vignettes/advanced_examples.Rmd
advanced_examples.RmdThis function can be very handy is you maintain a list of countries and parameters in a table like the one below. Note: This function only works with survey years. There is no fill_gaps option available
# Read values from a table data("sample_input") sample_input #> # A tibble: 5 x 5 #> country poverty_line year ppp coverage_type #> <chr> <dbl> <dbl> <dbl> <chr> #> 1 ALB 3.2 1996 50 national #> 2 AGO 1.9 2008 50 national #> 3 KAZ 3.2 1993 50 national #> 4 BOL 3.2 1992 50 urban #> 5 ZAF 5.5 1993 50 national
# Use table values to send a request to the API # Only works for survey years povcalnet_cl(country = sample_input$country, povline = sample_input$poverty_line, year = sample_input$year, ppp = sample_input$ppp) #> # 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 AGO Angola SSA N 2008 2008. consump~ #> 3 KAZ Kazakhstan ECA N 1993 1993 income #> 4 BOL Bolivia LAC N 1992 1992 income #> 5 ZAF South Afri~ SSA N 1993 1993 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>
povcalnet_info() %>% glimpse() #> Rows: 180 #> Columns: 9 #> $ country_code <chr> "ALB", "DZA", "AGO", "ARG", "ARM", "AUS", "AUT", "AZ... #> $ country_name <chr> "Albania", "Algeria", "Angola", "Argentina", "Armeni... #> $ wb_region <chr> "ECA", "MNA", "SSA", "LAC", "ECA", "OHI", "OHI", "EC... #> $ un_region <chr> "EUS", "AFN", "AFM", "LAS", "ASW", "OCA", "EUW", "AS... #> $ income_region <chr> "UMC", "UMC", "LMC", "HIC", "UMC", "HIC", "HIC", "UM... #> $ coverage_level <chr> "national", "national", "national", "urban", "nation... #> $ coverage_type <chr> "national", "national", "national", "urban", "nation... #> $ coverage_code <chr> "3", "3", "3", "2", "3", "3", "3", "3", "3", "3", "3... #> $ year <list> [<1996, 2002, 2005, 2008, 2012, 2014, 2015, 2016, 2...
get_countries(c("ECA")) #> [1] "ALB" "ARM" "AZE" "BLR" "BIH" "BGR" "HRV" "CZE" "EST" "GEO" "HUN" "KAZ" #> [13] "XKX" "KGZ" "LVA" "LTU" "MKD" "MDA" "MNE" "POL" "ROU" "RUS" "SRB" "SVK" #> [25] "SVN" "TJK" "TUR" "TKM" "UKR" "UZB"
get_countries(c("LIC")) #> [1] "BEN" "BFA" "BDI" "CAF" "TCD" "COM" "COD" "ETH" "GMB" "GIN" "GNB" "HTI" #> [13] "LBR" "MDG" "MWI" "MLI" "MOZ" "NPL" "NER" "RWA" "SEN" "SLE" "SSD" "SYR" #> [25] "TJK" "TZA" "TGO" "UGA" "YEM" "ZWE"
income_groups <- c("LIC", "LMC", "UMC") poverty_lines <- c(1.9, 3.2, 5.5) map2_df(income_groups, poverty_lines, ~povcalnet(country = get_countries(.x), povline = .y, year = 2015, aggregate = TRUE) ) #> # A tibble: 3 x 9 #> regiontitle regioncode year povertyline mean headcount povertygap #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 XX XX 2015 1.9 88.3 0.448 0.171 #> 2 XX XX 2015 3.2 150. 0.439 0.139 #> 3 XX XX 2015 5.5 400. 0.245 0.0772 #> # ... with 2 more variables: povertygapsq <dbl>, population <dbl>