R/GGE_Plot.R

In geneticae: Statistical Tools for the Analysis of Multi Environment Agronomic Trials

#'GGE biplots with \pkg{ggplot2}
#'
#'@description GGE biplots are used for visual examination of the relationships
#'  between test environments, genotypes, and genotype-by-environment
#'  interactions. `GGEPlot()` produces a biplot as an object of class 'ggplot',
#'  using the output of  the \code{\link[geneticae]{GGEmodel}} function.
#'  Several types of biplots are offered which focus on different aspects of the
#'  analysis. Customization options are also included. This function is a
#'  modification of \code{\link[GGEBiplots]{GGEPlot}} from the
#'  \href{https://CRAN.R-project.org/package=GGEBiplots}{GGEBiplots package}.
#'
#'@param GGEModel An object of class \code{GGEModel}.
#'@param type type of biplot to produce.
#'\itemize{
#'  \item \code{"Biplot"}: Basic biplot.
#'  \item \code{"Selected Environmen"t}: Ranking of cultivars based on
#'  their performance in any given environment.
#'  \item \code{"Selected Genotype"}: Ranking of environments based on the
#'  performance of any given cultivar.
#'  \item \code{"Relationship Among Environments"}.
#'  \item \code{"Comparison of Genotype"}.
#'  \item \code{"Which Won Where/What"}: Identifying the 'best'
#'  cultivar in each environment.
#'  \item \code{"Discrimination vs. representativeness"}: Evaluating the
#'  environments based on both discriminating ability and representativeness.
#'  \item \code{"Ranking Environments"}: Ranking environments with respect to
#'  the ideal environment.
#'  \item \code{"Mean vs. stability"}: Evaluating cultivars based on both average
#'  yield and stability.
#'  \item \code{"Ranking Genotypes"}: Ranking genotypes with respect to the
#'  ideal genotype.}
#'@param d1 PCA component to plot on x axis. Defaults to 1.
#'@param d2 PCA component to plot on y axis. Defaults to 2.
#'@param selectedE name of the environment to evaluate when `type="Selected
#'  Environment"`.
#'@param selectedG name of the genotype to evaluate when `type="Selected
#'  Genotype"`.
#'@param selectedG1, name of the genotype to compare to `selectedG2`  when
#'  `type="Comparison of Genotype"`.
#'@param selectedG2, name of the genotype to compare to `selectedG1`  when
#'  `type="Comparison of Genotype"`.
#'@param colGen genotype attributes colour. Defaults to `"gray47"`.
#'@param colEnv environment attributes colour. Defaults to `"darkred"`.
#'@param colSegment segment or circle lines colour. Defaults to `"gray30"`.
#'@param colHull hull colour when `type="Which Won Where/What"`. Defaults to
#'  "gray30".
#'@param sizeGen genotype labels text size. Defaults to 4.
#'@param sizeEnv environment labels text size. Defaults to 4.
#'@param largeSize larger labels text size to use for two selected genotypes in
#'  `type="Comparison of Genotype"`, and for the outermost genotypes in
#'  `type="Which Won Where/What"`. Defaults to 4.5.
#'@param axis_expand multiplication factor to expand the axis limits by to
#'  enable fitting of labels. Defaults to 1.2.
#'@param axislabels logical, if this argument is `TRUE` labels for axes are
#'  included. Defaults to `TRUE`.
#'@param axes logical, if this argument is `TRUE` x and y axes going through the
#'  origin are drawn. Defaults to `TRUE`.
#'@param limits logical, if this argument is `TRUE` the axes are re-scaled.
#'  Defaults to `TRUE`.
#'@param titles logical, if this argument is `TRUE` a plot title is included.
#'  Defaults to `TRUE`.
#'@param footnote logical, if this argument is `TRUE` a footnote is included.
#'  Defaults to `TRUE`.
#'@keywords GGE Biplot
#'@return A biplot of class \code{ggplot}
#'@references Yan W, Kang M (2003). \emph{GGE Biplot Analysis: A Graphical Tool
#'  for Breeders, Geneticists, and Agronomists}. CRC Press.
#'@references Sam Dumble (2017). GGEBiplots: GGE Biplots with 'ggplot2'. R
#'  package version 0.1.1. \url{https://CRAN.R-project.org/package=GGEBiplots}
#'@export
#'@examples
#'  library(geneticae)
#'
#'  # Data without replication
#'  library(agridat)
#'  data(yan.winterwheat)
#'  GGE1 <- GGEmodel(yan.winterwheat)
#'  GGEPlot(GGE1)
#'
#'  # Data with replication
#'  data(plrv)
#'  GGE2 <- GGEmodel(plrv, genotype = "Genotype", environment = "Locality",
#'                   response = "Yield", rep = "Rep")
#'  GGEPlot(GGE2)
#'
#'@importFrom ggplot2 aes arrow coord_fixed element_text geom_abline geom_hline
#'  geom_point geom_polygon geom_segment geom_text geom_vline ggplot ggtitle
#'  labs layer_scales scale_color_manual scale_size_manual scale_x_continuous
#'  scale_y_continuous theme theme_bw xlab ylab element_blank theme_classic
#'@importFrom ggforce geom_arc geom_circle
#'@importFrom scales alpha squish
#'@importFrom grDevices chull
#'@importFrom grid unit

