openalex是一个大型免费学术资源索引，该名称源于古埃及的亚历山大图书馆馆名。openalex既提供免费版，也提供收费的高级服务。免费版API的每人每天最大访问量是10万次。 openalex数据集描述了5类学术实体，以及这些实体之间的联系，每类实体分别对应的目标有：
成果：包括论文、书籍、数据集等，会引用其他成果
作者：做出成果的人
出处：刊载成果的期刊或资源库
机构：（通过作者）与成果相关的大学或其他组织
概念：以话题方式对成果进行标签

openalex实体结构

该数据库可以通过API访问。如果我们已经知道了图中所包含的作者姓名或ORCID、论文DOI、概念、作者所在机构、国籍、文献来源、主题、概念等，都可以进行检索。在R语言中，可以使用openalexR包对其进行访问。下表列出了openalex的五个主要功能：

功能	说明
oa_fetch	由以下三个函数组成，即oa_query
oa_query	根据用户提供的一组参数生成一个有效的查询，该查询以遵循OpenAlex API的语法编写。
oa_request	下载与oa_query创建的查询或用户手动编写的查询相匹配的实体集合，并以列表格式返回JSON对象。
oa2df	将JSON对象转换为经典书目/数据框架。
oa_random	获取随机实体，例如，oa_random (“works”)每次运行时给出不同的结果

一、设置

安装openalexR包的途径有两个，一是从github上安装开发版：

options(repos = c(CRAN = "https://cran.rstudio.com/"))
utils::install.packages("remotes")

## 程序包'remotes'打开成功，MD5和检查也通过
## 
## 下载的二进制程序包在
##  C:\Users\RENJIANCHAO\AppData\Local\Temp\RtmpcTsarq\downloaded_packages里

remotes::install_github("ropensci/openalexR")

## Downloading GitHub repo ropensci/openalexR@HEAD

## Running `R CMD build`...

## * checking for file 'C:\Users\RENJIANCHAO\AppData\Local\Temp\RtmpcTsarq\remotes43803b863e86\ropensci-openalexR-e4ffbe3/DESCRIPTION' ... OK
## * preparing 'openalexR':
## * checking DESCRIPTION meta-information ... OK
## * checking for LF line-endings in source and make files and shell scripts
## * checking for empty or unneeded directories
## Removed empty directory 'openalexR/vignettes'
## * building 'openalexR_1.3.0.tar.gz'

二是从CRAN上安装发行版

install.packages("openalexR")

## 
##   有二进制版本的，但源代码版本是后来的:
##           binary source needs_compilation
## openalexR  1.2.3  1.3.0             FALSE

## 安装源码包'openalexR'

为了获得比较好的体验，在开始之前最好将把个人邮箱添加到API请求（当然，如果有OpenAlex高级版，可以将API密钥添加到openalexR的Apikey选项）：

options(openalexR.mailto = "847807047@sina.com")
options(openalexR.apikey = "EXAMPLE_APIKEY")

另外，也可以通过输入命令file.edit(“~/.Renviron”) 打开.Renviron直接添加 openalexR.mailto = 你的邮箱
openalexR.apikey = EXAMPLE_APIKEY

二、举例

1、成果（WORKS）

（1）基于已知的文献信息（DOIs）下载该文献的所有信息

使用doi作为成果过滤器：

library(openalexR)

## Thank you for using openalexR!
## To acknowledge our work, please cite the package by calling `citation("openalexR")`.
## To suppress this message, add `openalexR.message = suppressed` to your .Renviron file.

works_from_dois <- oa_fetch(
  entity = "works",
  doi = c("10.1016/j.joi.2017.08.007", "https://doi.org/10.1007/s11192-013-1221-3"),
  verbose = TRUE
)

## Requesting url: https://api.openalex.org/works?filter=doi%3A10.1016%2Fj.joi.2017.08.007%7Chttps%3A%2F%2Fdoi.org%2F10.1007%2Fs11192-013-1221-3

