Replace missing PPPs by MERs instead of with 1

R/adapt_source.R

@@ -129,10 +129,12 @@ adapt_source <- function(gdp, source, with_regions, replace_NAs, require_year_co
       dplyr::arrange(.data$iso3c, .data$year) %>%
       dplyr::select(-"gd")
 
-    # Replace missing MER and PPP values with US values (=1)
-    source_adapted <- source_adapted %>%
-      tidyr::replace_na(list("MER (LCU per US$)" = 1,
-                             "PPP conversion factor, GDP (LCU per international $)" = 1))
+    # If there is no PPP data whatsoever for the country, use MERs
+    source_adapted <- source_adapted  %>%
+      dplyr::mutate("PPP conversion factor, GDP (LCU per international $)" =
+                      dplyr::if_else(is.na(.data$`PPP conversion factor, GDP (LCU per international $)`),
+                                     .data$`MER (LCU per US$)`,
+                                     .data$`PPP conversion factor, GDP (LCU per international $)`))
 
     # If there is no deflator data whatsoever for the country, use US values
     ec <- dplyr::group_by(source_adapted, .data$iso3c) %>%

tests/testthat/test-06_replace_NAs.R

@@ -154,6 +154,15 @@ test_that("convertGDP replace_NAs = with_USA", {
   expect_true(!any(is.na(gdp_conv3$result$value)))
 })
 
+test_that("convertGDP replace_NAs = with_USA, no NAs for MADRAT countries", {
+  gdp <- tibble::tibble("iso3c" = madrat::toolGetMapping("regionmappingH12.csv")$CountryCode,
+                        value = 1) %>%
+    convertGDP(unit_in = "constant 2005 US$MER",
+               unit_out = "constant 2017 Int$PPP",
+               replace_NAs = "with_USA")
+  expect_true(!any(is.na(gdp$value)))
+})
+
 test_that("lin_int_ext", {
   x <- c(NA,NA,NA,NA,NA,NA,2,3,4,5,NA,7,8,NA,NA,NA,NA,NA,NA)
   expect_equal(lin_int_ext(x), -4:14)