R - Taylor chart diagram

Question

I am trying to build several models on a Taylor diagram and am struggling a bit with code. I managed to create a diagram (see image), but I can’t understand how to reduce the axes, because they are too large, standardize the axes that should be marked 1,2,3,4, and add marks on the correlation - with tick I wanted to have main ticks every 0.1 and small ticks every 0.05 to 0.9, after which I tried to get main ticks at 0.95 and small ticks every 0.01 at this moment (if that makes sense). Any help / advice with the above will help. I used "taylor.diagram" in the "plotrix" package (and read the manuals for it, but I'm relatively inexperienced with R) and have tied my (somewhat basic) code so far, but my plot looks pretty dirty. thanks

all.models <- as.data.frame(cbind(Sy.One, Sy.Two, Sy.Three, Sy.Four, Sy.Five, Sy.Six, Sy.Seven, Sy.Eight, Sy.Nine, Sy.Ten)) taylor.diagram(CSR, Sy.One, sd.arcs=T, ref.sd=T, pcex=1.5, main=NULL, pos.cor=F, xlab="Standard Deviation (cm)", ylab="Standard Deviation (cm)") for (i in 1:dim(all.models)[2]) { model.wanted <- all.models[,i] taylor.diagram(CSR, model.wanted, sd.arcs=T, ref.sd=T, pcex=1.5, col=i, add=T, pos.cor=F)} # Add legend model.names <- c("Sy=1%","Sy=2%","Sy=3%","Sy=4%","Sy=5%","Sy=6%","Sy=7%","Sy=8%","Sy=9%","Sy=10%") legend("top", model.names, pch=19, col=i, cex=1.0, bty="n", ncol=5) 

Answer 1

One option is to use another package, such as openair , which may be more flexible. Due to your specific requirements, it may be easier to use code designed for your requirements. I wrote code to create the next graph, which is close to your desired plot. You can crack the code to configure the schedule in the desired format.

 # code to make a Taylor diagram # formulas found in http://www-pcmdi.llnl.gov/about/staff/Taylor/CV/Taylor_diagram_primer.pdf # and http://rainbow.llnl.gov/publications/pdf/55.pdf # correlations and tick marks (only major will have a line to the center) # minor will have a tick mark correlation_major <- c(seq(-1,1,0.1),-0.95,0.95) correlation_minor <- c(seq(-1,-0.95,0.01),seq(-0.9,9,0.05),seq(0.95,1,0.01)) # test standard deviation tick marks (only major will have a line) sigma_test_major <- seq(1,4,1) sigma_test_minor <- seq(0.5,4,0.5) # rms lines locations rms_major <- seq(1,6,1) # reference standard deviation (observed) sigma_reference <- 2.9 # color schemes for the liens correlation_color <- 'black' sigma_test_color <- 'blue' rms_color <- 'green' # line types correlation_type <- 1 sigma_test_type <- 1 rms_type <- 1 # plot parameters par(pty='s') par(mar=c(3,3,3,3)+0.1) # creating plot with correct space based on the sigma_test limits plot(NA ,NA ,xlim=c(-1*max(sigma_test_major),max(sigma_test_major)) ,ylim=c(-1*max(sigma_test_major),max(sigma_test_major)) ,xaxt='n' ,yaxt='n' ,xlab='' ,ylab='' ,bty='n') #### adding sigma_test (standard deviation) # adding semicircles for(i in 1:length(sigma_test_major)){ lines(sigma_test_major[i]*cos(seq(0,pi,pi/1000)) ,sigma_test_major[i]*sin(seq(0,pi,pi/1000)) ,col=sigma_test_color ,lty=sigma_test_type ,lwd=1 ) } # adding horizontal axis lines(c(-1*max(sigma_test_major),max(sigma_test_major)) ,c(0,0) ,col=sigma_test_color ,lty=sigma_test_type ,lwd=1) # adding labels text(c(-1*sigma_test_major,0,sigma_test_major) ,-0.2 ,as.character(c(-1*sigma_test_major,0,sigma_test_major)) ,col=sigma_test_color ,cex=0.7) # adding title text(0 ,-0.6 ,"Standard Deviation" ,col=sigma_test_color ,cex=1) #### adding correlation lines, tick marks, and lables # adding lines for(i in 1:length(correlation_major)){ lines(c(0,1.02*max(sigma_test_major)*cos(acos(correlation_major[i]))) ,c(0,1.02*max(sigma_test_major)*sin(acos(correlation_major[i]))) ,lwd=2 ,lty=correlation_type ,col=correlation_color ) } # adding minor tick marks for correlation for(i in 1:length(correlation_minor)){ lines(max(sigma_test_major)*cos(acos(correlation_minor[i]))*c(1,1.01) ,max(sigma_test_major)*sin(acos(correlation_minor[i]))*c(1,1.01) ,lwd=2 ,lty=correlation_type ,col=correlation_color ) } # adding labels for correlation text(1.05*max(sigma_test_major)*cos(acos(correlation_major)) ,1.05*max(sigma_test_major)*sin(acos(correlation_major)) ,as.character(correlation_major) ,col=correlation_color ,cex=0.5) # adding correlation title text(0 ,max(sigma_test_major)+0.5 ,"Correlation" ,col=correlation_color ,cex=1) #### adding rms difference lines # adding rms semicircles for(i in 1:length(rms_major)){ inds <- which((rms_major[i]*cos(seq(0,pi,pi/1000))+sigma_reference)^2 + (rms_major[i]*sin(seq(0,pi,pi/1000)))^2 < max(sigma_test_major)^2) lines(rms_major[i]*cos(seq(0,pi,pi/1000))[inds]+sigma_reference ,rms_major[i]*sin(seq(0,pi,pi/1000))[inds] ,col=rms_color ,lty=rms_type ,lwd=1 ) } # adding observed point points(sigma_reference ,0 ,pch=19 ,col=rms_color ,cex=1) # adding labels for the rms lines text(-1*rms_major*cos(pi*rms_major/40)+sigma_reference , rms_major*sin(pi*rms_major/40) ,as.character(rms_major) ,col=rms_color ,cex=0.7 ,adj=1) # adding title text(0 ,-1.5 ,'Centered RMS Difference' ,col=rms_color ,cex=1 ,adj=0.5) ###################### adding points ##################### names <- paste("model",seq(1,8),sep='') correl_names <- seq(-0.6,0.8,by=0.2) std_names <- seq(2,4,by=0.26) color_names <- topo.colors(length(names)) points(std_names*cos(acos(correl_names)) ,std_names*sin(acos(correl_names)) ,col=color_names ,pch=19 ,cex=1.5) # making legend par(xpd=TRUE) legend(0,-2 ,names ,pc=19 ,col=color_names ,ncol=3 ,bty='n' ,xjust=0.5)