The goal of the R package institutions is to provide access to institutional data in R from the Global Research Identifier Database (GRID) which is available under the Creative Commons Public Domain 1.0 International licence (see https://grid.ac). This dataset is used by for example Altmetric, Dimensions, Figshare and others.
This package also embeds data from from the Research Organization Registry Community (see https://ror.org/). All ROR IDs and metadata are provided under the Creative Commons CC0 1.0 Universal Public Domain Dedication. There are no restrictions on access to and use of ROR IDs and metadata. Some more information about this dataset can be found at: https://ror.org/facts/#core-components. The ror dataset provides acccess to this data from within R; the ror$ror_ids table contains external identifers including, for all records, the GRID identifier. This identifier can be used to link to the ROR data with the GRID dataset exposed in this R package.
R “data package” background
This R package is a kind of “data package” primarily intended to be used to provide reference data (from GRID and ROR) when used in other workflows. It is not a “data only package”, since it provides some functions that might also be useful when doing data wrangling against this reference data (mainly for supporting geocoding and full text search lookups).
The package downloads some curated data from https://digitalscience.figshare.com/ndownloader/files/20151785 and migrates this into a full text search indexed SQLite database residing locally. This FTS-enabled database is then exposed through a couple of functions by way of tibbles.
The idea is to show how to provide access to a potentially provide access to a larger database and how to enable use of in-built full text search capabilities in SQLite by downloading potentially big remote data and installing it locally using rappdirs.
With this approach, the R package can stay small. There are a few minor practical drawbacks - mostly that your package will initially not work off-line until at least one initial successfull call to download the data has been made which would require a connection to the Internet.
The upside is being able to tap into things like Full Text Search for datasets and with this approach the package can stay small and pass the CRAN checks without requiring exceptions, while the dataset size is only limited to 2TB (an SQLite limitation).
Installation
You can install the development version of institutions from GitHub with:
Example
This is a basic example which shows you how to get started:
library(institutions)
# on first run, make sure to download the data which will generate the SQLite db
institutions_download()
# where are the data files - remotely and locally?
institutions_cfg()
#> $src_url
#> [1] "https://ndownloader.figshare.com/files/30895309"
#>
#> $dest
#> [1] "~/.config/institutions"
#>
#> $zip
#> [1] "~/.config/institutions/grid.zip"
#>
#> $db
#> [1] "~/.config/institutions/grid.db"To do a full text search for institutional addresses and locations (see details on FTS syntax here):
# do a full text search for institutions matching the search query "Royal AND Technology
institutions_search("Royal AND Technology")
#> # A tibble: 1 × 15
#> institutes grid_id line_1 line_2 line_3 lat lng postcode primary city
#> <chr> <chr> <chr> <chr> <int> <dbl> <dbl> <chr> <int> <chr>
#> 1 Royal Institu… grid.5… <NA> <NA> NA 59.3 18.1 <NA> 0 Stoc…
#> # ℹ 5 more variables: state <chr>, state_code <chr>, country <chr>,
#> # country_code <chr>, geonames_city_id <dbl>All tables in the database can be enumerated and accessed individually:
# enumerate all db tables
institutions_tables()
#> [1] "acronyms" "addresses" "aliases" "external_ids"
#> [5] "geonames" "grid" "institutes" "labels"
#> [9] "links" "relationships" "types"
# get acronym data
institutions_table("acronyms")
#> # A tibble: 42,840 × 2
#> grid_id acronym
#> <chr> <chr>
#> 1 grid.1001.0 ANU
#> 2 grid.1003.2 UQ
#> 3 grid.1005.4 UNSW
#> 4 grid.1007.6 UOW
#> 5 grid.1009.8 UTAS
#> 6 grid.1011.1 JCU
#> 7 grid.1012.2 UWA
#> 8 grid.1013.3 USYD
#> 9 grid.1016.6 CSIRO
#> 10 grid.1017.7 RMIT
#> # ℹ 42,830 more rowsCustom queries can be made:
# a custom query, first get an id to work with
id <- institutions_search("Royal Institute of Technology")$grid_id
# assemble some sql using that id
sql <- sprintf("select * from links where grid_id = '%s'", id)
institutions_query(sql)
#> # A tibble: 1 × 2
#> grid_id link
#> <chr> <chr>
#> 1 grid.5037.1 http://www.kth.se/en