Analysing LLM outputs
Burnet Bioinformatics group
2026-03-12
library("gplots")
library("kableExtra")PMC data
Read corpus data from PMC.
x <- readRDS("pmcres.Rds")
x$issn1 <- unlist(lapply(x$journalIssn, function(issn) {
gsub("-","",unlist(strsplit(issn,"; "))[1])
} ))
x$issn2 <- unlist(lapply(x$journalIssn, function(issn) {
i2 <- gsub("-","",unlist(strsplit(issn,"; "))[2])
} ))
sjr <- read.csv("scimagojr_2024.csv",header=TRUE, sep=";")
sjr$Issn1 <- sapply(strsplit(sjr$Issn,", "),"[[",1)
sjr$Issn2 <- lapply(strsplit(sjr$Issn,", "),function(x) { x[length(x)]} )
x$sjr1 <- sjr[match(x$issn1,sjr$Issn1),"SJR"]
x$sjr2 <- sjr[match(x$issn1,sjr$Issn2),"SJR"]
x$sjr3 <- sjr[match(x$issn2,sjr$Issn1),"SJR"]
x$sjr4 <- sjr[match(x$issn2,sjr$Issn2),"SJR"]
x$sjr <- apply(data.frame(x$sjr1,x$sjr2,x$sjr3,x$sjr4),1,function(x) {
x2 <- as.numeric(gsub(",",".",x)) # convert to numerical
x3 <- c(x2,0) # add a zero to the values, smart
x4 <- x3[!is.na(x3)] # remove na values, zeroes can remain
max(x4) # get the max value. seems to work
} )
# remove redundant columns
x <- x[,-which(colnames(x) %in% c("sjr1","sjr2","sjr3","sjr4"))]
hist(x$sjr)
message("High impact articles with SJR>5")## High impact articles with SJR>5xt <- table(x$sjr>5)
xt##
## FALSE TRUE
## 66091 1798xt[2] / sum(xt) * 100 #percent with sjr>5 = 2.65 %## TRUE
## 2.648441message("Medium impact articles with SJR between 2 and 5")## Medium impact articles with SJR between 2 and 5xt <- table(x$sjr>2 & x$sjr<=5 )
xt##
## FALSE TRUE
## 57532 10357xt[2] / sum(xt) * 100 #percent with 2<SJR< 5 = 15.26%## TRUE
## 15.25579message("Low impact articles with SJR between 0 and 2")## Low impact articles with SJR between 0 and 2xt <- table(x$sjr>0 & x$sjr<=2 )
xt##
## FALSE TRUE
## 21329 46560xt[2] / sum(xt) * 100 #percent with sjr<2 = 68.58%## TRUE
## 68.58254message("Low impact articles with SJR=0")## Low impact articles with SJR=0xt <- table(x$sjr==0)
xt##
## FALSE TRUE
## 58715 9174xt[2] / sum(xt) * 100 #percent with no sjr## TRUE
## 13.51323Read LLM results
Here we will attempt to read the LLM results.
llmres_files <- head(list.files("llmoutputs/",full.names=TRUE),1000)
extract_responses <- function(i) {
txtdat <- readLines(llmres_files[i])
txtdat[(length(txtdat)-9):length(txtdat)]
txtdat <- gsub("\\*","",txtdat)
PMCID <- sapply(strsplit(sapply(strsplit(llmres_files[i],"//"),"[[",2),"_"),"[[",1)
# Question 1 - tool used
q1response <- txtdat[grep("\\| 1",txtdat)]
q1response <- unlist(strsplit(q1response,"\\|"))
q1response <- q1response[length(q1response)]
q1response <- gsub(" $","",gsub("^ ","",q1response))
# Question 2 - tool version
q2response <- txtdat[grep("\\| 2",txtdat)]
q2response <- unlist(strsplit(q2response,"\\|"))
q2response <- q2response[length(q2response)]
q2response <- gsub(" $","",gsub("^ ","",q2response))
# Question 3 - gene sets used
q3response <- txtdat[grep("\\| 3",txtdat)]
q3response <- unlist(strsplit(q3response,"\\|"))
q3response <- q3response[length(q3response)]
q3response <- gsub(" $","",gsub("^ ","",q3response))
# Question 4 - background specified
q4response <- txtdat[grep("\\| 4",txtdat)]
q4response <- unlist(strsplit(q4response,"\\|"))
q4response <- q4response[length(q4response)]
q4response <- gsub(" $","",gsub("^ ","",q4response))
# Question 5 - stat test described
q5response <- txtdat[grep("\\| 5",txtdat)]
q5response <- unlist(strsplit(q5response,"\\|"))
q5response <- q5response[length(q5response)]
q5response <- gsub(" $","",gsub("^ ","",q5response))
# Question 6 - FDR described
q6response <- txtdat[grep("\\| 6",txtdat)]
q6response <- unlist(strsplit(q6response,"\\|"))
q6response <- q6response[length(q6response)]
q6response <- gsub(" $","",gsub("^ ","",q6response))
res <- c("PMCID"=PMCID,"Q1"=q1response,"Q2"=q2response,
"Q3"=q3response,"Q4"=q4response,"Q5"=q5response,"Q6"=q6response)
return(res)
}
llmres_files <- list.files("llmoutputs/",full.names=TRUE)
llmresl <- lapply(1:length(llmres_files),extract_responses)
llmresdf <- data.frame(do.call(rbind,llmresl))## Warning in (function (..., deparse.level = 1) : number of columns of result is
## not a multiple of vector length (arg 62)Exclude horizontal tables
HORIZONTAL <- grep("PMC",llmresdf[,7])
llmresdf <- llmresdf[-HORIZONTAL,]
dim(llmresdf)## [1] 65998 7HORIZONTAL## [1] 325 3135 43691Initial analysis
llmscores <- lapply(1:nrow(llmresdf), function(i) {
Q1score <- ! grepl("^No",llmresdf[i,"Q1"] )
Q2score <- ! grepl("^No",llmresdf[i,"Q2"] )
Q3score <- ! grepl("^No",llmresdf[i,"Q3"] )
Q4score <- ! grepl("^No",llmresdf[i,"Q4"] )
Q5score <- ! grepl("^No",llmresdf[i,"Q5"] )
Q6score <- ! grepl("^No",llmresdf[i,"Q6"] )
scores <- c("Q1score"=Q1score,"Q2score"=Q2score,"Q3score"=Q3score,
"Q4score"=Q4score,"Q5score"=Q5score,"Q6score"=Q6score)
return(as.numeric(scores))
})
llmresdf2 <- data.frame(llmresdf,do.call(rbind,llmscores))
q1 <- table(llmresdf2$X1)
message("Reporting tool")## Reporting toolunname(q1[2]/sum(q1))## [1] 0.8265857q2 <- table(llmresdf2$X2)
message("Reporting tool version")## Reporting tool versionunname(q2[2]/sum(q2))## [1] 0.2630534q3 <- table(llmresdf2$X3)
message("Reporting gene sets")## Reporting gene setsunname(q3[2]/sum(q3))## [1] 0.9060275q4 <- table(llmresdf2$X4)
message("Reporting background")## Reporting backgroundunname(q4[2]/sum(q4))## [1] 0.04409225q5 <- table(llmresdf2$X5)
message("Reporting stat test")## Reporting stat testunname(q5[2]/sum(q5))## [1] 0.1862632q6 <- table(llmresdf2$X6)
message("Reporting p-value correction")## Reporting p-value correctionunname(q6[2]/sum(q6))## [1] 0.2613716Combine bibiographic and methodological data
head(llmresdf2,1) #PMCID## PMCID Q1 Q2
## 1 PMC10000051 EnrichR Not described
## Q3 Q4
## 1 KEGG pathways; Gene Ontology (GO) Biological Process terms Not described
## Q5 Q6 X1 X2 X3 X4 X5 X6
## 1 Not described No (not mentioned) 1 0 1 0 0 0head(x,1) #pmcid## # A data frame: 1 x 31
## id source pmid pmcid doi title authorString journalTitle journalVolume
## * <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 407043~ MED 4070~ PMC1~ 10.1~ PDLI~ Nicaise Y, ~ MicroPubl B~ 2025
## # i 22 more variables: pubYear <chr>, journalIssn <chr>, pubType <chr>,
## # isOpenAccess <chr>, inEPMC <chr>, inPMC <chr>, hasPDF <chr>, hasBook <chr>,
## # hasSuppl <chr>, citedByCount <int>, hasReferences <chr>,
## # hasTextMinedTerms <chr>, hasDbCrossReferences <chr>, hasLabsLinks <chr>,
## # hasTMAccessionNumbers <chr>, firstIndexDate <chr>,
## # firstPublicationDate <chr>, issue <chr>, pageInfo <chr>, issn1 <chr>,
## # issn2 <chr>, sjr <dbl>m <- merge(x,llmresdf2,by.x="pmcid",by.y="PMCID")
head(m,2)## pmcid id source pmid doi
## 1 PMC10000051 36900171 MED 36900171 10.3390/cancers15051378
## 2 PMC10000056 36899744 MED 36899744 10.3390/ani13050887
## title
## 1 Downregulation of Dystrophin Expression Occurs across Diverse Tumors, Correlates with the Age of Onset, Staging and Reduced Survival of Patients.