## Getting 1 page of results with a total of 2 records...

# 我们可以在RStudio中交互式地查看works_from_dois输出的tibble/dataframe,，或者使用str或head等基本函数检查输出结果。目前的版本还提供了实验性的show_works函数来简化结果(例如，删除一些列，保留第一/最后作者)，以便于查看。

#注意:下面的表被包装在knitr:: able()中，以便在这个自述文件中很好地显示，但是您很可能不需要这个函数。
# str(works_from_dois, max.level = 2)
# head(works_from_dois)
# show_works(works_from_dois)

works_from_dois |>
  show_works() |>
  knitr::kable()

id	display_name	first_author	last_author	so	url	is_oa	top_concepts
W2755950973	bibliometrix : An R-tool for comprehensive science mapping analysis	Massimo Aria	Corrado Cuccurullo	Journal of informetrics	https://doi.org/10.1016/j.joi.2017.08.007	FALSE	Workflow, Bibliometrics, Software
W2038196424	Coverage and adoption of altmetrics sources in the bibliometric community	Stefanie Haustein	Jens Terliesner	Scientometrics	https://doi.org/10.1007/s11192-013-1221-3	FALSE	Altmetrics, Bookmarking, Social media

（2）基于作者信息（ORCIDs）下载该作者的所有成果

以下是使用author.orcid作为过滤器（带或不带 https://orcid.org/前缀都可以）

library(openalexR)
works_from_orcids <- oa_fetch(
  entity = "works",
  author.orcid = c("0000-0001-6187-6610", "0000-0002-8517-9411"),
  verbose = TRUE
)

## Requesting url: https://api.openalex.org/works?filter=author.orcid%3A0000-0001-6187-6610%7C0000-0002-8517-9411

## Getting 2 pages of results with a total of 252 records...

## Warning in oa_request(oa_query(filter = filter_i, multiple_id = multiple_id, : 
## The following work(s) have truncated lists of authors: W4230863633.
## Query each work separately by its identifier to get full list of authors.
## For example:
##   lapply(c("W4230863633"), \(x) oa_fetch(identifier = x))
## Details at https://docs.openalex.org/api-entities/authors/limitations.

works_from_orcids |>
  show_works() |>
  knitr::kable()

id	display_name	first_author	last_author	so	url	is_oa	top_concepts
W2755950973	bibliometrix : An R-tool for comprehensive science mapping analysis	Massimo Aria	Corrado Cuccurullo	Journal of informetrics	https://doi.org/10.1016/j.joi.2017.08.007	FALSE	Workflow, Bibliometrics, Software
W2741809807	The state of OA: a large-scale analysis of the prevalence and impact of Open Access articles	Heather Piwowar	Stefanie Haustein	PeerJ	https://doi.org/10.7717/peerj.4375	TRUE	Citation, License, Bibliometrics
W2122130843	Scientometrics 2.0: New metrics of scholarly impact on the social Web	Jason Priem	Bradely H. Hemminger	First Monday	https://doi.org/10.5210/fm.v15i7.2874	FALSE	Bookmarking, Altmetrics, Social media
W2038196424	Coverage and adoption of altmetrics sources in the bibliometric community	Stefanie Haustein	Jens Terliesner	Scientometrics	https://doi.org/10.1007/s11192-013-1221-3	FALSE	Altmetrics, Bookmarking, Social media
W2396414759	The Altmetrics Collection	Jason Priem	Dario Taraborelli	PloS one	https://doi.org/10.1371/journal.pone.0048753	TRUE	Social media, Citation, Altmetrics
W2408216567	Foundations and trends in performance management. A twenty-five years bibliometric analysis in business and public administration domains	Corrado Cuccurullo	Fabrizia Sarto	Scientometrics	https://doi.org/10.1007/s11192-016-1948-8	FALSE	Domain (mathematical analysis), Content analysis, Public domain

