library(institutions)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
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()
#> Rows: 1
#> Columns: 12
#> $ id <chr> "https://ror.org/026vcq606"
#> $ name <chr> "KTH Royal Institute of Technology"
#> $ email_address <lgl> NA
#> $ established <int> 1827
#> $ status <chr> "active"
#> $ wikipedia_url <chr> "https://en.m.wikipedia.org/wiki/KTH_Royal_Institute_of_…
#> $ country_name <chr> "Sweden"
#> $ country_code <chr> "SE"
#> $ FundRef <chr> NA
#> $ GRID <chr> "grid.5037.1"
#> $ ISNI <chr> NA
#> $ Wikidata <chr> NA
# what other identifiers are associated with this org?
ror$ror_ids %>% filter(id == id_kth_ror)
#> # A tibble: 4 × 3
#> id name value
#> <chr> <chr> <chr>
#> 1 https://ror.org/026vcq606 FundRef 501100004270
#> 2 https://ror.org/026vcq606 GRID grid.5037.1
#> 3 https://ror.org/026vcq606 ISNI 0000 0001 2158 1746
#> 4 https://ror.org/026vcq606 Wikidata Q854280
# ROR child table entries for labels, aliases, acronyms, links and types?
# what aliases?
ror$ror_aliases %>% filter(id == id_kth_ror)
#> # A tibble: 1 × 2
#> id aliases
#> <chr> <chr>
#> 1 https://ror.org/026vcq606 Royal Institute of Technology
# what acronyms?
ror$ror_acronyms %>% filter(id == id_kth_ror)
#> # A tibble: 1 × 2
#> id acronyms
#> <chr> <chr>
#> 1 https://ror.org/026vcq606 KTH
# what links?
ror$ror_links %>% filter(id == id_kth_ror)
#> # A tibble: 1 × 2
#> id links
#> <chr> <chr>
#> 1 https://ror.org/026vcq606 https://www.kth.se
# what types?
ror$ror_types %>% filter(id == id_kth_ror)
#> # A tibble: 2 × 2
#> id types
#> <chr> <chr>
#> 1 https://ror.org/026vcq606 Education
#> 2 https://ror.org/026vcq606 FunderSome summaries
The full dataset is available and can be summarized.
Using “child tables” for summaries
A few examples:
# what top five types of orgs are represented in this data?
ror$ror_types %>% count(types) %>% arrange(desc(n)) %>% head(5)
#> # A tibble: 5 × 2
#> types n
#> <chr> <int>
#> 1 Company 30784
#> 2 Education 22186
#> 3 Funder 17122
#> 4 Nonprofit 15068
#> 5 Healthcare 13933
# top 5 companies with many aliases
ror$ror_aliases %>% count(aliases) %>% arrange(desc(n)) %>% head(5)
#> # A tibble: 5 × 2
#> aliases n
#> <chr> <int>
#> 1 Merck Sharp & Dohme 15
#> 2 Hoffmann-La Roche 13
#> 3 ASEA Brown Boveri 11
#> 4 E. I. du Pont de Nemours and Company 11
#> 5 International Business Machines Corporation 11
# why does IBM have so many acronyms? seemingly because it is a multi-national corp.
ror$ror_aliases %>% filter(aliases == "International Business Machines Corporation") %>%
inner_join(ror$ror_orgs) %>% pull(name) %>% paste0(collapse = ", ")
#> Joining with `by = join_by(id)`
#> [1] "IBM (France), IBM (Belgium), IBM (India), IBM (Egypt), IBM (Denmark), IBM (Portugal), IBM (Czechia), IBM (Ireland), IBM (Brazil), IBM (Slovakia), IBM (Germany)"
# what is the most frequently used acronym and which orgs use this abbreviation for its name?
ror$ror_acronyms %>% count(acronyms) %>% arrange(desc(n))
#> # A tibble: 33,870 × 2
#> acronyms n
#> <chr> <int>
#> 1 CCC 44
#> 2 MU 33
#> 3 AU 32
#> 4 SCC 31
#> 5 HCC 30
#> 6 MCC 30
#> 7 CRC 29
#> 8 CMC 27
#> 9 MSU 27
#> 10 ARC 26
#> # ℹ 33,860 more rows
ror$ror_acronyms %>% filter(acronyms == "CCC") %>% inner_join(ror$ror_orgs) %>% pull(name) %>% paste0(collapse = ", ")
#> Joining with `by = join_by(id)`
#> [1] "Clatterbridge Cancer Centre NHS Foundation Trust, Committee on Climate Change, Camden County College, Capital Consulting Corporation (United States), IEA Clean Coal Centre, Colby Community College, Crile Carvey Consulting, Children's Cancer Center, Community Cancer Center, Capital Community College, Clatsop Community College, Central City Concern, Cleveland Community College, California Coastal Commission, California Conservation Corps, Chinese Culture Center of San Francisco, Clearwater Christian College, Clovis Community College, Coahoma Community College, Cumberland County College, Carolina Christian College, Consolidated Contractors Company (Greece), Community Care College, California Christian College, Craven Community College, Coffeyville Community College, Carteret Community College, Center for Community Change, Carbohydrate Competence Center, Cancer Research UK Cambridge Center, Sport Wales, Crichton Carbon Centre, Canola Council of Canada, Bowel Cancer UK, Cameron (United States), Cameron (United Kingdom), Childhood Cancer Canada Foundation, Comprehensive Cancer Center Vienna, City College of Calamba, Clinton Community College, SUNY Corning Community College, Clackamas Community College, Centro de Química de Coimbra, IUCN Climate Crisis Commission"
# some orgs have more than one link, seems to be duplicates?
ror$ror_links %>% group_by(id) %>% count(links) %>% filter(n > 1) %>% ungroup()
#> # A tibble: 0 × 3
#> # ℹ 3 variables: id <chr>, links <chr>, n <int>
ror$ror_links %>% filter(id == "https://ror.org/01eea1w69")
#> # A tibble: 1 × 2
#> id links
#> <chr> <chr>
#> 1 https://ror.org/01eea1w69 https://www.southernwater.co.ukExternal identifiers
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))
#> # A tibble: 4 × 2
#> # Groups: name [4]
#> name n
#> <chr> <int>
#> 1 GRID 113119
#> 2 ISNI 55891
#> 3 Wikidata 53254
#> 4 FundRef 20466
# 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)))| url |
|---|
| Q35794 |
| Q181892 |
| Q24679079 |
| Q10899168 |
| Q34433 |
| Q5260389 |
| Q56612600 |
| Q7529574 |
| Q6786826 |
| Q1095537 |