library(povcalnetR)
The povcalnetR package allows to easily query the PovcalNet API from R.
Here are a few examples to get you started:
By default, the povcalnet function return all available
povcalnet() #> # A tibble: 1,962 x 31 #> countrycode countryname regioncode coveragetype year datayear datatype #> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> #> 1 ALB Albania ECA N 1996 1996 consump~ #> 2 ALB Albania ECA N 2002 2002 consump~ #> 3 ALB Albania ECA N 2005 2005 consump~ #> 4 ALB Albania ECA N 2008 2008 consump~ #> 5 ALB Albania ECA N 2012 2012 consump~ #> 6 ALB Albania ECA N 2014 2014 consump~ #> 7 ALB Albania ECA N 2015 2015 consump~ #> 8 ALB Albania ECA N 2016 2016 consump~ #> 9 ALB Albania ECA N 2017 2017 consump~ #> 10 DZA Algeria MNA N 1988 1988 consump~ #> # ... with 1,952 more rows, and 24 more variables: isinterpolated <dbl>, #> # usemicrodata <dbl>, ppp <dbl>, povertyline <dbl>, mean <dbl>, #> # headcount <dbl>, povertygap <dbl>, povertygapsq <dbl>, watts <dbl>, #> # gini <dbl>, median <dbl>, mld <dbl>, polarization <dbl>, population <dbl>, #> # decile1 <dbl>, decile2 <dbl>, decile3 <dbl>, decile4 <dbl>, decile5 <dbl>, #> # decile6 <dbl>, decile7 <dbl>, decile8 <dbl>, decile9 <dbl>, decile10 <dbl>
# Specify ONE country povcalnet(country = "ALB") #> # A tibble: 9 x 31 #> countrycode countryname regioncode coveragetype year datayear datatype #> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> #> 1 ALB Albania ECA N 1996 1996 consump~ #> 2 ALB Albania ECA N 2002 2002 consump~ #> 3 ALB Albania ECA N 2005 2005 consump~ #> 4 ALB Albania ECA N 2008 2008 consump~ #> 5 ALB Albania ECA N 2012 2012 consump~ #> 6 ALB Albania ECA N 2014 2014 consump~ #> 7 ALB Albania ECA N 2015 2015 consump~ #> 8 ALB Albania ECA N 2016 2016 consump~ #> 9 ALB Albania ECA N 2017 2017 consump~ #> # ... with 24 more variables: isinterpolated <dbl>, usemicrodata <dbl>, #> # ppp <dbl>, povertyline <dbl>, mean <dbl>, headcount <dbl>, #> # povertygap <dbl>, povertygapsq <dbl>, watts <dbl>, gini <dbl>, #> # median <dbl>, mld <dbl>, polarization <dbl>, population <dbl>, #> # decile1 <dbl>, decile2 <dbl>, decile3 <dbl>, decile4 <dbl>, decile5 <dbl>, #> # decile6 <dbl>, decile7 <dbl>, decile8 <dbl>, decile9 <dbl>, decile10 <dbl> # Specify MULTIPLE countries povcalnet(country = c("ALB", "CHN")) #> # A tibble: 60 x 31 #> countrycode countryname regioncode coveragetype year datayear datatype #> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> #> 1 ALB Albania ECA N 1996 1996 consump~ #> 2 ALB Albania ECA N 2002 2002 consump~ #> 3 ALB Albania ECA N 2005 2005 consump~ #> 4 ALB Albania ECA N 2008 2008 consump~ #> 5 ALB Albania ECA N 2012 2012 consump~ #> 6 ALB Albania ECA N 2014 2014 consump~ #> 7 ALB Albania ECA N 2015 2015 consump~ #> 8 ALB Albania ECA N 2016 2016 consump~ #> 9 ALB Albania ECA N 2017 2017 consump~ #> 10 CHN China EAP A 1981 1981 income #> # ... with 50 more rows, and 24 more variables: isinterpolated <dbl>, #> # usemicrodata <dbl>, ppp <dbl>, povertyline <dbl>, mean <dbl>, #> # headcount <dbl>, povertygap <dbl>, povertygapsq <dbl>, watts <dbl>, #> # gini <dbl>, median <dbl>, mld <dbl>, polarization <dbl>, population <dbl>, #> # decile1 <dbl>, decile2 <dbl>, decile3 <dbl>, decile4 <dbl>, decile5 <dbl>, #> # decile6 <dbl>, decile7 <dbl>, decile8 <dbl>, decile9 <dbl>, decile10 <dbl>
By default, povcalnet will only return data if a survey is available for the requested country and the requested year
