73 lines
2.7 KiB
Python
73 lines
2.7 KiB
Python
from pathlib import Path
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import os
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import re
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## standard imports
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from IPython.display import display, Markdown, HTML
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import numpy as np
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import pandas as pd
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from matplotlib import pyplot as plt
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import seaborn as sns
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from tabulate import tabulate
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import bibtexparser
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sns.set_style("whitegrid")
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PROJECT_DIR=Path(os.getenv("QUARTO_PROJECT_DIR", "."))
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DATA_DIR=PROJECT_DIR.joinpath("02-data")
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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.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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# the complete library of sampled (and working) literature
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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.extract import load_data as load
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# each observation in a single dataframe
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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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df_by_intervention = (
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df
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.fillna("")
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.groupby(["author", "year", "title", "design", "method", "representativeness", "citation"])
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.agg(
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{
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"intervention": lambda _col: "; ".join(_col),
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}
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)
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.reset_index()
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.drop_duplicates()
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.assign(
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intervention=lambda _df: _df["intervention"].apply(
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lambda _cell: set([x.strip() for x in re.sub(r"\(.*\)", "", _cell).split(";")])
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),
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)
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.explode("intervention")
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)
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# Calc study validities (internal & external separated)
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from src.model import validity
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validities = validity.calculate(df_by_intervention)
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validities["identifier"] = validities["author"].str.replace(r',.*$', '', regex=True) + " (" + validities["year"].astype(str) + ")"
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validities = validities.loc[(validities["design"] == "quasi-experimental") | (validities["design"] == "experimental")]
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#validities["external_validity"] = validities["external_validity"].astype('category')
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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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