• many pieces of information at one glance
• explore your data
• make your point

click here kev

Workflow

          ## Why use R to do it? ### Workflow

Different valuable options

• The gridGraphics package
  
• base VS ggplot2
• Web-based graphs

Many, many, many packages available

• http://blog.revolutionanalytics.com/2017/01/cran-10000.html

• https://stackoverflow.blog/2017/10/10/impressive-growth-r/

• click here Kev

Few lines of codes

library(raster)
library(mapview)
elvBtn = getData('alt', country='BTN', path='./assets/')
mapview(elvBtn)

Few lines of codes

A large community

Effect Size (ES)

• log risk ratios,
• log odds ratios,
• …,
• non-standard ES

Studies

• by author
• by region
• by species

http://www.metafor-project.org/doku.php/plots:funnel_plot_with_trim_and_fill

http://www.metafor-project.org/doku.php/plots:forest_plot_with_subgroups

http://www.metafor-project.org/doku.php/plots:radial_plot

http://www.metafor-project.org/doku.php/plots:labbe_plot

Roca et al. 2016

Vellend et al. 2013

Vellend et al. 2013

• click here

• install.packages("ctv"); ctv::install.views("MetaAnalysis")

• Guido Schwarzer author of meta

• Schwarzer, Guido, James R. Carpenter, and Gerta Rücker. Meta-Analysis with R.

• Wolfgang Viechtbauer author metafor

• click here

• metabin()

Arguments:

 event.e: Number of events in experimental group.
     n.e: Number of observations in experimental group.
 event.c: Number of events in control group.
     n.c: Number of observations in control group.

• metabin()
  
• metagen()

Arguments:

      TE: Estimate of treatment effect.
    seTE: Standard error of treatment estimate.

meta:

- `metabin()` / `metagen()`
- `funnel`, `forest`, ...

install.packages('meta')
library(meta)
data(Olkin95)
# ?Olkin95
meta1 <- metabin(event.e, n.e, event.c, n.c,
                  data=Olkin95, subset=c(41,47,51,59),
                  sm="RR", method="I",
                  studlab=paste(author, year))

forest(meta1)

• click here kev

• Calculate effect size: escalc()

• Perform analysis: rma()

• Plots: funnel(), forest(), labbe(), …

install.packages('metafor')
library('metafor')

r<-c(0.5,0.6,0.4,0.2,0.7,0.45)
n<-c(40,90,25,400,60,50)
studynames<-c(1,2,3,4,5,6)
b<-data.frame(r,n,studynames)
eszcor <- escalc(measure="ZCOR", ri=r, ni=n, data=b)
FEmodel<-rma(yi=yi, vi=vi, data=eszcor, method="FE")
#forest plot fixed-effect model: pooled effect size,
forest(FEmodel)

r<-c(0.5,0.6,0.4,0.2,0.7,0.45)
n<-c(40,90,25,400,60,50)
studynames<-c(1,2,3,4,5,6)
b<-data.frame(r,n,studynames)

knitr::kable(head(b))

eszcor <- escalc(measure="ZCOR", ri=r, ni=n, data=b)
class(eszcor)

R>>  [1] "escalc"     "data.frame"

FEmodel<-rma(yi=yi, vi=vi, data=eszcor, method="FE")
class(FEmodel)

R>>  [1] "rma.uni" "rma"

forest(FEmodel)

forest(FEmodel,transf=transf.ztor)

funnel(FEmodel, yaxis="vinv", main="Inverse Sampling Variance")

Pros

• do everything for you
• good-looking plots with few lines of code

Cons

• standard ES
• not fully customizable
• not for ecologists?

ggplot2

• par()
• plot(), barplot(), funnel()

npt <- 15
vecX <- 1:npt
vecY <- runif(length(vecX))

plot(vecX, vecY)

plot(vecX, vecY, xlim=c(0,15)+.5)

plot(vecX, vecY, xlim=c(0,15)+.5, pch=19)

plot(vecX, vecY, xlim=c(0,15)+.5, pch=19, col="#0f7aa2")

plot(vecX, vecY, xlim=c(0,15)+.5, pch='S', col="#0f7aa2")

plot(vecX, vecY, xlim=c(0,15)+.5, type='h', col="#0f7aa2")

plot(vecX, vecY, xlim=c(0,15)+.5, type='h', col="#0f7aa2", lwd=12)

