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Return aggregation of PIP statistics

Source: R/pip.R
pip_group.Rd

Return aggregation of PIP statistics

Usage

pip_group(
  country = NULL,
  year = NULL,
  povline = 2.15,
  popshare = NULL,
  group_by = c("wb", "none"),
  fill_gaps = FALSE,
  welfare_type = c("all", "consumption", "income"),
  reporting_level = c("all", "national", "rural", "urban"),
  additional_ind = FALSE,
  release_version = NULL,
  ppp_version = NULL,
  version = NULL
)

Source

https://pip.worldbank.org/api

Arguments

country

character() countries for which statistics are to be computed, specified as ISO3 codes. Default NULL.

year

character() | numeric() year(s) for which statistics are to be computed, specified as YYYY. Default NULL.

povline

numeric(1) poverty line to be used to compute poverty mesures. Poverty lines are only accepted up to 3 decimals. Default 2.15.

popshare

numeric(1) proportion of the population living below the poverty line. Will be ignored if povline is specified. Default NULL.

group_by

character(1) aggregate results by pre-defined sub-groups. Default "wb".

fill_gaps

logical(1) whether to fill gaps in the data. Default FALSE.

welfare_type

character(1) type of welfare measure to be used. Default "all".

reporting_level

character(1) level of reporting for the statistics. Default "all".

additional_ind

logical(1) whether to include additional indicators. Default FALSE.

release_version

character(1) version of the data release in YYYYMMDD format. Default NULL.

ppp_version

character(1) | numeric(1) version of the data. Default NULL.

version

character(1) version of the data. Default NULL.

Value

A data.frame() with the requested statistics.

See also

Other poverty and inequality statistics: pip_aux(), pip_citation(), pip_data(), pip_health_check(), pip_info(), pip_valid_params(), pip_versions()

Examples

# \donttest{
pip_group(c("AFE", "LAC"))
#> # A tibble: 88 × 14
#>    region_name     region_code reporting_year poverty_line headcount poverty_gap
#>    <chr>           <chr>                <int>        <dbl>     <dbl>       <dbl>
#>  1 Eastern and So… AFE                   1981         2.15     0.520       0.221
#>  2 Eastern and So… AFE                   1982         2.15     0.524       0.225
#>  3 Eastern and So… AFE                   1983         2.15     0.529       0.229
#>  4 Eastern and So… AFE                   1984         2.15     0.539       0.235
#>  5 Eastern and So… AFE                   1985         2.15     0.547       0.240
#>  6 Eastern and So… AFE                   1986         2.15     0.540       0.235
#>  7 Eastern and So… AFE                   1987         2.15     0.533       0.230
#>  8 Eastern and So… AFE                   1988         2.15     0.533       0.229
#>  9 Eastern and So… AFE                   1989         2.15     0.533       0.230
#> 10 Eastern and So… AFE                   1990         2.15     0.542       0.236
#> # ℹ 78 more rows
#> # ℹ 8 more variables: poverty_severity <dbl>, watts <dbl>, mean <dbl>,
#> #   spr <dbl>, pg <dbl>, estimate_type <chr>, reporting_pop <int>,
#> #   pop_in_poverty <int>
# }