PMC Survey Data Analysis
Mark Ziemann & Kaumadi Wijesooriya
2021-07-30
Intro
Here we are performing an analysis of the ways enrichment analysis has been done in a sample of 1500 PMC articles from 2019.
knitr::opts_chunk$set(fig.width=7, fig.height=5)
library("wordcloud")## Loading required package: RColorBrewerlibrary("RColorBrewer")
library("wordcloud")Overview of included and excluded analyses
x <- read.table("PMC_2019-analysis.tsv",header=TRUE,fill=TRUE,sep="\t")
head(x)## Pubmed.Central.ID Article.number Allocated Journal
## 1 PMC6317205 1 Mark J Transl Med
## 2 PMC6317219 2 Mark J Transl Med
## 3 PMC6318854 3 Mark BMC Med Genomics
## 4 PMC6318897 4 Mark BMC Genomics
## 5 PMC6322516 5 Mark Cancer Manag Res
## 6 PMC6323737 6 Mark BMC Plant Biol
## Omics.type Organism Gene.set.library
## 1 RNA-seq Homo sapiens Not stated
## 2 Gene expression array Homo sapiens GO, KEGG
## 3 DNA methylation array Homo sapiens GO
## 4 RNA-seq Gymnocypris przewalskii KEGG
## 5 Gene expression array Homo sapiens Ingenuity Knowledge Base
## 6 Gene expression array Arabidopsis GO
## GS.version Statistical.test.used FDR.Correction App.used
## 1 No No test <NA> DAVID
## 2 No Not stated No DAVID
## 3 No No test <NA> PANTHER
## 4 No Not stated Yes KOBAS
## 5 No Not stated No Ingenuity Pathway Analysis
## 6 Yes Not stated No topGO
## App.Version Code.availability Background.gene.set
## 1 No <NA> Not stated
## 2 No <NA> Not stated
## 3 No <NA> Not stated
## 4 No <NA> Not stated
## 5 No <NA> Not stated
## 6 Yes No Not stated
## Assumptions.violated Gene.lists.provided
## 1 Inference without test, Background No
## 2 Background, FDR No
## 3 Inference without test, Background No
## 4 Background Yes
## 5 Background, FDR No
## 6 Background, FDR Yescolnames(x)## [1] "Pubmed.Central.ID" "Article.number" "Allocated"
## [4] "Journal" "Omics.type" "Organism"
## [7] "Gene.set.library" "GS.version" "Statistical.test.used"
## [10] "FDR.Correction" "App.used" "App.Version"
## [13] "Code.availability" "Background.gene.set" "Assumptions.violated"
## [16] "Gene.lists.provided"dim(x)## [1] 1762 16exclude <- subset(x,x$GS.version=="EXCLUDE")
nrow(exclude)## [1] 133length(unique(exclude$Pubmed.Central.ID))## [1] 133x <- subset(x,x$GS.version!="EXCLUDE")
nrow(x)## [1] 1624length(unique(x$Pubmed.Central.ID))## [1] 1363Omics type
omics <- x$Omics.type
omics_split <- strsplit(omics,", ")
omics <- unlist(omics_split)
res <- table(omics)
res <- res[order(res)]
res## omics
## ATAC-seq CRISPR screen
## 1 1
## Cytokine array EST sequencing
## 1 1
## Metagenomics Methyation sequencing
## 1 1
## proteomics pyrosequencing
## 1 1
## QTL mapping RNAi screen
## 1 1
## TCGA database ChIP-seq
## 1 2
## DNA Gene expression array Genotyping array
## 2 2
## Metabolomics Microbiome
## 2 2
## NanoString PPI
## 2 2
## RNA-seqand Gene expression array Genome sequening
## 2 3
## Metablomics scRNA-seq
## 4 4
## PCR array Protein array
## 6 8
## miRNA expression array DNA methylation sequencing
## 11 13
## Genome sequencing miRNA-seq
## 30 38
## DNA methylation array Genotyping array
## 45 47
## Metabolomics Proteomics
## 68 149
## Database Gene expression array
## 273 500
## RNA-seq
## 562par(mar=c(1,1,1,1))
names(res) <- gsub("Gene expression array","RNA array",names(res))
wordcloud(words = names(res), freq = res, min.freq = 1,
max.words=200, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"))
par(mar=c(5,12,3,1))
barplot(tail(res,20),horiz=TRUE,las=1,cex.names = 0.7, xlab="no. analyses",
main = "Omics type", xlim=c(0,600))
grid()
other <- sum(res[1:(nrow(res)-10)])
res2 <- c(other,tail(res,9))
names(res2)[1] <- "Other"
par(mar=c(5,12,3,1))
barplot(res2,horiz=TRUE,las=1,cex.names = 0.7, xlab="no. analyses",
main = "Omics type", xlim=c(0,600))
grid()
Organism
org <- x$Organism
org_split <- strsplit(org,", ")
org <- unlist(org_split)
res <- table(org)
res <- res[order(res)]