GGEPlot<-function(GGEModel,type="Biplot",d1=1,d2=2, selectedE=NA , selectedG=NA,selectedG1=NA,selectedG2=NA,
                  colGen="gray47",colEnv="darkred",colSegment="gray30",colHull="gray30",sizeGen=4,sizeEnv=4,largeSize=4.5,axis_expand=1.2,
                  axislabels=TRUE,axes=TRUE,limits=TRUE,titles=TRUE,footnote=TRUE){

    stopifnot(
    class(GGEModel) == "GGEModel",
    type %in% c("Biplot", "Selected Environment","Selected Genotype","Relationship Among Environments",
                       "Comparison of Genotype","Which Won Where/What","Discrimination vs. representativeness",
                       "Ranking Environments","Mean vs. Stability","Ranking Genotypes"),
    class(d1) == "numeric",
    class(d2) == "numeric",
    class(colGen) == "character",
    class(colEnv) == "character",
    class(colSegment) == "character",
    class(colHull) == "character",
    class(sizeGen) == "numeric",
    class(sizeEnv) == "numeric",
    class(largeSize) == "numeric",
    class(axis_expand) == "numeric",
    class(axislabels)  == "logical",
    class(axes)  == "logical",
    class(limits)  == "logical",
    class(titles)  == "logical",
    class(footnote)  == "logical"
  )

  coordgenotype=GGEModel$coordgenotype[,c(d1,d2)];coordenviroment=GGEModel$coordenviroment[,c(d1,d2)]
  varexpl=GGEModel$varexpl[c(d1,d2)];labelgen=GGEModel$labelgen;
  labelenv=GGEModel$labelenv;labelaxes=GGEModel$labelaxes[c(d1,d2)];Data=GGEModel$Data
  SVP=GGEModel$SVP

  plotdata<-data.frame(rbind(data.frame(coordgenotype,type="genotype",label=labelgen),data.frame(coordenviroment,type="environment",label=labelenv)))
  colnames(plotdata)[1:2]<-c("d1","d2")
  plotdata$type<-factor(plotdata$type)


  GGE1<-ggplot(data=plotdata,aes(x=d1,y=d2,group="type"))+
    theme(panel.grid = element_blank())+
    scale_color_manual(values=c(colGen,colEnv)) +
    scale_size_manual(values=c(sizeGen,sizeEnv)) + theme_classic()

  if(axislabels==TRUE){
    GGE1<-GGE1+xlab(paste(labelaxes[1],format(varexpl[1],nsmall=2), "%", sep = " "))+ylab(paste(labelaxes[2], format(varexpl[2],nsmall=2),"%", sep = " "))
  }else{    GGE1<-GGE1+xlab(" ")+ylab(" ")
}
  if(axes==TRUE){
    GGE1<-GGE1+geom_hline(yintercept=0)+geom_vline(xintercept=0)
  }
  if(limits==TRUE){
    xlim<-c(min(plotdata$d1*axis_expand),max(plotdata$d1*axis_expand))
    ylim<-c(min(plotdata$d2*axis_expand),max(plotdata$d2*axis_expand))
    if(which(c(diff(xlim),diff(ylim))==max(c(diff(xlim),diff(ylim))))==1){
      xlim1<-xlim
      ylim1<-c(ylim[1]-(diff(xlim)-diff(ylim))/2,ylim[2]+(diff(xlim)-diff(ylim))/2)
    }
    if(which(c(diff(xlim),diff(ylim))==max(c(diff(xlim),diff(ylim))))==2){
      ylim1<-ylim
      xlim1<-c(xlim[1]-(diff(ylim)-diff(xlim))/2,xlim[2]+(diff(ylim)-diff(xlim))/2)
    }

