chore(code): Refactor data preparation into process module

01-codechunks/_prep-data.py

@@ -18,8 +18,12 @@ RAW_DATA=DATA_DIR.joinpath("raw")
 WORKING_DATA=DATA_DIR.joinpath("intermediate")
 PROCESSED_DATA=DATA_DIR.joinpath("processed")
 SUPPLEMENTARY_DATA=DATA_DIR.joinpath("supplementary")
+## Creates 3 important data structures:
+# df: The main dataframe containing all final sample studies
+# df_by_intervention: The same dataframe but split up by individual interventions per study
+# validities: The studies with their validities, containing only quasi-/experimental studies
 
-from src import prep_data
+from src.process import add_metadata as meta
 
 # raw database-search results
 bib_sample_raw_db = prep_data.bib_library_from_dir(RAW_DATA)
@@ -27,13 +31,13 @@ bib_sample_raw_db = prep_data.bib_library_from_dir(RAW_DATA)
 bib_sample = prep_data.bib_library_from_dir(WORKING_DATA)
 
 # load relevant studies
-from src import load_data
+from src.extract import load_data as load
 
 # each observation in a single dataframe
-df = prep_data.observations_with_metadata_df(
     raw_observations = load_data.from_yml(PROCESSED_DATA),
     study_metadata = prep_data.bib_metadata_df(bib_sample),
     country_groups = prep_data.country_groups_df(Path(f"{SUPPLEMENTARY_DATA}/wb-country-groupings.xlsx")),
+df = meta.observations_with_metadata_df(
 )
 
 # all observations but split per individual intervention
@@ -66,4 +70,3 @@ validities = validities.loc[(validities["design"] == "quasi-experimental") | (va
 validities["internal_validity"] = validities["internal_validity"].astype('category')
 validities["External Validity"] = validities["external_validity"]
 validities["Internal Validity"] = validities["internal_validity"]
-