Create data processing file

data_processing.R

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+target_feature_value <- params$added_feature
+target_feature_shap <- params$added_feature_shap
+nbhd <- ccao::nbhd_shp
+
+# Selecting and joining relevant data
+card_individual <- shap %>%
+  select(meta_pin, meta_card_num, pred_card_shap_baseline_fmv, {{ target_feature_value }}) %>%
+  rename(!!target_feature_shap := !!target_feature_value) %>%
+  inner_join(assessment_card, by = c("meta_pin", "meta_card_num")) %>%
+  inner_join(
+    assessment_data %>%
+      select(meta_pin, meta_card_num, meta_nbhd_code, loc_longitude, loc_latitude, meta_township_name),
+    by = c("meta_pin", "meta_card_num")
+  )
+
+
+# Summarizing data by neighborhood code
+card_nbhd <- card_individual %>%
+  group_by(meta_nbhd_code) %>%
+  summarize(
+    avg_target_feature_shap = mean(!!sym({{target_feature_shap}}), na.rm = TRUE),
+    avg_pred_card_shap_baseline_fmv = mean(pred_card_shap_baseline_fmv, na.rm = TRUE)
+  ) %>%
+  ungroup()
+
+
+pin_individual <- assessment_pin %>%
+  select(meta_pin, pred_pin_final_fmv, pred_pin_initial_fmv) %>%
+  rename(
+    pred_pin_final_fmv_new = pred_pin_final_fmv,
+    pred_pin_initial_fmv_new = pred_pin_initial_fmv
+  ) %>%
+  inner_join(
+    assessment_pin_comparison %>%
+      select(meta_pin, pred_pin_final_fmv, pred_pin_initial_fmv),
+    by = "meta_pin"
+  ) %>%
+  rename(
+    pred_pin_final_fmv_comp = pred_pin_final_fmv,
+    pred_pin_initial_fmv_comp = pred_pin_initial_fmv
+  ) %>%
+  mutate(
+    diff_pred_pin_final_fmv = round(((
+      pred_pin_final_fmv_new - pred_pin_final_fmv_comp) / pred_pin_final_fmv_comp) * 100, 2),
+    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) * 100, 2),
+    pred_pin_initial_fmv_new = dollar(pred_pin_initial_fmv_new),
+    pred_pin_initial_fmv_comp = dollar(pred_pin_initial_fmv_comp)
+  ) %>%
+  inner_join(
+    assessment_data %>%
+      select(meta_pin, meta_nbhd_code, loc_longitude, loc_latitude, meta_township_name, {{ target_feature_value }}),
+    by = "meta_pin"
+  )
+
+pin_nbhd <- pin_individual %>%
+  group_by(meta_nbhd_code) %>%
+  summarize(
+    !!paste0({{target_feature_value}}, "_neighborhood_mean") := mean(!!sym({{target_feature_value}}), na.rm = TRUE),
+    !!paste0({{target_feature_value}}, "_neighborhood_median") := median(!!sym({{target_feature_value}}), na.rm = TRUE),
+    !!paste0({{target_feature_value}}, "_neighborhood_90th") := quantile(!!sym({{target_feature_value}}), 0.9, na.rm = TRUE)
+  ) %>%
+  ungroup() %>%
+  inner_join(
+    nbhd,
+    by = c("meta_nbhd_code" = "town_nbhd")
+  )