{"id":20081093,"url":"https://github.com/trangdata/oarj","last_synced_at":"2026-03-09T04:07:50.025Z","repository":{"id":63002404,"uuid":"552951490","full_name":"trangdata/oarj","owner":"trangdata","description":"Examples of openalexR submitted to R Journal","archived":false,"fork":false,"pushed_at":"2023-02-10T00:23:00.000Z","size":108734,"stargazers_count":2,"open_issues_count":0,"forks_count":3,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-09-20T17:50:55.624Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/trangdata.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-10-17T13:33:55.000Z","updated_at":"2025-04-03T19:54:42.000Z","dependencies_parsed_at":"2023-02-12T22:15:43.973Z","dependency_job_id":null,"html_url":"https://github.com/trangdata/oarj","commit_stats":{"total_commits":24,"total_committers":3,"mean_commits":8.0,"dds":"0.29166666666666663","last_synced_commit":"ec1d29665ed414c21b0daf0b9e71a14757936969"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/trangdata/oarj","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/trangdata%2Foarj","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/trangdata%2Foarj/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/trangdata%2Foarj/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/trangdata%2Foarj/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/trangdata","download_url":"https://codeload.github.com/trangdata/oarj/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/trangdata%2Foarj/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":30282745,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-03-09T02:57:19.223Z","status":"ssl_error","status_checked_at":"2026-03-09T02:56:26.373Z","response_time":61,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-13T15:33:23.781Z","updated_at":"2026-03-09T04:07:49.996Z","avatar_url":"https://github.com/trangdata.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"Examples shown in the R Journal manuscript\n================\n2022-11-10\n\n**NOTE**: To replicate the analyses proposed in the manuscript, please\nuse the downloaded data at `data/oarj.rdata`. Because bibliographic\nmetadata change at high frequency, downloads made on different days\ncould provide slightly different results (*e.g.*, number of citations,\nnumber of published articles, *etc.*). The `oarj.rdata` file contains\nall the objects we needed for this analysis.\n\n``` r\nset.seed(1234)\nlibrary(openalexR)\nlibrary(tidyverse)\n```\n\n    ## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──\n    ## ✔ ggplot2 3.3.6.9000     ✔ purrr   0.3.4     \n    ## ✔ tibble  3.1.8          ✔ dplyr   1.0.10    \n    ## ✔ tidyr   1.2.1          ✔ stringr 1.4.1     \n    ## ✔ readr   2.1.2          ✔ forcats 0.5.2     \n    ## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──\n    ## ✖ dplyr::filter() masks stats::filter()\n    ## ✖ dplyr::lag()    masks stats::lag()\n\n``` r\nlibrary(gghighlight)\nlibrary(ggraph)\nlibrary(tidygraph)\n```\n\n    ## \n    ## Attaching package: 'tidygraph'\n    ## \n    ## The following object is masked from 'package:stats':\n    ## \n    ##     filter\n\n``` r\nlibrary(treemap)\ntheme_set(\n  theme_classic() +\n    theme(\n      plot.background = element_rect(fill = \"transparent\", colour = NA),\n      panel.background = element_rect(fill = \"transparent\", colour = NA),\n      strip.background = element_rect(fill = NA, color = \"grey20\")\n    )\n)\n```\n\n## Bibliometrics concept\n\n``` r\nconcept \u003c- oa_fetch(\n  entity = \"concepts\",\n  identifier = \"C178315738\" # OAID for \"bibliometrics\"\n)\n\ncat(concept$description, \"is a level\", concept$level, \"concept\")\n```\n\n    ## statistical analysis of written publications, such as books or articles is a level 2 concept\n\n``` r\nrelated_concepts \u003c- concept$related_concepts[[1]] |\u003e\n  mutate(relation = case_when(\n    level \u003c 2 ~ \"ancestor\",\n    level == 2 ~ \"equal level\",\n    TRUE ~ \"descendant\"\n  )) |\u003e\n  arrange(level) |\u003e\n  relocate(relation) |\u003e\n  select(-wikidata)\n\nrelated_concepts\n```\n\n    ##       relation                               id           display_name level\n    ## 1     ancestor   https://openalex.org/C41008148       Computer science     0\n    ## 2     ancestor   https://openalex.org/C36289849         Social science     1\n    ## 3     ancestor  https://openalex.org/C124101348            Data mining     1\n    ## 4  equal level  https://openalex.org/C525823164         Scientometrics     2\n    ## 5  equal level https://openalex.org/C2779455604          Impact factor     2\n    ## 6  equal level https://openalex.org/C2778407487             Altmetrics     2\n    ## 7  equal level  https://openalex.org/C521491914            Webometrics     2\n    ## 8  equal level https://openalex.org/C2781083858  Scientific literature     2\n    ## 9  equal level https://openalex.org/C2778805511               Citation     2\n    ## 10 equal level   https://openalex.org/C95831776    Information science     2\n    ## 11 equal level https://openalex.org/C2779172887               PageRank     2\n    ## 12 equal level  https://openalex.org/C138368954            Peer review     2\n    ## 13 equal level https://openalex.org/C2779810430 Knowledge organization     2\n    ## 14 equal level https://openalex.org/C2780416505 Collection development     2\n    ## 15  descendant  https://openalex.org/C105345328      Citation analysis     3\n    ## 16  descendant https://openalex.org/C2778793908        Citation impact     3\n    ## 17  descendant https://openalex.org/C2780378607           Informetrics     3\n    ## 18  descendant https://openalex.org/C2778032371         Citation index     3\n    ## 19  descendant   https://openalex.org/C83867959                 Scopus     3\n    ## 20  descendant https://openalex.org/C2776822937 Bibliographic coupling     3\n    ## 21  descendant https://openalex.org/C2779693592        Journal ranking     3\n    ## 22  descendant   https://openalex.org/C45462083  Documentation science     3\n    ## 23  descendant https://openalex.org/C2777765086            Co-citation     3\n    ##        score\n    ## 1  1.3350035\n    ## 2  1.6031636\n    ## 3  1.5347114\n    ## 4  6.6193560\n    ## 5  4.1035270\n    ## 6  2.5396087\n    ## 7  2.3026270\n    ## 8  1.6163236\n    ## 9  1.6110690\n    ## 10 1.5750017\n    ## 11 1.5363927\n    ## 12 1.4112837\n    ## 13 1.0037539\n    ## 14 0.8137859\n    ## 15 4.9036117\n    ## 16 4.0405297\n    ## 17 2.1396947\n    ## 18 1.8888942\n    ## 19 1.6536747\n    ## 20 1.3375385\n    ## 21 1.1321522\n    ## 22 0.8473609\n    ## 23 0.8002241\n\n``` r\nequal_ids \u003c- related_concepts |\u003e\n  filter(relation == \"equal level\") |\u003e\n  