added metis_countries, resolved dependencies, new ccyy_to_yyyy and ccyy_to_cname and fixed structure_to_plot

Author: Gonçalo Marques

R/checks_for_lissyuse.R

@@ -63,9 +63,9 @@ check_length_iso2 <- function(data) {
 #' @return Stops if all iso2 codes in `data` are invalid, and it warns if only some are invalid. 
 check_iso2 <- function(data, lws = FALSE) {
   valid_iso2 <- if (lws) {
-    lissyrtools::get_countries_lws()
+    lissyrtools::datasets %>% dplyr::filter(database == "LWS") %>% dplyr::select(iso2) %>% unique() %>% dplyr::pull() %>% as.vector()
   } else {
-    lissyrtools::get_countries_lis()
+    lissyrtools::datasets %>% dplyr::filter(database == "LIS") %>% dplyr::select(iso2) %>% unique() %>% dplyr::pull() %>% as.vector()
   }
 
   data_iso2 <- stringr::str_sub(data, 1, 2)

R/data.R

@@ -55,6 +55,16 @@
 #' @format A data frame with 9 variables:
 "value_label_c_data"
 
+
+#' Countries Names and ISO Codes.
+#'
+#' A data frame containing the country names, and its respective iso2 and iso3 codes. 
+#'
+#' @format A data frame with 3 variables:
+"metis_countries_df"
+
+
+
 #' LIS Variables 
 #'
 #' A character vector containing the names of the variables available in LIS.

R/get_labels_notes_surveys.R

@@ -20,7 +20,7 @@
 variable_labels <- function(vars = NULL) {
 
   if (is.null(vars)) {
-    output <- tibble::deframe(data_vars_labels)
+    output <- tibble::deframe(lissyrtools::data_vars_labels)
     return(output)
   } else if (!(is.null(vars))) {
     if (is.character(vars)) {
@@ -45,7 +45,7 @@ variable_labels <- function(vars = NULL) {
 
       vars_accepted <- vars[!vars %in% invalid_vars]
 
-      output <- tibble::deframe(data_vars_labels)[vars_accepted]
+      output <- tibble::deframe(lissyrtools::data_vars_labels)[vars_accepted]
       return(output)
     } else if (isTRUE(exists("vars"))) {
       if (!(is.list(vars))) {
@@ -54,7 +54,7 @@ variable_labels <- function(vars = NULL) {
         ))
       } else {
         columns <- names(vars[[1]])
-        output <- tibble::deframe(data_vars_labels)[columns]
+        output <- tibble::deframe(lissyrtools::data_vars_labels)[columns]
         return(output)
       }
     }
@@ -142,7 +142,7 @@ variable_has_note <- function(variable, iso2, lws = FALSE) {
 
     years <- get_years_function(i)[[1]]
 
-    existing_years <- data_with_warnings %>%
+    existing_years <- lissyrtools::data_with_warnings %>%
       dplyr::filter(database == db, iso2 == i, var_name == variable) %>%
       dplyr::pull(year)
 

R/structure_to_plot.R

@@ -134,20 +134,22 @@ structure_to_plot <- function(data_list, print_columns = TRUE) {
   # 1st structure
   if (
     all(
-      names(data_list) %in%
-        c(
-          names(lissyrtools::get_countries_lis()),
-          names(lissyrtools::get_countries_lws())
-        )
-    )
+      names(data_list) %in% (lissyrtools::metis_countries_df %>% dplyr::select(name) %>% unique() %>% dplyr::pull())    
+      )
   ) {
-    result_df <- purrr::list_rbind(purrr::imap(
+    
+    list_of_cc <- lissyrtools::metis_countries_df %>% dplyr::filter(name %in% names(data_list)) %>% dplyr::select(-iso3) %>% as.vector()
+    
+    vector_of_cc <- list_of_cc[["iso2"]]
+    names(vector_of_cc) <- list_of_cc[["name"]]
+    
+    result_df <- dplyr::bind_rows(purrr::imap(
       data_list,
       ~ tibble::enframe(.x, name = "year", value = "value") %>%
         dplyr::mutate(cname = .y)
     )) %>%
       dplyr::mutate(
-        cc = lissyrtools::get_countries_lis()[cname],
+        cc = vector_of_cc[cname], 
         yy = stringr::str_sub(year, 3, 4),
         dname = paste0(cc, yy),
         year = as.integer(year)
@@ -161,14 +163,14 @@ structure_to_plot <- function(data_list, print_columns = TRUE) {
         purrr::map_chr(data_list, ~ class(.x)[1]) %in% c("numeric", "integer")
       )
   ) {
-    result_df <- purrr::list_rbind(purrr::imap(
+    result_df <- dplyr::bind_rows(purrr::imap(
       data_list,
       ~ tibble::enframe(.x, name = "category", value = "value") %>%
         dplyr::mutate(dname = stringr::str_sub(.y,1,4))
     )) %>%
       dplyr::mutate(
-        cname = lissyrtools::ccyy_to_cname(dname),
-        year = lissyrtools::ccyy_to_yyyy(dname),
+        cname = purrr::map_chr(dname, lissyrtools::ccyy_to_cname),
+        year = as.integer(purrr::map_chr(dname, lissyrtools::ccyy_to_yyyy)),
         category = stringr::str_remove(category, "^\\[\\d+\\]"),
         year = as.integer(year)
       ) %>%
@@ -181,11 +183,11 @@ structure_to_plot <- function(data_list, print_columns = TRUE) {
         all(purrr::map_chr(data_list, ~ class(.x)[1]) == "list")
     )
   ) {
-    result_df <- purrr::list_rbind(purrr::imap(
+    result_df <- dplyr::bind_rows(purrr::imap(
       data_list,
       ~ {
         outer_name <- stringr::str_sub(.y,1,4)
-        purrr::list_rbind(purrr::imap(.x, function(sublist, subgroup) {
+        dplyr::bind_rows(purrr::imap(.x, function(sublist, subgroup) {
           # subgroup would be the categorical variable in run_weighted_mean or run_weighted_percentiles
           tibble::enframe(sublist, name = "vector_names", value = "value") %>% # vector_names: could be percentiles, shares, averages or the by var in run_weighted_count
             dplyr::mutate(
@@ -197,8 +199,8 @@ structure_to_plot <- function(data_list, print_columns = TRUE) {
       }
     )) %>%
       dplyr::mutate(
-        cname = lissyrtools::ccyy_to_cname(dname),
-        year = as.integer(lissyrtools::ccyy_to_yyyy(dname))
+        cname = purrr::map_chr(dname, lissyrtools::ccyy_to_cname),
+        year = as.integer(purrr::map_chr(dname, lissyrtools::ccyy_to_yyyy))
       ) %>%
       dplyr::select(cname, year, dname, category, name, value)