Known Quirks of JSTOR/DfR Data

Collecting all the quirks

Data from JSTOR/DfR is unlike most other data you encounter when doing text analysis. First and foremost, the data about articles and books come from a wide variety of journals and publishers. The level of detail and certain formats vary because of this. jstor tries to deal with this situation with two strategies:

try to recognise the format and read data accordingly
if this is not possible, read data as “raw” as possible, i.e. without any conversions

An example for the first case are references. Four different ways how references can be specified are known at this time, and all are imported in specific ways to deal this variation. There might however be other formats, which should lead to an informative error when trying to import them via jst_get_references().

An example for the latter case are page numbers. Most of the time, the entries for page numbers are simply 42, or 61. This is as expected, and could be parsed as integers. Sometimes, there are characters present, like M77. This would pose no problem either, we could simply extract all digits via regex and parse as character. Unfortunately, sometimes the page is specified like this: v75i2p84. Extracting all digits would result in 75284, which is wrong by a long shot. Since there might be other ways of specifying pages, jstor does not attempt to parse the pages to integers when importing. However, it offers a set of convenience functions which deal with a few common cases (see jst_augment() and below).

There are many other problems or peculiarities like this. This vignette tries to list as many as possible, and offer solutions for dealing with them. Unfortunately I have neither the time nor the interest to wade through all the data which you could get from DfR in order to find all possible quirks. The following list is thus inevitably incomplete. If you encounter new quirks/peculiarities, it would be greatly appreciated if you sent me an email, or opened an issue at GitHub. I will then include your findings in future version of this vignette, so this vignette can be a starting point for everybody who conducts text analysis with data from JSTOR/DfR.

Data augmentation

After importing data via jst_get_article(), there are at least two tasks you might typically want to undertake:

Merge different identifiers for journals into one, so you can filter journals.
Convert pages from character into integers and calculate the total number of pages per article.

There are four functions which help you to streamline this process:

jst_clean_page() attempts to turn a character vector with pages into an integer vector.
jst_add_total_pages() adds a column with the total number of pages per article.
jst_unify_journal_id() merges different identifiers for journals into one.
jst_augment() wraps the above functions for convenience.

Known quirks

In the following sections, known issues with data from DfR are described in greater detail.

Page numbers

Page numbers are a mess. Besides the issues mentioned above, page numbers might sometimes be specified as “pp. 1234-83” as in this article from the American Journal of Sociology. Of course, this results in first_page = 1234 and last_page = 83, and the computed number of total pages from jst_get_total_pages() will be negative. There is currently no general solution for this issue.

Calculating total pages

As outlined above, page numbers come in very different forms. Besides this problem, there is actually another issue. Imagine you would like to quantify the lengths of articles. Obviously you will need information on the first and the last page of the articles. Furthermore, the pages need to be parsed properly: you will run into troubles if you calculate page numbers like 75284 - 42 + 1, in case the number was parsed badly. jst_clean_page() tries to do this properly, based on a few known possibilities:

“2” -> 2
“A2” -> 2
“v75i2p84” -> 84

Parsing correctly is unfortunately not enough. Things like “Errata” might come to haunt you. For example there might be an article with first_page = 42 and last_page = 362, which would leave you puzzled as to if this can be true¹. There could be a simple explanation: the article might start on page 42, and end on page 65, and there is furthermore an erratum on page 362. Technically, last_page = 362 is true then, but it will cause problems for calculating the total number of pages. Quite often, there is information in another column which could resolve this: page_range, which in this case would look like 42 - 65, 362.

A small helper to deal with those situations is jst_get_total_pages(). It works for page ranges, but also for first and last pages:

library(jstor)
library(dplyr)

input <- tibble::tribble(
  ~first_page,   ~last_page,    ~page_range,
  NA_real_,      NA_real_,      NA_character_, 
  1,             10,            "1 - 10",
  1,             10,            NA_character_,
  1,             NA_real_,      NA_character_, 
  1,             NA_real_,      "1-10",
  NA_real_,       NA_real_,      "1, 5-10",
  NA_real_,       NA_real_,      "1-4, 5-10",
  NA_real_,       NA_real_,      "1-4, C5-C10"
)

input %>% 
  mutate(n_pages = jst_get_total_pages(first_page, last_page, page_range))
#> # A tibble: 8 × 4
#>   first_page last_page page_range  n_pages
#>        <dbl>     <dbl> <chr>         <dbl>
#> 1         NA        NA NA               NA
#> 2          1        10 1 - 10           10
#> 3          1        10 NA               10
#> 4          1        NA NA               NA
#> 5          1        NA 1-10             10
#> 6         NA        NA 1, 5-10           7
#> 7         NA        NA 1-4, 5-10        10
#> 8         NA        NA 1-4, C5-C10      10

