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
(
numeric(1))
Poverty line to be used to compute poverty measures. Poverty lines are only accepted up to 3 decimals. Default2.15.- release_version
(
NULL|character(1))
Version of the data release in YYYYMMDD format. DefaultNULL.- ppp_version
(
NULL|character(1)|numeric(1))
Version of the data. 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 2.15 0.1981366 0.6302572 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 - 2014 CONS 0.4303764 0.5695617 0.7595399
#> 4 2005 - 2014 CONS 0.3120983 0.4493806 0.6760894
#> 5 2005 - 2014 CONS 0.3040549 0.4362340 0.6642118
#> 6 2005 - 2014 CONS 0.3200362 0.4533112 0.6677979
#> headcount_national headcount_national_footnote gini.y theil
#> 1 NA NA 0.5933394 NA
#> 2 NA NA 0.5776966 NA
#> 3 0.666 1 0.6476209 0.8674991
#> 4 0.621 1 0.6300900 0.7911583
#> 5 0.532 1 0.6338269 0.7907242
#> 6 0.555 1 0.6302580 0.7790964
#> 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 0.4169467 0.5830532 0.3822711 0.6177289
#> 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 0.6543594 0.3456406 0.2538252 0.7461749
#> 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 0.5139337 0.4860663 0.3545554
#> 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 0.6454446 0.3012934 0.6987066
#> 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 0.616284 0.383716 0.545761 0.454239
#> 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 0.3552169 0.6447831 0.0555131 0.9444869 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 0.0220896 0.0233865 0.0409385
#> 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 0.3517148 0.1036116 0.3122781 0.3200449 0.0120041 0.1660777
#> 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 0.0083156 0.003853 0.1258807 6.5e-05 0.0038489 0.6799551
# }