pull(id)\n```\n\n## Trends of biliometrics-related concepts\n\n``` r\nconcept_df \u003c- oa_fetch(\n  entity = \"concepts\",\n  identifier = c(concept$id, equal_ids)\n)\n\nbiblio_concepts \u003c- concept_df |\u003e\n  select(display_name, counts_by_year) |\u003e\n  tidyr::unnest(counts_by_year) |\u003e\n  filter(year \u003c 2022) |\u003e\n  mutate(year = as.Date(paste0(\"1jan\", year), format = \"%d%b%Y\")) |\u003e\n  ggplot() +\n  aes(x = year, y = works_count, color = display_name) +\n  scale_color_viridis_d(option = \"B\", end = 0.8) +\n  facet_wrap(~display_name) +\n  geom_line(linewidth = 0.7) +\n  labs(x = NULL, y = \"Works count\") +\n  scale_y_log10() +\n  scale_x_date(labels = scales::date_format(\"'%y\")) +\n  guides(color = \"none\") +\n  gghighlight(use_direct_label = FALSE)\n\nbiblio_concepts\n```\n\n\u003cimg src=\"paper-examples_files/figure-gfm/unnamed-chunk-3-1.png\" width=\"100%\" /\u003e\n\n``` r\nggsave(\"images/biblio-concepts.png\", biblio_concepts,\n  dpi = 450, width = 7, height = 5\n)\n```\n\n## Bibliometrics papers\n\n``` r\noa_fetch(\n  entity = \"works\",\n  title.search = \"bibliometrics|science mapping\",\n  count_only = TRUE,\n  verbose = TRUE\n)\n\nbiblio_works \u003c- oa_fetch(\n  entity = \"works\",\n  title.search = \"bibliometrics|science mapping\",\n  count_only = FALSE,\n  verbose = TRUE\n)\n```\n\n``` r\nbiblio_works |\u003e\n  count(so) |\u003e\n  drop_na(so) |\u003e\n  slice_max(n, n = 5) |\u003e\n  pull(so)\n```\n\n    ## [1] \"Scientometrics\"                                                   \n    ## [2] \"Sustainability\"                                                   \n    ## [3] \"Social Science Research Network\"                                  \n    ## [4] \"International Journal of Environmental Research and Public Health\"\n    ## [5] \"Environmental Science and Pollution Research\"\n\n``` r\nbiblio_journal \u003c- biblio_works |\u003e\n  add_count(so, name = \"n_so\") |\u003e\n  count(so, publication_year, n_so, sort = TRUE) |\u003e\n  drop_na(so) |\u003e\n  mutate(so_rank = dense_rank(desc(n_so))) |\u003e\n  filter(so_rank \u003c 6, publication_year \u003c 2022) |\u003e\n  mutate(\n    so = gsub(\"International Journal of|Journal of the|Journal of\", \"I.J.\", so) |\u003e\n      as_factor() |\u003e\n      fct_reorder(so_rank)\n  ) |\u003e\n  complete(so, publication_year, fill = list(n = 0)) |\u003e\n  mutate(\n    label = if_else(publication_year == max(publication_year),\n      as.character(so), NA_character_\n    )\n  ) |\u003e\n  ggplot(aes(x = publication_year, y = n, fill = so)) +\n  geom_area(alpha = 0.7, color = \"white\") +\n  geom_text(aes(label = label, color = so, x = publication_year + 1),\n    position = position_stack(vjust = 0.5),\n    hjust = 0, na.rm = TRUE\n  ) +\n  scale_y_continuous(expand = expansion(add = c(0, 0))) +\n  scale_x_continuous(\n    expand = expansion(add = c(0, 22.5)),\n    breaks = c(1980, 2000, 2020)\n  ) +\n  scale_fill_brewer(palette = \"Dark2\") +\n  scale_color_brewer(palette = \"Dark2\") +\n  labs(y = \"Number of works\", x = NULL) +\n  theme_minimal() +\n  theme(panel.grid.minor.y = element_blank()) +\n  guides(fill = \"none\", color = \"none\")\n\nbiblio_journal\n```\n\n\u003cimg src=\"paper-examples_files/figure-gfm/biblio-journal-1.png\" width=\"100%\" /\u003e\n\n``` r\nggsave(\"images/biblio-journals.png\", biblio_journal,\n  