Return aggregation of PIP statistics

Usage

pip_group(
  country = NULL,
  year = NULL,
  povline = NULL,
  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, or "all" for every available year or "MRV" for the most recent year. Default NULL, which returns every available year.
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 NULL, which uses the international poverty line of the requested ppp_version, e.g. 3 for 2021 PPPs and 2.15 for 2017 PPPs.
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.

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            3
#> 2         AFE Eastern and Southern Africa           1982            3
#> 3         AFE Eastern and Southern Africa           1983            3
#> 4         AFE Eastern and Southern Africa           1984            3
#> 5         AFE Eastern and Southern Africa           1985            3
#> 6         AFE Eastern and Southern Africa           1986            3
#>   reporting_pop headcount poverty_gap poverty_severity     watts     mean
#> 1     238043099 0.5965929   0.2791532        0.1630652 0.4471287 4.620314
#> 2     245822010 0.5994378   0.2823006        0.1657086 0.4540460 4.564970
#> 3     253644643 0.6061125   0.2888617        0.1712578 0.4685138 4.484695
#> 4     261458202 0.6152549   0.2951063        0.1756477 0.4801159 4.429398
#> 5     269450407 0.6226119   0.3001387        0.1790228 0.4888074 4.362157
#> 6     277621771 0.6176100   0.2963107        0.1765062 0.4825891 4.388316
#>         spr       pg pop_in_poverty estimate_type
#> 1 0.6201646 15.83368      142014821    projection
#> 2 0.6222839 16.05505      147355011    projection
#> 3 0.6273182 16.50500      153737190    projection
#> 4 0.6352590 16.81880      160863442    projection
#> 5 0.6408765 17.02746      167763017    projection
#> 6 0.6360982 16.91017      171461969    projection
# }

Return aggregation of PIP statistics

Usage

Source

Arguments

Value

See also

Examples