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Collate selected humanitarian vulnerability indices

collate_indices.Rd

Combine any selection of the package's individual index datasets in either wide or long form. All 195 United Nations member and observer states are retained, including countries outside an index's published coverage.

Usage

collate_indices(indices, format = c("wide", "long"))

Arguments

indices

A non-empty character vector of index identifiers. Available identifiers are inform_risk, inform_severity, underfunded_crisis, oecd_fragility, worldrisk, nd_gain, hdi, mpi, ghi, ghs, wps, un_mvi, debt_distress, searo, disaster_displacement, internal_displacement, and clif_vi (2050 pessimistic projection). Identifiers must be unique and their supplied order is preserved.

format

Output layout: "wide" (the default) or "long".

Value

A data frame. Wide output has one row per country, four geography columns, *_score, *_rank, and *_decile columns for each selected index, applicable index-specific fields, and five country summary columns. Long output has one row per country-index combination, the 19 fields documented in individual_indices, and the same five summary columns.

Details

top_10 and top_20 identify ranks less than or equal to 10 and 20, respectively; they do not refer to deciles. Because ties receive the minimum rank, more than 10 or 20 countries can meet a threshold for an index.

The summary fields are:

  • indices_ranked_count: selected eligible indices for which the country has a non-missing rank.

  • top_10_count and top_20_count: the number of those ranks meeting each threshold.

  • top_10_proportion and top_20_proportion: each count divided by indices_ranked_count.

A country with no available eligible rank has zero counts and missing proportions. Missing scores are not treated as ranks outside the thresholds. In long output, country summaries are repeated on every selected index row.

Wide output includes ghi_score_label, mpi_reference_year, debt_distress_class, and debt_distress_ordinal when their corresponding indices are selected.

See also

individual_indices, humanitarian_index_sources

Examples

selected_wide <- collate_indices(
  c("inform_risk", "hdi", "mpi")
)
selected_wide[
  selected_wide$iso3 == "AFG",
  c("country", "inform_risk_rank", "top_10_proportion")
]
#>       country inform_risk_rank top_10_proportion
#> 1 Afghanistan                6         0.6666667

selected_long <- collate_indices(
  c("inform_risk", "hdi", "mpi"),
  format = "long"
)
subset(
  selected_long,
  iso3 == "AFG",
  select = c(index_name, score, rank, top_10)
)
#>                       index_name     score rank top_10
#> 1                    INFORM Risk 7.8000000    6   TRUE
#> 2        Human Development Index 0.4960000   13  FALSE
#> 3 Multidimensional Poverty Index 0.3603053    9   TRUE