## 2 Effects of Dietary Alpha-Lipoic Acid on Growth Performance, Serum Biochemical Indexes, Liver Antioxidant Capacity and Transcriptome of Juvenile Hybrid Grouper (<i>Epinephelus fuscoguttatus</i><U+2640> <U+00D7> <i>Epinephelus polyphekadion</i><U+2642>).
## authorString
## 1 Alnassar N, Borczyk M, Tsagkogeorga G, Korostynski M, Han N, G<U+00F3>recki DC.
## 2 Ou G, Xie R, Huang J, Huang J, Wen Z, Li Y, Jiang X, Ma Q, Chen G.
## journalTitle journalVolume pubYear journalIssn
## 1 Cancers (Basel) 15 2023 2072-6694
## 2 Animals (Basel) 13 2023 2076-2615
## pubType isOpenAccess inEPMC inPMC hasPDF hasBook
## 1 research-article; journal article Y Y Y Y N
## 2 research-article; journal article Y Y Y Y N
## hasSuppl citedByCount hasReferences hasTextMinedTerms hasDbCrossReferences
## 1 Y 15 Y Y N
## 2 N 5 Y Y N
## hasLabsLinks hasTMAccessionNumbers firstIndexDate firstPublicationDate issue
## 1 Y Y 2023-03-12 2023-02-21 5
## 2 Y Y 2023-03-12 2023-02-28 5
## pageInfo issn1 issn2 sjr Q1 Q2
## 1 1378 20726694 <NA> 1.462 EnrichR Not described
## 2 887 20762615 <NA> 0.733 Not described Not described
## Q3
## 1 KEGG pathways; Gene Ontology (GO) Biological Process terms
## 2 GO (Gene Ontology); KEGG (Kyoto Encyclopedia of Genes and Genomes)
## Q4 Q5 Q6 X1 X2 X3 X4 X5 X6
## 1 Not described Not described No (not mentioned) 1 0 1 0 0 0
## 2 Not described Not described Not described 0 0 1 0 0 0Clean data
Make sure we’re dealing with whole research papers.
Remove duplicated PMCIDs.
m <- m[grep("journal article",m$pubType),]
m <- m[-which(duplicated(m$pmid)),]Tool use
tail(sort(table(llmresdf$Q1)),20)##
## g:Profiler
## 201
## ClusterProfiler
## 205
## WebGestalt
## 251
## KOBAS
## 285
## MetaboAnalyst 5.0
## 314
## Not described (no specific software/package named)
## 371
## Gene Set Enrichment Analysis (GSEA)
## 394
## clusterProfiler
## 511
## MetaboAnalyst
## 518
## Enrichr
## 554
## GSEA (Gene Set Enrichment Analysis)
## 620
## Ingenuity Pathway Analysis (IPA)
## 627
## DAVID (Database for Annotation, Visualization, and Integrated Discovery)
## 650
## ClusterProfiler (R package)
## 666
## clusterProfiler R package
## 968
## Metascape
## 1317
## DAVID
## 1582
## DAVID (Database for Annotation, Visualization and Integrated Discovery)
## 1980
## clusterProfiler (R package)
## 3018
## Not described
## 9423tools <- c("clusterProfiler"=length(grep("CLUSTERPROFILER",llmresdf$Q1,ignore.case=TRUE)),
"DAVID"=length(grep("DAVID",llmresdf$Q1)),
"Not described"=length(grep("Not",llmresdf$Q1)),
"KOBAS"=length(grep("KOBAS",llmresdf$Q1,ignore.case=TRUE)),
"IPA"=length( unique(grep("Ingenuity",llmresdf$Q1), grep("IPA",llmresdf$Q1) ) ),
"Enrichr"=length(grep("EnrichR",llmresdf$Q1,ignore.case=TRUE)),
"GSVA"=length(grep("GSVA",llmresdf$Q1)),
"fgsea"=length(grep("FGSEA",llmresdf$Q1,ignore.case=TRUE)),
"ReactomePA"=length(grep("reactomepa",llmresdf$Q1,ignore.case=TRUE)),
"GSEA"=length(grep("GSEA",llmresdf$Q1)),
"Metascape"=length(grep("Metascape",llmresdf$Q1)),
"MetaboAnalyst"=length(grep("MetaboAnalyst",llmresdf$Q1)),
"WebGestalt"=length(grep("WebGestalt",llmresdf$Q1,ignore.case=TRUE)),
"gProfiler"=length(grep("g:Profiler",llmresdf$Q1)),
"GOseq"=length(grep("GOseq",llmresdf$Q1)) ,
"Cytoscape"=length(grep("cytoscape",llmresdf$Q1,ignore.case=TRUE)),
"STRING"=length(grep("string",llmresdf$Q1,ignore.case=TRUE)),
"ShinyGO"=length(grep("ShinyGO",llmresdf$Q1)) )
sort(tools)## ReactomePA ShinyGO fgsea gProfiler GOseq
## 464 564 605 755 958
## WebGestalt STRING Cytoscape GSVA IPA
## 1063 1387 1771 2157 2228
## Enrichr KOBAS Metascape MetaboAnalyst DAVID
## 2373 3189 3345 3575 10472
## GSEA Not described clusterProfiler
## 10898 11434 14080par(mar=c(5.1,10.1,4.1,2.1))
barplot(sort(tools),horiz=TRUE,las=1,main="Popular tools",
xlim=c(0,14000),xlab="no. articles")
abline(v=c(2000,4000,6000,8000,10000,12000,14000),lty=2,lwd=0.6,col="gray")
par(mar=c(5.1,4.1,4.1,2.1))Aggregate by SJR group
mylevels <- c("high","mid","low","vlow")
sjrl <- lapply(mylevels,function(x) {
if ( x == "high" ) { mx <- subset(m,sjr>5) }
if ( x == "mid" ) { mx <- subset(m,sjr<=5 & sjr>2) }
if ( x == "low" ) { mx <- subset(m,sjr<=2 & sjr>0) }
if ( x == "vlow" ) { mx <- subset(m,sjr==0) }
mxora <- mx[grep("GSEA",mx$Q1,invert=TRUE,ignore.case=TRUE),]
mxora <- mx[grep("GSVA",mx$Q1,invert=TRUE,ignore.case=TRUE),]
q1 <- c(length(which(mx$X1==0)),length(which(mx$X1==1)))
q2 <- c(length(which(mx$X2==0)),length(which(mx$X2==1)))
q3 <- c(length(which(mx$X3==0)),length(which(mx$X3==1)))
q4 <- c(length(which(mxora$X4==0)),length(which(mxora$X4==1)))
q5 <- c(length(which(mx$X5==0)),length(which(mx$X5==1)))
q6 <- c(length(which(mx$X6==0)),length(which(mx$X6==1)))
q1p <- q1[2]/sum(q1)
q2p <- q2[2]/sum(q2)
q3p <- q3[2]/sum(q3)
q4p <- q4[2]/sum(q4)
q5p <- q5[2]/sum(q5)
q6p <- q6[2]/sum(q6)
RES <- c("Q1"=q1p,"Q2"=q2p,"Q3"=q3p,"Q4"=q4p,"Q5"=q5p,"Q6"=q6p,
"MEAN"=mean(c(q1p,q2p,q3p,q4p,q5p,q6p)))
return(RES)
} )
sjrdf <- do.call(rbind,sjrl)
rownames(sjrdf) <- mylevels
sjrdf |>
kbl(caption="Mean reporting by SJR category") |>
kable_paper("hover", full_width = F)| Q1 | Q2 | Q3 | Q4 | Q5 | Q6 | MEAN | |
|---|---|---|---|---|---|---|---|
| high | 0.8481985 | 0.2575310 | 0.8050797 | 0.0603659 | 0.2569403 | 0.3077377 | 0.4226422 |
| mid | 0.7892239 | 0.2471577 | 0.8467622 | 0.0460238 | 0.1913989 | 0.2371725 | 0.3929565 |
| low | 0.8396371 | 0.2722095 | 0.9239047 | 0.0470772 | 0.1869287 | 0.2684497 | 0.4230345 |
| vlow | 0.8312775 | 0.2473568 | 0.9327093 | 0.0355272 | 0.1731278 | 0.2572687 | 0.4128779 |
Temporal
temporal <- lapply(2010:2025,function(y) {
my <- m[grep(paste(y,'-',sep=""),m$firstPublicationDate),]
myora <- my[grep("GSEA",my$Q1,invert=TRUE,ignore.case=TRUE),]
myora <- my[grep("GSVA",my$Q1,invert=TRUE,ignore.case=TRUE),]
q1 <- c(length(which(my$X1==0)),length(which(my$X1==1)))
q2 <- c(length(which(my$X2==0)),length(which(my$X2==1)))
q3 <- c(length(which(my$X3==0)),length(which(my$X3==1)))
q4 <- c(length(which(myora$X4==0)),length(which(myora$X4==1)))
q5 <- c(length(which(my$X5==0)),length(which(my$X5==1)))
q6 <- c(length(which(my$X6==0)),length(which(my$X6==1)))
q1p <- q1[2]/sum(q1)
q2p <- q2[2]/sum(q2)
q3p <- q3[2]/sum(q3)
q4p <- q4[2]/sum(q4)
q5p <- q5[2]/sum(q5)
q6p <- q6[2]/sum(q6)
cbind(c(q1,q1p),c(q2,q2p),c(q3,q3p),c(q4,q4p),c(q5,q5p),c(q6,q6p))
} )
plot(2010:2025,unlist(lapply(temporal, function(x) { x[3,1] } )),
main="Describe tool", ylab="Proportion of articles providing info")
grid()
plot(2010:2025,unlist(lapply(temporal, function(x) { x[3,2] } )),
main="Describe versions",ylab="Proportion of articles providing info")
grid()
plot(2010:2025,unlist(lapply(temporal, function(x) { x[3,3] } )),
main="Describe gene sets",ylab="Proportion of articles providing info")
grid()
plot(2010:2025,unlist(lapply(temporal, function(x) { x[3,4] } )),
main="Describe background",ylab="Proportion of articles providing info")
grid()
plot(2010:2025,unlist(lapply(temporal, function(x) { x[3,5] } )),
