The pipr package allows R users to compute poverty and inequality indicators for more than 160 countries and regions from the World Bank’s database of household surveys. It does so by accessing the Poverty and Inequality Platform (PIP) API. PIP is a computational tool that allows users to estimate poverty rates for regions, sets of countries or individual countries, over time and at any poverty line.
Installation
You can install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("worldbank/pipr")Example
This is a basic example that shows how to retrieve some key poverty and inequity statistics.
Retrieve statistics
library(dplyr)
library(pipr)
df <- get_stats(country = "ALB")
glimpse(df)
#> Rows: 15
#> Columns: 40
#> $ region_name <chr> "Europe & Central Asia", "Europe & Central Asia",~
#> $ region_code <chr> "ECA", "ECA", "ECA", "ECA", "ECA", "ECA", "ECA", ~
#> $ country_name <chr> "Albania", "Albania", "Albania", "Albania", "Alba~
#> $ country_code <chr> "ALB", "ALB", "ALB", "ALB", "ALB", "ALB", "ALB", ~
#> $ year <dbl> 1996, 2002, 2005, 2008, 2012, 2014, 2015, 2016, 2~
#> $ reporting_level <chr> "national", "national", "national", "national", "~
#> $ survey_acronym <chr> "EWS", "LSMS", "LSMS", "LSMS", "LSMS", "HBS", "HB~
#> $ survey_coverage <chr> "national", "national", "national", "national", "~
#> $ welfare_time <dbl> 1996, 2002, 2005, 2008, 2012, 2014, 2015, 2016, 2~
#> $ welfare_type <chr> "consumption", "consumption", "consumption", "con~
#> $ survey_comparability <dbl> 0, 1, 1, 1, 1, 2, 2, 2, 4, 2, 4, 3, 4, 3
#> $ comparable_spell <chr> "1996", "2002 - 2012", "2002 - 2012", "2002 - 201~
#> $ poverty_line <dbl> 2.15, 2.15, 2.15, 2.15, 2.15, 2.15, 2.15, 2.15, 2~
#> $ headcount <dbl> 0.0053484604, 0.0109264739, 0.0059108568, 0.00199~
#> $ poverty_gap <dbl> 9.821458e-04, 1.894350e-03, 8.972340e-04, 3.96411~
#> $ poverty_severity <dbl> 3.047592e-04, 5.942301e-04, 2.285120e-04, 8.69032~
#> $ watts <dbl> 1.203124e-03, 2.382840e-03, 1.042396e-03, 4.48972~
#> $ mean <dbl> 7.933157, 8.108228, 9.165974, 10.038168, 9.517231~
#> $ median <dbl> 6.972102, 6.688141, 7.799790, 8.400199, 8.240384,~
#> $ mld <dbl> 0.1191043, 0.1648116, 0.1544128, 0.1488934, 0.138~
#> $ gini <dbl> 0.2701034, 0.3173898, 0.3059566, 0.2998467, 0.289~
#> $ polarization <dbl> 0.2412933, 0.2689816, 0.2545287, 0.2473111, 0.249~
#> $ decile1 <dbl> 0.03863286, 0.03494002, 0.03482536, 0.03733625, 0~
#> $ decile2 <dbl> 0.05289347, 0.04859444, 0.04920109, 0.05136781, 0~
#> $ decile3 <dbl> 0.06378683, 0.05842059, 0.05977283, 0.06088472, 0~
#> $ decile4 <dbl> 0.07322042, 0.06738204, 0.06921183, 0.06983584, 0~
#> $ decile5 <dbl> 0.08379662, 0.07653102, 0.07988158, 0.07912079, 0~
#> $ decile6 <dbl> 0.09354903, 0.08839459, 0.09037069, 0.08924133, 0~
#> $ decile7 <dbl> 0.1082309, 0.1022859, 0.1037214, 0.1029873, 0.105~
#> $ decile8 <dbl> 0.1247387, 0.1198443, 0.1212641, 0.1192908, 0.122~
#> $ decile9 <dbl> 0.1489955, 0.1492508, 0.1483394, 0.1453520, 0.148~
#> $ decile10 <dbl> 0.2121557, 0.2543564, 0.2434117, 0.2445831, 0.229~
#> $ cpi <dbl> 0.3996353, 0.7016371, 0.7539579, 0.8201141, 0.917~
#> $ ppp <dbl> 50.35737, 50.35737, 50.35737, 50.35737, 50.35737,~
#> $ pop <dbl> 3168033, 3051010, 3011487, 2947314, 2900401, 2889~
#> $ gdp <dbl> 1633.552, 2247.497, 2675.508, 3298.478, 3736.339,~
#> $ hfce <dbl> 1714.813, 1685.368, 2079.244, 2819.736, 2989.866,~
#> $ is_interpolated <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, ~
#> $ distribution_type <chr> "micro", "micro", "micro", "micro", "micro", "mic~
#> $ estimation_type <chr> "survey", "survey", "survey", "survey", "survey",~Access data dictionary
get_aux("dictionary")
#> # A tibble: 41 x 2
#> variable definition
#> <chr> <chr>
#> 1 region_name World Bank region name
#> 2 region_code Three-letter World Bank abbreviation of world regions
#> 3 year Year
#> 4 country_name World Bank country name
#> 5 country_code Three-letter ISO (alpha-3) country code system for internati~
#> 6 reporting_level Reporting level
#> 7 survey_acronym Country survey acronym
#> 8 survey_coverage Geographic coverage of the country survey (i.e. national, ur~
#> 9 welfare_time Welfare time
#> 10 welfare_type Type of welfare vector used for estimates (income or consump~
#> # ... with 31 more rows
