https://github.com/trangdata/oarj
Examples of openalexR submitted to R Journal
https://github.com/trangdata/oarj
Last synced: 2 days ago
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Examples of openalexR submitted to R Journal
- Host: GitHub
- URL: https://github.com/trangdata/oarj
- Owner: trangdata
- Created: 2022-10-17T13:33:55.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-02-10T00:23:00.000Z (about 3 years ago)
- Last Synced: 2025-09-20T17:50:55.624Z (6 months ago)
- Language: R
- Size: 104 MB
- Stars: 2
- Watchers: 2
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
Examples shown in the R Journal manuscript
================
2022-11-10
**NOTE**: To replicate the analyses proposed in the manuscript, please
use the downloaded data at `data/oarj.rdata`. Because bibliographic
metadata change at high frequency, downloads made on different days
could provide slightly different results (*e.g.*, number of citations,
number of published articles, *etc.*). The `oarj.rdata` file contains
all the objects we needed for this analysis.
``` r
set.seed(1234)
library(openalexR)
library(tidyverse)
```
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6.9000 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ dplyr 1.0.10
## ✔ tidyr 1.2.1 ✔ stringr 1.4.1
## ✔ readr 2.1.2 ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
``` r
library(gghighlight)
library(ggraph)
library(tidygraph)
```
##
## Attaching package: 'tidygraph'
##
## The following object is masked from 'package:stats':
##
## filter
``` r
library(treemap)
theme_set(
theme_classic() +
theme(
plot.background = element_rect(fill = "transparent", colour = NA),
panel.background = element_rect(fill = "transparent", colour = NA),
strip.background = element_rect(fill = NA, color = "grey20")
)
)
```
## Bibliometrics concept
``` r
concept <- oa_fetch(
entity = "concepts",
identifier = "C178315738" # OAID for "bibliometrics"
)
cat(concept$description, "is a level", concept$level, "concept")
```
## statistical analysis of written publications, such as books or articles is a level 2 concept
``` r
related_concepts <- concept$related_concepts[[1]] |>
mutate(relation = case_when(
level < 2 ~ "ancestor",
level == 2 ~ "equal level",
TRUE ~ "descendant"
)) |>
arrange(level) |>
relocate(relation) |>
select(-wikidata)
related_concepts
```
## relation id display_name level
## 1 ancestor https://openalex.org/C41008148 Computer science 0
## 2 ancestor https://openalex.org/C36289849 Social science 1
## 3 ancestor https://openalex.org/C124101348 Data mining 1
## 4 equal level https://openalex.org/C525823164 Scientometrics 2
## 5 equal level https://openalex.org/C2779455604 Impact factor 2
## 6 equal level https://openalex.org/C2778407487 Altmetrics 2
## 7 equal level https://openalex.org/C521491914 Webometrics 2
## 8 equal level https://openalex.org/C2781083858 Scientific literature 2
## 9 equal level https://openalex.org/C2778805511 Citation 2
## 10 equal level https://openalex.org/C95831776 Information science 2
## 11 equal level https://openalex.org/C2779172887 PageRank 2
## 12 equal level