main="Describe stat test",ylab="Proportion of articles providing info")
grid()
plot(2010:2025,unlist(lapply(temporal, function(x) { x[3,6] } )),
main="Describe FDR",ylab="Proportion of articles providing info")
grid()
plot(2010:2025,unlist(lapply(temporal, function(x) { mean(x[3,1:6]) } )),
main="Mean reporting score",ylab="Proportion of articles providing info")
grid()
plot(2010:2025,unlist(lapply(temporal, function(x) { x[3,1] } )), bty="n",
ylim=c(0,1),col="black",type="b",lwd=1.5,xlim=c(2010,2030),
xlab="", ylab="Proportion of articles providing info") # tool
points(2010:2025,unlist(lapply(temporal, function(x) { x[3,2] } )),col="blue",type="b",lwd=1.5) # tool version
points(2010:2025,unlist(lapply(temporal, function(x) { x[3,3] } )),col="darkgreen",type="b",lwd=1.5) # gene sets
points(2010:2025,unlist(lapply(temporal, function(x) { x[3,4] } )),col="red",type="b",lwd=1.5) # background
points(2010:2025,unlist(lapply(temporal, function(x) { x[3,5] } )),col="darkorange",type="b",lwd=1.5) # stat test
points(2010:2025,unlist(lapply(temporal, function(x) { x[3,6] } )),col="cyan3",type="b",lwd=1.5) # FDR
legend("right", title="Detail",legend=c("Gene sets", "Tool", "Version", "FDR","Stat test","Background"),
col=c("darkgreen", "black", "blue", "cyan3", "darkorange", "red"), lty=1, cex=0.8,lwd=1.5, box.lty=0)
mtext("Pathway analysis reporting quality over time")
library(scales)
plot(2010:2025,unlist(lapply(temporal, function(x) { x[3,1] } )), bty="n",
ylim=c(0,1),col=alpha("black", 0.5),,type="b",lwd=1.5,xlim=c(2010,2031),
xlab="", ylab="Proportion of articles providing info") # tool
points(2010:2025,unlist(lapply(temporal, function(x) { x[3,2] } )),col=alpha("blue", 0.7),type="b",lwd=1.5) # tool version
points(2010:2025,unlist(lapply(temporal, function(x) { x[3,3] } )),col=alpha("darkgreen", 0.7),type="b",lwd=1.5) # gene sets
points(2010:2025,unlist(lapply(temporal, function(x) { x[3,4] } )),col=alpha("red", 0.7),type="b",lwd=1.5) # background
points(2010:2025,unlist(lapply(temporal, function(x) { x[3,5] } )),col=alpha("darkorange", 0.7),type="b",lwd=1.5) # stat test
points(2010:2025,unlist(lapply(temporal, function(x) { x[3,6] } )),col=alpha("cyan", 0.7),type="b",lwd=1.5) # FDR
points(2010:2025,unlist(lapply(temporal, function(x) mean(x[3,]) )),col="black",type="b",lwd=3)
legend("right", title="Detail",legend=c("Overall mean","Gene sets defined", "Tool defined", "Version defined", "FDR described","Stat test defined","Background defined"),
col=c("black", "darkgreen", "black", "blue", "cyan3", "darkorange", "red"), lty=1, cex=0.8,lwd=c(3,rep(1.5,6)), box.lty=0)
mtext("Pathway analysis reporting quality over time")
temporal## [[1]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 10.0000000 57.0000000 6.0000000 45.0000000 26.0000000 43.0000000
## [2,] 58.0000000 11.0000000 62.0000000 23.0000000 42.0000000 25.0000000
## [3,] 0.8529412 0.1617647 0.9117647 0.3382353 0.6176471 0.3676471
##
## [[2]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 30.0000000 101.0000000 13.000000 90.0000000 49.0000000 76.0000000
## [2,] 86.0000000 15.0000000 103.000000 26.0000000 67.0000000 40.0000000
## [3,] 0.7413793 0.1293103 0.887931 0.2241379 0.5775862 0.3448276
##
## [[3]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 34.0000000 165.0000000 33.0000000 166.000000 107.0000000 120.000000
## [2,] 169.0000000 38.0000000 170.0000000 37.000000 96.0000000 83.000000
## [3,] 0.8325123 0.1871921 0.8374384 0.182266 0.4729064 0.408867
##
## [[4]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 66.0000000 292.0000000 49.0000000 293.0000000 200.0000000 199.0000000
## [2,] 283.0000000 57.0000000 300.0000000 56.0000000 149.0000000 150.0000000
## [3,] 0.8108883 0.1633238 0.8595989 0.1604585 0.4269341 0.4297994
##
## [[5]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 124.0000000 426.0000000 75.0000000 413.0000000 278.000000 305.0000000
## [2,] 386.0000000 84.0000000 435.0000000 97.0000000 232.000000 205.0000000
## [3,] 0.7568627 0.1647059 0.8529412 0.1901961 0.454902 0.4019608
##
## [[6]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 152.0000000 622.0000000 102.0000000 649.0000000 453.0000000 450.0000000
## [2,] 593.0000000 123.0000000 643.0000000 95.0000000 292.0000000 295.0000000
## [3,] 0.7959732 0.1651007 0.8630872 0.1276882 0.3919463 0.3959732
##
## [[7]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 187.0000000 806.0000000 165.0000000 926.0000000 616.0000000 625.0000000
## [2,] 851.0000000 232.0000000 873.0000000 111.0000000 422.0000000 413.0000000
## [3,] 0.8198459 0.2235067 0.8410405 0.1070395 0.4065511 0.3978805
##
## [[8]]
## [,1] [,2] [,3] [,4] [,5]
## [1,] 259.0000000 1197.0000000 204.0000000 1.430000e+03 1042.0000000
## [2,] 1319.0000000 381.0000000 1374.0000000 1.470000e+02 536.0000000
## [3,] 0.8358682 0.2414449 0.8707224 9.321497e-02 0.3396705
## [,6]
## [1,] 986.0000000
## [2,] 592.0000000
## [3,] 0.3751584
##
## [[9]]
## [,1] [,2] [,3] [,4] [,5]
## [1,] 297.0000000 1451.0000000 247.0000000 1.835000e+03 1404.0000000
## [2,] 1678.0000000 524.0000000 1728.0000000 1.360000e+02 571.0000000
## [3,] 0.8496203 0.2653165 0.8749367 6.900051e-02 0.2891139
## [,6]
## [1,] 1368.0000000
## [2,] 607.0000000
## [3,] 0.3073418
##
## [[10]]
## [,1] [,2] [,3] [,4] [,5]
## [1,] 419.0000000 2185.0000000 337.0000000 2.776000e+03 2192.0000000
## [2,] 2566.0000000 800.0000000 2648.0000000 1.920000e+02 793.0000000
## [3,] 0.8596315 0.2680067 0.8871022 6.469003e-02 0.2656616
## [,6]
## [1,] 2073.0000000
## [2,] 912.0000000
## [3,] 0.3055276
##
## [[11]]
## [,1] [,2] [,3] [,4] [,5]
## [1,] 655.0000000 3283.0000000 508.0000000 4.248000e+03 3502.0000000
## [2,] 3885.0000000 1257.0000000 4032.0000000 2.470000e+02 1038.0000000
## [3,] 0.8557269 0.2768722 0.8881057 5.494994e-02 0.2286344
## [,6]
## [1,] 3164.0000000
## [2,] 1376.0000000
## [3,] 0.3030837
##
## [[12]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 893.0000000 5001.0000000 629.0000 6.377000e+03 5459.0000000 4795.0000000
## [2,] 5907.0000000 1799.0000000 6171.0000 2.950000e+02 1341.0000000 2005.0000000
## [3,] 0.8686765 0.2645588 0.9075 4.421463e-02 0.1972059 0.2948529
##
## [[13]]
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1453.0000000 7097.0000000 735.0000000 8.891000e+03 7980.0000000
## [2,] 8115.0000000 2471.0000000 8833.0000000 3.410000e+02 1588.0000000
## [3,] 0.8481396 0.2582567 0.9231814 3.693674e-02 0.1659699
## [,6]
## [1,] 7075.000000
## [2,] 2493.000000
## [3,] 0.260556
##
## [[14]]
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1377.0000000 6393.0000000 743.0000000 8.006000e+03 7299.0000000
## [2,] 7300.0000000 2284.0000000 7934.0000000 2.800000e+02 1378.0000000
## [3,] 0.8413046 0.2632246 0.9143713 3.379194e-02 0.1588106
## [,6]
## [1,] 6562.0000000
## [2,] 2115.0000000
## [3,] 0.2437478
##
## [[15]]
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1841.0000000 7450.0000000 839.0000000 9.369000e+03 8670.000000
