wow-inequalities/notebooks/rank_validities.qmd

load data, boilerplate:

```{python}
#| label: load-data
#| echo: false
from pathlib import Path
import re
## standard imports
from IPython.core.display import Markdown as md
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import seaborn as sns
from tabulate import tabulate
import bibtexparser

sns.set_style("whitegrid")

DATA_DIR=Path("./02-data")
RAW_DATA=DATA_DIR.joinpath("raw")
WORKING_DATA=DATA_DIR.joinpath("intermediate")
PROCESSED_DATA=DATA_DIR.joinpath("processed")
SUPPLEMENTARY_DATA=DATA_DIR.joinpath("supplementary")

from src import prep_data

# raw database-search results
bib_sample_raw_db = prep_data.bib_library_from_dir(RAW_DATA)
# the complete library of sampled (and working) literature
bib_sample = prep_data.bib_library_from_dir(WORKING_DATA)

# load relevant studies
from src import load_data

bib_df = prep_data.observations_with_metadata_df(
    raw_observations = load_data.from_yml(PROCESSED_DATA),
    study_metadata = prep_data.bib_metadata_df(bib_sample),
    country_groups = prep_data.country_groups_df(Path(f"{SUPPLEMENTARY_DATA}/wb-country-groupings.xlsx")),
)
raw_observations = None
zot_df = None
df_country_groups = None
```

prep data:

Map a 0-5 external validity score based on 'representativeness' to rows:

```{python}
df=bib_df
vd=df[(df['design'] == 'quasi-experimental') | (df['design'] == 'experimental')]

vd = vd.assign(valid_ext=0)
vd["representativeness"] = vd["representativeness"].fillna("")

mask_subnational = vd['representativeness'].str.contains("subnational")
mask_national = vd['representativeness'].str.contains("national")
mask_regional = vd['representativeness'].str.contains("regional")
mask_local = vd['representativeness'].str.contains("local")

vd.loc[mask_regional, 'valid_ext'] = 5
vd.loc[mask_national, 'valid_ext'] = 4
vd.loc[mask_subnational, 'valid_ext'] = 3
vd.loc[mask_local, 'valid_ext'] = 2

vd[['representativeness', 'valid_ext']]
```

Map an internal validity score based on study design/method:

```{python}
vd = vd.assign(valid_int=0)
vd["method"] = vd["method"].fillna("")

vd.loc[vd['method'].str.contains("RCT"), 'valid_int'] = 5.0
vd.loc[vd['method'].str.contains("|".join(["RD","regression.vdiscontinuity"])), 'valid_int'] = 4.5
vd.loc[vd['method'].str.contains("|".join(["IV","instrumental.variable"])), 'valid_int'] = 4.0
vd.loc[vd['method'].str.contains("|".join(["PSM","propensity.score.matching"])), 'valid_int'] = 3.5
vd.loc[vd['method'].str.contains("|".join(["DM","discontinuity.matching"])), 'valid_int'] = 3.0
vd.loc[vd['method'].str.contains("|".join(["DID","difference.in.difference"])), 'valid_int'] = 3.0
vd.loc[vd['method'].str.contains("|".join(["OLS","ordinary.least.square"])), 'valid_int'] = 2.0
vd[['method', 'valid_int']]
```

## visualize data:

Prep the by_intervention dataframe:

```{python}
#| label: fig-intervention-types
#| fig-cap: Available studies by primary type of intervention

by_intervention = (
    bib_df
    .fillna("")
    .groupby(["author", "year", "title", "design", "method", "region", "representativeness", "citation"])
    .agg(
        {
            "intervention": lambda _col: "; ".join(_col),
        }
    )
    .reset_index()
    .drop_duplicates()
    .assign(
        intervention=lambda _df: _df["intervention"].apply(
            lambda _cell: set([x.strip() for x in re.sub(r"\(.*\)", "", _cell).split(";")])
        ),
    )
    .explode("intervention")
)
sort_order = by_intervention["intervention"].value_counts().index

fig = plt.figure()
fig.set_size_inches(6, 3)
ax = sns.countplot(by_intervention, x="intervention", order=by_intervention["intervention"].value_counts().index)
plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
         rotation_mode="anchor")
plt.show()
```

then visualize:

validities as distplot with external as categorical x and internal as hue facet.
Nicely shows that lower-internal generally have higher external and there are two external humps at 3 and 5
(subnational and census-based)

```{python}
#| label: fig-validity-density
from src.model import validity
import seaborn.objects as so

validities = validity.calculate(by_intervention)
validities["identifier"] = validities["author"].str.replace(r',.*$', '', regex=True) + " (" + validities["year"].astype(str) + ")"


# As distplot to show hue-facetted density

sns.displot(
    data=validities,
    x="external_validity", hue="internal_validity",
    kind="kde",
    multiple="fill", clip=(0, None),
    palette="ch:rot=-0.5,hue=1.5,light=0.9",
)
```
As a point-plot which shows the x and y correlation and the spread (roughly) per external validity

```{python}
#| label: fig-validity-points
from src.model import validity
import seaborn.objects as so

validities = validity.calculate(by_intervention)
validities["identifier"] = validities["author"].str.replace(r',.*$', '', regex=True) + " (" + validities["year"].astype(str) + ")"



sns.pointplot(
    data=validities,
    x="internal_validity", y="external_validity"
)
```

As a relation-chart which shows the internal-external relation and the deviation from individual points.

```{python}
#| label: fig-validity-relation
#| fig-cap: "Relation between internal and external validity"
#| fig-height: 5
#| code-fold: true

from src.model import validity

validities = validity.calculate(by_intervention)
validities["identifier"] = validities["author"].str.replace(r',.*$', '', regex=True) + " (" + validities["year"].astype(str) + ")"
validities = validities.loc[(validities["design"] == "quasi-experimental") | (validities["design"] == "experimental")]
#validities["external_validity"] = validities["external_validity"].astype('category')
validities["internal_validity"] = validities["internal_validity"].astype('category')

sns.pointplot(
    data=validities,
    x="internal_validity", y="external_validity",
)
```

```{python}
#| label: fig-validity-distribution
#| fig-cap: "Distribution of internal validities"
#| fig-height: 5
#| code-fold: true

fig, ax = plt.subplots()

#sns.displot(
#    data=validities,
#    x="external_validity", hue="internal_validity",
#    kind="kde",
#    multiple="fill", clip=(0, None),
#    palette="ch:rot=-0.5,hue=1.5,light=0.9",
#    bw_adjust=.65, cut=0,
#    warn_singular = False
#)
```
Following plots need at least one axis, preferably external to be set to categorical.

As a heatmap plot for categorical data between x-y:

```{python}
#| label: fig-validity-distribution
sns.displot(
    data=validities,
    x="internal_validity", y="external_validity", hue="design",
    palette="ch:rot=-0.75,hue=1.5,light=0.9",
)
```

As a violin plot showing distribution of external along internal category:

```{python}
sns.violinplot(
    data=validities,
    x="internal_validity", y="external_validity", hue="design",
    cut=0, bw_method="scott",
    orient="x"
)
# optional swarmplot showing the actual amount of data points for each rank
sns.swarmplot(
    data=validities,
    x="internal_validity", y="external_validity",
    color="red",
    s=6
)
```