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.
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()
library(gghighlight)
library(ggraph)
library(tidygraph)##
## Attaching package: 'tidygraph'
##
## The following object is masked from 'package:stats':
##
## filter
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")
)
)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
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
equal_ids <- related_concepts |>
filter(relation == "equal level") |>
pull(id)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
ggsave("images/biblio-concepts.png", biblio_concepts,
dpi = 450, width = 7, height = 5
)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
)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"
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
ggsave("images/biblio-journals.png", biblio_journal,
dpi = 450, height = 5, width = 10
)biblio_authors_raw <- do.call(rbind.data.frame, biblio_works$author)
biblio_insts <- biblio_authors_raw |>
count(institution_display_name) |>
rename("name" = institution_display_name) |>
drop_na(name) |>
slice_max(n, n = 10) |>
mutate(type = "Institution")
biblio_authors <- biblio_authors_raw |>
count(au_display_name) |>
rename("name" = au_display_name) |>
drop_na(name) |>
slice_max(n, n = 10) |>
mutate(type = "Author")
biblio_aut_insts <- biblio_authors |>
bind_rows(biblio_insts) |>
group_by(type) |>
mutate(name = forcats::fct_reorder(name, n)) |>
ggplot() +
aes(x = n, y = name) +
geom_segment(aes(yend = name, x = 0, xend = n)) +
geom_point(aes(color = type), size = 3) +
facet_wrap(~type, scales = "free") +
scale_color_manual(values = c("#d46780", "#a3ad62"), guide = "none") +
labs(x = "Number of articles", y = NULL) +
theme(panel.spacing = unit(3, "lines"))
biblio_aut_insts
ggsave("images/biblio-authors-institutions.png", biblio_aut_insts,
dpi = 450, height = 3.5, width = 8
)seminal_works <- slice_max(biblio_works, cited_by_count, n = 10)
seminal_works |>
select(publication_year, display_name, so, cited_by_count)## # A tibble: 10 × 4
## publication_year display_name so cited…¹
## <int> <chr> <chr> <int>
## 1 2010 Software survey: VOSviewer, a computer progra… Scie… 5557
## 2 2017 bibliometrix : An R-tool for comprehensive sc… Jour… 2244
## 3 2015 Bibliometric Methods in Management and Organi… Orga… 1586
## 4 1976 A general theory of bibliometric and other cu… Jour… 1508
## 5 2015 Bibliometrics: The Leiden Manifesto for resea… Natu… 1181
## 6 2011 Science mapping software tools: Review, analy… Jour… 1131
## 7 2004 Changes in the intellectual structure of stra… Stra… 1044
## 8 2010 A unified approach to mapping and clustering … Jour… 948
## 9 2015 Green supply chain management: A review and b… Inte… 934
## 10 2021 How to conduct a bibliometric analysis: An ov… Jour… 837
## # … with abbreviated variable name ¹cited_by_count
sb_docs <- oa_snowball(
identifier = seminal_works$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...
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
# 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
tm <- treemap(
dtf = top_10,
index = c("ngram"),
vSize = "ngram_count",
vColor = "ngram"
) |>
invisible()head(tm$tm)## ngram vSize vColor stdErr vColorValue level x0 y0
## 1 circular economy 240 1 240 NA 1 0.0000000 0.3582888
## 2 ecosystem service 97 1 97 NA 1 0.5712786 0.5850914
## 3 fractional counting 92 1 92 NA 1 0.3447005 0.2909471
## 4 highly cite 72 1 72 NA 1 0.8368752 0.1782897
## 5 internal audit 102 1 102 NA 1 0.3447005 0.5850914
## 6 internal auditing 71 1 71 NA 1 0.6329692 0.0000000
## w h color
## 1 0.3447005 0.6417112 #D6A166
## 2 0.2154714 0.4149086 #50B6E0
## 3 0.2882688 0.2941443 #2DC194
## 4 0.1631248 0.4068018 #EB8DC1
## 5 0.2265781 0.4149086 #B2AF4F
## 6 0.3670308 0.1782897 #A1A5EC
tm_plot_data <- tm$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
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")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
##
## ──────────────────────────────────────────────────────────────────────────────