chore(script): Add descriptive labels to all compute cells

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Marty Oehme 2024-02-15 20:21:19 +01:00
parent 58ef7f84a0
commit a05f274a16
Signed by: Marty
GPG key ID: EDBF2ED917B2EF6A

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@ -14,6 +14,7 @@ subtitle: Scoping Review on 'What Works'
---
```{python}
#| label: load-data
#| echo: false
from pathlib import Path
import re
@ -45,9 +46,7 @@ for partial_bib in WORKING_DATA.glob("**/*.bib"):
with open(partial_bib) as f:
bib_string+="\n".join(f.readlines())
bib_sample = bibtexparser.parse_string(bib_string)
```
```{python}
# load relevant studies
from src import load_data
@ -424,6 +423,7 @@ they will in turn be crawled for cited sources in a 'snowballing' process,
and the sources will be added to the sample to undergo the same screening process explained above.
```{python}
#| label: calculate-scoping-flowchart
#| echo: false
#| output: asis
@ -472,6 +472,7 @@ The results to be identified in the matrix include a studys: i) key outcome m
## Data
```{python}
#| label: calculate-relevant-studies
#| echo: false
# TODO Remove redundant 'relevant' studies observation below once all studies are extracted.
nr_relevant = len([1 for kw in all_keywords if "relevant" in kw])
@ -611,6 +612,7 @@ and the reviewed studies discussed from a perspective of their analysed inequali
to better identify areas of strong analytical lenses or areas of more limited analyses.
```{python}
#| label: fig-validity
from src import prep_data
validities = prep_data.calculate_validities(by_intervention)
@ -631,6 +633,7 @@ g = sns.PairGrid(validities[["internal_validity", "external_validity", "identifi
::: {#tbl-findings-institutional}
```{python}
#| label: tbl-findings-institutional
from src.model import strength_of_findings as findings
findings_institutional = pd.read_csv("02-data/supplementary/findings-institutional.csv")
@ -861,6 +864,7 @@ One limitation of the study is the modelling assumption that workers will have t
::: {#tbl-findings-structural}
```{python}
#| label: tbl-findings-structural
from src.model import strength_of_findings as findings
findings_structural = pd.read_csv("02-data/supplementary/findings-structural.csv")
@ -1095,6 +1099,7 @@ Though the intervention clearly aims at strengthening some aspect of individual
::: {#tbl-findings-agency}
```{python}
#| label: tbl-findings-agency
from src.model import strength_of_findings as findings
findings_agency = pd.read_csv("02-data/supplementary/findings-agency.csv")
@ -1186,6 +1191,7 @@ The authors suggest the primary channel is the newly increased bargaining power
# Discussion & policy implications
```{python}
#| label: prep-inequalities-crosstabs
# dataframe containing each intervention inequality pair
df_inequality = (
bib_df[["region", "intervention", "inequality"]]
@ -1651,6 +1657,7 @@ while relying on indicators for measurement which are flexible yet overlapping e
## Full search query
```{python}
#| label: full-search-query
#| echo: false
#| output: asis
with open(f"{SUPPLEMENTARY_DATA}/query.txt") as f: