## ----include = FALSE----------------------------------------------------------
run_live <- identical(Sys.getenv('DSIR_BUILD_LIVE_VIGNETTE'), 'true')
knitr::opts_chunk$set(
  collapse = TRUE,
  comment  = "#>", 
  eval     = TRUE
)

## ----setup--------------------------------------------------------------------
library(DSIR)
library(dplyr)
library(ggplot2)

## -----------------------------------------------------------------------------
who_countries

## -----------------------------------------------------------------------------
wpro_cty
length(wpro_cty)   # 28 Member States in WPR (since May 2025)

## -----------------------------------------------------------------------------
who_countries |>
  filter(is_pic) |>
  select(iso3, name_short)

## -----------------------------------------------------------------------------
iso3_to_region(c("PHL", "FRA", "ZAF", "USA", "XYZ"))
# "WPR" "EUR" "AFR" "AMR" NA

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

# Case-insensitive; non-Member areas return NA
iso3_to_m49(c("phl", "PRI"))
# "608" NA

## ----eval = run_live----------------------------------------------------------
# # Does WHO have life-expectancy data for France?
# gho_has_data("WHOSIS_000001", area = "FRA")
# # TRUE
# 
# # Bulk-screen several indicators at once
# inds <- c("WHOSIS_000001", "NCDMORT3070", "MDG_0000000026")
# vapply(inds, gho_has_data, logical(1), area = "PHL")

## ----eval = run_live----------------------------------------------------------
# gho_count("WHOSIS_000001", area = wpro_cty)

## ----eval = run_live----------------------------------------------------------
# gho_coverage("WHOSIS_000001", area = c("FRA", "DEU", "JPN"))

## ----eval = run_live----------------------------------------------------------
# gho_indicators("UHC") |> head()

## ----eval = run_live----------------------------------------------------------
# uhc <- gho_data(
#   indicator    = "UHC_INDEX_REPORTED",
#   spatial_type = "country",
#   area         = wpro_cty,
#   year_from    = 2015
# )
# 
# uhc |> glimpse()

## ----eval = run_live----------------------------------------------------------
# uhc_clean <- gho_clean(uhc)
# uhc_clean

## -----------------------------------------------------------------------------
# Unweighted geometric mean
geomean(c(0.6, 0.8, 0.95))
#> 0.7720589

# With optional weights — useful when tracers have different 
# methodological importance
geomean(c(0.6, 0.8, 0.95), w = c(2, 1, 1))

## ----fig.width = 7, fig.height = 4, eval = run_live---------------------------
# uhc_clean |>
#   filter(iso3 %in% c("AUS", "CHN", "PHL", "FJI")) |>
#   left_join(who_countries, by = "iso3") |>
#   ggplot(aes(x = year, y = value_num, group = iso3, color = name_short)) +
#   geom_line(linewidth = .8) +
#   geom_point(size = 1.8) +
#   theme_dsi() +
#   labs(
#     title    = "UHC Service Coverage Index, selected WPR Member States",
#     subtitle = "2015 onwards",
#     x = NULL, y = "SCI", color = NULL
#   )

## ----fig.width = 7, fig.height = 4, eval = run_live---------------------------
# uhc_clean |>
#   filter(year == max(year)) |>
#   left_join(who_countries, by = "iso3") |>
#   arrange(desc(value_num)) |>
#   head(10) |>
#   ggplot(aes(reorder(name_short, value_num), value_num)) +
#   geom_col(fill = "#0093D5") +
#   coord_flip() +
#   scale_y_dsi_col() +
#   theme_dsi(grid = "x") +
#   labs(
#     title    = "UHC Service Coverage Index, top 10 WPR Member States",
#     subtitle = "Latest available year",
#     x = NULL, y = "SCI"
#   )