（3）基于研究主题下载文献

下载所有在2020年至2021年间发表的被引用50次以上的作品，在文献标题中包含“bibliometric analysis”或“science mapping”。也许我们还希望按总引用数降序对结果进行排序。

library(openalexR)
works_search <- oa_fetch(
  entity = "works",
  title.search = c("bibliometric analysis", "science mapping"),
  cited_by_count = ">50",
  from_publication_date = "2020-01-01",
  to_publication_date = "2021-12-31",
  options = list(sort = "cited_by_count:desc"),
  verbose = TRUE
)

## Requesting url: https://api.openalex.org/works?filter=title.search%3Abibliometric%20analysis%7Cscience%20mapping%2Ccited_by_count%3A%3E50%2Cfrom_publication_date%3A2020-01-01%2Cto_publication_date%3A2021-12-31&sort=cited_by_count%3Adesc

## Getting 2 pages of results with a total of 236 records...

works_search |>
  show_works() |>
  knitr::kable()

id	display_name	first_author	last_author	so	url	is_oa	top_concepts
W3160856016	How to conduct a bibliometric analysis: An overview and guidelines	Naveen Donthu	Weng Marc Lim	Journal of business research	https://doi.org/10.1016/j.jbusres.2021.04.070	TRUE	Bibliometrics, Field (mathematics), Resource (disambiguation)
W3038273726	Investigating the emerging COVID-19 research trends in the field of business and management: A bibliometric analysis approach	Surabhi Verma	Anders Gustafsson	Journal of business research	https://doi.org/10.1016/j.jbusres.2020.06.057	TRUE	Bibliometrics, Field (mathematics), Empirical research
W3001491100	Software tools for conducting bibliometric analysis in science: An up-to-date review	José A. Moral-Muñoz	Manuel J. Cobo	El Profesional de la información	https://doi.org/10.3145/epi.2020.ene.03	TRUE	Bibliometrics, Visualization, Set (abstract data type)
W2990450011	Forty-five years of Journal of Business Research: A bibliometric analysis	Naveen Donthu	Debidutta Pattnaik	Journal of business research	https://doi.org/10.1016/j.jbusres.2019.10.039	FALSE	Publishing, Bibliometrics, Empirical research
W3044902155	Financial literacy: A systematic review and bibliometric analysis	Kirti Goyal	Satish Kumar	International journal of consumer studies	https://doi.org/10.1111/ijcs.12605	FALSE	Financial literacy, Content analysis, Citation
W2990688366	A bibliometric analysis of board diversity: Current status, development, and future research directions	H. Kent Baker	Arunima Haldar	Journal of business research	https://doi.org/10.1016/j.jbusres.2019.11.025	FALSE	Diversity (politics), Ethnic group, Bibliometrics

2、作者（Authors）

(1)当我们知道作者的ORCID时，下载作者信息。

与前面不同，此处不使用author.orcid，而是直接使用orcid作为参数。此处需要注意的是，不同实体(作者而不是作品)需要一组不同的[过滤器]（https://ropensci.github.io/openalexR/articles/Filters.html#authors）。

library(openalexR)

authors_from_orcids <- oa_fetch(
  entity = "authors",
  orcid = c("0000-0001-6187-6610", "0000-0002-8517-9411")
)

authors_from_orcids |>
  show_authors() |>
  knitr::kable()

id	display_name	orcid	works_count	cited_by_count	affiliation_display_name	top_concepts
A5069892096	Massimo Aria	0000-0002-8517-9411	184	7750	University of Naples Federico II	Statistics, Internal medicine, Pathology
A5023888391	Jason Priem	0000-0001-6187-6610	67	2532	OurResearch	World Wide Web, Library science, Law