dpi = 450, height = 5, width = 10\n)\n```\n\n``` r\nbiblio_authors_raw \u003c- do.call(rbind.data.frame, biblio_works$author)\nbiblio_insts \u003c- biblio_authors_raw |\u003e\n  count(institution_display_name) |\u003e\n  rename(\"name\" = institution_display_name) |\u003e\n  drop_na(name) |\u003e\n  slice_max(n, n = 10) |\u003e\n  mutate(type = \"Institution\")\n\nbiblio_authors \u003c- biblio_authors_raw |\u003e\n  count(au_display_name) |\u003e\n  rename(\"name\" = au_display_name) |\u003e\n  drop_na(name) |\u003e\n  slice_max(n, n = 10) |\u003e\n  mutate(type = \"Author\")\n\nbiblio_aut_insts \u003c- biblio_authors |\u003e\n  bind_rows(biblio_insts) |\u003e\n  group_by(type) |\u003e\n  mutate(name = forcats::fct_reorder(name, n)) |\u003e\n  ggplot() +\n  aes(x = n, y = name) +\n  geom_segment(aes(yend = name, x = 0, xend = n)) +\n  geom_point(aes(color = type), size = 3) +\n  facet_wrap(~type, scales = \"free\") +\n  scale_color_manual(values = c(\"#d46780\", \"#a3ad62\"), guide = \"none\") +\n  labs(x = \"Number of articles\", y = NULL) +\n  theme(panel.spacing = unit(3, \"lines\"))\n\nbiblio_aut_insts\n```\n\n\u003cimg src=\"paper-examples_files/figure-gfm/biblio-authors-1.png\" width=\"100%\" /\u003e\n\n``` r\nggsave(\"images/biblio-authors-institutions.png\", biblio_aut_insts,\n  dpi = 450, height = 3.5, width = 8\n)\n```\n\n## Two most cited articles and their citations and references\n\n``` r\nseminal_works \u003c- slice_max(biblio_works, cited_by_count, n = 10)\nseminal_works |\u003e\n  select(publication_year, display_name, so, cited_by_count)\n```\n\n    ## # A tibble: 10 × 4\n    ##    publication_year display_name                                   so    cited…¹\n    ##               \u003cint\u003e \u003cchr\u003e                                          \u003cchr\u003e   \u003cint\u003e\n    ##  1             2010 Software survey: VOSviewer, a computer progra… Scie…    5557\n    ##  2             2017 bibliometrix : An R-tool for comprehensive sc… Jour…    2244\n    ##  3             2015 Bibliometric Methods in Management and Organi… Orga…    1586\n    ##  4             1976 A general theory of bibliometric and other cu… Jour…    1508\n    ##  5             2015 Bibliometrics: The Leiden Manifesto for resea… Natu…    1181\n    ##  6             2011 Science mapping software tools: Review, analy… Jour…    1131\n    ##  7             2004 Changes in the intellectual structure of stra… Stra…    1044\n    ##  8             2010 A unified approach to mapping and clustering … Jour…     948\n    ##  9             2015 Green supply chain management: A review and b… Inte…     934\n    ## 10             2021 How to conduct a bibliometric analysis: An ov… Jour…     837\n    ## # … with abbreviated variable name ¹​cited_by_count\n\n``` r\nsb_docs \u003c- oa_snowball(\n  identifier = seminal_works$id[1:2],\n  citing_filter = list(from_publication_date = \"2022-01-01\"),\n  verbose = TRUE\n)\n```\n\n    ## Requesting url: https://api.openalex.org/works?filter=openalex_id%3Ahttps%3A%2F%2Fopenalex.org%2FW2150220236%7Chttps%3A%2F%2Fopenalex.org%2FW2755950973\n\n    ## Getting 1 page of results with a total of 2 records...\n\n    ## Collecting all documents citing the target papers...\n\n    ## Requesting url: https://api.openalex.org/works?filter=cites%3AW2150220236%7CW2755950973%2Cfrom_publication_date%3A2022-01-01\n\n    ## Getting 16 pages of results with a total of 3037 records...