## ----fig.width = 8, fig.height = 5, eval = run_live---------------------------
# uhc_clean |>
#   left_join(who_countries, by = "iso3") |>
#   filter(is_pic) |>
#   ggplot(aes(x = year, y = value_num)) +
#   geom_line(color = "#0093D5", linewidth = 0.8) +
#   geom_point(color = "#0093D5", size = 1.5) +
#   facet_wrap(~ name_short, ncol = 4) +
#   theme_dsi_facet() +
#   labs(
#     title    = "UHC Service Coverage Index, Pacific Island Countries",
#     subtitle = "Each panel shows one country's trajectory",
#     x = NULL, y = "SCI"
#   )

## ----fig.width = 8, fig.height = 5, eval = run_live---------------------------
# uhc_clean |>
#   left_join(who_countries, by = "iso3") |>
#   filter(is_pic) |>
#   ggplot(aes(x = year, y = value_num)) +
#   geom_line(color = "#0093D5", linewidth = 0.8) +
#   facet_wrap(~ name_short, ncol = 4) +
#   theme_dsi_facet(strip_fill = "#E5F4FB") +
#   labs(title = "UHC SCI, PIC — with custom strip colour",
#        x = NULL, y = "SCI")

## ----eval = run_live----------------------------------------------------------
# library(flextable)
# dsi_flextable_defaults(font_family = "Geogria")
# 
# uhc_clean |>
#   filter(year == max(year)) |>
#   left_join(who_countries, by = "iso3") |>
#   select(name_short, value_num) |>
#   arrange(desc(value_num)) |>
#   flextable() |>
#   set_table_properties("autofit", width = .6) %>%
#   set_caption("UHC SCI in WPR, latest year")

## ----eval = run_live----------------------------------------------------------
# # All indicators that mention both mortality and cancer
# sdg_indicators("mortality cancer")
# 
# # Same as above, but with explicit terms (allows whitespace inside a term)
# sdg_indicators(c("maternal", "mortality"))

## ----eval = run_live----------------------------------------------------------
# # ISO3 — regional vector passed straight through
# sdg <- sdg_data(
#   indicator = "3.4.1",
#   area      = wpro_cty
# )
# sdg |> glimpse()
# 
# # M49 also works (e.g. when copy-pasting codes from sdg_areas())
# sdg_data("3.4.1", area = c("608", "250"))

## ----eval = run_live----------------------------------------------------------
# sdg_clean(sdg)

## ----eval = run_live----------------------------------------------------------
# raw <- sdg_data("3.8.2", area = "PHL")
# unique(raw$dimensions)
# sdg_clean(raw, keep_dimensions = TRUE)

## ----eval = run_live----------------------------------------------------------
# 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)

## ----eval = run_live----------------------------------------------------------
# sdg_dimensions("3.8.2", include_attributes = TRUE)

## ----eval = run_live----------------------------------------------------------
# detailed <- sdg_clean(raw, keep_dimensions = TRUE, keep_metadata = TRUE)
# detailed |>
#   select(any_of(c("iso3", "year", "value_num", "attr_units", "attr_nature", "data_source")))
# head(detailed$footnotes, 1L)

## ----eval = run_live----------------------------------------------------------
# gho_detailed <- gho_data("FINANCIALHARDSHIP_PROPORTIONOFPOP", area = "PHL",
#                          backend = "legacy") |>
#   gho_clean(keep_dimensions = TRUE, keep_metadata = TRUE)
# gho_detailed |>
#   select(provider, iso3, year, value_num, dim1_type, dim1, dim2_type, dim2)

## ----eval = run_live----------------------------------------------------------
# # Two indicators on the same topic from different APIs:
# #   GHO NCDMORT3070 (probability of premature NCD mortality)
# #   SDG 3.4.1       (mortality rate from NCDs)
# gho_ncd <- gho_data("NCDMORT3070", area = wpro_cty) |> gho_clean()
# sdg_ncd <- sdg_data("3.4.1",        area = wpro_cty) |> sdg_clean()
# bind_indicators(gho_ncd, sdg_ncd) |> glimpse()

## ----eval = run_live----------------------------------------------------------
# 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