chore(code): Refactor data preparation into process module
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2 changed files with 7 additions and 4 deletions
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@ -18,8 +18,12 @@ RAW_DATA=DATA_DIR.joinpath("raw")
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WORKING_DATA=DATA_DIR.joinpath("intermediate")
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PROCESSED_DATA=DATA_DIR.joinpath("processed")
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SUPPLEMENTARY_DATA=DATA_DIR.joinpath("supplementary")
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## Creates 3 important data structures:
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# df: The main dataframe containing all final sample studies
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# df_by_intervention: The same dataframe but split up by individual interventions per study
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# validities: The studies with their validities, containing only quasi-/experimental studies
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from src import prep_data
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from src.process import add_metadata as meta
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# raw database-search results
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bib_sample_raw_db = prep_data.bib_library_from_dir(RAW_DATA)
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@ -27,13 +31,13 @@ bib_sample_raw_db = prep_data.bib_library_from_dir(RAW_DATA)
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bib_sample = prep_data.bib_library_from_dir(WORKING_DATA)
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# load relevant studies
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from src import load_data
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from src.extract import load_data as load
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# each observation in a single dataframe
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df = prep_data.observations_with_metadata_df(
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raw_observations = load_data.from_yml(PROCESSED_DATA),
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study_metadata = prep_data.bib_metadata_df(bib_sample),
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country_groups = prep_data.country_groups_df(Path(f"{SUPPLEMENTARY_DATA}/wb-country-groupings.xlsx")),
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df = meta.observations_with_metadata_df(
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)
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# all observations but split per individual intervention
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@ -66,4 +70,3 @@ validities = validities.loc[(validities["design"] == "quasi-experimental") | (va
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validities["internal_validity"] = validities["internal_validity"].astype('category')
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validities["External Validity"] = validities["external_validity"]
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validities["Internal Validity"] = validities["internal_validity"]
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