Marty Oehme
4f9acd0816
Begin restructuring data dir by separating out references into their own data sub-dir containing only references and bibtex files.
120 lines
2.7 KiB
Plaintext
120 lines
2.7 KiB
Plaintext
---
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bibliography: ../data/intermediate/zotero-library.bib
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csl: /home/marty/documents/library/utilities/styles/APA-7.csl
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papersize: A4
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linestretch: 1.5
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fontfamily: lmodern
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fontsize: "12"
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geometry:
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- left=2.2cm
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- right=3.5cm
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- top=2.5cm
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- bottom=2.5cm
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toc: false
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link-citations: true
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link-bibliography: true
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number-sections: false
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lang: en
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title: Scoping review on 'what works'
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subtitle: Addressing inequalities in the World of Work
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---
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```{python}
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#| echo: false
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from pathlib import Path
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import src.globals as g
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data_dir = g.DATA_DIR
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## standard imports
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from IPython.core.display import Markdown as md
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import numpy as np
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import pandas as pd
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from matplotlib import pyplot as plt
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import seaborn as sns
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from tabulate import tabulate
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```
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```{python}
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sns.set_style("whitegrid")
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```
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```{python}
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#| echo: false
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# load and parse overall bibtex sample
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import bibtexparser
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bib_string=""
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sample_dir = g.REFERENCE_DATA.joinpath("01_wos-sample_2023-11-02")
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print(f"path: {sample_dir.absolute()}")
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for partial_bib in sample_dir.glob("*.bib"):
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with open(partial_bib) as f:
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bib_string+="\n".join(f.readlines())
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sample = bibtexparser.parse_string(bib_string)
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```
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## Description of results
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```{python}
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#| echo: false
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sample_size = len(sample.entries)
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md(f"""
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The exploratory execution of queries results in an initial sample of {sample_size} studies after the identification process.
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""")
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```
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yrs:
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```{python}
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reformatted = []
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for e in sample.entries:
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reformatted.append([e["Year"], e["Author"], e["Title"], e["Type"], e["Times-Cited"], e["Usage-Count-Since-2013"]])
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bib_df = pd.DataFrame(reformatted, columns = ["Year", "Author", "Title", "Type", "Cited", "Usage"])
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bib_df["Date"] = pd.to_datetime(bib_df["Year"], format="%Y")
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bib_df["Year"] = bib_df["Date"].dt.year
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bib_df
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```
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```{python}
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# RESTRICT FOR NEWER STUDIES
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bib_df = bib_df[bib_df["Year"] >= 2000]
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```
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Publications per year:
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```{python}
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ax = sns.countplot(bib_df[bib_df["Year"] >= 2000], x="Year")
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ax.tick_params(axis='x', rotation=45)
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plt.tight_layout()
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plt.show()
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```
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By type:
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```{python}
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bib_df["Type"].value_counts()
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bib_df["Literature"] = np.where(bib_df["Type"].str.contains("article", case=False, regex=False), "white", "gray")
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bib_df["Literature"] = bib_df["Literature"].astype("category")
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```
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Per type:
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```{python}
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ax = sns.countplot(bib_df[bib_df["Year"] >= 2000], x="Year", hue="Literature")
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ax.tick_params(axis='x', rotation=45)
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# ax.set_xlabel("")
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plt.tight_layout()
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plt.show()
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```
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Avg num of citations:
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```{python}
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bib_df["Cited"] = bib_df["Cited"].astype("int")
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grpd = bib_df.groupby(["Year"], as_index=False)["Cited"].mean()
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ax = sns.barplot(grpd, x="Year", y="Cited")
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ax.tick_params(axis='x', rotation=45)
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plt.tight_layout()
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plt.show()
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```
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