res## org
## Acinetobacter baumannii Acropora cervicornis
## 1 1
## Agave tequilana Ailuropoda melanoleuca
## 1 1
## Alternanthera philoxeroides Ananas comosus
## 1 1
## Anas platyrhynchus domesticus Annona × atemoya
## 1 1
## Apostichopus japonicus Arabidopsis
## 1 1
## Artemia franciscana Bacillus cereus
## 1 1
## Bombus Bubalus bubalis
## 1 1
## Cairina moschata Camellia oleifera
## 1 1
## Campylomormyrus compressirostris Campylomormyrus tshokwe
## 1 1
## Candida albicans Capra hircus
## 1 1
## Caragana intermedia Careolus capreolus
## 1 1
## Catalpa fargesii Catharanthus roseus
## 1 1
## Chlorella pyrenoidosa Chromochloris zofingiensis
## 1 1
## Coturnix japonica Ctenopharyngodon idella
## 1 1
## Cucumis sativus Cupriavidus necator
## 1 1
## Damnacanthus indicus Desmosdesmus sp.
## 1 1
## Diabrotica virgifera Drosophila
## 1 1
## Dugesia ryukyuensis Dunaliella salina
## 1 1
## Eisenia fetida Enterococcus faecalis
## 1 1
## Equus ferus caballus Eriobotrya japonica
## 1 1
## Eriocheir sinensis Euphorbia kansui
## 1 1
## Exaiptasia pallida Fundulus majalis
## 1 1
## Gerbera hybrida Gnathonemus petersii
## 1 1
## Gossypium arboreum Gossypium barbadense
## 1 1
## Gymnocypris przewalskii Haemaphysalis longicornis
## 1 1
## Haemophilus influenzae Hypophthalmichthys nobilis
## 1 1
## Incilaria fruhstorferi Juncus effusus
## 1 1
## Legionella pneumophila Lilium pumilum
## 1 1
## Linum usitatissimum Lolium perenne
## 1 1
## Lolium temulentum Longissimus thoracis
## 1 1
## Lupinus albus Lycoris longituba
## 1 1
## Lymantria dispar dispar Malapterurus electricus
## 1 1
## Malus sieversii Microbiota
## 1 1
## Moschus berezovskii Mycobacterium smegmatis
## 1 1
## Mycobacterium tuberculosis Mytilus galloprovincialis
## 1 1
## Mytilus sp. Nicotiana benthamiana
## 1 1
## Ocimum tenuiflorum Oreochromis niloticus
## 1 1
## Oryza Sativa Panax ginseng
## 1 1
## Papio anubis Phellinus igniarius
## 1 1
## Phyllosticta Plasmodium falciparum
## 1 1
## Pleurotus tuoliensis Populus deltoides
## 1 1
## Pseudomonas aeruginosa Pygoscelis antarcticus
## 1 1
## Pygoscelis papua Ribes nigrum
## 1 1
## Saccharomyces cerevisiae Saccharomyces cerevisiae\n
## 1 1
## Saccharum officinarum Saccharum sp.
## 1 1
## Salvelinus alpinus Serratia marcescens
## 1 1
## Setaria italica Sillago sihama
## 1 1
## Simiiformes catarrhini Sorghum bicolor
## 1 1
## Spica prunellae Sternopygus macrurus
## 1 1
## Suaeda salsa Taraxacum kok-saghyz
## 1 1
## Taxus sp. Trachemys scripta elegans
## 1 1
## Trollius chinensis Bunge Various order Chiroptera
## 1 1
## Vertebrata Vicia faba
## 1 1
## Vicia sativa Xanthomonas citri
## 1 1
## Zonotrichia albicollis Zootermopsis nevadensis
## 1 1
## Zygnema circumcarinatum Abelmoschus esculentus
## 1 2
## Aequipecten opercularis Anas platyrhynchos
## 2 2
## Apis mellifera Auricularia cornea
## 2 2
## Bemisia tabaci Brachymeria lasus
## 2 2
## Brassica alboglabra Brassica oleracea
## 2 2
## Brassica rapa Caenorhabditis elegans
## 2 2
## Danio rerio Escherichia coli
## 2 2