    GGE1<-GGE1+scale_x_continuous(limits=xlim1,expand=c(0,0))+
      scale_y_continuous(limits=ylim1,expand=c(0,0))+
      coord_fixed(1)
  }




  if(type=="Biplot"){

    GGE2<-GGE1+geom_segment(xend=0,yend=0,col=alpha(colEnv,0.5),data=subset(plotdata,type=="environment"))+
      geom_text(aes(label=label),show.legend = FALSE,data=subset(plotdata,type=="genotype"),col=colGen,size=sizeGen)+
      geom_text(aes(label=label),show.legend = FALSE,data=subset(plotdata,type=="environment"),col=colEnv,size=sizeEnv)

    if(titles==TRUE){GGE2<-GGE2+ggtitle("GGE Biplot")}
  }

  if(type=="Selected Environment"){
    if(!selectedE%in%labelenv){stop(paste(selectedE,"The environment selected is not in list of environment labels"))}
    venvironment<-labelenv==selectedE

    x1<-NULL
    for (i in 1:nrow(Data))
    {
      x <- solve(matrix(c(-coordenviroment[venvironment,2], coordenviroment[venvironment,1], coordenviroment[venvironment,1], coordenviroment[venvironment, 2]), nrow = 2),
                 matrix(c(0, coordenviroment[venvironment,1] * coordgenotype[i, 1] +  coordenviroment[venvironment, 2] *coordgenotype[i, 2]), ncol = 1))

      x1<-rbind(x1,t(x))
    }

    plotdata$x1_x<-NA
    plotdata$x1_x[plotdata$type=="genotype"]<-x1[,1]

    plotdata$x1_y<-NA
    plotdata$x1_y[plotdata$type=="genotype"]<-x1[,2]

    GGE2<-GGE1+
      geom_abline(slope=coordenviroment[venvironment, 2]/coordenviroment[venvironment,1],intercept=0,col=colEnv,)+
      geom_abline(slope=-coordenviroment[venvironment, 1]/coordenviroment[venvironment,2],intercept=0,col=colEnv)+
      geom_segment(xend=0,yend=0,col=alpha(colEnv,0.5),data=subset(plotdata,type=="environment"&label==selectedE),
                   arrow =arrow(ends ="first",length=unit(0.2,"inches") ),size=1)+
      geom_segment(aes(xend=x1_x,yend=x1_y),col=colGen,data=subset(plotdata,type=="genotype"),linetype=2)+
      geom_text(aes(label=label),show.legend = FALSE,data=subset(plotdata,type=="genotype"),col=colGen,size=sizeGen)

    if(titles==TRUE){GGE2<-GGE2+ggtitle(paste("Selected Environment =",selectedE))}

  }


  if(type=="Selected Genotype"){
    if(!selectedG%in%labelgen){stop(paste(selectedG,"The genotype selected is not in list of genotype labels"))}
    vgenotype<-labelgen==selectedG

    x1<-NULL
    for (i in 1:ncol(Data))
    {
      x <- solve(matrix(c(-coordgenotype[vgenotype,2], coordgenotype[vgenotype,1], coordgenotype[vgenotype,1], coordgenotype[vgenotype, 2]), nrow = 2),
                 matrix(c(0, coordgenotype[vgenotype,1] * coordenviroment[i, 1] +  coordgenotype[vgenotype, 2] *coordenviroment[i, 2]), ncol = 1))

      x1<-rbind(x1,t(x))
    }

    plotdata$x1_x<-NA
    plotdata$x1_x[plotdata$type=="environment"]<-x1[,1]

    plotdata$x1_y<-NA
    plotdata$x1_y[plotdata$type=="environment"]<-x1[,2]

    GGE2<-GGE1+
      geom_abline(slope=coordgenotype[vgenotype, 2]/coordgenotype[vgenotype,1],intercept=0,col=alpha(colSegment,0.5),size=0.7)+
      geom_abline(slope=-coordgenotype[vgenotype, 1]/coordgenotype[vgenotype,2],intercept=0,col=alpha(colSegment,0.5),size=0.7)+
      geom_segment(xend=0,yend=0,col=alpha(colSegment,0.5),data=subset(plotdata,type=="genotype"&label==selectedG),
                   arrow =arrow(ends ="first",length=unit(0.15,"inches") ),size=1)+
      geom_segment(aes(xend=x1_x,yend=x1_y),col=colEnv,data=subset(plotdata,type=="environment"),linetype=2)+
      geom_text(aes(label=label),show.legend = FALSE,data=subset(plotdata,type=="environment"),col=colEnv,size=sizeEnv)

