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?
library(openalexR)
library(dplyr)
#> Error in get(paste0(generic, ".", class), envir = get_method_env()) :
#> object 'type_sum.accel' not found
library(tidyr)
library(ggplot2)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 |
| 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 2012 1108 2012 1420
#> 2 2013 4072 2013 5006
#> 3 2014 4158 2014 5118
#> 4 2015 4206 2015 5266
#> 5 2016 3781 2016 5383
#> 6 2017 3665 2017 5746
#> 7 2018 3940 2018 6378
#> 8 2019 4018 2019 6805
#> 9 2020 4235 2020 8145
#> 10 2021 4044 2021 8218
#> 11 2022 3832 2022 7745
#> 12 2023 4343 2023 7292
#> 13 2024 5889 2024 5190Finally, 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")