Journal Impact Factor & Google Scholar

I am currently writing a grant, and grant agencies are very interested in Journal Impact Factors (IF). Sometimes this might be explicit, but in this case I just want to flag the journals I have published in that are the most well known and IF seems to be a good indicator of this. I am not very interested in IF, and so I am not really sure which of my publications are in good places, and which of them are not. So, here is an attempt to automate figuring out which of my publications are in high impact factor places using a combination of Google Scholar and Journal Citation Reports (JCR).

First, I can use the scholar package in R to download all my published articles. This will create a dataframe where each row is a paper, and there are some columns describing that paper, like the title, authors, and most importantly, the journal.

library(knitr)
library(dplyr)
library(scholar)

# My scholar ID
id = 'zrjq62QAAAAJ'

# Get my publications (a data frame)
publications = get_publications(id)

publications[,c("title", "author", "journal", "year", "cites")] %>% 
  head(.) %>% 
  kable(.)

title	author	journal	year	cites
Grambank reveals the importance of genealogical constraints on linguistic diversity and highlights the impact of language loss	H Skirgård, HJ Haynie, DE Blasi, H Hammarström, J Collins, JJ Latarche, …	Science Advances	2023	101
Sequence alignment of folk song melodies reveals cross-cultural regularities of musical evolution	PE Savage, S Passmore, G Chiba, TE Currie, H Suzuki, QD Atkinson	Current Biology	2022	67
CHIELD: the causal hypotheses in evolutionary linguistics database	SG Roberts, A Killin, A Deb, C Sheard, SJ Greenhill, K Sinnemäki, …	Journal of language evolution	2020	43
The Global Jukebox: A public database of performing arts and culture.	ALC Wood, KR Kirby, CR Ember, S Silbert, H Daikoku, J McBride, …	PLOS ONE	2022	39
No universals in the cultural evolution of kinship terminology	S Passmore, FM Jordan	Evolutionary Human Sciences	2020	35
Shared cultural history as a predictor of political and economic changes among nation states	LJ Matthews, S Passmore, PM Richard, RD Gray, QD Atkinson	PloS one	2016	35

Then, I can also use a lesser known package called JCRImpactFactor, to cross-reference the journals I have published in with their Impact Factor as recorded in this package. This package only contains the Impact Factor values up to 2019, but that will do for my purposes.

library(JCRImpactFactor)

# Find impact factors
journals = find.IF.JCR(publications$journal, year = 2019) 

[1] "The following 13 journal(s) are not in JCR"
 [1] "Journal of language evolution" "Evolutionary Human Sciences"  
 [3] "Biological theory"             "Journal of Cognition"         
 [5] "Religion, Brain & Behavior"    "Open Mind"                    
 [7] ""                              ""                             
 [9] "OSF"                           "OSF"                          
[11] "OSF"                           ""                             
[13] "OSF"                          

# I have published in some journals more than once, so remove duplicates
journals = journals[!duplicated(journals$Full.Journal.Title),]

journals %>% 
  arrange(desc(IF2019)) %>%
  kable(., row.names = FALSE)

Full.Journal.Title	IF2019
Science Advances	13.116
Nature Communications	12.121
CURRENT BIOLOGY	9.601
COGNITION	3.294
PLoS One	2.740
Royal Society Open Science	2.647
Topics in Cognitive Science	2.511

It is worth noting that not all Journals have Impact factors recorded by JCR. Notably, this database contains no information on: The Journal of Language Evolution (IF = 2.25), Evolutionary Human Sciences (2.53), Biological Theory (1.14), or Religion, Brain, and Behavior (1.25).

So, in the end it was relatively straightforward to automate, although it doesn’t give perfect results. If anyone knows of a more up-to-date location for Journal Impact Factors, let me know!