lintr

Data_ingest_and_transformation.R

@@ -84,15 +84,15 @@ all_vars <- ls()
 for (var_name in all_vars) {
   # Check if the variable is a dataframe and ends with _run_id
   if (exists(var_name) && is.data.frame(get(var_name)) &&
-      grepl(paste0("_", params$run_id, "$"), var_name)) {
+    grepl(paste0("_", params$run_id, "$"), var_name)) {
     new_name <- sub(paste0("_", params$run_id, "$"), "_new", var_name)
     assign(new_name, get(var_name), envir = .GlobalEnv)
     rm(list = var_name, envir = .GlobalEnv)
   }
 
   # Check if the variable is a dataframe and ends with _comp_run_id
   if (exists(var_name) && is.data.frame(get(var_name)) &&
-      grepl(paste0("_", params$comp_run_id, "$"), var_name)) {
+    grepl(paste0("_", params$comp_run_id, "$"), var_name)) {
     new_name <- sub(paste0("_", params$comp_run_id, "$"), "_comparison", var_name)
     assign(new_name, get(var_name), envir = .GlobalEnv)
     rm(list = var_name, envir = .GlobalEnv)
@@ -103,15 +103,15 @@ for (var_name in all_vars) {
 for (var_name in all_vars) {
   # Check if the variable is a dataframe and ends with _run_id
   if (exists(var_name) && is.data.frame(get(var_name)) &&
-      grepl(paste0("_", params$run_id, "$"), var_name)) {
+    grepl(paste0("_", params$run_id, "$"), var_name)) {
     new_name <- sub(paste0("_", params$run_id, "$"), "_new", var_name)
     assign(new_name, get(var_name), envir = .GlobalEnv)
     rm(list = var_name, envir = .GlobalEnv)
   }
 
   # Check if the variable is a dataframe and ends with _comp_run_id
   if (exists(var_name) && is.data.frame(get(var_name)) &&
-      grepl(paste0("_", params$comp_run_id, "$"), var_name)) {
+    grepl(paste0("_", params$comp_run_id, "$"), var_name)) {
     new_name <- sub(paste0("_", params$comp_run_id, "$"), "_comparison", var_name)
     assign(new_name, get(var_name), envir = .GlobalEnv)
     rm(list = var_name, envir = .GlobalEnv)
@@ -174,13 +174,13 @@ pin_individual <- assessment_pin_new %>%
     diff_pred_pin_final_fmv = round(((
       pred_pin_final_fmv_new -
         pred_pin_final_fmv_comp) /
-        pred_pin_final_fmv_comp), 4),
+      pred_pin_final_fmv_comp), 4),
     pred_pin_final_fmv_new = dollar(pred_pin_final_fmv_new),
     pred_pin_final_fmv_comp = dollar(pred_pin_final_fmv_comp),
     diff_pred_pin_initial_fmv = round(((
       pred_pin_initial_fmv_new -
         pred_pin_initial_fmv_comp) /
-        pred_pin_initial_fmv_comp), 4),
+      pred_pin_initial_fmv_comp), 4),
     pred_pin_initial_fmv_new = dollar(pred_pin_initial_fmv_new),
     pred_pin_initial_fmv_comp = dollar(pred_pin_initial_fmv_comp)
   ) %>%

Helper File.R

@@ -81,6 +81,3 @@ print(subset_paths)
 
 # Calling the function directly with the subset paths
 model_fetch_run_subset("2024-07-09-cool-takuya", "2024", subset_paths, TRUE)
-
-
-

analyses/leaflet_map.R

@@ -1,5 +1,3 @@
-
-
 create_leaflet_map <- function(dataset, legend_value, legend_title, order_scheme = "high", longitude = "loc_longitude", latitude = "loc_latitude") {
   # Filter neighborhoods that have at least one observation
   nbhd_borders <- nbhd %>%
@@ -26,7 +24,7 @@ create_leaflet_map <- function(dataset, legend_value, legend_title, order_scheme
         "<br>", "FMV Difference: ", dollar(diff_pred_pin_final_fmv),
         collapse = "<br>---<br>"
       ),
-      .groups = 'drop'
+      .groups = "drop"
     )
 
   # Create the leaflet map
@@ -37,7 +35,7 @@ create_leaflet_map <- function(dataset, legend_value, legend_title, order_scheme
       lat = ~ get(latitude),
       radius = 5,
       color = ~ pal(dataset_grouped[[legend_value]]),
-      popup = ~ popup_content
+      popup = ~popup_content
     ) %>%
     addPolygons(
       data = nbhd_borders,
@@ -55,7 +53,7 @@ create_leaflet_map <- function(dataset, legend_value, legend_title, order_scheme
 
 
 highest_100 <- leaflet_data %>%
-  arrange(desc({{target_feature_value}})) %>%
+  arrange(desc({{ target_feature_value }})) %>%
   slice(1:100)
 
-create_leaflet_map(highest_100, {{target_feature_value}}, "Largest 100 Values", order_scheme = "high")
+create_leaflet_map(highest_100, {{ target_feature_value }}, "Largest 100 Values", order_scheme = "high")