（2）获取有关此package作者的信息。

我们可以使用其他过滤器，如display_name和has_orcid:

library(openalexR)
authors_from_names <- oa_fetch(
  entity = "authors",
  display_name = c("Massimo Aria", "Jason Priem"),
  has_orcid = TRUE
)
authors_from_names |>
  show_authors() |>
  knitr::kable()

id	display_name	orcid	works_count	cited_by_count	affiliation_display_name	top_concepts
A5069892096	Massimo Aria	0000-0002-8517-9411	184	7750	University of Naples Federico II	Statistics, Internal medicine, Pathology
A5023888391	Jason Priem	0000-0001-6187-6610	67	2532	OurResearch	World Wide Web, Library science, Law

（3）下载某一单位的所有文献

下载在那不勒斯费德里科二世大学工作，并发表了至少500篇出版物的学者的所有作者记录(OpenAlex ID: I71267560)。
首先检查有多少记录与查询匹配，然后下载整个集合。接下来可以这样做:首先定义一个参数列表，然后将count_only(默认为FALSE)添加到该列表中:

library(openalexR)
my_arguments <- list(
  entity = "authors",
  last_known_institutions.id = "I71267560",
  works_count = ">499"
)

do.call(oa_fetch, c(my_arguments, list(count_only = TRUE)))

##      count db_response_time_ms page per_page
## [1,]    34                 223    1        1

#>      count db_response_time_ms page per_page
#> [1,]    33                 127    1        1

if (do.call(oa_fetch, c(my_arguments, list(count_only = TRUE)))[1]>0){
do.call(oa_fetch, my_arguments) |>
  show_authors() |>
  knitr::kable()
}

id	display_name	orcid	works_count	cited_by_count	affiliation_display_name	top_concepts
A5066548119	D. della Volpe	0000-0001-8530-7447	1708	67588	University of Naples Federico II	Quantum mechanics, Nuclear physics, Particle physics
A5032217427	Nicola Longo	0000-0002-3677-1216	1175	12977	University of Naples Federico II	Internal medicine, Genetics, Pathology
A5076706548	Salvatore Capozziello	0000-0003-4886-2024	913	34110	INFN Sezione di Napoli	Quantum mechanics, Astronomy, Theoretical physics
A5072318694	G. Chiefari	NA	883	46442	INFN Sezione di Napoli	Quantum mechanics, Particle physics, Nuclear physics
A5064797795	A. O. M. Iorio	NA	798	46038	INFN Sezione di Napoli	Particle physics, Nuclear physics, Quantum mechanics
A5035636337	S. Patricelli	NA	795	43655	INFN Sezione di Napoli	Quantum mechanics, Particle physics, Nuclear physics

三、实例分析

1、追踪生物学概念（Biology）的流行程度。

我们首先下载涉及100万件以上作品的所有一级概念/关键词的记录:

library(openalexR)
library(gghighlight)

## 载入需要的程辑包：ggplot2

library(dplyr)

## 
## 载入程辑包：'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

concept_df <- oa_fetch(
  entity = "concepts",
  level = 1,
  ancestors.id = "https://openalex.org/C86803240", # Biology
  works_count = ">1000000"
)

concept_df |>
  select(display_name, counts_by_year) |>
  tidyr::unnest(counts_by_year) |>
  filter(year < 2022) |>
  ggplot() +
  aes(x = year, y = works_count, color = display_name) +
  facet_wrap(~display_name) +
  geom_line(linewidth = 0.7) +
  scale_color_brewer(palette = "Dark2") +
  labs(
    x = NULL, y = "Works count",
    title = "Virology spiked in 2020."
  ) +
  guides(color = "none") +
  gghighlight(
    max(works_count) > 200000,
    min(works_count) < 400000,
    label_params = list(nudge_y = 10^5, segment.color = NA)
  )

## label_key: display_name

#> label_key: display_name

2、根据总引用次数对意大利的机构进行排名。

我们希望下载所有关于意大利机构(country_code:it)的记录，这些机构被分类为教育(type:education)。同样，我们检查有多少记录与查询相匹配，然后下载相应的记录集合:

italy_insts <- oa_fetch(
  entity = "institutions",
  country_code = "it",
  type = "education",
  verbose = TRUE
)

