Getting Started With The treedata.table Package
Josef Uyeda, Cristian Roman-Palacios, April Wright
08/08/2020
Source:vignettes/AA_treedata.table_intro_english.Rmd
AA_treedata.table_intro_english.RmdGetting Started With The treedata.table Package
The aim of the treedata.table R package is to allow
researchers to access and manipulate phylogenetic data using tools from
the data.table package. data.table has many
functions for rapidly manipulating data in a memory efficient way.
Using the treedata.table package begins with creating a
treedata.table object. The treedata.table
matches the tip.labels of the phylogeny to a column of names in your
data.frame. This allows you to manipulate the data, and the
corresponding tree together.
Importantly, the character matrix must must include a column with the
taxa names and should be of class data.frame. The tree must
be of class phylo or multiPhylo.
A treedata.table is created using the
as.treedata.table function. Here we use the Anolis
dataset from treeplyr. Traits in this dataset were randomly
generated for a set of 100 species.
## Thank you for using the {treedata.table} R package!
##
## 🙂Happy coding!!🙂
# Load example data
data(anolis)
#Create treedata.table object with as.treedata.table
td <- as.treedata.table(tree = anolis$phy, data = anolis$dat)## Tip labels detected in column: X## Phylo object detected## All tips from original tree/dataset were preservedWe may inspect our object by calling it by name. You will notice that
your data.frame is now a data.table. A
data.table is simply an advanced version of a
data.frame that, among other, increase speed in data
manipulation steps while simplifying syntax.
td## $phy
##
## Phylogenetic tree with 100 tips and 99 internal nodes.
##
## Tip labels:
## ahli, allogus, rubribarbus, imias, sagrei, bremeri, ...
##
## Rooted; includes branch lengths.
##
## $dat
## tip.label SVL PCI_limbs PCII_head PCIII_padwidth_vs_tail
## <char> <num> <num> <num> <num>
## 1: ahli 4.039125 -3.248286 0.3722519 -1.0422187
## 2: allogus 4.040138 -2.845570 0.6001134 -1.0253056
## 3: rubribarbus 4.078469 -2.238349 1.1199779 -1.1929572
## 4: imias 4.099687 -3.048917 2.3320349 0.1616442
## 5: sagrei 4.067162 -1.741055 2.0228243 0.1693635
## 6: bremeri 4.113371 -1.813611 2.6067501 0.6399320
## PCIV_lamella_num awesomeness hostility attitude ecomorph island
## <num> <num> <num> <num> <char> <char>
## 1: -2.414742 -0.2416517 -0.1734769 0.6443771 TG Cuba
## 2: -2.463311 0.6244689 -0.5000962 0.7128910 TG Cuba
## 3: -2.087433 -0.4277574 0.4800445 -0.9674263 TG Cuba
## 4: -2.112606 0.1694260 -0.4108123 0.1963580 TG Cuba
## 5: -1.375769 -0.6304338 0.7193130 -1.2228276 TG Cuba
## 6: -1.626299 -1.7543006 1.4127184 0.1832345 TG CubaFurthermore, the new data.table has been reordered into
the same order as the tip.labels of your tree.
td$phy$tip.label == td$dat$tip.label## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUEManipulating Data
Coindexing
Your data table can be indexed in the same way any other
data.table object would be. For example, if we wanted to
look at our snout-vent length column, we can do that like so.
