knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
The tool Data for Research (DfR) by JSTOR is a
valuable source for citation analysis and text mining.
provides functions and suggests workflows for importing
datasets from DfR.
When using DfR, requests for datasets can
be made for small excerpts (max. 25,000 records) or large ones, which require an
agreement between the researcher and JSTOR.
jstor was developed to deal with
very large datasets which require an agreement, but can be used with smaller
ones as well.
The most important set of functions is a group of
jst_get_article(for journal documents and pamphlets)
jst_get_authors(for all sources)
jst_get_references(for journal documents)
jst_get_footnotes(for journal documents)
jst_get_book(for books and research reports)
jst_get_chapters(for books and possibly research reports)
I will demonstrate their usage using the sample dataset which is provided by JSTOR on their website.
All functions from the
jst_get_* family which are concerned with meta
data operate along the same lines:
The functions are similar in that all operate on single files
(article, book, research report or pamphlet). Depending on the content of the
file, the output of the functions might have one or multiple rows.
jst_get_article always returns a
tibble with one row: the core meta data
(like title, id, or first page of the article) are single items,
and only one article is processed at a time.
jst_get_authors for the same article might give you a tibble with one
or multiple rows, depending on the number of authors the article has. The same
is true for
jst_get_footnotes. If a file has no data on
references (they might still exist, but JSTOR might not have parsed them), the
output is only one row, with missing references. If there is data on references,
each entry gets its own row. Note however, that the number of rows does not
equal the number of references. References usually start with a title like
"References", which is obviously not a reference to another article.
Be sure to think carefully about your assumptions and to check the content of
your data before you make inferences.
Books work a bit differently. Searching for data on
https://www.jstor.org/dfr/results lets you filter for books, which are actually
book chapters. If you receive data from DfR on a book chapter, you always get
one xml-file with the whole book, including data on all chapters. Ngram or
full-text data for the same entry however is processed only from single
for more detail.]. Thus, the output of
jst_get_book for a single file is
similar to the one from
jst_get_article: it is one row with general data about
jst_get_chapters gives you data on all chapters, and the resulting
tibble therefore might have multiple rows.
The following sections showcase the different functions separately.
jstor we only need to load
dplyr for matching records and
for printing nice tables.
library(jstor) library(dplyr) library(knitr)
The basic usage of the
jst_get_* functions is very simple. They take only one
argument, the path to the file to import:
meta_data <- jst_get_article(file_path = jst_example("article_with_references.xml"))
The resulting object is a
tibble with one row and 17 columns. The columns
correspond to most of the elements documented here: https://www.jstor.org/dfr/about/technical-specifications.
The columns are:
Since the output from all functions are tibbles, the result is nicely formatted:
meta_data %>% kable()
Extracting the authors works in similar fashion:
authors <- jst_get_authors(jst_example("article_with_references.xml")) kable(authors)
Here we have the following columns:
Albert Einstein, or
Albert Einstein, II..
The number of rows matches the number of authors -- each author get its' own row.
references <- jst_get_references(jst_example("article_with_references.xml")) # # we need to remove line breaks for knitr::kable() to work properly for printing references <- references %>% mutate(ref_unparsed = stringr::str_remove_all(ref_unparsed, "\\\n"))
We have two columns:
Here I display 5 random entries:
references %>% sample_n(5) %>% kable()
This example shows several things:
file_name is identical among rows, since
it identifies the article and all references came from one article. The the
sample file doesn't follow a typical convention (it was published in 1922),
therefore there are several different headings (
ref_title). Usually, this is
only "Bibliography" or "References".
Since the references were not parsed by JSTOR, we only get an unparsed version.
In general, the content of references (
unparsed_refs) is in quite a raw state,
quite often the result of digitising scans via OCR. For example, the last entry
reads like this:
MACHADO, A.1911 Zytologische Untersuchungen fiber Trypanosoma rotatorium ....
There is an error here:
fiber should be
über. The language of the source
is German, but the OCR-software assumed English. Therefore, it didn't
recognize the Umlaut. Similar errors are common for text read via OCR.
For other files, we can set
parse_refs = TRUE, so references will be imported
in their parsed form, whenever they are available.
jst_get_references( jst_example("parsed_references.xml"), parse_refs = TRUE ) %>% kable()
Note, that there might be other content present like endnotes, in case the article used endnotes rather than footnotes.
jst_get_footnotes(jst_example("article_with_references.xml")) %>% kable()
Very commonly, articles either have footnotes or references. The sample file
used here does not have footnotes, therefore a simple
tibble with missing
footnotes is returned.
I will use another file to demonstrate footnotes.
footnotes <- jst_get_footnotes(jst_example("article_with_footnotes.xml")) footnotes %>% mutate(footnotes = stringr::str_remove_all(footnotes, "\\\n")) %>% kable()
In general, you might need to combine
jst_get_references() to get all available information on citation data.
The function to extract full texts can't be demonstrated with proper data, since
the full texts are only supplied upon special request with DfR. The function
guesses the encoding of the specified file via
the whole file and returns a
I created a file that looks similar to files supplied by DfR with sample text:
full_text <- jst_get_full_text(jst_example("full_text.txt")) full_text %>% mutate(full_text = stringr::str_remove_all(full_text, "\\\n")) %>% kable()
Different parts of meta-data can be combined by using
meta_data %>% left_join(authors) %>% select(file_name, article_title, pub_year, given_name, surname) %>% kable()
meta_data %>% left_join(references) %>% select(file_name, article_title, volume, pub_year, ref_unparsed) %>% head(5) %>% kable()
Quite recently DfR added book chapters to their stack. To import metadata about
the books and chapters, jstor supplies
jst_get_book is very similar to
jst_get_article. We obtain general information
about the complete book:
jst_get_book(jst_example("book.xml")) %>% knitr::kable()
A single book might contain many chapters.
jst_get_chapters extracts all of them.
Due to this, the function is a bit slower than most of jstor's other functions.
chapters <- jst_get_chapters(jst_example("book.xml")) str(chapters)
Without the abstracts (they are rather long) the first 10 chapters look like this:
chapters %>% select(-abstract) %>% head(10) %>% kable()
Since extracting all authors for all chapters needs considerably more time, by default authors are not extracted. You can import them like so:
author_chap <- jst_get_chapters(jst_example("book.xml"), authors = TRUE)
The authors are supplied in a list column:
You can expand this list with
author_chap %>% tidyr::unnest(authors) %>% select(part_id, given_name, surname) %>% head(10) %>% kable()
You can learn more about the concept of list-columns in Hadley Wickham's book R for Data Science.
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.