## Requesting url: https://api.openalex.org/institutions?filter=country_code%3Ait%2Ctype%3Aeducation

## Getting 2 pages of results with a total of 232 records...

#> Requesting url: https://api.openalex.org/institutions?filter=country_code%3Ait%2Ctype%3Aeducation
#> Getting 2 pages of results with a total of 232 records...

italy_insts |>
  slice_max(cited_by_count, n = 8) |>
  mutate(display_name = forcats::fct_reorder(display_name, cited_by_count)) |>
  ggplot() +
  aes(x = cited_by_count, y = display_name, fill = display_name) +
  geom_col() +
  scale_fill_viridis_d(option = "E") +
  guides(fill = "none") +
  labs(
    x = "Total citations", y = NULL,
    title = "Italian references"
  ) +
  coord_cartesian(expand = FALSE)

3、他们发表在哪些刊物上？

# The package wordcloud needs to be installed to run this chunk
library(wordcloud)

## 载入需要的程辑包：RColorBrewer

concept_cloud <- italy_insts |>
  select(inst_id = id, x_concepts) |>
  tidyr::unnest(x_concepts) |>
  filter(level == 1) |>
  select(display_name, score) |>
  group_by(display_name) |>
  summarise(score = sum(score))

pal <- c("black", scales::brewer_pal(palette = "Set1")(5))
set.seed(1)
wordcloud::wordcloud(
  concept_cloud$display_name,
  concept_cloud$score,
  scale = c(2, .4),
  colors = pal
)

4、大型期刊主题的可视化。

我们首先下载所有发表作品超过30万篇的期刊记录，然后将其评分概念可视化:

# The package ggtext needs to be installed to run this chunk
# library(ggtext)

jours_all <- oa_fetch(
  entity = "sources",
  works_count = ">200000",
  verbose = TRUE
)

## Requesting url: https://api.openalex.org/sources?filter=works_count%3A%3E200000

## Getting 1 page of results with a total of 42 records...

jours <- jours_all |>
  filter(!is.na(x_concepts), type != "ebook platform") |>
  slice_max(cited_by_count, n = 9) |>
  distinct(display_name, .keep_all = TRUE) |>
  select(jour = display_name, x_concepts) |>
  tidyr::unnest(x_concepts) |>
  filter(level == 0) |>
  left_join(concept_abbrev, by = join_by(id, display_name)) |>
  mutate(
    abbreviation = gsub(" ", "<br>", abbreviation),
    jour = gsub("Journal of|Journal of the", "J.", gsub("\\(.*?\\)", "", jour))
  ) |>
  tidyr::complete(jour, abbreviation, fill = list(score = 0)) |>
  group_by(jour) |>
  mutate(
    color = if_else(score > 10, "#1A1A1A", "#D9D9D9"), # CCCCCC
    label = paste0("<span style='color:", color, "'>", abbreviation, "</span>")
  ) |>
  ungroup()

jours |>
  ggplot() +
  aes(fill = jour, y = score, x = abbreviation, group = jour) +
  facet_wrap(~jour) +
  geom_hline(yintercept = c(45, 90), colour = "grey90", linewidth = 0.2) +
  geom_segment(
    aes(x = abbreviation, xend = abbreviation, y = 0, yend = 100),
    color = "grey95"
  ) +
  geom_col(color = "grey20") +
  coord_polar(clip = "off") +
  theme_bw() +
  theme(
    plot.background = element_rect(fill = "transparent", colour = NA),
    panel.background = element_rect(fill = "transparent", colour = NA),
    panel.grid = element_blank(),
    panel.border = element_blank(),
    axis.text = element_blank(),
    axis.ticks.y = element_blank()
  ) +
  ggtext::geom_richtext(
    aes(y = 120, label = label),
    fill = NA, label.color = NA, size = 3
  ) +
  scale_fill_brewer(palette = "Set1", guide = "none") +
  labs(y = NULL, x = NULL, title = "Journal clocks")

