Querying PubMed using R

I've seen talks over the years where the speaker shows a bar chart with the number of articles in PubMed that contain a certain keyword and tallied per year. In most of the cases the speaker was trying to illustrate the growing number of articles that contain the keyword. Here I try to do the same by querying PubMed using R.

#install the RISmed package
install.packages("RISmed")
library(RISmed)

#now let's look up this dude called Dave Tang
res <- EUtilsSummary('dave tang', type='esearch', db='pubmed')

summary(res)
Query:
Tang, Dave[Full Author Name] 

Result count:  10

#what are the PubMed ids for the Author Dave Tang?
QueryId(res)
 [1] "23180801" "22976001" "22722852" "21888672" "21386911" "20510229" "19648138" "19501082" "19393063"
[10] "19270757"

#limit by date
res2 <- EUtilsSummary('dave tang', type='esearch', db='pubmed', mindate='2012', maxdate='2012')

summary(res2)
Query:
Tang, Dave[Full Author Name] AND 2012[EDAT] : 2012[EDAT] 

Result count:  3

#three publications in 2012
QueryId(res2)
[1] "23180801" "22976001" "22722852"

I'm interested in the number of publications containing the word "retrotransposon" that are in PubMed, tallied per year, from 1970 (the year the discovery of reverse transcriptase was published) to 2013.

#first how many total articles containing retrotransposon
res3 <- EUtilsSummary('retrotransposon', type='esearch', db='pubmed')

summary(res3)
Query:
"retroelements"[MeSH Terms] OR "retroelements"[All Fields] OR "retrotransposon"[All Fields] 

Result count:  8123

#if you only want the number of articles
QueryCount(res3)
[1] 8123

#tally each year beginning at 1970
#In order not to overload the E-utility servers, NCBI recommends that users post no more than three
#URL requests per second and limit large jobs to either weekends or between 9:00 PM and 5:00 AM
#Eastern time during weekdays. Failure to comply with this policy may result in an IP address being
#blocked from accessing NCBI.

tally <- array()
x <- 1
for (i in 1970:2013){
  Sys.sleep(1)
  r <- EUtilsSummary('retrotransposon', type='esearch', db='pubmed', mindate=i, maxdate=i)
  tally[x] <- QueryCount(r)
  x <- x + 1
}

names(tally) <- 1970:2013
max(tally)
[1] 573

barplot(tally, las=2, ylim=c(0,600), main="Number of PubMed articles containing retrotransposon")

How about the word "transposon"?

transposon <- array()
x <- 1
for (i in 1970:2013){
  Sys.sleep(1)
  r <- EUtilsSummary('transposon', type='esearch', db='pubmed', mindate=i, maxdate=i)
  transposon[x] <- QueryCount(r)
  x <- x + 1
}

names(transposon) <- 1970:2013
max(transposon)
[1] 1634

barplot(transposon, las=2, ylim=c(0,2000), main="Number of PubMed articles containing transposon")

Is there an upward trend for any keyword, due to the increase in the number of journals and database entries?

trna <- array()
x <- 1
for (i in 1970:2013){
  Sys.sleep(1)
  r <- EUtilsSummary('trna', type='esearch', db='pubmed', mindate=i, maxdate=i)
  trna[x] <- QueryCount(r)
  x <- x + 1
}

names(trna) <- 1970:2013
max(trna)
[1] 2015

barplot(trna, las=2, ylim=c(0,2000), main="Number of PubMed articles containing trna")

Normalising by total article counts

To get the total number of articles for a given year, just use an empty query.

test <- EUtilsSummary('', type='esearch', db='pubmed', mindate=1970, maxdate=1970)
summary(test)
Query:
1970[EDAT] : 1970[EDAT] 

Result count:  218690

#number of articles each year
total <- array()
x <- 1
for (i in 1970:2013){
  Sys.sleep(1)
  r <- EUtilsSummary('', type='esearch', db='pubmed', mindate=i, maxdate=i)
  total[x] <- QueryCount(r)
  x <- x + 1
}

names(total) <- 1970:2013
max(total)
[1] 917657

barplot(total, las=2, ylim=c(0,1000000), main="Number of PubMed articles each year")

The dip in 1997 is a consequence of the 1997 Asian financial crisis?

Now to normalise the previous searches:

tally_norm <- tally / total
transposon_norm <- transposon / total
trna_norm <- trna / total

par(mfrow=c(1,3))
barplot(tally_norm, las=2)
barplot(transposon_norm, las=2)
barplot(trna_norm, las=2)
#reset
par(mfrow=c(1,1))

Conclusions

So there you have it, querying PubMed using R via the CRAN package RISmed. More complicated queries can be built and for those interested have a look at the manual.