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 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 1098 2012 1478
#> 2 2013 3941 2013 5388
#> 3 2014 4163 2014 5613
#> 4 2015 4226 2015 5897
#> 5 2016 4160 2016 6195
#> 6 2017 3873 2017 6768
#> 7 2018 4389 2018 7347
#> 8 2019 4447 2019 7813
#> 9 2020 4589 2020 9338
#> 10 2021 4355 2021 9696
#> 11 2022 4514 2022 8924
#> 12 2023 5458 2023 7924
#> 13 2024 3937 2024 3112
#> 14 2025 1 <NA> NA
Finally, 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")