四、滚雪球搜索

用户还可以使用oa_snowball执行滚雪球操作。滚雪球是一种文献检索技术，研究人员从一组文章开始，找到引用或被原始集合引用的文章。Oa_snowball返回一个包含两个元素的列表:节点和边（nodes and edges）。与oa_fetch类似，oa_snowball查找并返回满足某些条件的核心文章集的信息，但与oa_fetch不同的是，它还返回引用过和被该核心文章集引用过的文章的信息。

# The packages ggraph and tidygraph need to be installed to run this chunk
library(ggraph)
library(tidygraph)

## 
## 载入程辑包：'tidygraph'

## The following object is masked from 'package:stats':
## 
##     filter

#> 
#> Attaching package: 'tidygraph'
#> The following object is masked from 'package:stats':
#> 
#>     filter

snowball_docs <- oa_snowball(
  identifier = c("W1964141474", "W1963991285"),
  verbose = TRUE
)

## Requesting url: https://api.openalex.org/works?filter=openalex%3AW1964141474%7CW1963991285

## 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%3AW1963991285%7CW1964141474

## Getting 3 pages of results with a total of 533 records...

## Collecting all documents cited by the target papers...

## Requesting url: https://api.openalex.org/works?filter=cited_by%3AW1963991285%7CW1964141474

## Getting 1 page of results with a total of 91 records...

#> Requesting url: https://api.openalex.org/works?filter=openalex%3AW1964141474%7CW1963991285
#> 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%3AW1963991285%7CW1964141474
#> Getting 3 pages of results with a total of 533 records...
#> Collecting all documents cited by the target papers...
#> Requesting url: https://api.openalex.org/works?filter=cited_by%3AW1963991285%7CW1964141474
#> Getting 1 page of results with a total of 91 records...

ggraph(graph = as_tbl_graph(snowball_docs), layout = "stress") +
  geom_edge_link(aes(alpha = after_stat(index)), show.legend = FALSE) +
  geom_node_point(aes(fill = oa_input, size = cited_by_count), shape = 21, color = "white") +
  geom_node_label(aes(filter = oa_input, label = id), nudge_y = 0.2, size = 3) +
  scale_edge_width(range = c(0.1, 1.5), guide = "none") +
  scale_size(range = c(3, 10), guide = "none") +
  scale_fill_manual(values = c("#a3ad62", "#d46780"), na.value = "grey", name = "") +
  theme_graph() +
  theme(
    plot.background = element_rect(fill = "transparent", colour = NA),
    panel.background = element_rect(fill = "transparent", colour = NA),
    legend.position = "bottom"
  ) +
  guides(fill = "none")

五、N-grams

OpenAlex提供了对Work实体(这些实体的id以“W”开头)的全文N-grams(有限)支持。给定一个实体id向量，oa_ngrams返回每个实体的n个gram数据的数据框(在ngrams列表列中)。

ngrams_data <- oa_ngrams(
  works_identifier = c("W1964141474", "W1963991285"),
  verbose = TRUE
)

ngrams_data

## # A tibble: 2 × 4
##   id                               doi                              count ngrams
##   <chr>                            <chr>                            <int> <list>
## 1 https://openalex.org/W1964141474 https://doi.org/10.1016/j.conb.…  2733 <df>  
## 2 https://openalex.org/W1963991285 https://doi.org/10.1126/science…  2338 <df>

#> # A tibble: 2 × 4
#>   id                               doi                              count ngrams
#>   <chr>                            <chr>                            <int> <list>
#> 1 https://openalex.org/W1964141474 https://doi.org/10.1016/j.conb.…  2733 <df>  
#> 2 https://openalex.org/W1963991285 https://doi.org/10.1126/science…  2338 <df>

lapply(ngrams_data$ngrams, head, 3)

