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:
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
An example for the latter case are page numbers. Most of the time, the entries for page numbers are simply
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.
After importing data via
jst_get_article(), there are at least two tasks you might typically want to undertake:
There are four functions which help you to streamline this process:
In the following sections, known issues with data from DfR are described in greater detail.
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.
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:
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 x 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
Identifiers for the journal usually appear in three columns:
|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_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
With this algorithm, it should be possible to reliably match them to general information about the respective journals, which are available from
|journal_title||Transactions of the American Microscopical Society|
|article_title||On the Protozoa Parasitic in Frogs|
|title||Transactions of the American Microscopical Society|
|discipline||Biological Sciences ; Science & Mathematics ; Zoology|
|publisher||American Microscopical Society ; Wiley|
|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.
Information on langues is not consistent. For the sample article,
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.
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.
Although it sounds absurd, this can actually be true. There are some articles which are 200 pages long. Obviously, they are not standard research articles. You will need to decide if they fall into your sample or not.↩