## [2,] 8342.0000000 2733.0000000 9344.0000000 3.230000e+02 1513.000000
## [3,] 0.8192085 0.2683885 0.9176078 3.332645e-02 0.148581
## [,6]
## [1,] 7912.0000000
## [2,] 2271.0000000
## [3,] 0.2230188
##
## [[16]]
## [,1] [,2] [,3] [,4] [,5]
## [1,] 3.24700e+03 1.150700e+04 1.186000e+03 1.47340e+04 1.378300e+04
## [2,] 1.27240e+04 4.464000e+03 1.478500e+04 4.76000e+02 2.188000e+03
## [3,] 7.96694e-01 2.795066e-01 9.257404e-01 3.12952e-02 1.369983e-01
## [,6]
## [1,] 1.237300e+04
## [2,] 3.598000e+03
## [3,] 2.252833e-01High impact journal.
temporal_high <- lapply(2010:2025,function(y) {
my <- m[grep(paste(y,'-',sep=""),m$firstPublicationDate),]
my <- subset(my,sjr>=5)
myora <- my[grep("GSEA",my$Q1,invert=TRUE,ignore.case=TRUE),]
myora <- my[grep("GSVA",my$Q1,invert=TRUE,ignore.case=TRUE),]
q1 <- c(length(which(my$X1==0)),length(which(my$X1==1)))
q2 <- c(length(which(my$X2==0)),length(which(my$X2==1)))
q3 <- c(length(which(my$X3==0)),length(which(my$X3==1)))
q4 <- c(length(which(myora$X4==0)),length(which(myora$X4==1)))
q5 <- c(length(which(my$X5==0)),length(which(my$X5==1)))
q6 <- c(length(which(my$X6==0)),length(which(my$X6==1)))
q1p <- q1[2]/sum(q1)
q2p <- q2[2]/sum(q2)
q3p <- q3[2]/sum(q3)
q4p <- q4[2]/sum(q4)
q5p <- q5[2]/sum(q5)
q6p <- q6[2]/sum(q6)
cbind(c(q1,q1p),c(q2,q2p),c(q3,q3p),c(q4,q4p),c(q5,q5p),c(q6,q6p))
} )
temporal_mid <- lapply(2010:2025,function(y) {
my <- m[grep(paste(y,'-',sep=""),m$firstPublicationDate),]
my <- subset(my,sjr<5 & sjr>2)
myora <- my[grep("GSEA",my$Q1,invert=TRUE,ignore.case=TRUE),]
myora <- my[grep("GSVA",my$Q1,invert=TRUE,ignore.case=TRUE),]
q1 <- c(length(which(my$X1==0)),length(which(my$X1==1)))
q2 <- c(length(which(my$X2==0)),length(which(my$X2==1)))
q3 <- c(length(which(my$X3==0)),length(which(my$X3==1)))
q4 <- c(length(which(myora$X4==0)),length(which(myora$X4==1)))
q5 <- c(length(which(my$X5==0)),length(which(my$X5==1)))
q6 <- c(length(which(my$X6==0)),length(which(my$X6==1)))
q1p <- q1[2]/sum(q1)
q2p <- q2[2]/sum(q2)
q3p <- q3[2]/sum(q3)
q4p <- q4[2]/sum(q4)
q5p <- q5[2]/sum(q5)
q6p <- q6[2]/sum(q6)
cbind(c(q1,q1p),c(q2,q2p),c(q3,q3p),c(q4,q4p),c(q5,q5p),c(q6,q6p))
} )
temporal_low <- lapply(2010:2025,function(y) {
my <- m[grep(paste(y,'-',sep=""),m$firstPublicationDate),]
my <- subset(my,sjr<2 & sjr>0)
myora <- my[grep("GSEA",my$Q1,invert=TRUE,ignore.case=TRUE),]
myora <- my[grep("GSVA",my$Q1,invert=TRUE,ignore.case=TRUE),]
q1 <- c(length(which(my$X1==0)),length(which(my$X1==1)))
q2 <- c(length(which(my$X2==0)),length(which(my$X2==1)))
q3 <- c(length(which(my$X3==0)),length(which(my$X3==1)))
q4 <- c(length(which(myora$X4==0)),length(which(myora$X4==1)))
q5 <- c(length(which(my$X5==0)),length(which(my$X5==1)))
q6 <- c(length(which(my$X6==0)),length(which(my$X6==1)))
q1p <- q1[2]/sum(q1)
q2p <- q2[2]/sum(q2)
q3p <- q3[2]/sum(q3)
q4p <- q4[2]/sum(q4)
q5p <- q5[2]/sum(q5)
q6p <- q6[2]/sum(q6)
cbind(c(q1,q1p),c(q2,q2p),c(q3,q3p),c(q4,q4p),c(q5,q5p),c(q6,q6p))
} )
temporal_vlow <- lapply(2010:2025,function(y) {
my <- m[grep(paste(y,'-',sep=""),m$firstPublicationDate),]
my <- subset(my,sjr==0)
myora <- my[grep("GSEA",my$Q1,invert=TRUE,ignore.case=TRUE),]
myora <- my[grep("GSVA",my$Q1,invert=TRUE,ignore.case=TRUE),]
q1 <- c(length(which(my$X1==0)),length(which(my$X1==1)))
q2 <- c(length(which(my$X2==0)),length(which(my$X2==1)))
q3 <- c(length(which(my$X3==0)),length(which(my$X3==1)))
q4 <- c(length(which(myora$X4==0)),length(which(myora$X4==1)))
q5 <- c(length(which(my$X5==0)),length(which(my$X5==1)))
q6 <- c(length(which(my$X6==0)),length(which(my$X6==1)))
q1p <- q1[2]/sum(q1)
q2p <- q2[2]/sum(q2)
q3p <- q3[2]/sum(q3)
q4p <- q4[2]/sum(q4)
q5p <- q5[2]/sum(q5)
q6p <- q6[2]/sum(q6)
cbind(c(q1,q1p),c(q2,q2p),c(q3,q3p),c(q4,q4p),c(q5,q5p),c(q6,q6p))
} )
plot(2010:2025,unlist(lapply(temporal_high, function(x) { x[3,1] } )),ylim=c(0.4,1),
main="Describe tool", ylab="Proportion of articles providing info",type="b", col="darkgreen")
grid()
points(2010:2025,unlist(lapply(temporal_mid, function(x) { x[3,1] } )),type="b",col="black")
points(2010:2025,unlist(lapply(temporal_low, function(x) { x[3,1] } )),type="b",col="red")
points(2010:2025,unlist(lapply(temporal_vlow, function(x) { x[3,1] } )),type="b",col="blue")
legend("bottomright", title="SJR",legend=c(">5", ">2", ">0","0"),
col=c("darkgreen", "black", "red", "blue"), lty=1, cex=1)
plot(2010:2025,unlist(lapply(temporal_high, function(x) { x[3,2] } )),ylim=c(0,0.4),
main="Describe versions", ylab="Proportion of articles providing info",type="b", col="darkgreen")
grid()
points(2010:2025,unlist(lapply(temporal_mid, function(x) { x[3,2] } )),type="b",col="black")
points(2010:2025,unlist(lapply(temporal_low, function(x) { x[3,2] } )),type="b",col="red")
points(2010:2025,unlist(lapply(temporal_vlow, function(x) { x[3,2] } )),type="b",col="blue")
legend("bottomright", title="SJR",legend=c(">5", ">2", ">0","0"),
col=c("darkgreen", "black", "red", "blue"), lty=1, cex=1)
plot(2010:2025,unlist(lapply(temporal_high, function(x) { x[3,3] } )),ylim=c(0.4,1),
main="Describe gene sets", ylab="Proportion of articles providing info",type="b", col="darkgreen")
grid()
points(2010:2025,unlist(lapply(temporal_mid, function(x) { x[3,3] } )),type="b",col="black")
points(2010:2025,unlist(lapply(temporal_low, function(x) { x[3,3] } )),type="b",col="red")
points(2010:2025,unlist(lapply(temporal_vlow, function(x) { x[3,3] } )),type="b",col="blue")
legend("bottomright", title="SJR",legend=c(">5", ">2", ">0","0"),
col=c("darkgreen", "black", "red", "blue"), lty=1, cex=1)
plot(2010:2025,unlist(lapply(temporal_high, function(x) { x[3,4] } )),ylim=c(0,0.41),
main="Describe background", ylab="Proportion of articles providing info",type="b", col="darkgreen")
grid()
points(2010:2025,unlist(lapply(temporal_mid, function(x) { x[3,4] } )),type="b",col="black")
points(2010:2025,unlist(lapply(temporal_low, function(x) { x[3,4] } )),type="b",col="red")
points(2010:2025,unlist(lapply(temporal_vlow, function(x) { x[3,4] } )),type="b",col="blue")
legend("topright", title="SJR",legend=c(">5", ">2", ">0","0"),
col=c("darkgreen", "black", "red", "blue"), lty=1, cex=1)
plot(2010:2025,unlist(lapply(temporal_high, function(x) { x[3,5] } )),ylim=c(0,0.75),
main="Describe stat test", ylab="Proportion of articles providing info",type="b", col="darkgreen")
grid()
points(2010:2025,unlist(lapply(temporal_mid, function(x) { x[3,5] } )),type="b",col="black")
points(2010:2025,unlist(lapply(temporal_low, function(x) { x[3,5] } )),type="b",col="red")