    if(titles==TRUE){GGE2<-GGE2+ggtitle(paste("Selected Genotype =",selectedG))}
  }

  if(type=="Relationship Among Environments"){


    GGE2<-GGE1+
      geom_segment(xend=0,yend=0,col=alpha(colEnv,0.5),data=subset(plotdata,type=="environment"),
                   arrow =arrow(ends ="first",length=unit(0.1,"inches") ),size=0.7, linetype =2)+
      geom_text(aes(label=label),show.legend = FALSE,hjust="outward",vjust="outward",data=subset(plotdata,type=="environment"),col=colEnv,size=sizeEnv)

    if(titles==TRUE){GGE2<-GGE2+ggtitle("Relationship Among Environments")}
  }

  if(type=="Comparison of Genotype"){

    if(!selectedG1%in%labelgen){stop(paste(selectedG1,"The genotype selected is not in list of genotype labels"))}
    if(!selectedG2%in%labelgen){stop(paste(selectedG2,"The genotype selected is not in list of genotype labels"))}
    if(selectedG1==selectedG2){stop(paste("Cannot compare the same genotype to itself"))}
    vgenotype1<-labelgen==selectedG1
    vgenotype2<-labelgen==selectedG2
    # Compara dos genotipos
    GGE2<-GGE1+geom_point(data=subset(plotdata,type=="genotype"&label%in%c(selectedG1,selectedG2)))+
      geom_segment(x=plotdata$d1[plotdata$label==selectedG1&plotdata$type=="genotype"],
                   xend=plotdata$d1[plotdata$label==selectedG2&plotdata$type=="genotype"],
                   y=plotdata$d2[plotdata$label==selectedG1&plotdata$type=="genotype"],
                   yend=plotdata$d2[plotdata$label==selectedG2&plotdata$type=="genotype"],col=colGen,size=1)+
      geom_abline(intercept = 0, slope = -(coordgenotype[vgenotype1, 1] - coordgenotype[vgenotype2, 1])/(coordgenotype[vgenotype1,2] - coordgenotype[vgenotype2, 2]),
                  col = colGen,size=0.7)+
      geom_text(aes(label=label),show.legend = FALSE,data=subset(plotdata,type=="environment"),col=colEnv,size=sizeEnv)+
      geom_text(aes(label=label),show.legend = FALSE,data=subset(plotdata,type=="genotype"&!label%in%c(selectedG1,selectedG2)),col=alpha(colGen,0.4),size=sizeGen)+
      geom_text(aes(label=label),show.legend = FALSE,data=subset(plotdata,type=="genotype"&label%in%c(selectedG1,selectedG2)),
                col=colGen,size=largeSize,hjust="outward",vjust="outward")

    if(titles==TRUE){GGE2<-GGE2+ggtitle(paste("Comparison of Genotype",selectedG1,"with Genotype",selectedG2))}

  }

  if(type=="Which Won Where/What"){
    # Which-won-where
    indice = c(grDevices::chull(coordgenotype[, 1], coordgenotype[,2]))
    www<-data.frame(coordgenotype[indice,])


    indice<-c(indice,indice[1])
    ######################################
    segs<-NULL


    limx<-layer_scales(GGE1)$x$limits
    limy<-layer_scales(GGE1)$y$limits

    i <- 1
    while (is.na(indice[i + 1]) == FALSE)
    {
      m<-(coordgenotype[indice[i], 2] - coordgenotype[indice[i + 1], 2])/(coordgenotype[indice[i],1]-coordgenotype[indice[i + 1],1])
      mperp<--1/m
      c2<-coordgenotype[indice[i + 1], 2] - m*coordgenotype[indice[i + 1],1]
      xint<--c2/(m-mperp)
      xint<-ifelse(xint<0,min(coordenviroment[, 1],coordgenotype[, 1]), max(coordenviroment[, 1],coordgenotype[, 1]))
      yint<-mperp*xint

      xprop<-ifelse(xint<0,xint/limx[1],xint/limx[2])
      yprop<-ifelse(yint<0,yint/limy[1],yint/limy[2])