td$dat[,'SVL']## SVL
## <num>
## 1: 4.039125
## 2: 4.040138
## 3: 4.078469
## 4: 4.099687
## 5: 4.067162
## 6: 4.113371
## 7: 3.901619
## 8: 3.637962
## 9: 3.987147
## 10: 3.952605
## 11: 4.032806
## 12: 3.938442
## 13: 3.877457
## 14: 4.769473
## 15: 3.838376
## 16: 4.154274
## 17: 4.321524
## 18: 4.128612
## 19: 4.482607
## 20: 4.165605
## 21: 3.869881
## 22: 3.886500
## 23: 3.799022
## 24: 4.188105
## 25: 3.820378
## 26: 4.091535
## 27: 4.189820
## 28: 3.874155
## 29: 3.911743
## 30: 3.831810
## 31: 3.916546
## 32: 3.928796
## 33: 3.663932
## 34: 3.588941
## 35: 3.696631
## 36: 3.793899
## 37: 3.657991
## 38: 4.288780
## 39: 3.800471
## 40: 4.097479
## 41: 4.316542
## 42: 4.051111
## 43: 4.121684
## 44: 4.210982
## 45: 3.983003
## 46: 4.242103
## 47: 4.274271
## 48: 4.079485
## 49: 5.053056
## 50: 5.076958
## 51: 5.013963
## 52: 4.128504
## 53: 3.884652
## 54: 4.875012
## 55: 5.003946
## 56: 5.038034
## 57: 5.042349
## 58: 5.036953
## 59: 3.842994
## 60: 3.845670
## 61: 3.657088
## 62: 4.375390
## 63: 4.258991
## 64: 3.757869
## 65: 3.697941
## 66: 3.466860
## 67: 3.701240
## 68: 3.763884
## 69: 3.773967
## 70: 3.682924
## 71: 3.815705
## 72: 3.788595
## 73: 3.802961
## 74: 3.554891
## 75: 3.537439
## 76: 3.758726
## 77: 3.462014
## 78: 3.630161
## 79: 3.526655
## 80: 3.715765
## 81: 3.626206
## 82: 3.971307
## 83: 4.198915
## 84: 4.280547
## 85: 4.802849
## 86: 5.042780
## 87: 5.083473
## 88: 5.035096
## 89: 5.101085
## 90: 5.113994
## 91: 3.770613
## 92: 3.827445
## 93: 3.908550
## 94: 3.859835
## 95: 4.302036
## 96: 4.036557
## 97: 4.057997
## 98: 4.275448
## 99: 4.297965
## 100: 3.663049
## SVLYou can also use double bracket syntax to directly return column data as a named list.
td[["SVL"]]## ahli allogus rubribarbus imias sagrei
## 4.039125 4.040138 4.078469 4.099687 4.067162
## bremeri quadriocellifer ophiolepis mestrei jubar
## 4.113371 3.901619 3.637962 3.987147 3.952605
## homolechis confusus guafe garmani opalinus
## 4.032806 3.938442 3.877457 4.769473 3.838376
## grahami valencienni lineatopus reconditus evermanni
## 4.154274 4.321524 4.128612 4.482607 4.165605
## stratulus krugi pulchellus gundlachi poncensis
## 3.869881 3.886500 3.799022 4.188105 3.820378
## cooki cristatellus brevirostris caudalis marron
## 4.091535 4.189820 3.874155 3.911743 3.831810
## websteri distichus barbouri alumina semilineatus
## 3.916546 3.928796 3.663932 3.588941 3.696631
## olssoni etheridgei fowleri insolitus whitemani
## 3.793899 3.657991 4.288780 3.800471 4.097479
## haetianus breslini armouri cybotes shrevei
## 4.316542 4.051111 4.121684 4.210982 3.983003
## longitibialis strahmi marcanoi baleatus barahonae
## 4.242103 4.274271 4.079485 5.053056 5.076958
## ricordii eugenegrahami christophei cuvieri barbatus
## 5.013963 4.128504 3.884652 4.875012 5.003946
## porcus chamaeleonides guamuhaya altitudinalis oporinus
## 5.038034 5.042349 5.036953 3.842994 3.845670
## isolepis allisoni porcatus argillaceus centralis
## 3.657088 4.375390 4.258991 3.757869 3.697941
## pumilis loysiana guazuma placidus sheplani
## 3.466860 3.701240 3.763884 3.773967 3.682924
## alayoni angusticeps paternus alutaceus inexpectatus
## 3.815705 3.788595 3.802961 3.554891 3.537439
## clivicola cupeyalensis cyanopleurus alfaroi macilentus
## 3.758726 3.462014 3.630161 3.526655 3.715765
## vanidicus argenteolus lucius bartschi vermiculatus
## 3.626206 3.971307 4.198915 4.280547 4.802849
## baracoae noblei smallwoodi luteogularis equestris
## 5.042780 5.083473 5.035096 5.101085 5.113994
## monticola bahorucoensis dolichocephalus hendersoni darlingtoni
## 3.770613 3.827445 3.908550 3.859835 4.302036
## aliniger singularis chlorocyanus coelestinus occultus
## 4.036557 4.057997 4.275448 4.297965 3.663049The same functionality can also be accomplished through the
extractVector function. Both the double bracket syntax and
the extractVector function will return a named vector.
