip() or geom_col(orientation = "y")).

Pass any other ⁠scale_*_continuous()⁠ argument (labels, breaks, limits, ...) through ....

Value

A ggplot2 Scale object, to be added to a plot with +.

Examples

library(ggplot2)

# Vertical bars
ggplot(mtcars, aes(factor(cyl))) +
  geom_bar(fill = "#0093D5") +
  scale_y_dsi_col() +
  theme_dsi()

List SDG Geographic Areas

Description

Fetches the list of geographic areas available from the UN SDG database.

Usage

sdg_areas()

Value

A tibble with area codes and names, or NULL when the service is unreachable.

See Also

sdg_data().

Examples

sdg_areas()

Tidy an SDG Data Frame

Description

Selects, renames, and type-casts the most useful columns from an SDG observation table returned by sdg_data(), producing a compact tibble in the unified DSIR cleaned-indicator schema — the same schema produced by gho_clean(), so the two outputs can be combined directly with bind_indicators().

Usage

sdg_clean(df)

Arguments

Details

The mapping (SDG source → unified column) is:

• indicator (list-column, flattened) → id (e.g. "3.4.1")

• seriesDescription → indicator (human-readable label; NA if the API response does not include it)

• geoAreaCode → location (UN M49 numeric, as character); also iso3 via m49_to_iso3() for WHO Member States — region / world aggregates and non-Member areas get iso3 = NA

• location_name is resolved by looking up iso3 against who_countries (so a WHO Member State has the same location_name here and in gho_clean() output), with a fallback to the SDG API's raw geoAreaName for non-Member-State rows (e.g. regional / world aggregates)

• timePeriodStart → year (integer)

• value → value (character; raw) and value_num (numeric; NA for non-numeric entries like "<0.1" or aggregate notes)

• lowerBound, upperBound → low, high (numeric)

• series → series

Three columns are always present but never populated for SDG output: dim1, dim2, dim3 (GHO-only concepts).

Value

A tibble with 15 columns: source (always "sdg"), id, indicator, location, iso3, location_name, year, value, value_num, low, high, series, dim1 (NA), dim2 (NA), dim3 (NA). Sorted by location then year. Empty input returns an empty tibble with the same columns and types.

See Also

sdg_data(), gho_clean(), bind_indicators(), m49_to_iso3().

Examples

sdg_data("3.2.1", area = "156", year_from = 2015) |>
  sdg_clean()

Explore Series Coverage of an SDG Indicator

Description

A single SDG indicator (for example "3.4.1", NCD mortality) is typically published as several series stratified by sex, age, or cause. Different series may have different country and year coverage. sdg_coverage() summarises year range and observation count per ⁠(location, series)⁠ combination, so you can see which series exist for an indicator and how each one is covered before committing to a downstream analysis.

Usage

sdg_coverage(indicator, area = NULL, year_from = NULL, year_to = NULL)

Arguments

Details

Unlike the GHO availability helpers, this function is a series-exploration tool rather than a payload-saving precheck: SDG data is generally complete enough that GHO-style has_data() / count() helpers add little value, so they are intentionally not provided. The SDG API also offers no payload-reduction option (no ⁠$select⁠ equivalent), so sdg_coverage() calls sdg_data() internally and aggregates the result client-side.

Value

A tibble with one row per ⁠(location, series)⁠ and columns:

• location (chr) — area code (geoAreaCode).

• series (chr) — SDG series code.

• year_min (int) — earliest year with data.

• year_max (int) — latest year with data.

• n_obs (int) — number of observations.

  Sorted by location then series. Empty input or service failure returns an empty tibble with the same five columns.

See Also

sdg_data(), sdg_indicators(), gho_coverage().

Examples

# Series available for NCD mortality in China and Brazil
sdg_coverage("3.4.1", area = c("156", "076"))

# Filter to a year range
sdg_coverage("3.4.1", area = "156", year_from = 2015)

Fetch SDG Data

Description

Retrieves data for one or more SDG indicators from the UN SDG API, with optional filters by area and year.

Usage

sdg_data(
  indicator,
  area = NULL,
  year_from = NULL,
  year_to = NULL,
  page_size = 1000L
)

Arguments

Value

A tibble of indicator observations, or an empty tibble when the service is unreachable or there are no matching rows.

See Also

sdg_indicators(), sdg_areas(), iso3_to_m49().