This is actually identical to using jst_add_total_pages():

input %>% 
  jst_add_total_pages()
#> # A tibble: 8 × 4
#>   first_page last_page page_range  n_pages
#>        <dbl>     <dbl> <chr>         <dbl>
#> 1         NA        NA NA               NA
#> 2          1        10 1 - 10           10
#> 3          1        10 NA               10
#> 4          1        NA NA               NA
#> 5          1        NA 1-10             10
#> 6         NA        NA 1, 5-10           7
#> 7         NA        NA 1-4, 5-10        10
#> 8         NA        NA 1-4, C5-C10      10

Journal identifiers

Identifiers for the journal usually appear in three columns:

journal_doi
journal_jcode
journal_pub_id

sample_article <- jst_get_article(jst_example("article_with_references.xml")) 

knitr::kable(sample_article)

file_name	journal_doi	journal_jcode	journal_pub_id	journal_title	article_doi	article_pub_id	article_jcode	article_type	article_title	volume	issue	language	pub_day	pub_month	pub_year	first_page	last_page	page_range
article_with_references	NA	tranamermicrsoci	NA	Transactions of the American Microscopical Society	10.2307/3221896	NA	NA	research-article	On the Protozoa Parasitic in Frogs	41	2	eng	1	4	1922	59	76	59-76

From my samples, it seems that the information in journal_pub_id is often missing, as is journal_doi. The most important identifier is thus journal_jcode. In cases where both journal_jcode and journal_pub_id are present, at least in my samples, the format of journal_jcode was different. For consistency, jst_unify_journal_id() thus takes content of journal_pub_id if it is present, and that of journal_jcode otherwise.

With this algorithm, it should be possible to reliably match them to general information about the respective journals, which are available from jst_get_journal_overview():

sample_article %>% 
  jst_unify_journal_id() %>% 
  left_join(jst_get_journal_overview()) %>% 
  tidyr::gather(variable, value) %>% 
  knitr::kable()
#> Joining with `by = join_by(journal_id)`

variable	value
file_name	article_with_references
journal_title	Transactions of the American Microscopical Society
article_doi	10.2307/3221896
article_pub_id	NA
article_jcode	NA
article_type	research-article
article_title	On the Protozoa Parasitic in Frogs
volume	41
issue	2
language	eng
pub_day	1
pub_month	4
pub_year	1922
first_page	59
last_page	76
page_range	59-76
journal_id	tranamermicrsoci
title	Transactions of the American Microscopical Society
issn	00030023
eissn	NA
doi	10.2307/j100072
url	https://www.jstor.org/journal/tranamermicrsoci
discipline	Biological Sciences ; Science & Mathematics ; Zoology
publisher	American Microscopical Society ; Wiley
coverage_range	1878-1994
oclc_catalog_identifier	61241470
lccn_catalog_identifier	2005 237209
archive_release_date	2005-08-10
collections	Biological Sciences Collection ; Corporate & For-Profit Access Initiative Collection ; JSTOR Archival Journal & Primary Source Collection ; Life Sciences Collection

Duplicated ngrams

Source	time span	Part
American Journal of Sociology	Unknown	Book Reviews

For the AJS, ngrams for book reviews are calculated per issue. There are numerous reviews per issue, and each of them has an identical file of ngrams, containing ngrams for all book reviews of this issue.

A possible strategy for dealing with this is either not to use those ngrams, since they are calculated on all reviews in the issue, irrespective of whether actually all reviews of the given issue are in the sample or not. Alternatively, one could group by issues, and only take one set of ngrams per issue.

Language codes

Information on langues is not consistent. For the sample article, language is eng.

sample_article %>% 
  pull(language)
#> [1] "eng"

In other cases it might be en. It is thus advisable to take a quick look at different variants via distinct(meta_data, language) or count(meta_data, language).

Incorrect/odd references

When analysing data about references and footnotes, you will encounter many inconsistencies and errors. Most of them are not due to errors from DfR, but stem simply from the fact, that humans make mistakes when creating manuscripts, and not all errors with references are caught before printing.

Problems with non-english characters

A common problem are names with non-english characters like german umlauts (Ferdinand Tönnies) or nordic names (Gøsta Esping-Andersen). These will appear in many different variations: Tonnies, Tönnies, Gosta, Gösta, etc.

OCR-Issues

For older articles, you might encounter issues that stem from digitising text with OCR-software. A common problem is distinguishing I from l, like in the phrase “In love”. Depending on which names appear in your data, this might lead to inconsistencies.

Errors by article authors

There are many examples where authors make mistakes and your summary statistics end up being skewed. This article about “Ethics Education in the Workplace” cites the same items multiple times, which is possibly an artifact. The advantage of using JSTOR/DfR data is, that you can inspect all sources and check, if a specific pattern you see is an artifact or genuine.

Thomas Klebel

2024-11-14