https://openalex.org/C138368954 Peer review 2
## 13 equal level https://openalex.org/C2779810430 Knowledge organization 2
## 14 equal level https://openalex.org/C2780416505 Collection development 2
## 15 descendant https://openalex.org/C105345328 Citation analysis 3
## 16 descendant https://openalex.org/C2778793908 Citation impact 3
## 17 descendant https://openalex.org/C2780378607 Informetrics 3
## 18 descendant https://openalex.org/C2778032371 Citation index 3
## 19 descendant https://openalex.org/C83867959 Scopus 3
## 20 descendant https://openalex.org/C2776822937 Bibliographic coupling 3
## 21 descendant https://openalex.org/C2779693592 Journal ranking 3
## 22 descendant https://openalex.org/C45462083 Documentation science 3
## 23 descendant https://openalex.org/C2777765086 Co-citation 3
## score
## 1 1.3350035
## 2 1.6031636
## 3 1.5347114
## 4 6.6193560
## 5 4.1035270
## 6 2.5396087
## 7 2.3026270
## 8 1.6163236
## 9 1.6110690
## 10 1.5750017
## 11 1.5363927
## 12 1.4112837
## 13 1.0037539
## 14 0.8137859
## 15 4.9036117
## 16 4.0405297
## 17 2.1396947
## 18 1.8888942
## 19 1.6536747
## 20 1.3375385
## 21 1.1321522
## 22 0.8473609
## 23 0.8002241
``` r
equal_ids <- related_concepts |>
filter(relation == "equal level") |>
pull(id)
```
## Trends of biliometrics-related concepts
``` r
concept_df <- oa_fetch(
entity = "concepts",
identifier = c(concept$id, equal_ids)
)
biblio_concepts <- concept_df |>
select(display_name, counts_by_year) |>
tidyr::unnest(counts_by_year) |>
filter(year < 2022) |>
mutate(year = as.Date(paste0("1jan", year), format = "%d%b%Y")) |>
ggplot() +
aes(x = year, y = works_count, color = display_name) +
scale_color_viridis_d(option = "B", end = 0.8) +
facet_wrap(~display_name) +
geom_line(linewidth = 0.7) +
labs(x = NULL, y = "Works count") +
scale_y_log10() +
scale_x_date(labels = scales::date_format("'%y")) +
guides(color = "none") +
gghighlight(use_direct_label = FALSE)
biblio_concepts
```
``` r
ggsave("images/biblio-concepts.png", biblio_concepts,
dpi = 450, width = 7, height = 5
)
```
## Bibliometrics papers
``` r
oa_fetch(
entity = "works",
title.search = "bibliometrics|science mapping",
count_only = TRUE,
verbose = TRUE
)
biblio_works <- oa_fetch(
entity = "works",
title.search = "bibliometrics|science mapping",
count_only = FALSE,
verbose = TRUE
)
```
``` r
biblio_works |>
count(so) |>
drop_na(so) |>
slice_max(n, n = 5) |>
pull(so)
```
## [1] "Scientometrics"
## [2] "Sustainability"
## [3] "Social Science Research Network"
## [4] "International Journal of Environmental Research and Public Health"
## [5] "Environmental Science and Pollution Research"
``` r
biblio_journal <- biblio_works |>
add_count(so, name = "n_so") |>
count(so, publication_year, n_so, sort = TRUE) |>
drop_na(so) |>
mutate(so_rank = dense_rank(desc(n_so))) |>
filter(so_rank < 6, publication_year < 2022) |>
mutate(
so = gsub("International Journal of|Journal of the|Journal of", "I.J.", so) |>
as_factor() |>
fct_reorder(so_rank)
) |>
complete(so, publication_year, fill = list(n = 0)) |>
mutate(
label = if_else(publication_year == max(publication_year),
as.character(so), NA_character_