## Fenneropenaeus chinensis Granulicatella adiacens
## 2 2
## Hordeum vulgare Humulus lupulus
## 2 2
## Ipomoea batatas Magnolia wufengensis
## 2 2
## Malus domestica Mauremys reevesii
## 2 2
## Megalobrama amblycephala Meleagris gallopavo silvestris
## 2 2
## Mesocricetus auratus Mizuhopecten yessoensis
## 2 2
## Morchella importuna Nicotiana glutinosa
## 2 2
## Nicotiana tabaccum Oreochromis aureus
## 2 2
## Pagrus major Pseudotsuga menziesii
## 2 2
## Raphanus sativus Rhacophorus omeimontis
## 2 2
## Schistosoma japonicum Sclerotinia sclerotiorum
## 2 2
## Solanum sisymbriifolium Staphylococcus aureus
## 2 2
## Vibrio parahaemolyticus Vitis vinifera
## 2 2
## Aedes aegypti Carica papaya
## 3 3
## Macaca mulatta Zea mays
## 3 3
## Bos grunniens Canis lupus familiaris
## 4 4
## Capra aegagrus hircus Citrus sinensis
## 4 4
## Cyprinus carpio Oryctolagus cuniculus
## 4 4
## Camellia sinensis Equus caballus
## 5 5
## Glycine max Triticum aestivum
## 5 6
## Gossypium hirsutum Ovis aries
## 8 8
## Arabidopsis thaliana Brassica napus
## 9 9
## Oryza sativa Gallus gallus
## 15 16
## Sus scrofa Bos taurus
## 27 28
## Rattus norvegicus Mus musculus
## 54 136
## Homo sapiens
## 1104par(mar=c(1,1,1,1))
names(res) <- gsub("Homo sapiens","human",names(res))
wordcloud(words = names(res), freq = res, min.freq = 1,
max.words=200, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), scale=c(4,.5))
par(mar=c(5,12,3,1))
names(res) <- gsub("human","Homo sapiens",names(res))
barplot(tail(res,20),horiz=TRUE,las=1,cex.names = 0.7, xlab="no. analyses",
main = "Organism", xlim=c(0,1200))
grid()
other <- sum(res[1:(nrow(res)-10)])
res2 <- c(other,tail(res,9))
names(res2)[1] <- "Other"
par(mar=c(5,12,3,1))
barplot(res2,horiz=TRUE,las=1,cex.names = 0.7, xlab="no. analyses",
main = "Organism", xlim=c(0,1200))
grid()
Gene set library
GSL <-x$Gene.set.library
GSL_split <- strsplit(GSL,", ")
GSL <- unlist(GSL_split)
res <- table(GSL)
res <- res[order(res)]
which(names(res)=="Not stated")/sum(res)*100## [1] 3.844614res## GSL
## Arabidopsis thaliana metabolic pathway AraCyc
## 1 1
## CCGA ChEMBL
## 1 1
## ChemRICH ChemSpider
## 1 1
## ConsensusPathDB Custom
## 1 1
## Cytoband dbPTB
## 1 1
## DGIdb DISEASES
## 1 1
## DSigDB EcoCyc
## 1 1
## eggNOG EHMN
## 1 1
## Enrichr FUMA
## 1 1
## FunDO G2SBC
## 1 1
## Gene Set Knowledgebase GenMAPP
## 1 1
## GEO GOslim
## 1 1
## GSEA GWAS-catalog
## 1 1
## Human Metabolome Database IMPaLA
## 1 1
## Ingenuity Ingenuity Canonical Pathways
## 1 1
## INOH Jensen Diseases database
## 1 1
## MetaboLync METLIN
## 1 1
## MGI miEAA
## 1 1
## miRBase NCBI
## 1 1
## NCBI PubChem BioAssay NCI
## 1 1
## NDeX Omic Path
## 1 1
## pathDIP Pathway Ontology
## 1 1
## Pathway Studio Pathway Studio Ontology
## 1 1
## PDSP Ki DB PharmGKB
## 1 1
## PHAROS Phytozome annotations
## 1 1
## plantCyc PubChem
## 1 1
## REACTOME Schizo-Pi
## 1 1
## SGD SignaLink
## 1 1
## SIGNOR SMART
## 1 1
## SMPDB STITCH
## 1 1
## STRING ToppCluster
## 1 1
## ToppFun TRANSFAC
## 1 1
## Vectorbase Vesiclepedia
