chore(code): Rename ymlgrab to explore notebook
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@ -94,6 +94,8 @@ by_study = (
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```{python}
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import re
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from matplotlib import pyplot as plt
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import seaborn as sns
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by_intervention = (
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df.groupby(["author", "year", "title"])
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.agg(
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@ -120,3 +122,50 @@ plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
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plt.show()
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by_intervention = None
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```
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```{python}
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#| label: fig-publications-per-year
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#| fig-cap: Publications per year
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df_study_years = (
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df.groupby(["author", "year", "title"])
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.first()
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.reset_index()
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.drop_duplicates()
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)
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# plot by year TODO decide if we want to distinguish by literature type/region/etc as hue
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# FIXME should be timeseries plot so no years are missing
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ax = sns.countplot(df_study_years, x="year", native_scale=True)
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ax.tick_params(axis='x', rotation=45)
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ax.set_xlabel("")
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plt.tight_layout()
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plt.show()
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df_study_years = None
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```
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```{python}
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#| label: tbl-income-crosstab
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#| tbl-cap: Interventions targeting income inequality
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df_income = df.copy()
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df_income['Inequality'] = df_income['inequality'].str.split(";").explode(ignore_index=True).str.strip()
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df_income = df_income.loc[df_income['Inequality'] == "income"].copy()
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df_income['Intervention'] = df_income['intervention'].str.split(";").explode(ignore_index=True).str.replace(r"\(.+\)", "", regex=True).str.strip()
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pd.crosstab(df_income["Intervention"], df_income["Inequality"])
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```
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```{python}
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#| label: tbl-income-crosstab
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#| tbl-cap: Interventions targeting income inequality
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def inequality_crosstab(df, inequality:str):
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temp_df = df.copy()
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temp_df['Inequality'] = temp_df['inequality'].str.split(";").explode(ignore_index=True).str.strip()
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temp_df = temp_df.loc[temp_df['Inequality'] == inequality].copy()
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temp_df['Intervention'] = temp_df['intervention'].str.split(";").explode(ignore_index=True).str.replace(r"\(.+\)", "", regex=True).str.strip()
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tab = pd.crosstab(temp_df["Intervention"], temp_df["Inequality"])
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temp_df=None
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return tab
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inequality_crosstab(df, "income")
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```
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