knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
library(institutions) library(dplyr) institutions_download()
A static dataset from October 2020 from the Research Organization Registry Community has been bundled in this R package.
It provides GRID identifiers for research organizations and can therefore be used to link to the data otherwise provided through functions in this R package.
Some examples of usage follow:
# get a GRID identifier for a specific institution id_kth_grid <- institutions_search("Royal Institute of Technology")$grid_id # get the corresponding ROR identifier id_kth_ror <- ror$ror_orgs %>% filter(GRID == id_kth_grid) %>% pull(id) # what ROR core info can we get for this identifier? ror$ror_orgs %>% filter(id == id_kth_ror) %>% glimpse() # what other identifiers are associated with this org? ror$ror_ids %>% filter(id == id_kth_ror) # ROR child table entries for labels, aliases, acronyms, links and types? # what aliases? ror$ror_aliases %>% filter(id == id_kth_ror) # what acronyms? ror$ror_acronyms %>% filter(id == id_kth_ror) # what links? ror$ror_links %>% filter(id == id_kth_ror) # what types? ror$ror_types %>% filter(id == id_kth_ror)
The full dataset is available and can be summarized.
A few examples:
# what top five types of orgs are represented in this data? ror$ror_types %>% count(types) %>% arrange(desc(n)) %>% head(5) # top 5 companies with many aliases ror$ror_aliases %>% count(aliases) %>% arrange(desc(n)) %>% head(5) # why does IBM have so many acronyms? seemingly because it is a multi-national corp. ror$ror_aliases %>% filter(aliases == "International Business Machines Corporation") %>% inner_join(ror$ror_orgs) %>% pull(name) %>% paste0(collapse = ", ") # what is the most frequently used acronym and which orgs use this abbreviation for its name? ror$ror_acronyms %>% count(acronyms) %>% arrange(desc(n)) ror$ror_acronyms %>% filter(acronyms == "CCC") %>% inner_join(ror$ror_orgs) %>% pull(name) %>% paste0(collapse = ", ") # some orgs have more than one link, seems to be duplicates? ror$ror_links %>% group_by(id) %>% count(links) %>% filter(n > 1) %>% ungroup() ror$ror_links %>% filter(id == "https://ror.org/01eea1w69")
extids <- ror$ror_ids # which external identifiers are most frequently provided? # one given identifier can have multiple other identifiers esp FundRef extids %>% group_by(name) %>% count() %>% arrange(desc(n)) # which WikiData identifiers does a set of orgs have? ids_wikidata <- extids %>% filter( id %in% c("https://ror.org/052gg0110", "https://ror.org/013meh722"), name == "Wikidata") %>% distinct() %>% pull("value") knitr::kable(escape = FALSE, tibble(url = sprintf("<a href='https://www.wikidata.org/wiki/%s'>%s</a>", ids_wikidata, ids_wikidata)))
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