## 1 1
## YEASTRACT Hallmark
## 1 2
## HPO HumanCyc
## 2 2
## MapMan MetaCore
## 2 2
## Mummichog NetPath
## 2 2
## NHGRI Pathway Commons
## 2 2
## Swiss-Prot COG
## 2 3
## CORUM DisGeNET
## 3 3
## DO KOG
## 3 3
## OMIM WikiPathway
## 3 4
## HMDB PFAM
## 5 6
## BioCyc UniProt
## 7 7
## InterPro PID
## 8 10
## Metacore WikiPathways
## 12 13
## MetaboAnalyst PANTHER
## 15 17
## BioCarta Not stated
## 28 85
## Reactome MSigDB
## 85 103
## Ingenuity Knowledge Base KEGG
## 139 892
## GO
## 956par(mar=c(1,1,1,1))
names(res) <- gsub("Homo sapiens","human",names(res))
wordcloud(words = names(res), freq = res, min.freq = 1,
max.words=200, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"))
par(mar=c(5,12,3,1))
barplot(tail(res,20),horiz=TRUE,las=1,cex.names = 0.7, xlab="no. analyses",
main = "Gene set library", xlim=c(0,1000))
grid()
other <- sum(res[1:(nrow(res)-10)])
res2 <- c(other,tail(res,9))
names(res2)[1] <- "Other"
par(mar=c(5,12,3,1))
barplot(res2,horiz=TRUE,las=1,cex.names = 0.7, xlab="no. analyses",
main = "Gene set library", xlim=c(0,1200))
grid()
Gene set version
GSV <-x$GS.version
res <- table(GSV)
res## GSV
## No Yes
## 1516 108res[1]/sum(res)*100## No
## 93.34975par(mar=c(5,12,3,1))
barplot(tail(res,20),horiz=TRUE,las=1,cex.names = 1, xlab="no. analyses",
main = "Gene set version defined", xlim=c(0,1600))
grid()
Statistical test used
test <-x$Statistical.test.used
test <- strsplit(test,", ")
test <- unlist(test)
res <- table(test)
res <- res[order(res)]
res[which(names(res)=="Not stated")] / sum(res) * 100## Not stated
## 63.57535res## test
## Binomial edgeR R package
## 1 1
## fisher FIsher
## 1 1
## GSVA Kolmogorov–Smirnov
## 1 1
## Kolmogorov–Smirnov\n Kruskal-Wallis
## 1 1
## Kruskal–Wallis Mann-Whitney U-test
## 1 1
## modified Chi-squared test PASCAL
## 1 1
## Singular Enrichment Analysis (SEA) SPIA
## 1 1
## GVSA Binomial
## 2 3
## Chi-squared Permutation
## 3 3
## ssGSEA Not Stated
## 3 4
## MSEA ANOVA
## 5 6
## EASE No test
## 10 69
## Hypergeometric Fisher
## 120 171
## GSEA Not stated
## 184 1042par(mar=c(1,1,1,1))
wordcloud(words = names(res), freq = res, min.freq = 1,
max.words=200, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"))
par(mar=c(5,12,3,1))
barplot(tail(res,20),horiz=TRUE,las=1,cex.names = 0.7, xlab="no. analyses",
main = "Test used", xlim=c(0,1200))
grid()
other <- sum(res[1:(nrow(res)-10)])
res2 <- c(other,tail(res,9))
names(res2)[1] <- "Other"
par(mar=c(5,12,3,1))
barplot(res2,horiz=TRUE,las=1,cex.names = 0.7, xlab="no. analyses",
main = "Test used", xlim=c(0,1200))
grid()
FDR Correction
fdr <-x$FDR.Correction
fdr <- strsplit(fdr,", ")
fdr <- unlist(fdr)
res <- table(fdr)
res <- res[order(res)]
sum(res[which(names(res)!="Yes")])/sum(res)*100## [1] 52.69762res## fdr
## Not stated Yes No
## 48 754 792par(mar=c(1,1,1,1))
wordcloud(words = names(res), freq = res, min.freq = 1,
max.words=200, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"))
par(mar=c(5,12,3,1))
barplot(tail(res,20),horiz=TRUE,las=1,cex.names = 0.7, xlab="no. analyses",