points(2010:2025,unlist(lapply(temporal_vlow, function(x) { x[3,5] } )),type="b",col="blue")
legend("topright", title="SJR",legend=c(">5", ">2", ">0","0"),
col=c("darkgreen", "black", "red", "blue"), lty=1, cex=1)
plot(2010:2025,unlist(lapply(temporal_high, function(x) { x[3,6] } )),ylim=c(0,0.6),
main="Describe FDR", ylab="Proportion of articles providing info",type="b", col="darkgreen")
grid()
points(2010:2025,unlist(lapply(temporal_mid, function(x) { x[3,6] } )),type="b",col="black")
points(2010:2025,unlist(lapply(temporal_low, function(x) { x[3,6] } )),type="b",col="red")
points(2010:2025,unlist(lapply(temporal_vlow, function(x) { x[3,6] } )),type="b",col="blue")
legend("topright", title="SJR",legend=c(">5", ">2", ">0","0"),
col=c("darkgreen", "black", "red", "blue"), lty=1, cex=1)
temporal_high## [[1]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 1.0000000 7 1.0000000 6.0000000 4.0000000 5.0000000
## [2,] 6.0000000 0 6.0000000 1.0000000 3.0000000 2.0000000
## [3,] 0.8571429 0 0.8571429 0.1428571 0.4285714 0.2857143
##
## [[2]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 5.0000000 9 5.0000000 8.0000000 4.0000000 7.0000000
## [2,] 4.0000000 0 4.0000000 1.0000000 5.0000000 2.0000000
## [3,] 0.4444444 0 0.4444444 0.1111111 0.5555556 0.2222222
##
## [[3]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 5.0000000 16.0000000 5.0000000 16.0000000 9.0 13.0000000
## [2,] 13.0000000 2.0000000 13.0000000 2.0000000 9.0 5.0000000
## [3,] 0.7222222 0.1111111 0.7222222 0.1111111 0.5 0.2777778
##
## [[4]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 4.0000000 16.0000000 4.0000000 20.00000000 14.0000000 13.0000000
## [2,] 17.0000000 5.0000000 17.0000000 1.00000000 7.0000000 8.0000000
## [3,] 0.8095238 0.2380952 0.8095238 0.04761905 0.3333333 0.3809524
##
## [[5]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 3.0000000 22.0000000 6.0000000 25.00000000 17.0000000 15.0000000
## [2,] 23.0000000 4.0000000 20.0000000 1.00000000 9.0000000 11.0000000
## [3,] 0.8846154 0.1538462 0.7692308 0.03846154 0.3461538 0.4230769
##
## [[6]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 6.0000000 36.00000000 10.0000000 36.00000000 25.0000000 23.0000000
## [2,] 32.0000000 2.00000000 28.0000000 2.00000000 13.0000000 15.0000000
## [3,] 0.8421053 0.05263158 0.7368421 0.05263158 0.3421053 0.3947368
##
## [[7]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 7.000 49.000 11.0000000 50.00000000 35.000 36.0000000
## [2,] 49.000 7.000 45.0000000 5.00000000 21.000 20.0000000
## [3,] 0.875 0.125 0.8035714 0.09090909 0.375 0.3571429
##
## [[8]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 6.0000000 40.0000000 12.0000000 50.00000000 40.0000000 32.000000
## [2,] 45.0000000 11.0000000 39.0000000 1.00000000 11.0000000 19.000000
## [3,] 0.8823529 0.2156863 0.7647059 0.01960784 0.2156863 0.372549
##
## [[9]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 9.0000000 61.000000 8.000000 62.0000000 42.0000000 48.0000000
## [2,] 60.0000000 8.000000 61.000000 7.0000000 27.0000000 21.0000000
## [3,] 0.8695652 0.115942 0.884058 0.1014493 0.3913043 0.3043478
##
## [[10]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 6.0000000 67.0000000 23.0000000 83.00000000 67.0000000 56.0000000
## [2,] 82.0000000 21.0000000 65.0000000 4.00000000 21.0000000 32.0000000
## [3,] 0.9318182 0.2386364 0.7386364 0.04597701 0.2386364 0.3636364
##
## [[11]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 21.0000000 100.0000000 33.0000000 117.00000000 88.0000000 87.0000000
## [2,] 105.0000000 26.0000000 93.0000000 6.00000000 38.0000000 39.0000000
## [3,] 0.8333333 0.2063492 0.7380952 0.04878049 0.3015873 0.3095238
##
## [[12]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 27.0000000 127.000000 27.0000000 152.00000000 122.0000000 103.0000000
## [2,] 138.0000000 38.000000 138.0000000 7.00000000 43.0000000 62.0000000
## [3,] 0.8363636 0.230303 0.8363636 0.04402516 0.2606061 0.3757576
##
## [[13]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 24.0000000 137.0000000 39.0000000 168.00000000 135.0000000 123.0000000
## [2,] 161.0000000 48.0000000 146.0000000 10.00000000 50.0000000 62.0000000
## [3,] 0.8702703 0.2594595 0.7891892 0.05617978 0.2702703 0.3351351
##
## [[14]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 33.0000000 173.0000000 44.000000 215.00000000 181.0000000 174.0000000
## [2,] 204.0000000 64.0000000 193.000000 16.00000000 56.0000000 63.0000000
## [3,] 0.8607595 0.2700422 0.814346 0.06926407 0.2362869 0.2658228
##
## [[15]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 55.0000000 208.000000 52.0000000 271.00000000 225.0000000 224.0000000
## [2,] 239.0000000 86.000000 242.0000000 12.00000000 69.0000000 70.0000000
## [3,] 0.8129252 0.292517 0.8231293 0.04240283 0.2346939 0.2380952
##
## [[16]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 45.0000000 189.0000000 50.0000000 262.00000000 250.0000000 213.0000000
## [2,] 258.0000000 114.0000000 253.0000000 23.00000000 53.0000000 90.0000000
## [3,] 0.8514851 0.3762376 0.8349835 0.08070175 0.1749175 0.2970297temporal_mid## [[1]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 1.0000000 8.0000000 1.0000000 6.0000000 3.0000000 4.0000000
## [2,] 8.0000000 1.0000000 8.0000000 3.0000000 6.0000000 5.0000000
## [3,] 0.8888889 0.1111111 0.8888889 0.3333333 0.6666667 0.5555556
##
## [[2]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 4.0000000 13.00000000 4.0000000 14 8.0000000 11.0000000
## [2,] 10.0000000 1.00000000 10.0000000 0 6.0000000 3.0000000
## [3,] 0.7142857 0.07142857 0.7142857 0 0.4285714 0.2142857
##
## [[3]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 3.0000000 18.0000000 8.0000000 21.00000000 16.0000000 11.0000000
## [2,] 20.0000000 5.0000000 15.0000000 2.00000000 7.0000000 12.0000000
## [3,] 0.8695652 0.2173913 0.6521739 0.08695652 0.3043478 0.5217391
##
## [[4]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 9.0000000 44.00000000 15.0000000 40.0000000 34.0000000 35.0000000
## [2,] 38.0000000 3.00000000 32.0000000 7.0000000 13.0000000 12.0000000
## [3,] 0.8085106 0.06382979 0.6808511 0.1489362 0.2765957 0.2553191
##
## [[5]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 13.0000000 49.0000000 17.0000000 57.00000000 37.0000000 44.0000000
## [2,] 49.0000000 13.0000000 45.0000000 5.00000000 25.0000000 18.0000000
## [3,] 0.7903226 0.2096774 0.7258065 0.08064516 0.4032258 0.2903226
##
