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: 1,753 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 DZA Algeria MNA N 1988 1988 consump~
#> 7 DZA Algeria MNA N 1995 1995 consump~
#> 8 DZA Algeria MNA N 2011 2011. consump~
#> 9 AGO Angola SSA N 2000 2000. consump~
#> 10 AGO Angola SSA N 2008 2008. consump~
#> # ... with 1,743 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: 5 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~
#> # ... 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: 53 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 CHN China EAP A 1981 1981 income
#> 7 CHN China EAP A 1984 1984 income
#> 8 CHN China EAP A 1987 1987 income
#> 9 CHN China EAP A 1990 1990 consump~
#> 10 CHN China EAP A 1993 1993 consump~
#> # ... with 43 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: 5 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~
#> # ... 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: 15 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~
#> # ... 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: 15 x 9
#> regiontitle regioncode year povertyline mean headcount povertygap
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 World Total WLD 2015 1.9 459. 0.0998 0.0306
#> 2 World Total WLD 2013 1.9 442. 0.112 0.0336
#> 3 World Total WLD 2012 1.9 432. 0.128 0.0377
#> 4 World Total WLD 2011 1.9 425. 0.137 0.0407
#> 5 World Total WLD 2010 1.9 418. 0.157 0.0466
#> 6 World Total WLD 2008 1.9 409. 0.182 0.0541
#> 7 World Total WLD 2005 1.9 381. 0.207 0.0631
#> 8 World Total WLD 2002 1.9 348. 0.255 0.0823
#> 9 World Total WLD 1999 1.9 339. 0.286 0.0953
#> 10 World Total WLD 1996 1.9 329. 0.294 0.0979
#> 11 World Total WLD 1993 1.9 315. 0.340 0.120
#> 12 World Total WLD 1990 1.9 321. 0.359 0.127
#> 13 World Total WLD 1987 1.9 318. 0.353 0.125
#> 14 World Total WLD 1984 1.9 304. 0.392 0.146
#> 15 World Total WLD 1981 1.9 297. 0.421 0.179
#> # ... with 2 more variables: povertygapsq <dbl>, population <dbl>
# Custom aggregate
povcalnet(country = c("CHL", "ARG", "BOL"), aggregate = TRUE)
#> # A tibble: 15 x 9
#> regiontitle regioncode year povertyline mean headcount povertygap
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 XX XX 2015 1.9 645. 0.0172 0.00811
#> 2 XX XX 2013 1.9 644. 0.0174 0.00794
#> 3 XX XX 2012 1.9 625. 0.0205 0.00956
#> 4 XX XX 2011 1.9 612. 0.0206 0.00851
#> 5 XX XX 2010 1.9 575. 0.0322 0.0140
#> 6 XX XX 2008 1.9 544. 0.0382 0.0169
#> 7 XX XX 2005 1.9 473. 0.0582 0.0260
#> 8 XX XX 2002 1.9 334. 0.130 0.0574
#> 9 XX XX 1999 1.9 452. 0.0762 0.0418
#> 10 XX XX 1996 1.9 468. 0.0622 0.0334
#> 11 XX XX 1993 1.9 477. 0.0443 0.0188
#> 12 XX XX 1990 1.9 458. 0.0377 0.0123
#> 13 XX XX 1987 1.9 576. 0.0423 0.0127
#> 14 XX XX 1984 1.9 598. 0.0496 0.0151
#> 15 XX XX 1981 1.9 583. 0.0308 0.00983
#> # ... with 2 more variables: povertygapsq <dbl>, population <dbl>