main = "FDR", xlim=c(0,800))
grid()
App used
App <-x$App.used
App_split <- strsplit(App,", ")
App <- unlist(App_split)
res <- table(App)
res <- res[order(res)]
res[which(names(res)=="Not stated")]/sum(res)*100## Not stated
## 10.69453res## App
## anamiR Basespace Correlation engine
## 1 1
## BioCloud Biosystem
## 1 1
## BlastKOALA BLASTx
## 1 1
## CAMERA ChemRICH
## 1 1
## CluePedia ConsensuspathDB
## 1 1
## CPDB Custom Perl script
## 1 1
## DEPICT DIAMOND
## 1 1
## ESGEA exRNA atlas
## 1 1
## FUMA gene2func g:GOSt
## 1 1
## Genelibs GeneMANIA
## 1 1
## GeneSpring GeneTrail2
## 1 1
## GenFam GenGen
## 1 1
## globaltest GO-Elite
## 1 1
## GO::TermFinder GOTermFinder
## 1 1
## GSA-SNP GVSA
## 1 1
## i‐Gsea4Gwas KSEA
## 1 1
## limma limma goana
## 1 1
## limma kegga LinkedOmics
## 1 1
## MAGENTA MATHT
## 1 1
## Metabo Analyst Metabolon
## 1 1
## MetaboLync MiRNet
## 1 1
## miRSystem missMethyl
## 1 1
## missMethyl R package Molecule Annotation System
## 1 1
## MS Excel NOA web app
## 1 1
## Omicshare PANOGA
## 1 1
## PAPi Path-Finder
## 1 1
## pathDIP Pathifier
## 1 1
## pathVar Pathview
## 1 1
## PathVisio Pathway Studio
## 1 1
## PathwayConnector PIGE
## 1 1
## Plant Pathway Studio PlantGSEA
## 1 1
## ProteINSIDE PSEA
## 1 1
## QuickGO RAVIGO
## 1 1
## ReactomePA REViGO
## 1 1
## SEA SetRank
## 1 1
## SGD Gene Ontology Slim Mapper Speed
## 1 1
## SPIA SPSS
## 1 1
## ssGSEA (GenePattern) topGo
## 1 1
## ToppCluster VLAD
## 1 1
## webMeV Cluepedia
## 1 2
## ConsensusPathDB Custom Python script
## 2 2
## DOSE FGSEA
## 2 2
## g:Profiler GAGE
## 2 2
## GeneAnswers GOEAST
## 2 2
## i-GSEA4GWAS Ingenuity pathway analysis
## 2 2
## MapMan MetaCore
## 2 2
## MeTPA NetworkAnalyst
## 2 2
## OmicsBean PIANO
## 2 2
## Reactome FI/Cytoscape ToppFun
## 2 2
## Uniprot/GOA FUMA
## 2 3
## GenCLiP GeneCodis
## 3 3
## GeneGo Metacore GOstats
## 3 3
## GREAT GSVA
## 3 3
## MetPA MSEA
## 3 3
## Partek Pascal
## 3 3
## REVIGO AmiGO
## 3 4
## Custom script WEGO
## 4 4
## GSEA web app Reactome web app
## 5 5
## Cytoscape (No plugin stated) GOATOOLS
## 6 6
## KEGG mapper MAGMA
## 6 6
## Mummichog GOrilla
## 6 7
## DIANA-miRPath Custom R script
## 9 10
## KAAS topGO
## 10 10
## ToppGene BiNGO/Cytoscape
## 10 12
## Metascape Metacore
## 12 13
## FunRich STRING
## 14 18
## agriGO Blast2GO
## 19 23
## Enrichr MetaboAnalyst
## 28 30
## WebGestalt ClueGO/Cytoscape
## 30 33
## GOseq PANTHER
## 38 41
## KOBAS clusterProfiler
## 69 87
## Ingenuity Pathway Analysis Not stated
## 158 174
## GSEA DAVID
## 186 379par(mar=c(1,1,1,1))
wordcloud(words = names(res), freq = res, min.freq = 1,
max.words=200, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"))
par(mar=c(5,12,3,1))
barplot(tail(res,20),horiz=TRUE,las=1,cex.names = 0.7, xlab="no. analyses",
main = "App used", xlim=c(0,400))
grid()
other <- sum(res[1:(nrow(res)-10)])
res2 <- c(other,tail(res,9))
names(res2)[1] <- "Other"
par(mar=c(5,12,3,1))
barplot(res2,horiz=TRUE,las=1,cex.names = 0.7, xlab="no. analyses",
