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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")