I’ve previously written about Google Scholar. Its usefulness and its instability. I just read a post by Jon Tennant on how to harvest Google Scholar data in R and I thought I would use his code as the basis to generate some nice plots based on Google Scholar data.
A script for R is below and can be found here. Graphics are base R but do the job.
First of all I took it for a spin on my own data. The outputs are shown here:
These were the most interesting plots that sprang to mind. First is a ranked citation plot which also shows y=x to find the Hirsch number. Second, was to look at total citations per year to all papers over time. Google Scholar shows the last few years of this plot in the profile page. Third, older papers accrue more citations, but how does this look for all papers? Finally, a prediction of what my H-index will do over time (no prizes for guessing that it will go up!). As Jon noted, the calculation comes from this paper.
While that’s interesting, we need to get the data of a scholar with a huge number of papers and citations. Here is George Church.
At the time of writing he has 763 papers with over 90,000 citations in total and a H-index of 147. Interestingly ~10% of his total citations come from a monster paper in PNAS with Wally Gilbert in the mid 80s on genome sequencing.
Feel free to grab/fork this code and have a play yourself. If you have other ideas for plots or calculations, add a comment here or an issue at GitHub.
if(!require(scholar)){
install.packages("scholar")
}
library(scholar)
# Add Google Scholar ID of interest here
ID <- ""
# If you didn't add one to the script prompt user to add one
if(ID == ""){
ID <- readline(prompt="Enter Scholar ID: ")
}
# Get the citation history
citeByYear<-get_citation_history(ID)
# Get profile information
profile <- get_profile(ID)
# Get publications and save as a csv
pubs <- get_publications(ID)
write.csv(pubs, file = "citations.csv")
# Predict h-index
hIndex <- predict_h_index(ID)
# Now make some plots
# Plot of total citations by year
png(file = "citationsByYear.png")
plot(citeByYear$year,citeByYear$cites,
type="h", xlab="Year", ylab = "Total Cites")
dev.off()
# Plot of ranked paper by citation with h
png(file = "citationsAndH.png")
plot(pubs$cites, type="l",
xlab="Paper rank", ylab = "Citations per paper")
abline(0,1)
text(nrow(pubs),max(pubs$cites, na.rm = TRUE),
profile$h_index)
dev.off()
# Plot of cites to paper by year
png(file = "citesByYear.png")
plot(pubs$year, pubs$cites,
xlab="Year", ylab = "Citations per paper")
dev.off()
# Plot of h-index prediction
thisYear <- as.integer(format(Sys.Date(), "%Y"))
png(file = "hPred.png")
plot(hIndex$years_ahead+thisYear,hIndex$h_index,
ylim = c(0, max(hIndex$h_index, na.rm = TRUE)),
type = "h",
xlab="Year", ylab = "H-index prediction")
dev.off()
Note that my previous code used a python script to grab Google Scholar data. While that script worked well, the scholar package for R seems a lot more reliable.
—
I have a surprising number of tracks in my library with Rollercoaster in the title. This time I will go with the Jesus & Mary Chain track from Honey’s Dead.