Examples

# One indicator, one country — the typical entry point
sdg_data("1.1.1", area = "PHL")

# Specific area and year range (M49 code)
sdg_data("3.2.1", area = "156", year_from = 2015, year_to = 2023)

# ISO3 codes work directly — DSIR's regional vectors can be passed in
sdg_data("3.4.1", area = c("PHL", "FRA", "JPN"))

List SDG Goals

Description

Fetches the list of Sustainable Development Goals from the UN SDG API.

Usage

sdg_goals(include_children = FALSE)

Arguments

Value

A list (or tibble) of SDG goals, or NULL when the service is unreachable.

See Also

sdg_targets(), sdg_indicators(), sdg_data().

Examples

sdg_goals()
sdg_goals(include_children = TRUE)

List SDG Indicators

Description

Fetches the list of SDG indicators from the UN SDG API, with optional keyword filtering on the indicator description.

Usage

sdg_indicators(search = NULL)

Arguments

Value

A list (or tibble) of SDG indicators, or NULL when the service is unreachable. When search matches no rows, an empty tibble with the same columns as the unfiltered response is returned.

See Also

sdg_targets(), sdg_data().

Examples

# Full list
sdg_indicators()

# Single keyword
sdg_indicators("mortality")

# Multi-keyword — AND semantics
sdg_indicators("mortality cancer")
sdg_indicators(c("maternal", "mortality"))

List SDG Targets

Description

Fetches the list of SDG targets from the UN SDG API.

Usage

sdg_targets(include_children = FALSE)

Arguments

Value

A list (or tibble) of SDG targets, or NULL when the service is unreachable.

See Also

sdg_goals(), sdg_indicators().

Examples

sdg_targets()

DSIR ggplot2 theme — WHO publication-style

Description

A clean, modern theme tuned for WHO and global-health publications. Removes the panel border, draws light grid lines, and uses a muted text colour so that the data — not the chart chrome — is the visual focus. The grid argument controls which direction(s) the grid lines run, so the theme works equally well for vertical bars, horizontal bars (via coord_flip()), scatter plots, and line charts.

Usage

theme_dsi(
  base_size = 12,
  base_family = "",
  accent = "#0093D5",
  grid_color = "grey92",
  grid = c("both", "x", "y", "none"),
  legend_position = "bottom"
)

Arguments

Value

A ggplot2 theme object.

See Also

theme_dsi_facet() for a sibling theme tuned for faceted plots.

Examples

library(ggplot2)

# Default — grid in both directions, works under coord_flip()
ggplot(mtcars, aes(wt, mpg, color = factor(cyl))) +
  geom_point(size = 3) +
  theme_dsi() +
  labs(title = "Fuel efficiency by weight",
       x = "Weight (1000 lbs)", y = "Miles per gallon",
       color = "Cylinders")

# Minimal look — only horizontal grid lines
ggplot(mtcars, aes(wt, mpg)) +
  geom_point(size = 3, color = "#0093D5") +
  theme_dsi(grid = "y") +
  labs(x = "Weight (1000 lbs)", y = "Miles per gallon")

DSIR ggplot2 theme for faceted plots

Description

A sibling of theme_dsi() tuned for faceted plots. Where theme_dsi() uses half-frame axis lines and only horizontal grid lines — a look that suits a single panel — repeating those across every facet looks heavy and the panels run together. theme_dsi_facet() replaces the axis lines with a light panel border, draws grid lines on both axes, gives the strip a soft background, and inserts whitespace between panels.

Usage

theme_dsi_facet(
  base_size = 12,
  base_family = "",
  accent = "#0093D5",
  grid_color = "grey92",
  strip_fill = "grey95",
  strip_color = "grey20",
  grid = c("both", "x", "y", "none"),
  legend_position = "bottom"
)

Arguments

Details

Use theme_dsi() for single-panel plots and theme_dsi_facet() for plots with facet_wrap() or facet_grid(). Shared elements (text styles, title block, legend, plot margins) match theme_dsi() exactly, so the two themes feel like the same family.

Value

A ggplot2 theme object.

See Also

theme_dsi() for the single-panel sibling.

Examples

library(ggplot2)
ggplot(mtcars, aes(wt, mpg)) +
  geom_point(size = 2, color = "#0093D5") +
  facet_wrap(~ cyl, labeller = label_both) +
  theme_dsi_facet() +
  labs(title = "Fuel efficiency by cylinder count",
       x = "Weight (1000 lbs)", y = "Miles per gallon")

WHO Member States with regional and Pacific classifications

Description

A tibble of the 194 World Health Organization (WHO) Member States, with standard country identifiers and WHO/Pacific groupings used across DSIR analytical workflows.

