jstor: An R Package for Analysing Scientific Articles

Slides for the talk "jstor: An R Package for Analysing Scientific Articles" at useR!2018 (https://user2018.r-project.org).

The package and instructions for installation can be found at https://ropensci.github.io/jstor/

Transcript

1. jstor: An R Package for Analysing Scientific
   Articles
   Thomas Klebel
   13 July 2018
   University of Graz
   https://github.com/tklebel
   https://twitter.com/klebel_t
   Package: http://bit.ly/jstor2018
   Slides: http://bit.ly/jstor2018_slides
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2. Main message
   if (interested_in_research_on_sciences) {
   get_data_from_JSTOR() %>%
   use_package_jstor() %>%
   deal_with_data_limitations()
   } else {
   hopefully(
   get_interested_in_research_on_sciences()
   )
   }
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3. JSTOR and Data for Research (DfR)
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4. Analysing citation patterns
   Figure 1: (Bjork, Offer, and Söderberg 2014, 191)
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5. Analysing ngrams
   Figure 2: http://bit.ly/jstor_ngrams
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6. Why do we need a new package?
   Many existing packages can be used for analysis:
   • topic modeling:
   • topicmodels, lda, mallet, . . .
   • ngram analysis
   • tidytext, tm, quanteda, . . .
   • citation analysis
   • base regex, stringr, . . .
   But: No existing solution to import DfR-metadata in a convenient
   way.
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7. How to use the package - a fictional example
   Analysing articles about “facebook”
   Possible research questions:
   • In which fields are researchers interested in facebook and why?
   • In which ways do they write about facebook, what are the key
   topics?
   • . . . .
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8. Requesting a dataset at: https://www.jstor.org/dfr/
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10. Get overview of zip-archive
    jst_preview_zip("facebook.zip")
    type meta_type n
    metadata book_chapter 7068
    metadata journal_article 11494
    metadata research_report 430
    ngram1 ngram1 19493
    ngram2 ngram2 19493
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11. Structure of meta-data files
    
    
    ...
    A title
    2011
    
    
    ...
    
    H. Wickham. ggplot2: Elegant Graphics for Data
    Analysis. Springer-Verlag New York, 2016.
    
    
    
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12. Extract parts of meta-data files with jstor
    • Articles:
    • jst_get_article
    • jst_get_references
    • jst_get_footnotes
    • Books:
    • jst_get_book
    • jst_get_chapters
    • Both:
    • jst_get_authors
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13. jst_get_article("example.xml") %>%
    tidyr::gather(columns, rows)
    columns rows
    file_name example
    journal_title Journal of Transport and Land Use
    article_title Photos, tweets, and trails
    pub_year 2017
    . . . . . .
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14. jst_get_article("example.xml") %>%
    tidyr::gather(columns, rows)
    columns rows
    file_name example
    journal_title Journal of Transport and Land Use
    article_title Photos, tweets, and trails
    pub_year 2017
    . . . . . .
    jst_get_references("example.xml")
    file_name references
    example Backstrom, L., Sun, E., & Marlow, C. (2010). Fi. . .
    example Cranshaw, J., Schwartz, R., Hong, J. I., & Sade. . .
    . . . . . .
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15. Joining separate tables
    XML-file(s) jst_get_article filter(pub_year > 2000)
    left_join
    combined data.frame
    jst_get_references
    file_name article_title pub_year references
    example Photos, tweets, a. . . 2017 References
    example Photos, tweets, a. . . 2017 Backstrom, L., Su. . .
    example Photos, tweets, a. . . 2017 Cranshaw, J., Sch. . .
    example Photos, tweets, a. . . 2017 El Esawey, M., Li. . .
    . . . . . . . . . . . .
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16. Import from archive: jst_import_zip
    • Read data directly from .zip-file
    • Choose which parts you want to import
    • Write data to .csv-files
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17. Import from archive: jst_import_zip
    • Read data directly from .zip-file
    • Choose which parts you want to import
    • Write data to .csv-files
    jst_import_zip(
    zip_archive = "facebook.zip",
    import_spec = jst_define_import(
    article = c(jst_get_article,
    jst_get_footnotes,
    jst_get_references),
    ngram2 = jst_get_ngram
    ),
    out_file = "out_file"
    )
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18. Data Limitations – Citation Analysis
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19. 0%
    25%
    50%
    75%
    100%
    0%
    25%
    50%
    75%
    100%
    1890 1920 1950 1980 2010
    1890 1920 1950 1980 2010
    Proportion of articles with data on references
    Proportion of articles with data on footnotes
    Articles (n = 192,986) come from 215 sociological journals.
    The red line is a running median over 11 years.
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20. 0%
    25%
    50%
    75%
    100%
    1940 1950 1960 1970 1980 1990 2000 2010
    Proportion with footnotes Proportion with references
    Social Research
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21. 0%
    25%
    50%
    75%
    100%
    1940 1960 1980 2000
    Proportion with footnotes Proportion with references
    American Sociological Review
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22. 0%
    25%
    50%
    75%
    100%
    1890 1920 1950 1980 2010
    Proportion with footnotes Proportion with references
    American Journal of Sociology
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23. Collecting all the quirks
    Collected known quirks about data from DfR:
    https://ropensci.github.io/jstor/articles/known-quirks.html
    Contributions are welcome!
    • Open an issue: https://github.com/ropensci/jstor/issues
    • Make a pull request
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24. Installation and Documentation
    Thanks to rOpenSci, Elin Waring and Jason Becker for the package
    review!
    Install the package with: install.packages("jstor")1
    Documentation: http://bit.ly/jstor2018
    Slides: http://bit.ly/jstor2018_slides
    1Edited after the talk to reflect acceptance to CRAN.
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25. References
    Bjork, Samuel, Avner Offer, and Gabriel Söderberg. 2014. “Time
    Series Citation Data: The Nobel Prize in Economics.” Scientometrics
    98 (1):185–96. https://doi.org/10.1007/s11192-013-0989-5.
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