Following the template in OpenAlex’s oa-percentage tutorial, this vignette uses openalexR to answer:
How many of recent journal articles from the University of Pennsylvania are open access? And how many aren’t?
We first need to find the openalex.id for University of Pennsylvania. We can do this by fetching for the institutions entity and put “University of Pennsylvania” in display_name or display_name.search:
oa_fetch(
entity = "inst", # same as "institutions"
display_name.search = "\"University of Pennsylvania\""
) %>%
select(display_name, ror) %>%
knitr::kable()| display_name | ror |
|---|---|
| University of Pennsylvania | https://ror.org/00b30xv10 |
| California University of Pennsylvania | https://ror.org/01spssf70 |
| Hospital of the University of Pennsylvania | https://ror.org/02917wp91 |
| University of Pennsylvania Health System | https://ror.org/04h81rw26 |
| Indiana University of Pennsylvania | https://ror.org/0511cmw96 |
| Cheyney University of Pennsylvania | https://ror.org/02nckwn80 |
| University of Pennsylvania Museum of Archaeology and Anthropology | https://ror.org/02aej5879 |
| Raymond and Ruth Perelman School of Medicine at the University of Pennsylvania | https://ror.org/041phzg73 |
We will use the first ror, 00b30xv10, as one of the filters for our query.
Alternatively, we could go to the autocomplete endpoint at https://explore.openalex.org/ to search for “University of Pennsylvania” and find the ror there!
All other filters are straightforward and explained in detailed in the original jupyter notebook tutorial. The only difference here is that, instead of grouping by is_oa, we’re interested in the “trend” over the years, so we’re going to group by publication_year, and perform the query twice, one for is_oa = "true" and one for is_oa = "false" .
open_access <- oa_fetch(
entity = "works",
institutions.ror = "00b30xv10",
type = "article",
from_publication_date = "2012-08-24",
is_paratext = "false",
is_oa = "true",
group_by = "publication_year",
count_only = TRUE
)
closed_access <- oa_fetch(
entity = "works",
institutions.ror = "00b30xv10",
type = "article",
from_publication_date = "2012-08-24",
is_paratext = "false",
is_oa = "false",
group_by = "publication_year",
count_only = TRUE
)
uf_df <- closed_access %>%
select(- key_display_name) %>%
full_join(open_access, by = "key", suffix = c("_ca", "_oa"))
uf_df
#> key count_ca key_display_name count_oa
#> 1 2023 5978 2023 6738
#> 2 2020 5198 2020 10179
#> 3 2021 4855 2021 10315
#> 4 2022 4855 2022 9015
#> 5 2019 4822 2019 8292
#> 6 2018 4786 2018 7756
#> 7 2015 4543 2015 5757
#> 8 2016 4497 2016 6261
#> 9 2014 4371 2014 5447
#> 10 2017 4187 2017 6956
#> 11 2013 4128 2013 5195
#> 12 2012 1148 2012 1433
#> 13 2024 49 2024 6Finally, we compare the number of open vs. closed access articles over the years:
uf_df %>%
filter(key <= 2021) %>% # we do not yet have complete data for 2022 and after
pivot_longer(cols = starts_with("count")) %>%
mutate(
year = as.integer(key),
is_oa = recode(
name,
"count_ca" = "Closed Access",
"count_oa" = "Open Access"
),
label = if_else(key < 2021, NA_character_, is_oa)
) %>%
select(year, value, is_oa, label) %>%
ggplot(aes(x = year, y = value, group = is_oa, color = is_oa)) +
geom_line(size = 1) +
labs(
title = "University of Pennsylvania's progress towards Open Access",
x = NULL, y = "Number of journal articles") +
scale_color_brewer(palette = "Dark2", direction = -1) +
scale_x_continuous(breaks = seq(2010, 2024, 2)) +
geom_text(aes(label = label), nudge_x = 0.1, hjust = 0) +
coord_cartesian(xlim = c(NA, 2022.5)) +
guides(color = "none")