## [[6]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 19.0000000 79.0000000 32.000000 87.00000000 68.0000000 61.000000
## [2,] 74.0000000 14.0000000 61.000000 6.00000000 25.0000000 32.000000
## [3,] 0.7956989 0.1505376 0.655914 0.06451613 0.2688172 0.344086
##
## [[7]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 27.0000000 113.0000000 32.0000000 129.00000000 88.0000000 97.0000000
## [2,] 114.0000000 28.0000000 109.0000000 12.00000000 53.0000000 44.0000000
## [3,] 0.8085106 0.1985816 0.7730496 0.08510638 0.3758865 0.3120567
##
## [[8]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 38.0000000 164.000000 51.0000000 194.00000000 150.0000000 138.0000000
## [2,] 171.0000000 45.000000 158.0000000 14.00000000 59.0000000 71.0000000
## [3,] 0.8181818 0.215311 0.7559809 0.06730769 0.2822967 0.3397129
##
## [[9]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 43.0000000 223.0000000 61.0000000 284.00000000 228.0000000 215.0000000
## [2,] 269.0000000 89.0000000 251.0000000 25.00000000 84.0000000 97.0000000
## [3,] 0.8621795 0.2852564 0.8044872 0.08090615 0.2692308 0.3108974
##
## [[10]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 74.0000000 360.0000000 95.0000000 405.00000000 328.0000000 331.0000000
## [2,] 371.0000000 85.0000000 350.0000000 35.00000000 117.0000000 114.0000000
## [3,] 0.8337079 0.1910112 0.7865169 0.07954545 0.2629213 0.2561798
##
## [[11]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 99.0000000 417.0000000 118.0000000 523.00000000 430.0000000 412.0000000
## [2,] 456.0000000 138.0000000 437.0000000 26.00000000 125.0000000 143.0000000
## [3,] 0.8216216 0.2486486 0.7873874 0.04735883 0.2252252 0.2576577
##
## [[12]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 150.0000000 636.0000000 133.0000000 786.00000000 652.0000000 584.0000000
## [2,] 689.0000000 203.0000000 706.0000000 37.00000000 187.0000000 255.0000000
## [3,] 0.8212157 0.2419547 0.8414779 0.04495747 0.2228844 0.3039333
##
## [[13]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 236.0000000 802.0000000 171.0000000 982.00000000 841.0000000 799.0000000
## [2,] 817.0000000 251.0000000 882.0000000 43.00000000 212.0000000 254.0000000
## [3,] 0.7758784 0.2383666 0.8376068 0.04195122 0.2013295 0.2412156
##
## [[14]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 267.0000000 986.000000 199.0000000 1.227000e+03 1084.0000000 1036.000000
## [2,] 1081.0000000 362.000000 1149.0000000 6.600000e+01 264.0000000 312.000000
## [3,] 0.8019288 0.268546 0.8523739 5.104408e-02 0.1958457 0.231454
##
## [[15]]
## [,1] [,2] [,3] [,4] [,5]
## [1,] 405.0000000 1416.0000000 244.0000000 1.757000e+03 1609.0000000
## [2,] 1511.0000000 500.0000000 1672.0000000 8.400000e+01 307.0000000
## [3,] 0.7886221 0.2609603 0.8726514 4.562738e-02 0.1602296
## [,6]
## [1,] 1497.0000000
## [2,] 419.0000000
## [3,] 0.2186848
##
## [[16]]
## [,1] [,2] [,3] [,4] [,5]
## [1,] 744.0000000 2287.000000 369.0000000 2.857000e+03 2603.0000000
## [2,] 2305.0000000 762.000000 2680.0000000 8.700000e+01 446.0000000
## [3,] 0.7559856 0.249918 0.8789767 2.955163e-02 0.1462775
## [,6]
## [1,] 2441.0000000
## [2,] 608.0000000
## [3,] 0.1994096temporal_low## [[1]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 6.0000000 37.0000000 4.0000000 28.0000000 17.0000000 31.0000000
## [2,] 39.0000000 8.0000000 41.0000000 17.0000000 28.0000000 14.0000000
## [3,] 0.8666667 0.1777778 0.9111111 0.3777778 0.6222222 0.3111111
##
## [[2]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 21.000000 70.0000000 3.0000000 60.0000000 32.0000000 52.000000
## [2,] 62.000000 13.0000000 80.0000000 23.0000000 51.0000000 31.000000
## [3,] 0.746988 0.1566265 0.9638554 0.2771084 0.6144578 0.373494
##
## [[3]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 24.0000000 117.0000000 18.0000000 119.0000000 73.0000000 89.0000000
## [2,] 121.0000000 28.0000000 127.0000000 26.0000000 72.0000000 56.0000000
## [3,] 0.8344828 0.1931034 0.8758621 0.1793103 0.4965517 0.3862069
##
## [[4]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 47.0000000 199.0000000 24.000000 201.0000000 133.0000000 133.0000000
## [2,] 199.0000000 47.0000000 222.000000 45.0000000 113.0000000 113.0000000
## [3,] 0.8089431 0.1910569 0.902439 0.1829268 0.4593496 0.4593496
##
## [[5]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 95.0000000 308.0000000 47.0000000 289.0000000 197.0000000 209.0000000
## [2,] 274.0000000 61.0000000 322.0000000 80.0000000 172.0000000 160.0000000
## [3,] 0.7425474 0.1653117 0.8726287 0.2168022 0.4661247 0.4336043
##
## [[6]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 105.0000000 435.0000000 50.0000000 457.0000000 307.0000000 317.0000000
## [2,] 431.0000000 101.0000000 486.0000000 78.0000000 229.0000000 219.0000000
## [3,] 0.8041045 0.1884328 0.9067164 0.1457944 0.4272388 0.4085821
##
## [[7]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 127.0000000 543.0000000 106.0000000 633.000000 419.0000000 417.0000000
## [2,] 586.0000000 170.0000000 607.0000000 80.000000 294.0000000 296.0000000
## [3,] 0.8218794 0.2384292 0.8513324 0.112202 0.4123422 0.4151473
##
## [[8]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 182.0000000 861.0000000 128.0000000 1022.0000000 748.0000000 711.0000000
## [2,] 957.0000000 278.0000000 1011.0000000 117.0000000 391.0000000 428.0000000
## [3,] 0.8402107 0.2440737 0.8876207 0.1027217 0.3432836 0.3757682
##
## [[9]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 202.000000 986.0000000 162.0000000 1.277000e+03 989.0000000 946.0000000
## [2,] 1164.000000 380.0000000 1204.0000000 8.800000e+01 377.0000000 420.0000000
## [3,] 0.852123 0.2781845 0.8814056 6.446886e-02 0.2759883 0.3074671
##
## [[10]]
## [,1] [,2] [,3] [,4] [,5]
## [1,] 293.0000000 1531.0000000 190.0000000 1.989000e+03 1560.0000000
## [2,] 1841.0000000 603.0000000 1944.0000000 1.350000e+02 574.0000000
## [3,] 0.8626992 0.2825679 0.9109653 6.355932e-02 0.2689784
## [,6]
## [1,] 1473.000000
## [2,] 661.000000
## [3,] 0.309747
##
## [[11]]
## [,1] [,2] [,3] [,4] [,5]
## [1,] 445.0000000 2319.0000000 315.0000000 3.035000e+03 2497.0000000
## [2,] 2810.0000000 936.0000000 2940.0000000 1.890000e+02 758.0000000
## [3,] 0.8632873 0.2875576 0.9032258 5.862283e-02 0.2328725
## [,6]
## [1,] 2232.0000000
## [2,] 1023.0000000
## [3,] 0.3142857
##
## [[12]]
## [,1] [,2] [,3] [,4] [,5]
## [1,] 556.000000 3159.0000000 377.0000000 4.114000e+03 3502.0000000
## [2,] 3830.000000 1227.0000000 4009.0000000 2.100000e+02 884.0000000
## [3,] 0.873233 0.2797538 0.9140447 4.856614e-02 0.2015504
## [,6]
## [1,] 3083.0000000
## [2,] 1303.0000000
## [3,] 0.2970816
##
## [[13]]
## [,1] [,2] [,3] [,4] [,5]
## [1,] 869.0000000 4533.0000000 419.000000 5.741000e+03 5163.0000000
## [2,] 5324.0000000 1660.0000000 5774.000000 2.340000e+02 1030.0000000
## [3,] 0.8596803 0.2680446 0.932343 3.916318e-02 0.1663168
## [,6]
## [1,] 4536.0000000
## [2,] 1657.0000000
## [3,] 0.2675601
##
## [[14]]
## [,1] [,2] [,3] [,4] [,5]
## [1,] 899.0000000 4369.0000000 425.0000000 5.517000e+03 5070.0000000
## [2,] 5058.0000000 1588.0000000 5532.0000000 1.580000e+02 887.0000000
## [3,] 0.8490851 0.2665771 0.9286554 2.784141e-02 0.1489005
## [,6]
## [1,] 4501.0000000
## [2,] 1456.0000000
## [3,] 0.2444183
##
## [[15]]
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1190.0000000 5084.0000000 463.0000000 6.405000e+03 5972.0000000
