Promoting the use of R in the NHS

Blog Article

This post was originally published on this site

(This article was first published on R-english – Freakonometrics, and kindly contributed to R-bloggers)

In a previous post, I discussed how it was possible to scrap the NSERC website to get stats about discovery grants. Since we just got the new 2018 figures, I thought it would be a good opportunity to update my graphs,

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library(XML)
library(stringr)
url="http://www.nserc-crsng.gc.ca/NSERC-CRSNG/FundingDecisions-DecisionsFinancement/ResearchGrants-SubventionsDeRecherche/ResultsGSC-ResultatsCSS_eng.asp"
download.file(url,destfile = "GSC.html")
library(XML)
tables=readHTMLTable("GSC.html")
GSC=tables[[1]]$V1
GSC=as.character(GSC[-(1:2)])
namesGSC=tables[[1]]$V2
namesGSC=as.character(namesGSC[-(1:2)])
Correction = function(x) as.numeric(gsub('[$,]', '', x))
YEAR=2013:2018
for(i in 1:length(YEAR)){
y=YEAR[i]
grants= function(gsc){
  url=paste("http://www.nserc-crsng.gc.ca/NSERC-CRSNG/FundingDecisions-DecisionsFinancement/ResearchGrants-SubventionsDeRecherche/ResultsGSCDetail-ResultatsCSSDetails_eng.asp?Year=",y,"&GSC=",gsc,sep="")
  download.file(url,destfile = "GSC.html")
  library(XML)
  tables=readHTMLTable("GSC.html")
  X=as.character(tables[[1]]$"Awarded Amount")
  A=as.numeric(Vectorize(Correction)(X))
  return(c(median(A),mean(A),as.numeric(quantile(A,(1:99)/100))))
}
M=Vectorize(grants)(GSC[1:12])
plot(M[3:101,8],(1:99)/100,type="s",xlim=c(0,130000),xlab=
paste("Annual Discovery Grant (CAN) - ",y,sep=""),ylab="")
lines(M[3:101,5],(1:99)/100,type="s",col="red")
lines(M[3:101,4],(1:99)/100,type="s",col="blue")
abline(v=M[3,5],lty=2,col=rgb(1,0,0,.4))
idx=which(M[3:101,8]<M[3,5])
lines(M[2+idx,8],(idx)/100,type="s",lwd=4)
legend("bottomright",c("maths","physics","chemestry"),
col=c("black","red","blue"),lty=1,bty="n")}

With those functions, I plot the cumulative distribution functions for three disciplines, manely maths, physics and chemistry. I added a line for the lowest value in physics (the vertical line), and the bold line shows the proportion of researchers in maths who got less than the lowest amount in physics,

Hence, in 2013, 60% of the researchers in maths get less than any researcher in physics (and more than 90% in maths get less than any researcher in chemistry). Then, from 2014 to 2018, we get

It is rather constant : 50% of the researchers in mathematics in Canada get less than any researcher in physics, or in chemistry. I don’t understand why, but it’s interesting to observe that this is very stable…

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