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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

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

year

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

povline

(NULL | numeric(1))
Poverty line to be used to compute poverty measures, between 0 and 2700. Poverty lines are only accepted up to 3 decimals. Default 2.15.

popshare

(NULL | numeric(1))
Proportion of the population living below the poverty line, between 0 and 1. Takes precedence over povline: if both are supplied, the poverty line is derived from popshare. 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

(NULL | character(1) | numeric(1))
Version of the data release in YYYYMMDD format. Default NULL.

ppp_version

(NULL | character(1) | numeric(1))
Version of the data in YYYY format. Default NULL.

version

(NULL | 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_cp(), pip_data(), pip_health_check(), pip_info(), pip_valid_params(), pip_versions()

Examples

# \donttest{
grp <- pip_group(c("AFE", "LAC"))
head(grp)
#>   region_code                 region_name reporting_year poverty_line
#> 1         AFE Eastern and Southern Africa           1981         2.15
#> 2         AFE Eastern and Southern Africa           1982         2.15
#> 3         AFE Eastern and Southern Africa           1983         2.15
#> 4         AFE Eastern and Southern Africa           1984         2.15
#> 5         AFE Eastern and Southern Africa           1985         2.15
#> 6         AFE Eastern and Southern Africa           1986         2.15
#>   reporting_pop headcount poverty_gap poverty_severity     watts     mean
#> 1     238043099 0.4491860   0.1803920       0.09601235 0.2722762 4.620314
#> 2     245822010 0.4529310   0.1833875       0.09820633 0.2780057 4.564970
#> 3     253644643 0.4610831   0.1896610       0.10285639 0.2900256 4.484695
#> 4     261458202 0.4703827   0.1947065       0.10595121 0.2985325 4.429398
#> 5     269450407 0.4785722   0.1986180       0.10817873 0.3045951 4.362157
#> 6     277621771 0.4720145   0.1955952       0.10661543 0.3003416 4.388316
#>         spr       pg pop_in_poverty estimate_type
#> 1 0.6201646 15.83368      106925621    projection
#> 2 0.6222839 16.05505      111340398    projection
#> 3 0.6273182 16.50500      116951254    projection
#> 4 0.6352590 16.81880      122985422    projection
#> 5 0.6408765 17.02746      128951486    projection
#> 6 0.6360982 16.91017      131041498    projection
# }