Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
Sometimes it’s useful to do a bibliometric analysis. To this end, the rscopus_plus
functions (Bernabeu, 2024) extend the R package rscopus
(Muschelli, 2022) to administer the search quota and to enable specific searches and comparisons.
scopus_search_plus
runsrscopus::scopus_search
as many times as necessary based on the number of results and the search quota.scopus_search_DOIs
gets DOIs fromscopus_search_plus
, which can then be imported into a reference manager, such as Zotero, to create a list of references.scopus_search_additional_DOIs
searches for additional DOIs.scopus_comparison
compares counts of publications on various topics during a certain period.plot_scopus_comparison
draws a line plot with the output fromscopus_comparison
.
Note. Before using any of these functions, the user must read in their Scopus API key confidentially (see rscopus guidelines).
Example of use
As an example (also available on GitHub), we’ll visualise the prevalence of three executive functions in the literatures on second and third language throughout the past two decades.
First, we’ll use the scopus_comparison
function (fragment shown below).
library(rscopus) # set_api_key('your_key_here') # (see https://cran.r-project.org/web/packages/rscopus/vignettes/api_key.html) # I'll read in mine from a file. If you do this, make sure not to share your file. api_key = readLines('scopus_key.txt') set_api_key(api_key) library(dplyr) library(patchwork) # Load in Scopus search functions from https://github.com/pablobernabeu/rscopus_plus source('https://raw.githubusercontent.com/pablobernabeu/rscopus_plus/main/scopus_comparison.R') source('https://raw.githubusercontent.com/pablobernabeu/rscopus_plus/main/plot_scopus_comparison.R') # General parameters search_period = 2000:2023 quota = 20 # Prevalence of three executive functions in second language studies from 2000 to 2023. # In addition to "second language", the reference query includes the terms # "learning" and "cognition" to make the scope of the search more relevant to # the topic of interest. reference_query = '"second language"' comparison_terms = c( '"working memory"', 'inhibition', '"implicit learning"' ) N_comparison_terms = length(comparison_terms) L2_EF = scopus_comparison(reference_query, comparison_terms, search_period, quota, verbose = FALSE, reference_query_field_tag = 'TITLE-ABS-KEY') saveRDS(L2_EF, 'L2_EF.rds') L2_EF = readRDS('L2_EF.rds') # it's possible to load results directly plot_L2_EF = plot_scopus_comparison(L2_EF, pub_count_in_legend = FALSE, pub_count_in_lines = TRUE) + scale_color_manual( values = c( "[ref.] + '\"working memory\"'" = scales::hue_pal()(N_comparison_terms)[1], "[ref.] + 'inhibition'" = scales::hue_pal()(N_comparison_terms)[2], "[ref.] + '\"implicit learning\"'" = scales::hue_pal()(N_comparison_terms)[3] ) ) + guides(colour = guide_legend(override.aes = list(alpha = 1))) + # Prepare layout for the multi-plot combination theme(axis.text.x = element_blank(), axis.title.x = element_blank(), legend.position = 'none', plot.margin = margin(0, 0, 15, 0)) # Prevalence of three executive functions in third language studies from 2000 to 2023. # In addition to "third language", the reference query includes the terms "learning" # and "cognition" to make the scope of the search more relevant to the topic of # interest. reference_query = '"third language"' L3_EF = scopus_comparison(reference_query, comparison_terms, search_period, quota, verbose = FALSE, reference_query_field_tag = 'TITLE-ABS-KEY') saveRDS(L3_EF, 'L3_EF.rds') L3_EF = readRDS('L3_EF.rds') # it's possible to load results directly plot_L3_EF = plot_scopus_comparison(L3_EF, pub_count_in_legend = FALSE, pub_count_in_lines = TRUE) + scale_color_manual( values = c( "[ref.] + '\"working memory\"'" = scales::hue_pal()(N_comparison_terms)[1], "[ref.] + 'inhibition'" = scales::hue_pal()(N_comparison_terms)[2], "[ref.] + '\"implicit learning\"'" = scales::hue_pal()(N_comparison_terms)[3] ) ) + guides(colour = guide_legend(override.aes = list(alpha = 1))) + # Prepare layout for the multi-plot combination theme(axis.text.x = element_text(margin = margin(7, 0, 0, 0, 'pt')), axis.title.x = element_text(margin = margin(8, 0, 0, 0, 'pt')), legend.justification = 'right', plot.margin = margin(0, 0, 0, 0)) # Combine plots plot_L2_EF + plot_L3_EF + plot_layout(ncol = 1, axes = 'collect') & theme(axis.text = element_text(size = 10), axis.title = element_text(vjust = 0.5, size = 13), plot.title = element_markdown(hjust = 0.5, size = 12), legend.text = element_text(size = 12), legend.background = element_rect(color = 'grey70'), legend.margin = margin(-5, 5, 2, 0))
The total number of publications over the current period is shown between brackets after each query.
References
Bernabeu, P. (2024). rscopus_plus (v1.1.5). Zenodo. https://doi.org/10.5281/zenodo.10689747
Muschelli, J. (2022). Package ’rscopus’. CRAN. https://cran.r-project.org/web/packages/rscopus/rscopus.pdf
R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.