)
) |>
ggplot(aes(x = publication_year, y = n, fill = so)) +
geom_area(alpha = 0.7, color = "white") +
geom_text(aes(label = label, color = so, x = publication_year + 1),
position = position_stack(vjust = 0.5),
hjust = 0, na.rm = TRUE
) +
scale_y_continuous(expand = expansion(add = c(0, 0))) +
scale_x_continuous(
expand = expansion(add = c(0, 22.5)),
breaks = c(1980, 2000, 2020)
) +
scale_fill_brewer(palette = "Dark2") +
scale_color_brewer(palette = "Dark2") +
labs(y = "Number of works", x = NULL) +
theme_minimal() +
theme(panel.grid.minor.y = element_blank()) +
guides(fill = "none", color = "none")
biblio_journal
```
``` r
ggsave("images/biblio-journals.png", biblio_journal,
dpi = 450, height = 5, width = 10
)
```
``` r
biblio_authors_raw <- do.call(rbind.data.frame, biblio_works$author)
biblio_insts <- biblio_authors_raw |>
count(institution_display_name) |>
rename("name" = institution_display_name) |>
drop_na(name) |>
slice_max(n, n = 10) |>
mutate(type = "Institution")
biblio_authors <- biblio_authors_raw |>
count(au_display_name) |>
rename("name" = au_display_name) |>
drop_na(name) |>
slice_max(n, n = 10) |>
mutate(type = "Author")
biblio_aut_insts <- biblio_authors |>
bind_rows(biblio_insts) |>
group_by(type) |>
mutate(name = forcats::fct_reorder(name, n)) |>
ggplot() +
aes(x = n, y = name) +
geom_segment(aes(yend = name, x = 0, xend = n)) +
geom_point(aes(color = type), size = 3) +
facet_wrap(~type, scales = "free") +
scale_color_manual(values = c("#d46780", "#a3ad62"), guide = "none") +
labs(x = "Number of articles", y = NULL) +
theme(panel.spacing = unit(3, "lines"))
biblio_aut_insts
```
``` r
ggsave("images/biblio-authors-institutions.png", biblio_aut_insts,
dpi = 450, height = 3.5, width = 8
)
```
## Two most cited articles and their citations and references
``` r
seminal_works <- slice_max(biblio_works, cited_by_count, n = 10)
seminal_works |>
select(publication_year, display_name, so, cited_by_count)
```
## # A tibble: 10 × 4
## publication_year display_name so cited…¹
##
## 1 2010 Software survey: VOSviewer, a computer progra… Scie… 5557
## 2 2017 bibliometrix : An R-tool for comprehensive sc… Jour… 2244
## 3 2015 Bibliometric Methods in Management and Organi… Orga… 1586
## 4 1976 A general theory of bibliometric and other cu… Jour… 1508
## 5 2015 Bibliometrics: The Leiden Manifesto for resea… Natu… 1181
## 6 2011 Science mapping software tools: Review, analy… Jour… 1131
## 7 2004 Changes in the intellectual structure of stra… Stra… 1044
## 8 2010 A unified approach to mapping and clustering … Jour… 948
## 9 2015 Green supply chain management: A review and b… Inte… 934
## 10 2021 How to conduct a bibliometric analysis: An ov… Jour… 837
## # … with abbreviated variable name ¹cited_by_count
``` r
sb_docs <- oa_snowball(
identifier = seminal_works$id[1:2],
citing_filter = list(from_publication_date = "2022-01-01"),
verbose = TRUE
)
```