## [2,] 5784.0000000 1890.0000000 6511.0000000 2.090000e+02 1002.0000000
## [3,] 0.8293662 0.2710066 0.9336106 3.159964e-02 0.1436765
## [,6]
## [1,] 5391.0000000
## [2,] 1583.0000000
## [3,] 0.2269859
##
## [[16]]
## [,1] [,2] [,3] [,4] [,5]
## [1,] 2062.0000000 7776.000000 6.490000e+02 9.981000e+03 9436.0000000
## [2,] 8815.0000000 3101.000000 1.022800e+04 3.300000e+02 1441.0000000
## [3,] 0.8104257 0.285097 9.403328e-01 3.200466e-02 0.1324814
## [,6]
## [1,] 8373.0000000
## [2,] 2504.0000000
## [3,] 0.2302105temporal_vlow## [[1]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 2.0000000 5.0000000 0 5.0000000 2.0000000 3.0000000
## [2,] 5.0000000 2.0000000 7 2.0000000 5.0000000 4.0000000
## [3,] 0.7142857 0.2857143 1 0.2857143 0.7142857 0.5714286
##
## [[2]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0 9.0 1.0 8.0 5.0 6.0
## [2,] 10 1.0 9.0 2.0 5.0 4.0
## [3,] 1 0.1 0.9 0.2 0.5 0.4
##
## [[3]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 2.0000000 14.0000000 2.0000000 10.0000000 9.0000000 7.0000000
## [2,] 15.0000000 3.0000000 15.0000000 7.0000000 8.0000000 10.0000000
## [3,] 0.8823529 0.1764706 0.8823529 0.4117647 0.4705882 0.5882353
##
## [[4]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 6.0000000 33.00000000 6.0000000 32.00000000 19.0000000 18.0000000
## [2,] 29.0000000 2.00000000 29.0000000 3.00000000 16.0000000 17.0000000
## [3,] 0.8285714 0.05714286 0.8285714 0.08571429 0.4571429 0.4857143
##
## [[5]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 13.000000 47.0000000 5.0000000 42.0000000 27.000000 37.0000000
## [2,] 40.000000 6.0000000 48.0000000 11.0000000 26.000000 16.0000000
## [3,] 0.754717 0.1132075 0.9056604 0.2075472 0.490566 0.3018868
##
## [[6]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 22.0000000 72.00000000 10.0000000 69.0000000 53.0000000 49.0000000
## [2,] 56.0000000 6.00000000 68.0000000 9.0000000 25.0000000 29.0000000
## [3,] 0.7179487 0.07692308 0.8717949 0.1153846 0.3205128 0.3717949
##
## [[7]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 26.000000 101.0000000 16.000 114.000000 74.000000 75.0000000
## [2,] 102.000000 27.0000000 112.000 14.000000 54.000000 53.0000000
## [3,] 0.796875 0.2109375 0.875 0.109375 0.421875 0.4140625
##
## [[8]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 33.0000000 132.0000000 13.0000000 164.00000000 104.0000000 105.0000000
## [2,] 146.0000000 47.0000000 166.0000000 15.00000000 75.0000000 74.0000000
## [3,] 0.8156425 0.2625698 0.9273743 0.08379888 0.4189944 0.4134078
##
## [[9]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 43.0000000 181.0000000 16.0000000 212.00000000 145.0000000 159.0000000
## [2,] 185.0000000 47.0000000 212.0000000 16.00000000 83.0000000 69.0000000
## [3,] 0.8114035 0.2061404 0.9298246 0.07017544 0.3640351 0.3026316
##
## [[10]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 46.0000000 227.0000000 29.000000 299.00000000 237.000000 213.0000000
## [2,] 272.0000000 91.0000000 289.000000 18.00000000 81.000000 105.0000000
## [3,] 0.8553459 0.2861635 0.908805 0.05678233 0.254717 0.3301887
##
## [[11]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 90.0000000 447.0000000 42.0000000 573.00000000 487.0000000 433.0000000
## [2,] 514.0000000 157.0000000 562.0000000 26.00000000 117.0000000 171.0000000
## [3,] 0.8509934 0.2599338 0.9304636 0.04340568 0.1937086 0.2831126
##
## [[12]]
## [,1] [,2] [,3] [,4] [,5]
## [1,] 160.0000000 1079.0000000 92.0000000 1.325000e+03 1183.0000000
## [2,] 1250.0000000 331.0000000 1318.0000000 4.100000e+01 227.0000000
## [3,] 0.8865248 0.2347518 0.9347518 3.001464e-02 0.1609929
## [,6]
## [1,] 1025.0000000
## [2,] 385.0000000
## [3,] 0.2730496
##
## [[13]]
## [,1] [,2] [,3] [,4] [,5]
## [1,] 324.0000000 1625.0000000 106.0000000 2.000000e+03 1841.0000000
## [2,] 1813.0000000 512.0000000 2031.0000000 5.400000e+01 296.0000000
## [3,] 0.8483856 0.2395882 0.9503978 2.629017e-02 0.1385119
## [,6]
## [1,] 1617.0000000
## [2,] 520.0000000
## [3,] 0.2433318
##
## [[14]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 178.0000000 865.0000000 75.0000000 1.047000e+03 964.0000000 851.0000000
## [2,] 957.0000000 270.0000000 1060.0000000 4.000000e+01 171.0000000 284.0000000
## [3,] 0.8431718 0.2378855 0.9339207 3.679853e-02 0.1506608 0.2502203
##
## [[15]]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 191.0000000 742.0000000 80.0000000 936.00000000 864.0000000 800.0000000
## [2,] 808.0000000 257.0000000 919.0000000 18.00000000 135.0000000 199.0000000
## [3,] 0.8088088 0.2572573 0.9199199 0.01886792 0.1351351 0.1991992
##
## [[16]]
## [,1] [,2] [,3] [,4] [,5]
## [1,] 396.0000000 1255.0000000 118.0000000 1.634000e+03 1494.0000000
## [2,] 1346.0000000 487.0000000 1624.0000000 3.600000e+01 248.0000000
## [3,] 0.7726751 0.2795637 0.9322618 2.155689e-02 0.1423651
## [,6]
## [1,] 1346.0000000
## [2,] 396.0000000
## [3,] 0.2273249Now mean score across all six questions.
plot(2010:2025,unlist(lapply(temporal_high, function(x) { mean(x[3,1:6]) } )),ylim=c(0.29,0.6),
main="mean reporting score", ylab="Proportion of articles providing info",type="b", col="darkgreen")
grid()
points(2010:2025,unlist(lapply(temporal_mid, function(x) { mean(x[3,1:6]) } )),type="b",col="black")
points(2010:2025,unlist(lapply(temporal_low, function(x) { mean(x[3,1:6]) } )),type="b",col="red")
points(2010:2025,unlist(lapply(temporal_vlow, function(x) { mean(x[3,1:6]) } )),type="b",col="blue")
legend("topright", title="SJR",legend=c(">5", ">2", ">0","0"),
col=c("darkgreen", "black", "red", "blue"), lty=1, cex=1)
Number of journal articles in SJR categories by year.