# Survey year NOT available povcalnet(country = "ALB", year = 2012) #> # A tibble: 1 x 31 #> countrycode countryname regioncode coveragetype year datayear datatype #> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> #> 1 ALB Albania ECA N 2012 2012 consump~ #> # ... with 24 more variables: isinterpolated <dbl>, usemicrodata <dbl>, #> # ppp <dbl>, povertyline <dbl>, mean <dbl>, headcount <dbl>, #> # povertygap <dbl>, povertygapsq <dbl>, watts <dbl>, gini <dbl>, #> # median <dbl>, mld <dbl>, polarization <dbl>, population <dbl>, #> # decile1 <dbl>, decile2 <dbl>, decile3 <dbl>, decile4 <dbl>, decile5 <dbl>, #> # decile6 <dbl>, decile7 <dbl>, decile8 <dbl>, decile9 <dbl>, decile10 <dbl> # Survey year NOT available - Empty response povcalnet(country = "ALB", year = 2018) #> # A tibble: 0 x 31 #> # ... with 31 variables: countrycode <chr>, countryname <chr>, #> # regioncode <chr>, coveragetype <chr>, year <chr>, datayear <chr>, #> # datatype <chr>, isinterpolated <chr>, usemicrodata <chr>, ppp <chr>, #> # povertyline <chr>, mean <chr>, headcount <chr>, povertygap <chr>, #> # povertygapsq <chr>, watts <chr>, gini <chr>, median <chr>, mld <chr>, #> # polarization <chr>, population <chr>, decile1 <chr>, decile2 <chr>, #> # decile3 <chr>, decile4 <chr>, decile5 <chr>, decile6 <chr>, decile7 <chr>, #> # decile8 <chr>, decile9 <chr>, decile10 <chr>
povcalnet(country = "ALB", povline = 3.2) #> # A tibble: 9 x 31 #> countrycode countryname regioncode coveragetype year datayear datatype #> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> #> 1 ALB Albania ECA N 1996 1996 consump~ #> 2 ALB Albania ECA N 2002 2002 consump~ #> 3 ALB Albania ECA N 2005 2005 consump~ #> 4 ALB Albania ECA N 2008 2008 consump~ #> 5 ALB Albania ECA N 2012 2012 consump~ #> 6 ALB Albania ECA N 2014 2014 consump~ #> 7 ALB Albania ECA N 2015 2015 consump~ #> 8 ALB Albania ECA N 2016 2016 consump~ #> 9 ALB Albania ECA N 2017 2017 consump~ #> # ... with 24 more variables: isinterpolated <dbl>, usemicrodata <dbl>, #> # ppp <dbl>, povertyline <dbl>, mean <dbl>, headcount <dbl>, #> # povertygap <dbl>, povertygapsq <dbl>, watts <dbl>, gini <dbl>, #> # median <dbl>, mld <dbl>, polarization <dbl>, population <dbl>, #> # decile1 <dbl>, decile2 <dbl>, decile3 <dbl>, decile4 <dbl>, decile5 <dbl>, #> # decile6 <dbl>, decile7 <dbl>, decile8 <dbl>, decile9 <dbl>, decile10 <dbl>
The fill_gaps argument will trigger the interpolation / extrapolation of poverty estimates when survey year is not available
# fill_gaps = FALSE (default) povcalnet(country = "HTI") #> # A tibble: 3 x 31 #> countrycode countryname regioncode coveragetype year datayear datatype #> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> #> 1 HTI Haiti LAC N 2001 2001 income #> 2 HTI Haiti LAC N 2012 2012 consump~ #> 3 HTI Haiti LAC N 2012 2012 income #> # ... with 24 more variables: isinterpolated <dbl>, usemicrodata <dbl>, #> # ppp <dbl>, povertyline <dbl>, mean <dbl>, headcount <dbl>, #> # povertygap <dbl>, povertygapsq <dbl>, watts <dbl>, gini <dbl>, #> # median <dbl>, mld <dbl>, polarization <dbl>, population <dbl>, #> # decile1 <dbl>, decile2 <dbl>, decile3 <dbl>, decile4 <dbl>, decile5 <dbl>, #> # decile6 <dbl>, decile7 <dbl>, decile8 <dbl>, decile9 <dbl>, decile10 <dbl> # fill_gaps = TRUE povcalnet(country = "HTI", fill_gaps = TRUE) #> # A tibble: 16 x 31 #> countrycode countryname regioncode coveragetype year datayear datatype #> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> #> 1 HTI Haiti XX N 1981 2001 income #> 2 HTI Haiti XX N 1984 2001 income #> 3 HTI Haiti XX N 1987 2001 income #> 4 HTI Haiti XX N 1990 2001 income #> 5 HTI Haiti XX N 1993 2001 income #> 6 HTI Haiti XX N 1996 2001 income #> 7 HTI Haiti XX N 1999 2001 income #> 8 HTI Haiti XX N 2002 NA income #> 9 HTI Haiti XX N 2005 NA income #> 10 HTI Haiti XX N 2008 NA income #> 11 HTI Haiti XX N 2010 NA income #> 12 HTI Haiti XX N 2011 NA income #> 13 HTI Haiti XX N 2012 2012 consump~ #> 14 HTI Haiti XX N 2013 2012 consump~ #> 15 HTI Haiti XX N 2015 2012 consump~ #> 16 HTI Haiti XX N 2018 2012 consump~ #> # ... with 24 more variables: isinterpolated <dbl>, usemicrodata <dbl>, #> # ppp <dbl>, povertyline <dbl>, mean <dbl>, headcount <dbl>, #> # povertygap <dbl>, povertygapsq <dbl>, watts <dbl>, gini <dbl>, #> # median <dbl>, mld <dbl>, polarization <dbl>, population <dbl>, #> # decile1 <dbl>, decile2 <dbl>, decile3 <dbl>, decile4 <dbl>, decile5 <dbl>, #> # decile6 <dbl>, decile7 <dbl>, decile8 <dbl>, decile9 <dbl>, decile10 <dbl>
The povcalnet function can also be used to compute aggregate welfare statistics of custom group of countries
# World aggregate povcalnet(country = "all", aggregate = TRUE) #> # A tibble: 16 x 9 #> regiontitle regioncode year povertyline mean headcount povertygap #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 World Total WLD 2018 1.9 NA NA NA #> 2 World Total WLD 2015 1.9 462. 0.100 0.0310 #> 3 World Total WLD 2013 1.9 445. 0.112 0.0334 #> 4 World Total WLD 2012 1.9 436. 0.128 0.0375 #> 5 World Total WLD 2011 1.9 429. 0.137 0.0405 #> 6 World Total WLD 2010 1.9 423. 0.157 0.0465 #> 7 World Total WLD 2008 1.9 412. 0.181 0.0540 #> 8 World Total WLD 2005 1.9 384. 0.207 0.0630 #> 9 World Total WLD 2002 1.9 361. 0.255 0.0823 #> 10 World Total WLD 1999 1.9 340. 0.286 0.0953 #> 11 World Total WLD 1996 1.9 331. 0.295 0.0981 #> 12 World Total WLD 1993 1.9 317. 0.340 0.120 #> 13 World Total WLD 1990 1.9 322. 0.360 0.128 #> 14 World Total WLD 1987 1.9 317. 0.355 0.126 #> 15 World Total WLD 1984 1.9 303. 0.394 0.147 #> 16 World Total WLD 1981 1.9 296. 0.423 0.180 #> # ... with 2 more variables: povertygapsq <dbl>, population <dbl> # Custom aggregate povcalnet(country = c("CHL", "ARG", "BOL"), aggregate = TRUE) #> # A tibble: 16 x 9 #> regiontitle regioncode year povertyline mean headcount povertygap #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 XX XX 2018 1.9 651. 0.0137 0.00518 #> 2 XX XX 2015 1.9 652. 0.0147 0.00657 #> 3 XX XX 2013 1.9 655. 0.0162 0.00721 #> 4 XX XX 2012 1.9 634. 0.0188 0.00869 #> 5 XX XX 2011 1.9 619. 0.0183 0.00739 #> 6 XX XX 2010 1.9 583. 0.0294 0.0124 #> 7 XX XX 2008 1.9 551. 0.0355 0.0153 #> 8 XX XX 2005 1.9 476. 0.0566 0.0254 #> 9 XX XX 2002 1.9 334. 0.130 0.0575 #> 10 XX XX 1999 1.9 452. 0.0764 0.0419 #> 11 XX XX 1996 1.9 464. 0.0651 0.0345 #> 12 XX XX 1993 1.9 470. 0.0496 0.0206 #> 13 XX XX 1990 1.9 452. 0.0502 0.0179 #> 14 XX XX 1987 1.9 570. 0.0547 0.0183 #> 15 XX XX 1984 1.9 591. 0.0628 0.0211 #> 16 XX XX 1981 1.9 575. 0.0416 0.0149 #> # ... with 2 more variables: povertygapsq <dbl>, population <dbl>