      m3<-which(c(xprop,yprop)==max(c(xprop,yprop)))
      m2<-abs(c(xint,yint)[m3])
      if(m3==1&xint<0)sl1<-(c(xint,yint)/m2)*abs(limx[1])
      if(m3==1&xint>0)sl1<-(c(xint,yint)/m2)*limx[2]
      if(m3==2&yint<0)sl1<-(c(xint,yint)/m2)*abs(limy[1])
      if(m3==2&yint>0)sl1<-(c(xint,yint)/m2)*limy[2]

      segs<-rbind(segs,sl1)
      i <- i + 1
    }
    rownames(segs)<-NULL
    colnames(segs)<-NULL
    segs<-data.frame(segs)



    colnames(www)<-c("X1","X2")

    winners<-plotdata[plotdata$type=="genotype",][indice[-1],]
    others<-plotdata[!rownames(plotdata)%in%rownames(winners),]

    GGE2<-GGE1+geom_polygon(aes(x=X1,y=X2),data=www,fill=NA,col=colHull,size=0.7)+
      geom_segment(data=segs,aes(x=X1,y=X2),xend=0,yend=0,linetype=2,col=colSegment,size=0.7) +
      geom_text(aes(label=label),show.legend = FALSE,data=subset(others,type=="genotype"),col=colGen,size=sizeGen)+
      geom_text(aes(label=label),show.legend = FALSE,data=subset(others,type=="environment"),col=colEnv,size=sizeEnv)+
      geom_text(aes(label=label,x=d1,y=d2),show.legend = FALSE,hjust="outward",vjust="outward",data=winners,col=colGen,size=largeSize,fontface="bold")

    if(titles==TRUE){GGE2<-GGE2+ggtitle("Which Won Where/What")}

  }

  if(type=="Discrimination vs. representativeness"){

    circles<-data.frame(x0=0,y0=0,start=0,end=pi*2,radio =1:5* max((max(coordenviroment[1, ]) - min(coordenviroment[1, ])), (max(coordenviroment[2,]) - min(coordenviroment[2, ])))/10)


    GGE2<-GGE1+geom_arc(aes(r=radio,x0=x0,y0=y0,start=start,end=end),data=circles,col=colSegment,inherit.aes=F, na.rm=TRUE,linetype =2)+
      geom_segment(xend=0,yend=0,x=mean(coordenviroment[,1]),y=mean(coordenviroment[,2]),arrow =arrow(ends ="first",length=unit(0.1,"inches") ),size=1,col=colSegment)+
      geom_abline(intercept=0,slope=mean(coordenviroment[, 2])/mean(coordenviroment[,1]),col=colSegment)+
      geom_segment(xend=0,yend=0,col=alpha(colEnv,0.5),data=subset(plotdata,type=="environment"),linetype=2)+
      geom_text(aes(col=type,label=label,size=type),show.legend = FALSE)


    if(titles==TRUE){GGE2<-GGE2+ggtitle("Discrimination vs. representativeness")}

  }

  if(type=="Ranking Environments"){

    med1 = mean(coordenviroment[, 1])
    med2 = mean(coordenviroment[, 2])
    mod = max((coordenviroment[, 1]^2 + coordenviroment[,2]^2)^0.5)
    xcoord = sign(med1) * (mod^2/(1 + med2^2/med1^2))^0.5
    ycoord = (med2/med1) * xcoord

    circles<-data.frame(x0=xcoord,y0=ycoord,start=0,end=pi*2,radio=1:8*((xcoord - med1)^2 + (ycoord - med2)^2)^0.5/3)

    GGE2<-GGE1+
      geom_abline(intercept=0,slope=med2/med1,col=colSegment,size=0.7)+
      geom_abline(intercept=0,slope=-med1/med2,col=colSegment,size=0.7)+
      geom_arc(aes(r=radio,x0=x0,y0=y0,start=start,end=end),data=circles,col=colSegment,inherit.aes=F, na.rm=TRUE,linetype =2)+
      geom_segment(x=0, y=0,xend=xcoord,yend=ycoord,arrow =arrow(length=unit(0.15,"inches") ),size=1,col=colSegment)+
      geom_text(aes(label=label),show.legend = FALSE,data=subset(plotdata,type=="environment"),col=colEnv,size=sizeEnv)


    if(titles==TRUE){GGE2<-GGE2+ggtitle("Ranking Environments")}
  }


  if(type=="Mean vs. Stability"){

    med1 = mean(coordenviroment[, 1])
    med2 = mean(coordenviroment[, 2])

    x1<-NULL

    for (i in 1:nrow(Data))
    {
      x <- solve(matrix(c(-med2, med1, med1, med2), nrow = 2), matrix(c(0, med2 * coordgenotype[i, 2] + med1 * coordgenotype[i, 1]),ncol = 1))
      x1<-rbind(x1,t(x))

