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()
#> Warning: Note: `oa_fetch` and `oa2df` now return new names for some columns in openalexR v2.0.0.
#> See NEWS.md for the list of changes.
#> Call `get_coverage()` to view the all updated columns and their original names in OpenAlex.
#> This warning is displayed once every 8 hours.| 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 Press | https://ror.org/03xwa9562 |
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"
)
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"
)
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 2012 1059 2012 1433
#> 2 2013 3889 2013 5105
#> 3 2014 4014 2014 5202
#> 4 2015 4057 2015 5356
#> 5 2016 3664 2016 5477
#> 6 2017 3528 2017 5865
#> 7 2018 3796 2018 6507
#> 8 2019 3899 2019 6901
#> 9 2020 4075 2020 8286
#> 10 2021 3895 2021 8339
#> 11 2022 3622 2022 7917
#> 12 2023 4183 2023 7439
#> 13 2024 5041 2024 4651Finally, 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")