Return country profile data
Usage
pip_cp(
country = NULL,
povline = 2.15,
release_version = NULL,
ppp_version = NULL,
version = NULL
)Arguments
- country
(
NULL|character())
Countries for which statistics are to be computed, specified as ISO3 codes. DefaultNULL.- povline
(
NULL|numeric(1))
Poverty line to be used to compute poverty measures, between0and2700. Poverty lines are only accepted up to 3 decimals. Default2.15.- release_version
(
NULL|character(1)|numeric(1))
Version of the data release in YYYYMMDD format. DefaultNULL.- ppp_version
(
NULL|character(1)|numeric(1))
Version of the data in YYYY format. DefaultNULL.- version
(
NULL|character(1))
Version of the data. DefaultNULL.
Value
A data.frame() with country profile statistics including headcount ratios, inequality
measures, and demographic breakdowns.
See also
Other poverty and inequality statistics:
pip_aux(),
pip_citation(),
pip_data(),
pip_group(),
pip_health_check(),
pip_info(),
pip_valid_params(),
pip_versions()
Examples
# \donttest{
cp <- pip_cp("ZAF")
head(cp)
#> country_code reporting_year poverty_line headcount gini.x welfare_time
#> 1 ZAF 1993 NA NA NA 1993.00
#> 2 ZAF 2000 NA NA NA 2000.75
#> 3 ZAF 2005 NA NA NA 2005.00
#> 4 ZAF 2008 NA NA NA 2008.67
#> 5 ZAF 2010 NA NA NA 2010.00
#> 6 ZAF 2014 NA NA NA 2014.83
#> survey_coverage is_interpolated survey_acronym survey_comparability
#> 1 N FALSE KIDS 0
#> 2 N FALSE HIES 2
#> 3 N FALSE IES 3
#> 4 N FALSE LCS 3
#> 5 N FALSE IES 3
#> 6 N FALSE LCS 3
#> comparable_spell welfare_type headcount_ipl headcount_lmicpl headcount_umicpl
#> 1 1993 CONS 0.4540300 0.5670900 0.7588900
#> 2 2000 CONS 0.4869700 0.6004500 0.7901000
#> 3 2005 - 2022 CONS 0.4163192 0.5570654 0.7530689
#> 4 2005 - 2022 CONS 0.3795021 0.5095397 0.7114064
#> 5 2005 - 2022 CONS 0.2878022 0.4221426 0.6571829
#> 6 2005 - 2022 CONS 0.2877533 0.4265898 0.6628935
#> headcount_national headcount_national_footnote gini.y theil
#> 1 NA NA 0.5933394 NA
#> 2 NA NA 0.5776966 NA
#> 3 0.575 1 0.6500114 0.8784750
#> 4 0.557 1 0.6251807 0.7625942
#> 5 0.458 1 0.6087324 0.7083613
#> 6 0.467 1 0.5963911 0.6757182
#> share_b40_female share_t60_female share_b40_male share_t60_male
#> 1 NA NA NA NA
#> 2 NA NA NA NA
#> 3 NA NA NA NA
#> 4 NA NA NA NA
#> 5 NA NA NA NA
#> 6 0.4167145 0.5832855 0.382385 0.617615
#> share_b40_rural share_t60_rural share_b40_urban share_t60_urban
#> 1 NA NA NA NA
#> 2 NA NA NA NA
#> 3 NA NA NA NA
#> 4 NA NA NA NA
#> 5 NA NA NA NA
#> 6 0.6664004 0.3335996 0.2586327 0.7413673
#> share_b40agecat_0_14 share_t60agecat_0_14 share_b40agecat_15_64
#> 1 NA NA NA
#> 2 NA NA NA
#> 3 NA NA NA
#> 4 NA NA NA
#> 5 NA NA NA
#> 6 0.5182659 0.4817341 0.3543191
#> share_t60agecat_15_64 share_b40agecat_65p share_t60agecat_65p
#> 1 NA NA NA
#> 2 NA NA NA
#> 3 NA NA NA
#> 4 NA NA NA
#> 5 NA NA NA
#> 6 0.6456809 0.3171975 0.6828025
#> share_b40edu_noedu share_t60edu_noedu share_b40edu_pri share_t60edu_pri
#> 1 NA NA NA NA
#> 2 NA NA NA NA
#> 3 NA NA NA NA
#> 4 NA NA NA NA
#> 5 NA NA NA NA
#> 6 0.6286923 0.3713077 0.5526696 0.4473303
#> share_b40edu_sec share_t60edu_sec share_b40edu_ter share_t60edu_ter datatype
#> 1 NA NA NA NA NA
#> 2 NA NA NA NA NA
#> 3 NA NA NA NA NA
#> 4 NA NA NA NA NA
#> 5 NA NA NA NA NA
#> 6 0.3573225 0.6426775 0.053892 0.946108 1
#> display_cp mpm_education_attainment mpm_education_enrollment mpm_electricity
#> 1 NA NA NA NA
#> 2 NA NA NA NA
#> 3 NA NA NA NA
#> 4 NA NA NA NA
#> 5 NA NA NA NA
#> 6 1 0.0232594 0.0215872 0.0411189
#> mpm_sanitation mpm_water mpm_monetary mpm_headcount mpm_venn1 mpm_venn2
#> 1 NA NA NA NA NA NA
#> 2 NA NA NA NA NA NA
#> 3 NA NA NA NA NA NA
#> 4 NA NA NA NA NA NA
#> 5 NA NA NA NA NA NA
#> 6 0.3494727 0.0943263 0.2790575 0.2879641 0.0106208 0.1499087
#> mpm_venn3 mpm_venn4 mpm_venn5 mpm_venn6 mpm_venn7 mpm_venn8
#> 1 NA NA NA NA NA NA
#> 2 NA NA NA NA NA NA
#> 3 NA NA NA NA NA NA
#> 4 NA NA NA NA NA NA
#> 5 NA NA NA NA NA NA
#> 6 0.0076529 0.0044233 0.1108751 0.0001096 0.0043737 0.7120358
# }