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feat(scripts): Add figure for intervention and inequality types

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Marty Oehme 2023-12-06 23:49:14 +01:00
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scoping_review.qmd
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@ -476,9 +476,10 @@ for e in sample_relevant:
ed.get("type", Field(key="type", value=None)).value,
ed.get("times-cited", Field(key="times-cited", value=None)).value,
ed.get("usage-count-since-2013", Field(key="usage-count-since-2013", value=None)).value,
ed.get("keywords", Field(key="keywords", value=None)).value,
])
# FIXME do not just drop missing values
bib_df = pd.DataFrame(reformatted, columns = ["Year", "Author", "Title", "Type", "Cited", "Usage"])
bib_df = pd.DataFrame(reformatted, columns = ["Year", "Author", "Title", "Type", "Cited", "Usage", "Keywords"])
bib_df = bib_df.dropna(how="any")
bib_df["Date"] = pd.to_datetime(bib_df["Year"], format="mixed")
bib_df["Year"] = bib_df["Date"].dt.year
@ -538,6 +539,60 @@ This is because, as @fig-publications-per-year showed, the overall output was no
In all of these cases, such outliers should provide clear points of interest during the screening process for possible re-evaluation of current term clusters for scoping.
Should they point towards gaps (or over-optimization) of sepcific areas of interest during those time-frames or more generally, they may provide an impetus for tweaking the identification query terms to better align with the prevailing literature output.
```{python}
#| label: fig-intervention-types
#| fig-cap: Predominant type of intervention
#| fig-width: 18cm
#| column: page
interv_type_df = (
bib_df["Keywords"]
.str.replace(r"\_", " ")
.str.extractall(r"type::([\w ]+)")
.reset_index(drop=True)
.rename(columns = {0:"Intervention type"})
)
sort_order = interv_type_df["Intervention type"].value_counts(ascending=False).index
fig = plt.figure()
fig.set_size_inches(12, 4)
ax = sns.countplot(interv_type_df, x="Intervention type", order=sort_order)
plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
rotation_mode="anchor")
plt.show()
```
{{++ TODO: describe intervention types with complete dataset ++}}
```{python}
#| label: fig-inequality-types
#| fig-cap: Types of inequality analyzed
#| fig-width: 18cm
#| column: page
inequ_type_df = (
bib_df["Keywords"]
.str.replace(r"\_", " ")
.str.extractall(r"inequality::([\w ]+)")
.reset_index(drop=True)
.rename(columns = {0:"Inequality type"})
)
sort_order = inequ_type_df["Inequality type"].value_counts(ascending=False).index
fig = plt.figure()
fig.set_size_inches(12, 4)
ax = sns.countplot(inequ_type_df, x="Inequality type", order=sort_order)
plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
rotation_mode="anchor")
plt.show()
```
Income inequality is the primary type of inequality interrogated in most of the relevant studies.
This follows the identified lens income inequality can provide through which to understand other inequalities ---
many studies use income measurements and changes in income or income inequality over time as indicators to understand a variety of other inequalities' linkages through.
{{++ TODO: describe inequality types with complete dataset ++}}
# Synthesis of Evidence
This section will present a synthesis of evidence from the scoping review.