      # segments(coordgenotype[i, dimension1], coordgenotype[i,dimension2], x[1], x[2], lty = "dotted")
    }
    plotdata$x1_x<-NA
    plotdata$x1_x[plotdata$type=="genotype"]<-x1[,1]

    plotdata$x1_y<-NA
    plotdata$x1_y[plotdata$type=="genotype"]<-x1[,2]

    GGE2<-GGE1+
      geom_abline(intercept=0,slope=med2/med1,col=colSegment,size=0.7)+
      geom_abline(intercept=0,slope=-med1/med2,col=colSegment,size=0.7)+
      geom_segment(aes(xend=x1_x, yend=x1_y),col=colGen,linetype=2,data=subset(plotdata,type=="genotype"))+
      geom_segment(x=0, y=0,xend=med1,yend=med2,arrow =arrow(length=unit(0.15,"inches") ),size=1,col=colSegment)+
      geom_text(aes(label=label),show.legend = FALSE,data=subset(plotdata,type=="genotype"),col=colGen,size=sizeGen)+
      geom_text(aes(label=label),show.legend = FALSE,data=subset(plotdata,type=="environment"),col=colEnv,size=sizeEnv)


    if(titles==TRUE){GGE2<-GGE2+ggtitle("Mean vs. Stability")}
  }


  if(type=="Ranking Genotypes"){

    med1 = mean(coordenviroment[, 1])
    med2 = mean(coordenviroment[, 2])
    coordx <- 0
    coordy <- 0
    for (i in 1:nrow(Data)) {
      x <- solve(matrix(c(-med2, med1, med1, med2),nrow = 2), matrix(c(0, med2 * coordgenotype[i,2] + med1 * coordgenotype[i, 1]),ncol = 1))
      if (sign(x[1]) == sign(med1)) {
        if (abs(x[1]) > abs(coordx)) {
          coordx <- x[1]
          coordy <- x[2]
        }
      }
    }


    circles<-data.frame(x0=coordx,y0=coordy,radio=1:10*((coordx - med1)^2 + (coordy - med2)^2)^0.5/3)

    GGE2<-GGE1+
      geom_abline(intercept=0,slope=med2/med1,col=colGen,size=0.7)+
      geom_abline(intercept=0,slope=-med1/med2,col=colGen,size=0.7)+
      geom_arc(aes(r=radio,x0=x0,y0=y0,start=0,end=pi*2),data=circles,col=colSegment,inherit.aes=F,linetype =2)+
      geom_segment(x=0, y=0,xend=coordx,yend=coordy,arrow =arrow(length=unit(0.15,"inches") ),size=1,col=colGen)

    if(limits==F){
      GGE2<-GGE2+ scale_x_continuous(limits=c(min(plotdata$d1*axis_expand),max(plotdata$d1*axis_expand)),oob=squish)+
        scale_y_continuous(limits=c(min(plotdata$d2*axis_expand),max(plotdata$d2*axis_expand)),oob=squish)
    }
    if(axes==TRUE){
      GGE2<-GGE2+geom_hline(yintercept=0)+geom_vline(xintercept=0)
    }
    GGE2<-GGE2+
      geom_text(aes(label=label),show.legend = FALSE,data=subset(plotdata,type=="genotype"),col=colGen,size=sizeGen)+
      geom_text(aes(label=label),show.legend = FALSE,data=subset(plotdata,type=="environment"),col=colEnv,size=sizeEnv)
      if(titles==TRUE){GGE2<-GGE2+ggtitle("Ranking Genotypes")}


  }

  if(footnote==TRUE){
    SVPtext<-ifelse(SVP==1|SVP=="row","Row Metric Preserving SVP",
                    ifelse(SVP==2|SVP=="column","Column Metric Preserving SVP",
                           ifelse(SVP==3|SVP=="dual","Dual Metric Preserving SVP",
                                  ifelse(SVP==4|SVP=="symmetrical","Symmetrical SVP","NIPALS algorithm"))))
    footnotetxt=paste("\nGGE Biplot showing components ",d1," and ",d2," explaining ",sum(varexpl),"% of the total variation\nusing ",
                      SVPtext,sep="")

    GGE2<-GGE2+ labs(caption = footnotetxt)+theme(plot.caption = element_text(size=10,hjust=0,face="italic"))
  }


  return(GGE2)
}