par(lend=2); plot(vecX, vecY, xlim=c(0,15)+.5, type='h', col="#0f7aa2", lwd=12)

par(las=1)
plot(vecX, vecY, xlim=c(0,15)+.5, pch=19, col="#0f7aa2")

par(las=1)
plot(vecX, vecY, xlim=c(0,15)+.5, pch=19, col="#0f7aa2")
abline(v=c(0.5,15.5), lty=2)

par(las=1, xaxs='i')
plot(vecX, vecY, xlim=c(0,15)+.5, pch=19, col="#0f7aa2")
abline(v=c(0.5,15.5), lty=2)

vCol <- rep(1, npt)
vCol[c(2,5,8,9)] <- 2
vSize <- 1+2*runif(npt)

par(las=1, xaxs='i')
plot(vecX, vecY, xlim=c(0,15)+.5, pch=19, col=c("#0f7aa2", '#a70d72')[vCol])

par(las=1, xaxs='i')
plot(vecX, vecY, xlim=c(0,15)+.5, pch=19, col=c("#0f7aa2", '#a70d72')[vCol], cex=vSize)

par(las=1, xaxs='i')
plot(vecX, vecY, xlim=c(0,15)+.5, pch=19, col=c("#0f7aa2", '#a70d72')[vCol], cex=vSize)
abline(lm(vecY~vecX), col=2)

par(las=1, xaxs='i', mfrow=c(2,2))
for (i in 1:4){
  plot(vecX, vecY, xlim=c(0,15)+.5, pch=19, col=c("#0f7aa2", '#a70d72')[vCol], cex=vSize)
  mtext(3, at=2, text=LETTERS[i], line=.5, cex=1.2)
}

par(las=1, xaxs='i', mfrow=c(2,2), mar=c(2,2,2,1), oma=c(3,3,0,0), mgp=c(1,0.8,0))
##--
for (i in 1:4){
  plot(vecX, vecY, xlim=c(0,15)+.5, pch=19, col=c("#0f7aa2", '#a70d72')[vCol],
    cex=vSize, ann=FALSE)
  mtext(3, at=1, text=LETTERS[i], line=.5, cex=1.2)
}
##--
mtext(1, text='Time', outer=T, line=1, cex=1.4)
par(las=0)
mtext(2, text='Variable', outer=T, line=1, cex=1.4)

layout(matrix(c(2,1,0,3), 2), widths=c(1,.5), heights=c(.5,1))
layout.show(3)

layout(matrix(c(1,2,0,3), 2), widths=c(1,.5), heights=c(.6,1))
par(mar=c(2, 4, 2, 1), las=1)
for (i in 1:3){
  plot(vecX, vecY, xlim=c(0,15)+.5, pch=19, col=c("#0f7aa2", '#a70d72')[vCol],
    cex=vSize, ann=FALSE)
  mtext(3, at=1, text=LETTERS[i], line=.5, cex=1.2)
}

funnel(FEmodel, yaxis="vinv", main="Inverse Sampling Variance")

par(las=1, mar=c(5,6,4,1), mgp=c(4,1,0))
funnel(FEmodel, yaxis="vinv", main="Inverse Sampling Variance", col="#0f7aa2",
back='white')
legend('topright', legend='mypoints', pch=19, col="#0f7aa2", bty='n')

1- Roca et al. 2016

• Paper available online

• Data available on Github

2- Vellend et al. 2013

• Paper is available on line

• Data available on line as well Dataset S1

Figure 1 from Roca et al. 2016

Figure S2 Vellend et al. 2013

Find the R script I created to reproduce this figure here

Amanda Young kindly sent me her version using ggplot2

Find the R script I created to reproduce this figure here

To create the network that follows:

1- data available on Github;

2- R script available on Github.

• task view on CRAN

• 2 books:

  1. Chen, Ding-Geng, and Karl E. Peace. Applied Meta-Analysis with R. Chapman & Hall/CRC Biostatistics Series. Boca Raton: CRC Press/Taylor & Francis Group, 2013. + companion website

  2. Schwarzer, Guido, James R. Carpenter, and Gerta Rücker. Meta-Analysis with R. Use R! Cham: Springer International Publishing, 2015. https://doi.org/10.1007/978-3-319-21416-0.