This small R package supplies some functions for working with bibliographic data in RIS format from the MLA International Bibliography. The functions have online help accessible in the usual way, which gives a little more detail.
The easiest installation method requires the devtools package:
install.packages("devtools") # only if not already installed
devtools::install_github("agoldst/mlaibr")This will also install the packages this package depends on. Note that some of these dependencies, like dplyr, must normally be built from source and thus require a compiler.
I plan to add a tutorial vignette eventually.
Read in RIS data with read_ris("file.ris"). A vector of filenames may be supplied. RIS files compressed with zip can also be specified (read_ris("file.zip")). Note that by default not all RIS fields will be read in; use read_ris("file.ris", fields=NULL) to get them all.
The result is a "long" data frame, where each row corresponds to a single piece of bibliographic information, and each bibliography record has many rows (all with the same arbitrary ID assigned by read_ris). This form, known as "tidy" data, is efficient for some processing tasks but often quite inconvenient for further manipulation. To work with a "wide" data frame, use spread_ris.
Y1_year converts the Y1 field to a publication year in Date format (but note this discards any finer-grained date information that may also be present in that field).
In the MLAIB, important supplemental information is contained in the N1 field, including the bibliography's own record ID numbers ("accession numbers"). See ?N1_field for help extracting this information.
Finally, I include a couple of utilities for dealing with MLAIB subject headings, which are found in KW fields (many for each record). strip_subject_relation is for removing relation terms ("compared to," etc.). is_author uses the presence of birth-death dates as a simple heuristic for detecting personal names as subject headings (but works with a date range are misidentified as persons; see example(is_author)).
Automatic handling of zip files depends on R's zip support, which is wobbly on Windows.
Though it is listed as a requirement in the DESCRIPTION file, Python is not required to use this package. It is only a requirement for a now-deprecated alternative way of loading data, which uses a Python 2 script to convert the RIS file to a CSV, then loads that CSV. If you want to experiment with this, see convert_ris and read_ris_csv. It might gain you a little speed or memory efficiency, but no promises. I don't recommend this. read_ris is what I rely on.
I make no guarantees about how well this works. I am literature scholar who programs for reasons that are often unclear even to me. I am a member of the Modern Language Association but neither the MLA nor anyone else is affiliated with this programming project. I am releasing this source code under the permissive MIT license. If you copy or modify this code, please attribute the parts by me to me.
I am happy to hear from anyone who makes use of this, and I'd be grateful to learn of bugs via the issue tracker. I cannot promise to support anyone in using this package.