\n\n    ## Collecting all documents cited by the target papers...\n\n    ## Requesting url: https://api.openalex.org/works?filter=cited_by%3AW2150220236%7CW2755950973\n\n    ## Getting 1 page of results with a total of 72 records...\n\n``` r\nsg_1 \u003c- tidygraph::as_tbl_graph(sb_docs)\n\nAU \u003c- sb_docs$nodes |\u003e\n  select(author) |\u003e\n  unlist(recursive = FALSE) |\u003e\n  lapply(function(l) {\n    paste(l$au_display_name, collapse = \"; \")\n  }) |\u003e\n  unlist()\n\ng_citation \u003c- ggraph(graph = sg_1, layout = \"stress\") +\n  aes(size = cited_by_count) +\n  geom_edge_link(color = \"grey60\", alpha = 0.30, show.legend = FALSE) +\n  scale_edge_width(range = c(0.1, 1.5), guide = \"none\") +\n  scale_size(range = c(1, 3), guide = \"none\") +\n  geom_node_point(aes(filter = !oa_input), fill = \"#a3ad62\", shape = 21, color = \"white\") +\n  geom_node_point(aes(filter = oa_input), fill = \"#d46780\", shape = 21, color = \"white\") +\n  theme_graph() +\n  guides(fill = \"none\", size = \"none\") +\n  geom_node_label(aes(filter = oa_input, label = AU), nudge_y = 0.2, size = 3)\ng_citation\n```\n\n\u003cimg src=\"paper-examples_files/figure-gfm/citations-network-1.png\" width=\"100%\" /\u003e\n\nN-grams\n\n``` r\n# options(\"oa_ngrams.message.curlv5\" = TRUE)\nngrams_data \u003c- oa_ngrams(sample(biblio_works$id, 1000), verbose = TRUE)\ntop_10 \u003c- do.call(rbind.data.frame, ngrams_data$ngrams) |\u003e\n  filter(ngram_tokens == 2, nchar(ngram) \u003e 10) |\u003e\n  arrange(desc(ngram_count)) |\u003e\n  slice_max(ngram_count, n = 10, with_ties = FALSE)\n\ntop_10\n```\n\n    ##                          ngram ngram_tokens ngram_count term_frequency\n    ## 1             circular economy            2         240    0.022249003\n    ## 2              natural capital            2         134    0.021742658\n    ## 3               internal audit            2         102    0.006665795\n    ## 4            ecosystem service            2          97    0.014806900\n    ## 5  interorganizational network            2          96    0.009058313\n    ## 6          fractional counting            2          92    0.008700586\n    ## 7       rural entrepreneurship            2          91    0.009667481\n    ## 8           relate publication            2          90    0.007140024\n    ## 9                  highly cite            2          72    0.010990688\n    ## 10           internal auditing            2          71    0.004639916\n\n``` r\ntm \u003c- treemap(\n  dtf = top_10,\n  index = c(\"ngram\"),\n  vSize = \"ngram_count\",\n  vColor = \"ngram\"\n) |\u003e \n  invisible()\n```\n\n``` r\nhead(tm$tm)\n```\n\n    ##                 ngram vSize vColor stdErr vColorValue level        x0        y0\n    ## 1    circular economy   240      1    240          NA     1 0.0000000 0.3582888\n    ## 2   ecosystem service    97      1     97          NA     1 0.5712786 0.5850914\n    ## 3 fractional counting    92      1     92          NA     1 0.3447005 0.2909471\n    ## 4         highly cite    72      1     72          NA     1 0.8368752 0.1782897\n    ## 5      internal audit   102      1    102          NA     1 0.3447005 0.5850914\n    ## 6   internal auditing    71      1     71          NA     1 0.6329692 0.0000000\n    ##           w         