## Requesting url: https://api.openalex.org/works?filter=openalex_id%3Ahttps%3A%2F%2Fopenalex.org%2FW2150220236%7Chttps%3A%2F%2Fopenalex.org%2FW2755950973
## Getting 1 page of results with a total of 2 records...
## Collecting all documents citing the target papers...
## Requesting url: https://api.openalex.org/works?filter=cites%3AW2150220236%7CW2755950973%2Cfrom_publication_date%3A2022-01-01
## Getting 16 pages of results with a total of 3037 records...
## Collecting all documents cited by the target papers...
## Requesting url: https://api.openalex.org/works?filter=cited_by%3AW2150220236%7CW2755950973
## Getting 1 page of results with a total of 72 records...
``` r
sg_1 <- tidygraph::as_tbl_graph(sb_docs)
AU <- sb_docs$nodes |>
select(author) |>
unlist(recursive = FALSE) |>
lapply(function(l) {
paste(l$au_display_name, collapse = "; ")
}) |>
unlist()
g_citation <- ggraph(graph = sg_1, layout = "stress") +
aes(size = cited_by_count) +
geom_edge_link(color = "grey60", alpha = 0.30, show.legend = FALSE) +
scale_edge_width(range = c(0.1, 1.5), guide = "none") +
scale_size(range = c(1, 3), guide = "none") +
geom_node_point(aes(filter = !oa_input), fill = "#a3ad62", shape = 21, color = "white") +
geom_node_point(aes(filter = oa_input), fill = "#d46780", shape = 21, color = "white") +
theme_graph() +
guides(fill = "none", size = "none") +
geom_node_label(aes(filter = oa_input, label = AU), nudge_y = 0.2, size = 3)
g_citation
```
N-grams
``` r
# options("oa_ngrams.message.curlv5" = TRUE)
ngrams_data <- oa_ngrams(sample(biblio_works$id, 1000), verbose = TRUE)
top_10 <- do.call(rbind.data.frame, ngrams_data$ngrams) |>
filter(ngram_tokens == 2, nchar(ngram) > 10) |>
arrange(desc(ngram_count)) |>
slice_max(ngram_count, n = 10, with_ties = FALSE)
top_10
```
## ngram ngram_tokens ngram_count term_frequency
## 1 circular economy 2 240 0.022249003
## 2 natural capital 2 134 0.021742658
## 3 internal audit 2 102 0.006665795
## 4 ecosystem service 2 97 0.014806900
## 5 interorganizational network 2 96 0.009058313
## 6 fractional counting 2 92 0.008700586
## 7 rural entrepreneurship 2 91 0.009667481
## 8 relate publication 2 90 0.007140024
## 9 highly cite 2 72 0.010990688
## 10 internal auditing 2 71 0.004639916
``` r
tm <- treemap(
dtf = top_10,
index = c("ngram"),
vSize = "ngram_count",
vColor = "ngram"
) |>
invisible()
```
``` r
head(tm$tm)
```
## ngram vSize vColor stdErr vColorValue level x0 y0
## 1 circular economy 240 1 240 NA 1 0.0000000 0.3582888
## 2 ecosystem service 97 1 97 NA 1 0.5712786 0.5850914
## 3 fractional counting 92 1 92 NA 1 0.3447005 0.2909471
## 4 highly cite 72 1 72 NA 1 0.8368752 0.1782897
## 5 internal audit 102 1 102 NA 1 0.3447005 0.5850914
## 6 internal auditing 71 1 71 NA 1 0.6329692 0.0000000
## w h color
## 1 0.3447005 0.6417112 #D6A166
## 2 0.2154714 0.4149086 #50B6E0
## 3 0.2882688 0.2941443 #2DC194
## 4 0.1631248 0.4068018 #EB8DC1
## 5 0.2265781 0.4149086 #B2AF4F
## 6 0.3670308 0.1782897 #A1A5EC
``` r
tm_plot_data <- tm$tm |>
mutate(
# calculate end coordinates with height and width
x1 = x0 + w,
y1 = y0 + h,
# get center coordinates for labels
x = (x0 + x1) / 2,
y = (y0 + y1) / 2
)
ngram_plot <- ggplot(tm_plot_data, aes(xmin = x0, ymin = y0, xmax = x1, ymax = y1)) +
geom_rect(aes(fill = color), show.legend = FALSE, color = "black", alpha = .3) +