extractVector(td, 'SVL')## ahli allogus rubribarbus imias sagrei
## 4.039125 4.040138 4.078469 4.099687 4.067162
## bremeri quadriocellifer ophiolepis mestrei jubar
## 4.113371 3.901619 3.637962 3.987147 3.952605
## homolechis confusus guafe garmani opalinus
## 4.032806 3.938442 3.877457 4.769473 3.838376
## grahami valencienni lineatopus reconditus evermanni
## 4.154274 4.321524 4.128612 4.482607 4.165605
## stratulus krugi pulchellus gundlachi poncensis
## 3.869881 3.886500 3.799022 4.188105 3.820378
## cooki cristatellus brevirostris caudalis marron
## 4.091535 4.189820 3.874155 3.911743 3.831810
## websteri distichus barbouri alumina semilineatus
## 3.916546 3.928796 3.663932 3.588941 3.696631
## olssoni etheridgei fowleri insolitus whitemani
## 3.793899 3.657991 4.288780 3.800471 4.097479
## haetianus breslini armouri cybotes shrevei
## 4.316542 4.051111 4.121684 4.210982 3.983003
## longitibialis strahmi marcanoi baleatus barahonae
## 4.242103 4.274271 4.079485 5.053056 5.076958
## ricordii eugenegrahami christophei cuvieri barbatus
## 5.013963 4.128504 3.884652 4.875012 5.003946
## porcus chamaeleonides guamuhaya altitudinalis oporinus
## 5.038034 5.042349 5.036953 3.842994 3.845670
## isolepis allisoni porcatus argillaceus centralis
## 3.657088 4.375390 4.258991 3.757869 3.697941
## pumilis loysiana guazuma placidus sheplani
## 3.466860 3.701240 3.763884 3.773967 3.682924
## alayoni angusticeps paternus alutaceus inexpectatus
## 3.815705 3.788595 3.802961 3.554891 3.537439
## clivicola cupeyalensis cyanopleurus alfaroi macilentus
## 3.758726 3.462014 3.630161 3.526655 3.715765
## vanidicus argenteolus lucius bartschi vermiculatus
## 3.626206 3.971307 4.198915 4.280547 4.802849
## baracoae noblei smallwoodi luteogularis equestris
## 5.042780 5.083473 5.035096 5.101085 5.113994
## monticola bahorucoensis dolichocephalus hendersoni darlingtoni
## 3.770613 3.827445 3.908550 3.859835 4.302036
## aliniger singularis chlorocyanus coelestinus occultus
## 4.036557 4.057997 4.275448 4.297965 3.663049Multiple traits can also be extracted using
extractVector.
extractVector(td, 'SVL','ecomorph')## $SVL
## ahli allogus rubribarbus imias sagrei
## 4.039125 4.040138 4.078469 4.099687 4.067162
## bremeri quadriocellifer ophiolepis mestrei jubar
## 4.113371 3.901619 3.637962 3.987147 3.952605
## homolechis confusus guafe garmani opalinus
## 4.032806 3.938442 3.877457 4.769473 3.838376
## grahami valencienni lineatopus reconditus evermanni
## 4.154274 4.321524 4.128612 4.482607 4.165605
## stratulus krugi pulchellus gundlachi poncensis
## 3.869881 3.886500 3.799022 4.188105 3.820378
## cooki cristatellus brevirostris caudalis marron
## 4.091535 4.189820 3.874155 3.911743 3.831810
## websteri distichus barbouri alumina semilineatus
## 3.916546 3.928796 3.663932 3.588941 3.696631
## olssoni etheridgei fowleri insolitus whitemani
## 3.793899 3.657991 4.288780 3.800471 4.097479
## haetianus breslini armouri cybotes shrevei
## 4.316542 4.051111 4.121684 4.210982 3.983003
## longitibialis strahmi marcanoi baleatus barahonae
## 4.242103 4.274271 4.079485 5.053056 5.076958
## ricordii eugenegrahami christophei cuvieri barbatus
## 5.013963 4.128504 3.884652 4.875012 5.003946
## porcus chamaeleonides guamuhaya altitudinalis oporinus
## 5.038034 5.042349 5.036953 3.842994 3.845670
## isolepis allisoni porcatus argillaceus centralis
## 3.657088 4.375390 4.258991 3.757869 3.697941
## pumilis loysiana guazuma placidus sheplani
## 3.466860 3.701240 3.763884 3.773967 3.682924
## alayoni angusticeps paternus alutaceus inexpectatus
## 3.815705 3.788595 3.802961 3.554891 3.537439
## clivicola cupeyalensis cyanopleurus alfaroi macilentus
## 3.758726 3.462014 3.630161 3.526655 3.715765
## vanidicus argenteolus lucius bartschi vermiculatus
## 3.626206 3.971307 4.198915 4.280547 4.802849
## baracoae noblei smallwoodi luteogularis equestris
## 5.042780 5.083473 5.035096 5.101085 5.113994
## monticola bahorucoensis dolichocephalus hendersoni darlingtoni
## 3.770613 3.827445 3.908550 3.859835 4.302036
## aliniger singularis chlorocyanus coelestinus occultus
## 4.036557 4.057997 4.275448 4.297965 3.663049
##
## $ecomorph
## ahli allogus rubribarbus imias sagrei
## "TG" "TG" "TG" "TG" "TG"
## bremeri quadriocellifer ophiolepis mestrei jubar
## "TG" "TG" "GB" "TG" "TG"
## homolechis confusus guafe garmani opalinus
## "TG" "TG" "TG" "CG" "TC"
## grahami valencienni lineatopus reconditus evermanni
## "TC" "TW" "TG" "U" "TC"
## stratulus krugi pulchellus gundlachi poncensis
## "TC" "GB" "GB" "TG" "GB"
## cooki cristatellus brevirostris caudalis marron
## "TG" "TG" "T" "T" "T"
## websteri distichus barbouri alumina semilineatus
## "T" "T" "U" "GB" "GB"
## olssoni etheridgei fowleri insolitus whitemani
## "GB" "U" "U" "TW" "TG"
## haetianus breslini armouri cybotes shrevei
## "TG" "TG" "TG" "TG" "TG"
## longitibialis strahmi marcanoi baleatus barahonae
## "TG" "TG" "TG" "CG" "CG"
## ricordii eugenegrahami christophei cuvieri barbatus
## "CG" "U" "U" "CG" "U"
## porcus chamaeleonides guamuhaya altitudinalis oporinus
## "U" "U" "U" "TC" "TC"
## isolepis allisoni porcatus argillaceus centralis
## "TC" "TC" "TC" "U" "U"
## pumilis loysiana guazuma placidus sheplani
## "U" "T" "TW" "TW" "TW"
## alayoni angusticeps paternus alutaceus inexpectatus
## "TW" "TW" "TW" "GB" "GB"
## clivicola cupeyalensis cyanopleurus alfaroi macilentus
## "GB" "GB" "GB" "GB" "GB"
## vanidicus argenteolus lucius bartschi vermiculatus
## "GB" "U" "U" "U" "U"
## baracoae noblei smallwoodi luteogularis equestris
## "CG" "CG" "CG" "CG" "CG"
## monticola bahorucoensis dolichocephalus hendersoni darlingtoni
## "U" "GB" "GB" "GB" "TW"
## aliniger singularis chlorocyanus coelestinus occultus
## "TC" "TC" "TC" "TC" "TW"However, there’s a couple aspects that are unique to
[[.treedata.table() and extractVector().
