plotTree.barplot with multiple stacked barplots & a single tree

A phytools user recently requested the capability to plot multiple stacked barplots next to a phylogeny using plotTree.barplot. In fact, this was almost already possible & required only one tiny change to the code which checks if args.plotTree$plot==FALSE and, if so, sets par(new=TRUE).

It works as follows. Here, I imagine we have three different matrices in which each row of each matrix sums to 1.0 and we want to plot these matrices as three adjacent stacked barplots.

library(phytools)
library(RColorBrewer)
tree

## 
## Phylogenetic tree with 40 tips and 39 internal nodes.
## 
## Tip labels:
##  t39, t40, t10, t6, t18, t19, ...
## 
## Rooted; includes branch lengths.

X

##            [,1]       [,2]       [,3]
## t39 0.525179104 0.10026842 0.37455247
## t40 0.526804250 0.09444652 0.37874923
## t10 0.454712350 0.30239701 0.24289064
## t6  0.692387786 0.10601339 0.20159883
## t18 0.734208612 0.07943336 0.18635802
## t19 0.679013258 0.05800659 0.26298015
## t4  0.259917875 0.60114507 0.13893706
## t5  0.196385441 0.33866046 0.46495410
## t8  0.759235657 0.02862437 0.21213997
## t9  0.398878093 0.26267495 0.33844696
## t22 0.029222105 0.74330209 0.22747581
## t23 0.382022138 0.52175658 0.09622128
## t17 0.106185473 0.40834710 0.48546743
## t26 0.062975924 0.55596673 0.38105735
## t30 0.026656381 0.64656880 0.32677482
## t31 0.043506323 0.61764422 0.33884946
## t29 0.005912221 0.67623489 0.31785289
## t21 0.221507782 0.32307738 0.45541484
## t24 0.125329583 0.43069021 0.44398021
## t25 0.130316467 0.32002782 0.54965571
## t7  0.166474475 0.18771071 0.64581482
## t14 0.408753361 0.10513071 0.48611593
## t15 0.371510701 0.19752121 0.43096809
## t1  0.666583739 0.27400514 0.05941112
## t16 0.414224054 0.41079409 0.17498186
## t37 0.356511404 0.50465671 0.13883189
## t38 0.309634086 0.62894577 0.06142015
## t20 0.391565442 0.53535845 0.07307611
## t11 0.588463079 0.01232121 0.39921571
## t12 0.497284598 0.33453586 0.16817954
## t13 0.204676318 0.47018995 0.32513373
## t35 0.068670118 0.58135820 0.34997168
## t36 0.071858018 0.51778527 0.41035671
## t34 0.031562194 0.53214508 0.43629273
## t27 0.245969069 0.36092619 0.39310474
## t28 0.133252138 0.38461151 0.48213635
## t32 0.226088792 0.32321021 0.45070099
## t33 0.242253847 0.38862081 0.36912534
## t2  0.090611693 0.24371591 0.66567239
## t3  0.669291730 0.30368240 0.02702587

Y

##          [,1]       [,2]       [,3]
## t39 0.6500164 0.25160974 0.09837384
## t40 0.6194750 0.28185245 0.09867253
## t10 0.4784971 0.41797399 0.10352890
## t6  0.4942367 0.03511043 0.47065286
## t18 0.6685693 0.26447364 0.06695705
## t19 0.4566828 0.26926066 0.27405652
## t4  0.3877456 0.27129345 0.34096099
## t5  0.3574238 0.18452804 0.45804811
## t8  0.1877074 0.16769717 0.64459546
## t9  0.3750166 0.07019226 0.55479109
## t22 0.6248713 0.33260262 0.04252609
## t23 0.6133680 0.23298812 0.15364390
## t17 0.6143060 0.01698444 0.36870958
## t26 0.4775323 0.12669048 0.39577720
## t30 0.4885681 0.13927519 0.37215668
## t31 0.5016868 0.17091207 0.32740111
## t29 0.5699250 0.18713634 0.24293865
## t21 0.3558842 0.17479455 0.46932126
## t24 0.3470570 0.17959511 0.47334790
## t25 0.2582049 0.13750900 0.60428611
## t7  0.4851954 0.15034443 0.36446020
## t14 0.7417344 0.05042677 0.20783883
## t15 0.5467123 0.41922590 0.03406176
## t1  0.5785896 0.18257833 0.23883211
## t16 0.3420647 0.04585134 0.61208395
## t37 0.3890263 0.10524413 0.50572959
## t38 0.4055041 0.16297393 0.43152197
## t20 0.3700546 0.11439515 0.51555028
## t11 0.3957279 0.14167762 0.46259447
## t12 0.2746857 0.25512780 0.47018654
## t13 0.3314313 0.53104038 0.13752834
## t35 0.2885883 0.48372431 0.22768738
## t36 0.3702399 0.56026343 0.06949670
## t34 0.4733694 0.47525335 0.05137724
## t27 0.4070432 0.41648865 0.17646819
## t28 0.3493757 0.51506825 0.13555600
## t32 0.3372026 0.51098148 0.15181592
## t33 0.5469213 0.43735383 0.01572492
## t2  0.4490903 0.21609825 0.33481144
## t3  0.4839116 0.46820635 0.04788209