h   color\n    ## 1 0.3447005 0.6417112 #D6A166\n    ## 2 0.2154714 0.4149086 #50B6E0\n    ## 3 0.2882688 0.2941443 #2DC194\n    ## 4 0.1631248 0.4068018 #EB8DC1\n    ## 5 0.2265781 0.4149086 #B2AF4F\n    ## 6 0.3670308 0.1782897 #A1A5EC\n\n``` r\ntm_plot_data \u003c- tm$tm |\u003e\n  mutate(\n    # calculate end coordinates with height and width\n    x1 = x0 + w,\n    y1 = y0 + h,\n    # get center coordinates for labels\n    x = (x0 + x1) / 2,\n    y = (y0 + y1) / 2\n  )\n\nngram_plot \u003c- ggplot(tm_plot_data, aes(xmin = x0, ymin = y0, xmax = x1, ymax = y1)) +\n  geom_rect(aes(fill = color), show.legend = FALSE, color = \"black\", alpha = .3) +\n  scale_fill_identity() +\n  ggfittext::geom_fit_text(aes(label = ngram), min.size = 1) +\n  scale_x_continuous(expand = c(0, 0)) +\n  scale_y_continuous(expand = c(0, 0)) +\n  theme_void()\n\nngram_plot\n```\n\n\u003cimg src=\"paper-examples_files/figure-gfm/n-grams-plot-1.png\" width=\"100%\" /\u003e\n\n``` r\nggsave(\"images/citation-graph.png\", g_citation,\n  height = 5, width = 8\n)\nggsave(\"images/ngram-treemap.png\", ngram_plot,\n  height = 4, width = 8\n)\n\nsave.image(\"data/oarj.rdata\")\n```\n\n``` r\nsession_info()\n```\n\n    ## ─ Session info ───────────────────────────────────────────────────────────────\n    ##  setting  value\n    ##  version  R version 4.2.1 (2022-06-23)\n    ##  os       macOS Big Sur ... 10.16\n    ##  system   x86_64, darwin17.0\n    ##  ui       X11\n    ##  language (EN)\n    ##  collate  en_US.UTF-8\n    ##  ctype    en_US.UTF-8\n    ##  tz       America/New_York\n    ##  date     2023-02-07\n    ##  pandoc   2.18 @ /Applications/RStudio.app/Contents/MacOS/quarto/bin/tools/ (via rmarkdown)\n    ## \n    ## ─ Packages ───────────────────────────────────────────────────────────────────\n    ##  package       * version    date (UTC) lib source\n    ##  assertthat      0.2.1      2019-03-21 [1] CRAN (R 4.2.0)\n    ##  backports       1.4.1      2021-12-13 [1] CRAN (R 4.2.0)\n    ##  broom           1.0.1      2022-08-29 [1] CRAN (R 4.2.0)\n    ##  cachem          1.0.6      2021-08-19 [1] CRAN (R 4.2.0)\n    ##  callr           3.7.2      2022-08-22 [1] CRAN (R 4.2.0)\n    ##  cellranger      1.1.0      2016-07-27 [1] CRAN (R 4.2.0)\n    ##  cli             3.4.1      2022-09-23 [1] CRAN (R 4.2.0)\n    ##  colorspace      2.0-3      2022-02-21 [1] CRAN (R 4.2.0)\n    ##  crayon          1.5.1      2022-03-26 [1] CRAN (R 4.2.0)\n    ##  curl            5.0.0      2023-01-12 [1] CRAN (R 4.2.0)\n    ##  data.table      1.14.2     2021-09-27 [1] CRAN (R 4.2.0)\n    ##  DBI             1.1.3      2022-06-18 [1] CRAN (R 4.2.0)\n    ##  dbplyr          2.2.1      2022-06-27 [1] CRAN (R 4.2.0)\n    ##  devtools      * 2.4.4      2022-07-20 [1] CRAN (R 4.2.0)\n    ##  digest          0.6.29     2021-12-01 [1] CRAN (R 4.2.0)\n    ##  dplyr         * 1.0.10     2022-09-01 [1] CRAN (R 4.2.0)\n    ##  ellipsis        0.3.2      2021-04-29 [1] CRAN (R 4.2.0)\n    ##  evaluate        0.16       2022-08-09 [1] CRAN (R 4.2.0)\n    ##  fansi           1.0.3      2022-03-24 [1] CRAN (R 4.2.0)\n    ##  farver          2.1.1      2022-07-06 [1] CRAN (R 4.2.0)\n    ##  fastmap         1.1.0      2021-01-25 [1] CRAN (R 4.2.0)\n    ##  forcats       * 0.5.2      2022-08-19 [1] CRAN (R 4.2.0)\n    ## 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