Ingest CHR Preventable Hospitalization

python/datasources/chr.py

@@ -1,5 +1,44 @@
 from datasources.data_source import DataSource
-from ingestion import gcs_to_bq_util, standardized_columns as std_col
+from ingestion import dataset_utils, merge_utils, gcs_to_bq_util, standardized_columns as std_col
+from ingestion.constants import COUNTY_LEVEL, CURRENT
+
+CHR = 'chr'
+source_state_fips = 'State FIPS Code'
+source_county_fips = '5-digit FIPS Code'
+source_time_period = 'Release Year'
+
+source_cols = [source_time_period, source_state_fips, source_county_fips]
+
+prev_hosp_per_100k_col = std_col.generate_column_name(std_col.PREVENTABLE_HOSP_PREFIX, std_col.PER_100K_SUFFIX)
+
+source_race_to_id_map = {
+    'raw value': std_col.Race.ALL.value,
+    '(AIAN)': std_col.Race.AIAN_NH.value,
+    '(Asian/Pacific Islander)': std_col.Race.API_NH.value,
+    '(Black)': std_col.Race.BLACK_NH.value,
+    '(Hispanic)': std_col.Race.HISP.value,
+    '(White)': std_col.Race.WHITE_NH.value,
+}
+
+melt_map = {
+    prev_hosp_per_100k_col: {
+        f'Preventable Hospital Stays {source_race}': het_race_id
+        for source_race, het_race_id in source_race_to_id_map.items()
+    },
+}
+
+source_topic_cols = list(melt_map[prev_hosp_per_100k_col].keys())
+# NOTE: cols for numerator and denominator are all NULL
+
+source_dtypes = {
+    **{topic_col: 'float64' for topic_col in source_topic_cols},
+    **{col: 'str' for col in source_cols},
+}
+
+
+TIME_MAP = {
+    CURRENT: [prev_hosp_per_100k_col, std_col.POPULATION_PCT_COL, std_col.POPULATION_COL],
+}
 
 
 class CHRData(DataSource):
@@ -16,18 +55,44 @@ def upload_to_gcs(self, gcs_bucket, **attrs):
 
     def write_to_bq(self, dataset, gcs_bucket, **attrs):
         demographic = self.get_attr(attrs, "demographic")
-        geo_level = self.get_attr(attrs, "geographic")
         if demographic == std_col.RACE_COL:
             demographic = std_col.RACE_OR_HISPANIC_COL
 
+        use_cols = source_cols + source_topic_cols
+
         df = gcs_to_bq_util.load_csv_as_df_from_data_dir(
-            'chr',
+            CHR,
             "analytic_data2024.csv",
+            usecols=use_cols,
+            dtype=source_dtypes,
+            skiprows=[1],  # skip weird sub header row
         )
 
-        table_name = f"{demographic}_{geo_level}"
+        # drop national and state-level rows
+        df = df[~df[source_county_fips].str.endswith('000')]
+
+        df = dataset_utils.melt_to_het_style_df(df, std_col.RACE_CATEGORY_ID_COL, source_cols, melt_map)
+
+        df = df.rename(
+            columns={
+                source_county_fips: std_col.COUNTY_FIPS_COL,
+                source_state_fips: std_col.STATE_FIPS_COL,
+                source_time_period: std_col.TIME_PERIOD_COL,
+            }
+        )
+
+        df = merge_utils.merge_state_ids(df)
+        df = merge_utils.merge_county_names(df)
+        df = merge_utils.merge_yearly_pop_numbers(df, std_col.RACE_COL, COUNTY_LEVEL)
+        std_col.add_race_columns_from_category_id(df)
+
+        for table_type in [CURRENT]:
+            df = df.copy()
+            table_name = f"{demographic}_{COUNTY_LEVEL}_{table_type}"
+            time_cols = TIME_MAP[table_type]
 
-        print(table_name)
-        print(df)
+            df_for_bq, col_types = dataset_utils.generate_time_df_with_cols_and_types(
+                df, time_cols, table_type, demographic
+            )
 
-        # gcs_to_bq_util.add_df_to_bq(df_for_bq, dataset, table_name, column_types=col_types)
+            gcs_to_bq_util.add_df_to_bq(df_for_bq, dataset, table_name, column_types=col_types)

python/ingestion/dataset_utils.py

@@ -577,9 +577,14 @@ def generate_time_df_with_cols_and_types(
     DataFrame.
     """
     df = df.copy()
-    mandatory_cols = [std_col.TIME_PERIOD_COL, std_col.STATE_NAME_COL, std_col.STATE_FIPS_COL]
+    str_cols_to_keep = [std_col.TIME_PERIOD_COL, std_col.STATE_NAME_COL, std_col.STATE_FIPS_COL, dem_col]
 
-    all_cols = mandatory_cols + [dem_col] + numerical_cols_to_keep
+    if std_col.COUNTY_NAME_COL in df.columns:
+        str_cols_to_keep.append(std_col.COUNTY_NAME_COL)
+    if std_col.COUNTY_FIPS_COL in df.columns:
+        str_cols_to_keep.append(std_col.COUNTY_FIPS_COL)
+
+    all_cols = str_cols_to_keep + numerical_cols_to_keep
     df = df[all_cols]
 
     if table_type == CURRENT: