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 |
University of Pennsylvania Press | https://ror.org/03xwa9562 |
Cheyney University of Pennsylvania | https://ror.org/02nckwn80 |
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 2024 5847 2024 5636
#> 2 2023 4312 2023 7307
#> 3 2020 4271 2020 8123
#> 4 2015 4234 2015 5244
#> 5 2014 4175 2014 5101
#> 6 2013 4094 2013 4981
#> 7 2021 4061 2021 8213
#> 8 2019 4052 2019 6775
#> 9 2018 3982 2018 6335
#> 10 2022 3853 2022 7739
#> 11 2016 3810 2016 5361
#> 12 2017 3705 2017 5712
#> 13 2025 3152 2025 2373
#> 14 2012 1113 2012 1418
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")