main = "App used", xlim=c(0,1200))
grid()
App version
APV <-x$App.Version
res <- table(APV)
res## APV
## No Yes
## 1190 421res[1]/sum(res)*100## No
## 73.86716par(mar=c(5,12,3,1))
barplot(tail(res,20),horiz=TRUE,las=1,cex.names = 1, xlab="no. analyses",
main = "App version defined", xlim=c(0,1600))
grid()
Code available
code <-x$Code.availability
res <- table(code)
res## code
## No Yes
## 283 10res[1]/sum(res)*100## No
## 96.58703par(mar=c(5,12,3,1))
barplot(tail(res,20),horiz=TRUE,las=1,cex.names = 1, xlab="no. analyses",
main = "Code availability", xlim=c(0,300))
grid()
Background gene set
BG <-x$Background.gene.set
res <- table(BG)
res## BG
## No Not stated Stated, but incorrect
## 219 1159 15
## Yes
## 67sum(res[which(names(res)!="Yes")])/sum(res)*100## [1] 95.41096par(mar=c(5,12,3,1))
barplot(tail(res,20),horiz=TRUE,las=1,cex.names = 1, xlab="no. analyses",
main = "Background list defined", xlim=c(0,1200))
grid()
Gene lists provided
GL <-x$Gene.lists.provided
res <- table(GL)
res## GL
## No Yes
## 3 1039 579sum(res[which(names(res)!="Yes")])/sum(res)*100## [1] 64.28131par(mar=c(5,12,3,1))
barplot(tail(res,20),horiz=TRUE,las=1,cex.names = 1, xlab="no. analyses",
main = "Gene lists provided", xlim=c(0,1200))
grid()
colnames(x)## [1] "Pubmed.Central.ID" "Article.number" "Allocated"
## [4] "Journal" "Omics.type" "Organism"
## [7] "Gene.set.library" "GS.version" "Statistical.test.used"
## [10] "FDR.Correction" "App.used" "App.Version"
## [13] "Code.availability" "Background.gene.set" "Assumptions.violated"
## [16] "Gene.lists.provided"Assumptions violated
ok <- nrow(subset(x,Assumptions.violated=="No"))
ok## [1] 160bad <- nrow(subset(x,Assumptions.violated!="No"))
bad## [1] 1464ok/sum(bad,ok)*100## [1] 9.852217ass <-x$Assumptions.violated
ass <- strsplit(ass,", ")
ass <- unlist(ass)
res <- table(ass)
res <- res[order(res)]
res## ass
## Background\n Misinterpreted FDR values No data shown
## 2 3 32
## Inference without test No FDR
## 61 160 761
## Background
## 1361par(mar=c(1,1,1,1))
wordcloud(words = names(res), freq = res, min.freq = 1,
max.words=200, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"))
par(mar=c(5,12,3,1))
barplot(tail(res,20),horiz=TRUE,las=1,cex.names = 1, xlab="no. analyses",
main = "Assumptions violated", xlim=c(0,1400))
grid()
Session information
sessionInfo()## R version 4.1.0 (2021-05-18)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
##
## locale:
## [1] LC_CTYPE=en_AU.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_AU.UTF-8 LC_COLLATE=en_AU.UTF-8
## [5] LC_MONETARY=en_AU.UTF-8 LC_MESSAGES=en_AU.UTF-8
## [7] LC_PAPER=en_AU.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] wordcloud_2.6 RColorBrewer_1.1-2
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.7 digest_0.6.27 R6_2.5.0 jsonlite_1.7.2
## [5] magrittr_2.0.1 evaluate_0.14 highr_0.9 rlang_0.4.11
## [9] stringi_1.7.3 jquerylib_0.1.4 bslib_0.2.5.1 rmarkdown_2.9
## [13] tools_4.1.0 stringr_1.4.0 xfun_0.24 yaml_2.2.1
## [17] compiler_4.1.0 htmltools_0.5.1.1 knitr_1.33 sass_0.4.0