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 1013 2012 1318
#> 2 2013 3797 2013 4991
#> 3 2014 3975 2014 5177
#> 4 2015 4023 2015 5313
#> 5 2016 3705 2016 5512
#> 6 2017 3450 2017 5892
#> 7 2018 3833 2018 6473
#> 8 2019 3981 2019 6906
#> 9 2020 4115 2020 8229
#> 10 2021 3907 2021 8481
#> 11 2022 3751 2022 8090
#> 12 2023 4369 2023 7533
#> 13 2024 4419 2024 4029
#> 14 2025 2 <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")