silence many_to_many warnings (#441)

RMI-PACTA · Jul 19, 2023 · 0b482f8 · 0b482f8
1 parent 568d804
commit 0b482f8
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Showing 3 changed files with 20 additions and 6 deletions.
diff --git a/R/join_abcd_scenario.R b/R/join_abcd_scenario.R
@@ -61,8 +61,8 @@ join_abcd_scenario <- function(data,
   }
 
   out <- data %>%
-    left_join(abcd, by = abcd_columns()) %>%
-    inner_join(scenario, by = scenario_columns()) %>%
+    left_join(abcd, by = abcd_columns(), relationship = "many-to-many") %>%
+    inner_join(scenario, by = scenario_columns(), relationship = "many-to-many") %>%
     warn_if_has_zero_rows("Joining `scenario` outputs 0 rows.") %>%
     mutate(plant_location = tolower(.data$plant_location)) %>%
     inner_join(
@@ -119,7 +119,11 @@ add_green_technologies_to_abcd <- function(data, scenario) {
       .data$is_ultimate_owner
     ) %>%
     summarize() %>%
-    left_join(green_techs_in_scenario, by = "sector") %>%
+    left_join(
+      green_techs_in_scenario,
+      by = "sector",
+      relationship = "many-to-many"
+      ) %>%
     mutate(production = 0)
 
   dplyr::bind_rows(data, green_rows_to_add)

diff --git a/R/target_market_share.R b/R/target_market_share.R
@@ -332,7 +332,8 @@ calculate_abcd_benchmark <- function(abcd, region_isos, by_company) {
     mutate(plant_location = tolower(.data$plant_location)) %>%
     inner_join(
       region_isos,
-      by = c("plant_location" = "isos")
+      by = c("plant_location" = "isos"),
+      relationship = "many-to-many"
     ) %>%
     warn_if_has_zero_rows("Joining `region_isos` outputs 0 rows.") %>%
     # Return visibly

diff --git a/R/target_sda.R b/R/target_sda.R
@@ -143,13 +143,22 @@ target_sda <- function(data,
 
   abcd <- abcd %>%
     mutate(plant_location = tolower(.data$plant_location)) %>%
-    left_join(region_isos, by = c(plant_location = "isos")) %>%
+    left_join(
+      region_isos,
+      by = c(plant_location = "isos"),
+      relationship = "many-to-many"
+      ) %>%
     rename(scenario_source = "source")
 
   abcd_by_sector <- abcd %>%
     aggregate_excluding(c("technology", "plant_location", "country_of_domicile"))
 
-  data <- inner_join(data, abcd_by_sector, by = abcd_columns())
+  data <- inner_join(
+    data,
+    abcd_by_sector,
+    by = abcd_columns(),
+    relationship = "many-to-many"
+    )
 
   summary_groups <- c(
     "region",