Z

##           [,1]       [,2]       [,3]
## t39 0.37192362 0.02577426 0.60230212
## t40 0.40485849 0.01907763 0.57606388
## t10 0.65818009 0.08511950 0.25670041
## t6  0.44930662 0.44633551 0.10435787
## t18 0.12095062 0.50473687 0.37431251
## t19 0.05888720 0.54257449 0.39853832
## t4  0.39041801 0.03682941 0.57275257
## t5  0.02496900 0.04489223 0.93013876
## t8  0.74564440 0.13866896 0.11568665
## t9  0.71399680 0.13727289 0.14873031
## t22 0.36294795 0.04290760 0.59414445
## t23 0.28190651 0.19496530 0.52312819
## t17 0.34367023 0.05252129 0.60380848
## t26 0.26066189 0.01425516 0.72508295
## t30 0.32023372 0.17417535 0.50559093
## t31 0.39965915 0.13432711 0.46601374
## t29 0.24112467 0.13220168 0.62667365
## t21 0.82423380 0.04924114 0.12652506
## t24 0.30871314 0.55629214 0.13499472
## t25 0.46058285 0.13145529 0.40796185
## t7  0.19828952 0.38930760 0.41240288
## t14 0.34986860 0.13617143 0.51395997
## t15 0.29540064 0.11106646 0.59353290
## t1  0.26258445 0.48054473 0.25687081
## t16 0.30548936 0.25695995 0.43755069
## t37 0.13423430 0.46739358 0.39837212
## t38 0.10208503 0.47486939 0.42304558
## t20 0.11463696 0.44712501 0.43823802
## t11 0.34364773 0.32733844 0.32901383
## t12 0.26557733 0.34336603 0.39105664
## t13 0.20821399 0.23237136 0.55941464
## t35 0.44941577 0.28621676 0.26436747
## t36 0.69454286 0.18320516 0.12225198
## t34 0.65188707 0.29629385 0.05181908
## t27 0.49080474 0.21124254 0.29795272
## t28 0.09286513 0.59173091 0.31540395
## t32 0.14906068 0.59773679 0.25320254
## t33 0.08823629 0.63367537 0.27808834
## t2  0.13526464 0.24478334 0.61995202
## t3  0.07103310 0.36894937 0.56001753

cols<-brewer.pal(3,"YlGnBu")
par(mfrow=c(1,4)) ## we can also use layout
plotTree.barplot(tree,X,add=TRUE,args.barplot=list(xlab="X",col=cols,
    mar=c(5.1,0,2.1,2.1)))
plotTree.barplot(tree,Y,args.barplot=list(xlab="Y",mar=c(5.1,0,2.1,2.1),
    col=cols),args.plotTree=list(plot=FALSE),add=TRUE)
plotTree.barplot(tree,Z,args.barplot=list(xlab="Z",mar=c(5.1,0,2.1,2.1),
    col=cols),args.plotTree=list(plot=FALSE),add=TRUE)

Neat.

Data were simulated as follows:

tree<-pbtree(n=40,scale=1)
X<-fastBM(tree,nsim=3,bounds=c(0,Inf))
Y<-fastBM(tree,nsim=3,bounds=c(0,Inf))
Z<-fastBM(tree,nsim=3,bounds=c(0,Inf))
X<-X/rowSums(X)
Y<-Y/rowSums(Y)
Z<-Z/rowSums(Z)