The goal of pipapi is to provide a high level API to the computations and methods that power the Poverty and Inequality Platform (PIP).
World Bank staff who have read access to the PIP data can use the functions from this package directly, without hitting the PIP API.
Installation
You can install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("PIP-Technical-Team/pipapi")Getting started
The main function from the pipapi package is the pip() function. See ?pip for more information.
Data access
In order to use pip() you’ll need to have access to a PIP data_folder. The folder structure looks like this:
data-folder-root/
├─ _aux/
│ ├─ pop_regions.fst
│ ├─ pop.fst
│ ├─ ...
├─ estimations/
│ ├─ prod_svy_estimation.fst
│ ├─ prod_ref_estimation.fst
├─ survey_data/
│ ├─ survey_1.fst
│ ├─ ...
│ ├─ survey_n.fst
# Create a list of look-up tables from the root data folder
lkups <- create_lkups("<data-folder>")Usage
Pass the lkups list to the main pip() function to compute poverty and inequality statistics in your R session.
library(pipapi)
pip(country = "AGO",
year = 2000,
povline = 1.9,
lkup = lkups)
#> region_code country_code reporting_year survey_acronym survey_coverage
#> 1: SSA AGO 2000 HBS national
#> survey_year welfare_type survey_comparability comparable_spell poverty_line
#> 1: 2000.21 consumption 0 2000 1.9
#> headcount poverty_gap poverty_severity mean median mld gini
#> 1: 0.3637448 0.1636806 0.09982393 4.100014 2.593394 0.5125765 0.5195689
#> polarization watts decile1 decile2 decile3 decile4
#> 1: 0.4643401 0.2811239 0.00983246 0.02195307 0.03342455 0.04495307
#> decile5 decile6 decile7 decile8 decile9 decile10
#> 1: 0.05662774 0.07048758 0.08808485 0.1134946 0.158687 0.4024552
#> survey_mean_lcu survey_mean_ppp predicted_mean_ppp cpi cpi_data_level
#> 1: 11.23264 4.100014 NA 0.03385145 national
#> ppp ppp_data_level reporting_pop pop_data_level reporting_gdp
#> 1: 80.9318 national 16395473 national 2195.631
#> gdp_data_level reporting_pce pce_data_level is_interpolated
#> 1: national NA national FALSE
#> is_used_for_aggregation distribution_type estimation_type
#> 1: FALSE micro survey
