129 lines
4.7 KiB
Plaintext
129 lines
4.7 KiB
Plaintext
load data, boilerplate:
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```{python}
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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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bib_string=""
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for partial_bib in RAW_DATA.glob("**/*.bib"):
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with open(partial_bib) as f:
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bib_string+="\n".join(f.readlines())
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bib_sample_raw_db = bibtexparser.parse_string(bib_string)
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bib_string=""
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for partial_bib in WORKING_DATA.glob("**/*.bib"):
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with open(partial_bib) as f:
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bib_string+="\n".join(f.readlines())
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bib_sample = bibtexparser.parse_string(bib_string)
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```
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```{python}
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# load relevant studies
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from src import load_data
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# load zotero-based metadata: citations and uses
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zot_df = pd.DataFrame([
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[
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entry["doi"] if "doi" in entry.fields_dict else None,
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entry["times-cited"] if "times-cited" in entry.fields_dict else None,
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entry["usage"] if "usage" in entry.fields_dict else None,
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entry["keywords"] if "keywords" in entry.fields_dict else None,
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]
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for entry in bib_sample.entries
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], columns = ["doi", "cited", "usage", "keywords"]).drop_duplicates("doi").set_index("doi")
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# Add WB country grouping definitions (income group, world region)
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WB_COUNTRY_GROUPS_FILE = Path(f"{SUPPLEMENTARY_DATA}/wb-country-groupings.xlsx").resolve()
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df_country_groups = pd.read_excel(WB_COUNTRY_GROUPS_FILE).set_index("Economy")
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bib_df = (load_data.from_yml(f"{PROCESSED_DATA}")
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.assign(
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doi=lambda _df: _df["uri"].str.extract(r"https?://(?:dx\.)?doi\.org/(.*)", expand=False),
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zot_cited=lambda _df: _df["doi"].map(zot_df["cited"]),
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zot_usage=lambda _df: _df["doi"].map(zot_df["usage"]),
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zot_keywords=lambda _df: _df["doi"].map(zot_df["keywords"]),
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date = lambda _df: pd.to_datetime(_df["year"], format="%Y"),
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year = lambda _df: _df["date"].dt.year,
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region = lambda _df: _df["country"].map(df_country_groups["Region"]),
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income_group = lambda _df: _df["country"].map(df_country_groups["Income group"]),
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)
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.query("year >= 2000")
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)
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zot_df = None
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df_country_groups = None
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```
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```{python}
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df_country_groups = pd.concat([pd.read_excel(WB_COUNTRY_GROUPS_FILE), pd.DataFrame(data={'Economy':['global'],'Code':['WLD'],'Region':['Europe & Central Asia;South Asia;North America;East Asia & Pacific;Sub-Saharan Africa;Europe & Central Asia;Latin America & Caribbean'], 'Income group':[''], 'Lending category':['']})]).set_index("Economy")
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def countries_to_regions(countries:str):
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res = set()
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for c in countries.replace(" ;", ";").replace("; ",";").split(";"):
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if c in df_country_groups.index:
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region = df_country_groups.at[c,'Region']
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res.add(region)
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return ";".join(res)
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# countries_to_regions("India; Nicaragua")
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bib_df['region'] = bib_df['country'].map(countries_to_regions)
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bib_df['region'].value_counts().plot.bar()
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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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