plot(2010:2025,unlist(lapply(temporal_mid,function(x) { sum(x[1:2,1]) } )),ylab="No. articles")
plot(2010:2025,unlist(lapply(temporal_high,function(x) { sum(x[1:2,1]) } )),ylim=c(0,12000),
main="No. articles", ylab="No. articles by SJR category",type="b", col="darkgreen")
grid()
points(2010:2025,unlist(lapply(temporal_mid,function(x) { sum(x[1:2,1]) } )),type="b",col="black")
points(2010:2025,unlist(lapply(temporal_low,function(x) { sum(x[1:2,1]) } )),type="b",col="red")
points(2010:2025,unlist(lapply(temporal_vlow,function(x) { sum(x[1:2,1]) } )),type="b",col="blue")
legend("topleft", title="SJR",legend=c(">5", ">2", ">0","0"),
col=c("darkgreen", "black", "red", "blue"), lty=1, cex=1)
plot(2010:2025,unlist(lapply(temporal_high,function(x) { sum(x[1:2,1]) } )),ylim=c(5,12000),log="y",
main="No. articles", ylab="No. articles by SJR category",type="b", col="darkgreen")
grid()
points(2010:2025,unlist(lapply(temporal_mid,function(x) { sum(x[1:2,1]) } )),type="b",col="black")
points(2010:2025,unlist(lapply(temporal_low,function(x) { sum(x[1:2,1]) } )),type="b",col="red")
points(2010:2025,unlist(lapply(temporal_vlow,function(x) { sum(x[1:2,1]) } )),type="b",col="blue")
legend("topleft", title="SJR",legend=c(">5", ">2", ">0","0"),
col=c("darkgreen", "black", "red", "blue"), lty=1, cex=1)
Best and worst journal
m$journalTitle
m$sum <- ( m$X1 + m$X2 + m$X3 + m$X4 + m$X5 + m$X6 )
journal <- aggregate(sum ~ journalTitle,m,mean)
journal2 <- aggregate(sum ~ journalTitle,m,length)
jdf <- cbind(journal,journal2)
jdf <- jdf[,c(1,2,4)]
colnames(jdf) <- c("journalTitle","score","count")
jdf$sjr <- unlist(lapply( jdf$journalTitle, function(j) {
j_sjr <- m[which(m$journalTitle == j),"sjr"][1]
}))
plot(jdf$sjr,jdf$score,xlab="SJR",ylab="Reporting score",bty="n")
mylm <- lm(jdf$score ~ jdf$sjr)
abline(mylm,lty=2,col="red",lwd=2)
summary(mylm)##
## Call:
## lm(formula = jdf$score ~ jdf$sjr)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4870 -0.4747 -0.0078 0.5193 3.5249
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.48697 0.02368 105.007 <2e-16 ***
## jdf$sjr -0.01407 0.01039 -1.354 0.176
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8796 on 2161 degrees of freedom
## Multiple R-squared: 0.000848, Adjusted R-squared: 0.0003856
## F-statistic: 1.834 on 1 and 2161 DF, p-value: 0.1758jdf2 <- subset(jdf,count>=10)
plot(jdf2$sjr,jdf2$score,xlab="SJR",ylab="Reporting score",bty="n",ylim=c(0,4))
mylm2 <- lm(jdf2$score ~ jdf2$sjr)
abline(mylm2,lty=2,col="red",lwd=2)
summary(mylm2)##
## Call:
## lm(formula = jdf2$score ~ jdf2$sjr)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.64585 -0.18956 -0.00748 0.17834 1.26354
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.462025 0.016698 147.447 <2e-16 ***
## jdf2$sjr 0.000938 0.005042 0.186 0.852
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3305 on 620 degrees of freedom
## Multiple R-squared: 5.582e-05, Adjusted R-squared: -0.001557
## F-statistic: 0.03461 on 1 and 620 DF, p-value: 0.8525head(jdf2[order(-jdf2$score),],20) |>
kbl(caption="Top scoring journals") |>
kable_paper("hover", full_width = F)| journalTitle | score | count | sjr | |
|---|---|---|---|---|
| 746 | Environ Res | 3.727273 | 11 | 1.822 |
| 1704 | Nat Neurosci | 3.718750 | 32 | 11.197 |
| 1694 | Nat Genet | 3.562500 | 64 | 16.586 |
| 1761 | Noncoding RNA | 3.428571 | 14 | 0.000 |
| 975 | HGG Adv | 3.400000 | 15 | 1.880 |
| 2069 | Toxicol Sci | 3.400000 | 10 | 1.086 |
| 1866 | Physiol Genomics | 3.375000 | 32 | 0.911 |
| 659 | Data Brief | 3.363636 | 22 | 0.198 |
| 833 | Fluids Barriers CNS | 3.363636 | 11 | 1.931 |
| 90 | Alzheimers Res Ther | 3.333333 | 24 | 2.709 |
| 1404 | J Proteome Res | 3.303371 | 89 | 1.139 |
| 1734 | Neurol Neuroimmunol Neuroinflamm | 3.300000 | 10 | 3.008 |
| 1685 | Nat Aging | 3.297297 | 37 | 7.081 |
| 1652 | NAR Genom Bioinform | 3.285714 | 21 | 2.179 |
| 1654 | NPJ Aging | 3.250000 | 12 | 2.043 |
| 1698 | Nat Med | 3.230769 | 52 | 18.333 |
| 107 | Am J Physiol Cell Physiol | 3.200000 | 10 | 1.791 |
| 2103 | Turk J Biol | 3.200000 | 10 | 0.321 |
| 660 | Database (Oxford) | 3.153846 | 13 | 1.238 |
| 1919 | Proteomics | 3.125000 | 16 | 0.983 |
head(jdf2[order(jdf2$score),],20) |>
kbl(caption="Low scoring journals") |>
kable_paper("hover", full_width = F)| journalTitle | score | count | sjr | |
|---|---|---|---|---|
| 538 | Chem Sci | 0.8181818 | 11 | 2.138 |
| 1713 | Natl Sci Rev | 1.2000000 | 10 | 2.903 |
| 354 | Biomed Pharmacother | 1.3000000 | 10 | 1.775 |
| 365 | Bioresour Bioprocess | 1.7000000 | 10 | 1.039 |
| 968 | Gut | 1.7272727 | 33 | 8.874 |
| 1950 | Regen Biomater | 1.7500000 | 16 | 1.216 |
| 1994 | Science | 1.7857143 | 14 | 10.416 |
| 77 | Aging Dis | 1.8000000 | 15 | 2.143 |
| 1561 | Microb Biotechnol | 1.8125000 | 16 | 1.195 |
| 507 | Cell Mol Biol Lett | 1.8139535 | 43 | 2.458 |
| 933 | Genes Dis | 1.8169014 | 71 | 1.646 |
| 1461 | JHEP Rep | 1.8285714 | 35 | 3.525 |
| 298 | Bioact Mater | 1.8456790 | 162 | 0.000 |
| 555 | Circulation | 1.8518519 | 27 | 8.668 |
| 1357 | J Neurosci | 1.8666667 | 15 | 1.963 |
| 1392 | J Pharm Anal | 1.8979592 | 49 | 1.452 |
| 425 | Burns Trauma | 1.9000000 | 10 | 1.809 |
| 1105 | Int J Obes (Lond) | 1.9090909 | 11 | 1.589 |
| 642 | Curr Res Food Sci | 1.9117647 | 34 | 1.408 |
| 1562 | Microb Cell Fact | 1.9117647 | 34 | 1.103 |
top <- head(jdf2[order(-jdf2$count),],20)
top[order(-top$score),] |>
kbl(caption="Most prolific journals") |>
kable_paper("hover", full_width = F)| journalTitle | score | count | sjr | |
|---|---|---|---|---|
| 1814 | PLoS One | 2.859155 | 1704 | 0.803 |
| 237 | BMC Genomics | 2.842818 | 1107 | 1.003 |
| 1692 | Nat Commun | 2.828332 | 1433 | 4.761 |
| 1848 | PeerJ | 2.715790 | 570 | 0.625 |
| 928 | Genes (Basel) | 2.704992 | 661 | 0.858 |
| 1990 | Sci Rep | 2.685434 | 3570 | 0.874 |
| 867 | Front Genet | 2.658132 | 1445 | 0.863 |
| 1102 | Int J Mol Sci | 2.625507 | 2713 | 1.273 |
| 467 | Cancers (Basel) | 2.582255 | 541 | 1.462 |
| 260 | BMC Plant Biol | 2.554849 | 629 | 1.134 |
| 894 | Front Plant Sci | 2.529245 | 1060 | 0.000 |
| 869 | Front Immunol | 2.505281 | 1799 | 1.941 |
| 886 | Front Oncol | 2.503846 | 1040 | 0.000 |
| 75 | Aging (Albany NY) | 2.465890 | 601 | 1.078 |
| 984 | Heliyon | 2.446741 | 629 | 0.644 |
| 692 | Discov Oncol | 2.391386 | 534 | 0.880 |
| 875 | Front Microbiol | 2.368358 | 828 | 0.000 |
| 892 | Front Pharmacol | 2.351897 | 1239 | 1.220 |
| 1435 | J Transl Med | 2.320197 | 609 | 1.997 |
| 71 | Adv Sci (Weinh) | 2.095872 | 751 | 3.775 |
Session info
For reproducibility.
sessionInfo()## R version 4.5.2 (2025-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.5 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0 LAPACK version 3.10.0
##
## locale:
## [1] C
##
## time zone: Australia/Melbourne
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] scales_1.4.0 kableExtra_1.4.0 gplots_3.2.0
##
## loaded via a namespace (and not attached):
## [1] vctrs_0.7.1 svglite_2.2.2 cli_3.6.5 knitr_1.51
## [5] rlang_1.1.7 xfun_0.56 stringi_1.8.7 otel_0.2.0
## [9] KernSmooth_2.23-26 textshaping_1.0.4 jsonlite_2.0.0 gtools_3.9.5
## [13] glue_1.8.0 htmltools_0.5.9 sass_0.4.10 rmarkdown_2.30
## [17] evaluate_1.0.5 jquerylib_0.1.4 caTools_1.18.3 bitops_1.0-9
## [21] fastmap_1.2.0 yaml_2.3.12 lifecycle_1.0.5 stringr_1.6.0
## [25] compiler_4.5.2 RColorBrewer_1.1-3 rstudioapi_0.17.1 systemfonts_1.3.1
## [29] farver_2.1.2 digest_0.6.39 viridisLite_0.4.3 R6_2.6.1
## [33] dichromat_2.0-0.1 utf8_1.2.6 pillar_1.11.1 magrittr_2.0.4
## [37] bslib_0.10.0 tools_4.5.2 xml2_1.5.0 cachem_1.1.0