knitr::opts_chunk$set(tidy = FALSE, message = FALSE)
library("scholar") library("ggplot2") theme_set(theme_minimal())
## Define the id for Richard Feynman id <- 'B7vSqZsAAAAJ' ## Get his profile l <- get_profile(id) ## Print his name and affliation l$name l$affiliation ## Print his citation index l$h_index l$i10_index
get_publications()
return a data.frame
of publication records. It contains
information of the publications, including title, author list, page
number, citation number, publication year, etc..
The pubid
is the article ID used by Google Scholar and the identifier
that is used to retrieve the citation history of a selected publication.
## Get his publications (a large data frame) p <- get_publications(id) head(p, 3)
## Get his citation history, i.e. citations to his work in a given year ct <- get_citation_history(id) ## Plot citation trend library(ggplot2) ggplot(ct, aes(year, cites)) + geom_line() + geom_point()
Users can retrieve the citation history of a particular publication with
get_article_cite_history()
.
## The following publication will be used to demonstrate article citation history as.character(p$title[1]) ## Get article citation history ach <- get_article_cite_history(id, p$pubid[1]) ## Plot citation trend ggplot(ach, aes(year, cites)) + geom_segment(aes(xend = year, yend = 0), size=1, color='darkgrey') + geom_point(size=3, color='firebrick')
You can compare the citation history of scholars by fetching data with
compare_scholars
.
# Compare Feynman and Stephen Hawking ids <- c('B7vSqZsAAAAJ', 'qj74uXkAAAAJ') # Get a data frame comparing the number of citations to their work in # a given year cs <- compare_scholars(ids) ## remove some 'bad' records without sufficient information cs <- subset(cs, !is.na(year) & year > 1900) ggplot(cs, aes(year, cites, group=name, color=name)) + geom_line() + theme(legend.position="bottom") ## Compare their career trajectories, based on year of first citation csc <- compare_scholar_careers(ids) ggplot(csc, aes(career_year, cites, group=name, color=name)) + geom_line() + geom_point() + theme(legend.position=c(.2, .8))
# Be careful with specifying too many coauthors as the visualization of the # network can get very messy. coauthor_network <- get_coauthors('amYIKXQAAAAJ&hl', n_coauthors = 7) coauthor_network
And then we have a built-in function to plot this visualization.
plot_coauthors(coauthor_network)
Note however, that these are the coauthors listed in Google Scholar profile and not coauthors from all publications.
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