162 lines
4.9 KiB
Text
162 lines
4.9 KiB
Text
load data, boilerplate:
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```{python}
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#| label: load-data
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#| echo: false
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from pathlib import Path
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import re
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## standard imports
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from IPython.core.display import Markdown as md
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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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DATA_DIR=Path("./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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from src import prep_data
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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 import load_data
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bib_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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)
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raw_observations = None
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zot_df = None
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df_country_groups = None
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```
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prep data:
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Map a 0-5 external validity score based on 'representativeness' to rows:
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```{python}
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df=bib_df
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vd=df[(df['design'] == 'quasi-experimental') | (df['design'] == 'experimental')]
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vd = vd.assign(valid_ext=0)
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vd["representativeness"] = vd["representativeness"].fillna("")
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mask_subnational = vd['representativeness'].str.contains("subnational")
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mask_national = vd['representativeness'].str.contains("national")
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mask_regional = vd['representativeness'].str.contains("regional")
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mask_local = vd['representativeness'].str.contains("local")
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vd.loc[mask_regional, 'valid_ext'] = 5
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vd.loc[mask_national, 'valid_ext'] = 4
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vd.loc[mask_subnational, 'valid_ext'] = 3
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vd.loc[mask_local, 'valid_ext'] = 2
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vd[['representativeness', 'valid_ext']]
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```
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Map an internal validity score based on study design/method:
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```{python}
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vd = vd.assign(valid_int=0)
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vd["method"] = vd["method"].fillna("")
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vd.loc[vd['method'].str.contains("RCT"), 'valid_int'] = 5.0
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vd.loc[vd['method'].str.contains("|".join(["RD","regression.vdiscontinuity"])), 'valid_int'] = 4.5
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vd.loc[vd['method'].str.contains("|".join(["IV","instrumental.variable"])), 'valid_int'] = 4.0
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vd.loc[vd['method'].str.contains("|".join(["PSM","propensity.score.matching"])), 'valid_int'] = 3.5
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vd.loc[vd['method'].str.contains("|".join(["DM","discontinuity.matching"])), 'valid_int'] = 3.0
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vd.loc[vd['method'].str.contains("|".join(["DID","difference.in.difference"])), 'valid_int'] = 3.0
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vd.loc[vd['method'].str.contains("|".join(["OLS","ordinary.least.square"])), 'valid_int'] = 2.0
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vd[['method', 'valid_int']]
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```
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## visualize data:
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Prep the by_intervention dataframe:
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```{python}
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#| label: fig-intervention-types
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#| fig-cap: Available studies by primary type of intervention
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by_intervention = (
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bib_df
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.fillna("")
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.groupby(["author", "year", "title", "design", "method", "region", "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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sort_order = by_intervention["intervention"].value_counts().index
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fig = plt.figure()
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fig.set_size_inches(6, 3)
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ax = sns.countplot(by_intervention, x="intervention", order=by_intervention["intervention"].value_counts().index)
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plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
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rotation_mode="anchor")
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plt.show()
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```
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then visualize:
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validities as distplot with external as categorical x and internal as hue facet.
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Nicely shows that lower-internal generally have higher external and there are two external humps at 3 and 5
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(subnational and census-based)
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```{python}
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#| label: fig-validity-density
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from src.model import validity
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import seaborn.objects as so
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validities = validity.calculate(by_intervention)
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validities["identifier"] = validities["author"].str.replace(r',.*$', '', regex=True) + " (" + validities["year"].astype(str) + ")"
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# As distplot to show hue-facetted density
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sns.displot(
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data=validities,
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x="external_validity", hue="internal_validity",
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kind="kde",
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multiple="fill", clip=(0, None),
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palette="ch:rot=-0.5,hue=1.5,light=0.9",
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)
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```
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As a point-plot which shows the x and y correlation and the spread (roughly) per external validity
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```{python}
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#| label: fig-validity-points
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from src.model import validity
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import seaborn.objects as so
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validities = validity.calculate(by_intervention)
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validities["identifier"] = validities["author"].str.replace(r',.*$', '', regex=True) + " (" + validities["year"].astype(str) + ")"
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sns.pointplot(
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data=validities,
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x="internal_validity", y="external_validity"
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)
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```
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