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feat(data): Prepare loading WB country group data

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Marty Oehme 2023-12-07 20:11:27 +01:00
parent 8f0f57edcc
commit d88c733b6d
Signed by: Marty
GPG key ID: EDBF2ED917B2EF6A
4 changed files with 41 additions and 3 deletions
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02-data/supplementary/wb-country-groupings.xlsx Normal file
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27
poetry.lock generated
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@ -569,6 +569,17 @@ files = [
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@ -1747,6 +1758,20 @@ files = [
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et-xmlfile = "*"
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name = "overrides"
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@ -3021,4 +3046,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
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1
pyproject.toml
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@ -13,6 +13,7 @@ bibtexparser = {version = ">=2.0.0b1", allow-prereleases = true}
jupyter = "^1.0.0"
jupyter-cache = "^0.6.1"
tabulate = "^0.9.0"
openpyxl = "^3.1.2"
[tool.poetry.group.dev.dependencies]
pynvim = "^0.4.3"

16
scoping_review.qmd
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@ -463,8 +463,7 @@ as can be seen in @fig-publications-per-year.
Keeping in mind that these results are not yet screened for their full relevance to the topic at hand, so far only being *potentially* relevant in falling into the requirements of the search pattern, an increased results output does not necessarily mean a clearly rising amount of relevant literature.
```{python}
#| label: fig-publications-per-year
#| fig-cap: Publications per year
# load relevant studies
reformatted = []
for e in sample_relevant:
ed = e.fields_dict
@ -476,6 +475,7 @@ for e in sample_relevant:
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", "Keywords"])
bib_df = bib_df.dropna(how="any")
@ -485,6 +485,18 @@ bib_df["Year"] = bib_df["Date"].dt.year
# only keep newer entries
bib_df = bib_df[bib_df["Year"] >= 2000]
# Add WB country grouping definitions (income group, world region)
# TODO Re-enable for processed study pool
# WB_COUNTRY_GROUPS_FILE = Path(f"{SUPPLEMENTARY_DATA}/wb-country-groupings.xlsx").resolve()
# df_country_groups = pd.read_excel(WB_COUNTRY_GROUPS_FILE)
# bib_df["income group"] = bib_df["country"].map(df_country_groups.set_index("Economy")["Income group"])
# bib_df["region"] = bib_df["country"].map(df_country_groups.set_index("Economy")["Region"])
```
```{python}
#| label: fig-publications-per-year
#| fig-cap: Publications per year
# create dummy category for white or gray lit type (based on 'article' appearing in type)
bib_df["Type"].value_counts()
bib_df["Literature"] = np.where(bib_df["Type"].str.contains("article", case=False, regex=False), "white", "gray")