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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     1118             2012     1464
#> 2  2013     3987             2013     5360
#> 3  2014     4192             2014     5585
#> 4  2015     4273             2015     5880
#> 5  2016     4200             2016     6184
#> 6  2017     3916             2017     6751
#> 7  2018     4420             2018     7354
#> 8  2019     4501             2019     7815
#> 9  2020     4685             2020     9336
#> 10 2021     4448             2021     9690
#> 11 2022     4672             2022     8792
#> 12 2023     5758             2023     7584
#> 13 2024     3614             2024     2774
#> 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")