## [[1]]
##                                        ngram ngram_count ngram_tokens
## 1                 brain basis and core cause           2            5
## 2                     cause be not yet fully           2            5
## 3 include structural and functional magnetic           2            5
##   term_frequency
## 1   0.0006637902
## 2   0.0006637902
## 3   0.0006637902
## 
## [[2]]
##                                          ngram ngram_count ngram_tokens
## 1          intact but less accessible phonetic           1            5
## 2 accessible phonetic representation in Adults           1            5
## 3       representation in Adults with Dyslexia           1            5
##   term_frequency
## 1   0.0003756574
## 2   0.0003756574
## 3   0.0003756574

#> [[1]]
#>                                        ngram ngram_count ngram_tokens
#> 1                 brain basis and core cause           2            5
#> 2                     cause be not yet fully           2            5
#> 3 include structural and functional magnetic           2            5
#>   term_frequency
#> 1   0.0006637902
#> 2   0.0006637902
#> 3   0.0006637902
#> 
#> [[2]]
#>                                          ngram ngram_count ngram_tokens
#> 1          intact but less accessible phonetic           1            5
#> 2 accessible phonetic representation in Adults           1            5
#> 3       representation in Adults with Dyslexia           1            5
#>   term_frequency
#> 1   0.0003756574
#> 2   0.0003756574
#> 3   0.0003756574

ngrams_data |>
  tidyr::unnest(ngrams) |>
  filter(ngram_tokens == 2) |>
  select(id, ngram, ngram_count) |>
  group_by(id) |>
  slice_max(ngram_count, n = 10, with_ties = FALSE) |>
  ggplot(aes(ngram_count, forcats::fct_reorder(ngram, ngram_count))) +
  geom_col(aes(fill = id), show.legend = FALSE) +
  facet_wrap(~id, scales = "free_y") +
  labs(
    title = "Top 10 fulltext bigrams",
    x = "Count",
    y = NULL
  )

oa_ngrams有时会很慢，因为n-gram的数据可能会变得非常大，但考虑到n-gram是“通过CDN缓存的”[https://docs.openalex.org/api-entities/works/get-n-grams#api-endpoint]，您也可以考虑对这种特殊情况进行并行化(如果您使用{curl} >= v5.0.0, oa_ngrams会自动进行并行化)。

来源：https://github.com/ropensci/openalexR
https://baijiahao.baidu.com/s?id=1722906102002408666&wfr=spider&for=pc
https://zhuanlan.zhihu.com/p/643347576

利用R语言访问openalex学术索引

任建超

2024-05-04

一、设置

二、举例

1、成果（WORKS）

（1）基于已知的文献信息（DOIs）下载该文献的所有信息

（2）基于作者信息（ORCIDs）下载该作者的所有成果

（3）基于研究主题下载文献

2、作者（Authors）

(1)当我们知道作者的ORCID时，下载作者信息。

（2）获取有关此package作者的信息。

（3）下载某一单位的所有文献

三、实例分析

1、追踪生物学概念（Biology）的流行程度。

2、根据总引用次数对意大利的机构进行排名。

3、他们发表在哪些刊物上？

4、大型期刊主题的可视化。

四、滚雪球搜索

五、N-grams

利用R语言访问openalex学术索引

任建超

2024-05-04

一、设置

二、举例

1、成果（WORKS）

（1）基于已知的文献信息（DOIs）下载该文献的所有信息

（2）基于作者信息（ORCIDs）下载该作者的所有成果

（3） 基于研究主题下载文献

2、作者（Authors）

(1)当我们知道作者的ORCID时，下载作者信息。

（2）获取有关此package作者的信息。

（3） 下载某一单位的所有文献

三、实例分析

1、追踪生物学概念（Biology）的流行程度。

2、根据总引用次数对意大利的机构进行排名。

3、他们发表在哪些刊物上？

4、大型期刊主题的可视化。

四、滚雪球搜索

五、N-grams

（3）基于研究主题下载文献

（3）下载某一单位的所有文献