Usage

who_countries

Format

A tibble with 194 rows and 7 columns:

iso3

ISO 3166-1 alpha-3 code (3-letter, e.g. "PHL").

iso2

ISO 3166-1 alpha-2 code (2-letter, e.g. "PH").

m49_code

UN M49 numeric code, stored as a 3-character string with leading zeros where present (e.g. "008" for Albania). Stored as character because some downstream APIs (notably the UN SDG API) expect the leading-zero form.

name_official

WHO official English name (e.g. "Iran (Islamic Republic of)"). Use for formal documents and reports.

name_short

A shorter form suitable for charts and tables (e.g. "Iran"). Equal to name_official for countries whose official name is already concise. See Details.

who_region

WHO region code: one of "AFR", "AMR", "SEAR", "EUR", "EMR", "WPR".

is_pic

Logical. TRUE for the 14 Pacific Island Country (PIC) Member States in WPR, FALSE otherwise.

Details

Scope. WHO has 194 Member States plus 2 Associate Members (Puerto Rico, Tokelau) and reports on additional non-Member areas (e.g. West Bank and Gaza Strip). This dataset includes only the 194 Member States. Cook Islands and Niue are full WHO Member States and are included even though they are not UN member states.

Region coverage (as of May 2025, after WHO EB156 reassignment of Indonesia from SEAR to WPR):

• AFR (African Region): 47

• AMR (Region of the Americas): 35

• SEAR (South-East Asia Region): 10

• EUR (European Region): 53

• EMR (Eastern Mediterranean Region): 21

• WPR (Western Pacific Region): 28

Short names. name_short differs from name_official for 13 countries where the official name is a parenthetical descriptor or is otherwise long. Examples: "DPR Korea", "DR Congo", "Lao PDR", "United Kingdom". These short forms follow conventions used in WHO regional reports and OECD Health at a Glance: Asia/Pacific.

PICs. The Pacific Island Countries flag (is_pic) marks the 14 WHO Member States in the Pacific sub-region: Cook Islands, Fiji, Kiribati, Marshall Islands, Micronesia (Federated States of), Nauru, Niue, Palau, Papua New Guinea, Samoa, Solomon Islands, Tonga, Tuvalu, and Vanuatu. Non-Member Pacific areas (e.g. New Caledonia, French Polynesia, American Samoa) are not included in this dataset.

Source

• WHO official region listing: https://www.who.int/countries

• ISO 3166-1 codes and UN M49 numeric codes: UN Statistics Division https://unstats.un.org/unsd/methodology/m49/

Examples

# All WPR Member States, sorted alphabetically by short name
wpr <- who_countries[who_countries$who_region == "WPR", ]
wpr <- wpr[order(wpr$name_short), c("iso3", "name_short")]
head(wpr)

# Filter a data frame to PIC member states
# df_pic <- subset(my_data, country_iso3 %in% who_countries$iso3[who_countries$is_pic])

WHO regional Member State ISO3 vectors

Description

Convenience character vectors of ISO3 codes for the WHO Member States in each region. Each vector is the regional subset of who_countries's iso3 column, sorted alphabetically.

Usage

afro_cty

amro_cty

searo_cty

euro_cty

emro_cty

wpro_cty

Format

Character vectors of ISO 3166-1 alpha-3 codes.

An object of class character of length 47.

An object of class character of length 35.

An object of class character of length 10.

An object of class character of length 53.

An object of class character of length 21.

An object of class character of length 28.

Details

These are derived directly from who_countries and exist for ergonomic filtering, e.g. dplyr::filter(data, iso3 %in% wpro_cty). If you need to update group membership, edit data-raw/who_countries.R and re-run that script — the vectors regenerate from the master table.

afro_cty

47 Member States in the WHO African Region.

amro_cty

35 Member States in the WHO Region of the Americas.

searo_cty

10 Member States in the WHO South-East Asia Region.

euro_cty

53 Member States in the WHO European Region.

emro_cty

21 Member States in the WHO Eastern Mediterranean Region.

wpro_cty

28 Member States in the WHO Western Pacific Region. Includes Indonesia from May 2025 (per WHO EB156).

See Also

who_countries, pic_cty.

Examples

length(wpro_cty)        # 28
"IDN" %in% wpro_cty     # TRUE — Indonesia in WPR since May 2025