scale_fill_identity() +
ggfittext::geom_fit_text(aes(label = ngram), min.size = 1) +
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0)) +
theme_void()
ngram_plot
```
``` r
ggsave("images/citation-graph.png", g_citation,
height = 5, width = 8
)
ggsave("images/ngram-treemap.png", ngram_plot,
height = 4, width = 8
)
save.image("data/oarj.rdata")
```
``` r
session_info()
```
## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.2.1 (2022-06-23)
## os macOS Big Sur ... 10.16
## system x86_64, darwin17.0
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz America/New_York
## date 2023-02-07
## pandoc 2.18 @ /Applications/RStudio.app/Contents/MacOS/quarto/bin/tools/ (via rmarkdown)
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## package * version date (UTC) lib source
## assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.2.0)
## backports 1.4.1 2021-12-13 [1] CRAN (R 4.2.0)
## broom 1.0.1 2022-08-29 [1] CRAN (R 4.2.0)
## cachem 1.0.6 2021-08-19 [1] CRAN (R 4.2.0)
## callr 3.7.2 2022-08-22 [1] CRAN (R 4.2.0)
## cellranger 1.1.0 2016-07-27 [1] CRAN (R 4.2.0)
## cli 3.4.1 2022-09-23 [1] CRAN (R 4.2.0)
## colorspace 2.0-3 2022-02-21 [1] CRAN (R 4.2.0)
## crayon 1.5.1 2022-03-26 [1] CRAN (R 4.2.0)
## curl 5.0.0 2023-01-12 [1] CRAN (R 4.2.0)
## data.table 1.14.2 2021-09-27 [1] CRAN (R 4.2.0)
## DBI 1.1.3 2022-06-18 [1] CRAN (R 4.2.0)
## dbplyr 2.2.1 2022-06-27 [1] CRAN (R 4.2.0)
## devtools * 2.4.4 2022-07-20 [1] CRAN (R 4.2.0)
## digest 0.6.29 2021-12-01 [1] CRAN (R 4.2.0)
## dplyr * 1.0.10 2022-09-01 [1] CRAN (R 4.2.0)
## ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.2.0)
## evaluate 0.16 2022-08-09 [1] CRAN (R 4.2.0)
## fansi 1.0.3 2022-03-24 [1] CRAN (R 4.2.0)
## farver 2.1.1 2022-07-06 [1] CRAN (R 4.2.0)
## fastmap 1.1.0 2021-01-25 [1] CRAN (R 4.2.0)
## forcats * 0.5.2 2022-08-19 [1] CRAN (R 4.2.0)
## fs 1.5.2 2021-12-08 [1] CRAN (R 4.2.0)
## gargle 1.2.0 2021-07-02 [1] CRAN (R 4.2.0)
## generics 0.1.3 2022-07-05 [1] CRAN (R 4.2.0)
## ggfittext 0.9.1 2021-01-30 [1] CRAN (R 4.2.0)
## ggforce 0.4.1 2022-10-04 [1] CRAN (R 4.2.0)
## gghighlight * 0.4.0 2022-10-16 [1] CRAN (R 4.2.0)
## ggplot2 * 3.3.6.9000 2022-10-14 [1] Github (tidyverse/ggplot2@a58b48c)
## ggraph * 2.1.0 2022-10-09 [1] CRAN (R 4.2.0)
## ggrepel 0.9.1 2021-01-15 [1] CRAN (R 4.2.0)
## glue 1.6.2 2022-02-24 [1] CRAN (R 4.2.0)
## googledrive 2.0.0 2021-07-08 [1] CRAN (R 4.2.0)
## googlesheets4 1.0.1 2022-08-13 [1] CRAN (R 4.2.0)
## graphlayouts 0.8.2 2022-09-29 [1] CRAN (R 4.2.0)
## gridBase 0.4-7 2014-02-24 [1] CRAN (R 4.2.0)
## gridExtra 2.3 2017-09-09 [1] CRAN (R 4.2.0)
## gtable 0.3.1 2022-09-01 [1] CRAN (R 4.2.0)
## haven 2.5.1 2022-08-22 [1] CRAN (R 4.2.0)
## highr 0.9 2021-04-16 [1] CRAN (R 4.2.0)
## hms 1.1.2 2022-08-19 [1] CRAN (R 4.2.0)
## htmltools 0.5.3 2022-07-18 [1] CRAN (R 4.2.0)
## htmlwidgets 1.5.4 2021-09-08 [1] CRAN (R 4.2.0)
## httpuv 1.6.6 2022-09-08 [1] CRAN (R 4.2.0)
## httr 1.4.4 2022-08-17 [1] CRAN (R 4.2.0)
## igraph 1.3.4 2022-07-19 [1] CRAN (R 4.2.0)
## jsonlite 1.8.0 2022-02-22 [1] CRAN (R 4.2.0)
## knitr 1.40 2022-08-24 [1] CRAN (R 4.2.0)
## labeling 0.4.2 2020-10-20 [1] CRAN (R 4.2.0)
## later 1.3.0 2021-08-18 [1] CRAN (R 4.2.0)
## lifecycle 1.0.2 2022-09-09 [1] CRAN (R 4.2.0)
## lubridate 1.8.0 2021-10-07 [1] CRAN (R 4.2.0)
## magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.2.0)
## MASS 7.3-57 2022-04-22 [1] CRAN (R 4.2.1)
## memoise 2.0.1 2021-11-26 [1] CRAN (R 4.2.0)
## mime 0.12 2021-09-28 [1] CRAN (R 4.2.0)
## miniUI 0.1.1.1 2018-05-18 [1] CRAN (R 4.2.0)
## modelr 0.1.9 2022-08-19 [1] CRAN (R 4.2.0)
## munsell 0.5.0 2018-06-12 [1] CRAN (R 4.2.0)
## openalexR * 1.0.2.9000 2023-01-31 [1] local
## pillar 1.8.1 2022-08-19 [1] CRAN (R 4.2.0)
## pkgbuild 1.3.1 2021-12-20 [1] CRAN (R 4.2.0)
## pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.2.0)
## pkgload 1.3.0 2022-06-27 [1] CRAN (R 4.2.0)
## polyclip 1.10-0 2019-03-14 [1] CRAN (R 4.2.0)
## prettyunits 1.1.1 2020-01-24 [1] CRAN (R 4.2.0)
## processx 3.7.0 2022-07-07 [1] CRAN (R 4.2.0)
## profvis 0.3.7 2020-11-02 [1] CRAN (R 4.2.0)
## progress 1.2.2 2019-05-16 [1] CRAN (R 4.2.0)
## promises 1.2.0.1 2021-02-11 [1] CRAN (R 4.2.0)
## ps 1.7.1 2022-06-18 [1] CRAN (R 4.2.0)
## purrr * 0.3.4 2020-04-17 [1] CRAN (R 4.2.0)
## R6 2.5.1 2021-08-19 [1] CRAN (R 4.2.0)
## ragg 1.2.2 2022-02-21 [1] CRAN (R 4.2.0)
## RColorBrewer 1.1-3 2022-04-03 [1] CRAN (R 4.2.0)
## Rcpp 1.0.9 2022-07-08 [1] CRAN (R 4.2.0)
## readr * 2.1.2 2022-01-30 [1] CRAN (R 4.2.0)
## readxl 1.4.1 2022-08-17 [1] CRAN (R 4.2.0)
## remotes 2.4.2 2021-11-30 [1] CRAN (R 4.2.0)
## reprex 2.0.2 2022-08-17 [1] CRAN (R 4.2.0)
## rlang 1.0.6 2022-09-24 [1] CRAN (R 4.2.0)
## rmarkdown 2.16 2022-08-24 [1] CRAN (R 4.2.0)
## rstudioapi 0.14 2022-08-22 [1] CRAN (R 4.2.0)
## rvest 1.0.3 2022-08-19 [1] CRAN (R 4.2.0)
## scales 1.2.1 2022-08-20 [1] CRAN (R 4.2.0)
## sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.2.0)
## shiny 1.7.2 2022-07-19 [1] CRAN (R 4.2.0)
## stringi 1.7.8 2022-07-11 [1] CRAN (R 4.2.0)
## stringr * 1.4.1 2022-08-20 [1] CRAN (R 4.2.0)
## systemfonts 1.0.4 2022-02-11 [1] CRAN (R 4.2.0)
## textshaping 0.3.6 2021-10-13 [1] CRAN (R 4.2.0)
## tibble * 3.1.8 2022-07-22 [1] CRAN (R 4.2.0)
## tidygraph * 1.2.2 2022-08-22 [1] CRAN (R 4.2.0)
## tidyr * 1.2.1 2022-09-08 [1] CRAN (R 4.2.0)
## tidyselect 1.1.2 2022-02-21 [1] CRAN (R 4.2.0)
## tidyverse * 1.3.2 2022-07-18 [1] CRAN (R 4.2.0)
## treemap * 2.4-3 2021-08-22 [1] CRAN (R 4.2.0)
## tweenr 2.0.2 2022-09-06 [1] CRAN (R 4.2.0)
## tzdb 0.3.0 2022-03-28 [1] CRAN (R 4.2.0)
## urlchecker 1.0.1 2021-11-30 [1] CRAN (R 4.2.0)
## usethis * 2.1.6 2022-05-25 [1] CRAN (R 4.2.0)
## utf8 1.2.2 2021-07-24 [1] CRAN (R 4.2.0)
## vctrs 0.4.2 2022-09-29 [1] CRAN (R 4.2.0)
## viridis 0.6.2 2021-10-13 [1] CRAN (R 4.2.0)
## viridisLite 0.4.1 2022-08-22 [1] CRAN (R 4.2.0)
## withr 2.5.0 2022-03-03 [1] CRAN (R 4.2.0)
## xfun 0.33 2022-09-12 [1] CRAN (R 4.2.0)
## xml2 1.3.3 2021-11-30 [1] CRAN (R 4.2.0)
## xtable 1.8-4 2019-04-21 [1] CRAN (R 4.2.0)
## yaml 2.3.5 2022-02-21 [1] CRAN (R 4.2.0)
##
## [1] /Library/Frameworks/R.framework/Versions/4.2/Resources/library
##
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