Transition towards Open Access

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)
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()

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  2018     4297             2018     5455
#> 2  2025     4241             2025     7214
#> 3  2022     4107             2022     7354
#> 4  2019     4086             2019     6230
#> 5  2020     4073             2020     7723
#> 6  2021     4014             2021     7857
#> 7  2024     3886             2024     7877
#> 8  2015     3834             2015     4551
#> 9  2014     3777             2014     4423
#> 10 2013     3671             2013     4401
#> 11 2016     3621             2016     4866
#> 12 2017     3589             2017     5186
#> 13 2023     3463             2023     8227
#> 14 2012     1153             2012     1150
#> 15 2026      763             2026     1041

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")