First, [[.treedata.table() has an extra exact argument to
enable partial match (i.e. when target strings and those in the
treedata.table object match partially). Second,
extractVector() can extract multiple columns and accepts
non-standard evaluation (i.e. names are treated as string literals).
The real power in treedata.table is in co-indexing the tree and
table. For example, in the below command, we use data.table
syntax to take the first representative from each ecomorph. We retain
all data columns. If you examine the tree object, you will see that it
has had all the tips absent from the resultant
data.table.
td[, head(.SD, 1), by = "ecomorph"]## $phy
##
## Phylogenetic tree with 7 tips and 6 internal nodes.
##
## Tip labels:
## ahli, ophiolepis, garmani, opalinus, valencienni, reconditus, ...
##
## Rooted; includes branch lengths.
##
## $dat
## ecomorph tip.label SVL PCI_limbs PCII_head PCIII_padwidth_vs_tail
## <char> <char> <num> <num> <num> <num>
## 1: TG ahli 4.039125 -3.2482860 0.3722519 -1.0422187
## 2: GB ophiolepis 3.637962 0.7915117 1.4585760 -1.3152005
## 3: CG garmani 4.769473 -0.7735264 0.9371249 0.2594994
## 4: TC opalinus 3.838376 -1.7794371 -0.3245381 1.5569939
## 5: TW valencienni 4.321524 2.9424139 -0.8846007 1.8543308
## 6: U reconditus 4.482607 -2.7270416 -0.2104066 -2.3534242
## PCIV_lamella_num awesomeness hostility attitude island
## <num> <num> <num> <num> <char>
## 1: -2.4147423 -0.24165170 -0.17347691 0.64437708 Cuba
## 2: -2.2377514 0.35441877 0.05366142 -0.09389530 Cuba
## 3: 0.1051149 0.16779131 0.67675600 -0.69460080 Puerto Rico
## 4: 0.9366501 1.48302162 -0.90826653 0.72613483 Jamaica
## 5: 0.1288233 -0.08837008 0.46528679 -0.56754896 Jamaica
## 6: -0.7992905 0.26096544 -0.27169792 0.01367143 JamaicaWe could also do the same operation with multiple columns:
td[, head(.SD, 1), by = .(ecomorph, island)]## $phy
##
## Phylogenetic tree with 23 tips and 22 internal nodes.
##
## Tip labels:
## ahli, ophiolepis, garmani, opalinus, grahami, valencienni, ...
##
## Rooted; includes branch lengths.
##
## $dat
## ecomorph island tip.label SVL PCI_limbs PCII_head
## <char> <char> <char> <num> <num> <num>
## 1: TG Cuba ahli 4.039125 -3.2482860 0.3722519
## 2: GB Cuba ophiolepis 3.637962 0.7915117 1.4585760
## 3: CG Puerto Rico garmani 4.769473 -0.7735264 0.9371249
## 4: TC Jamaica opalinus 3.838376 -1.7794371 -0.3245381
## 5: TC Puerto Rico grahami 4.154274 -2.3056535 -1.9139369
## 6: TW Jamaica valencienni 4.321524 2.9424139 -0.8846007
## PCIII_padwidth_vs_tail PCIV_lamella_num awesomeness hostility attitude
## <num> <num> <num> <num> <num>
## 1: -1.0422187 -2.4147423 -0.24165170 -0.17347691 0.6443771
## 2: -1.3152005 -2.2377514 0.35441877 0.05366142 -0.0938953
## 3: 0.2594994 0.1051149 0.16779131 0.67675600 -0.6946008
## 4: 1.5569939 0.9366501 1.48302162 -0.90826653 0.7261348
## 5: 1.6852579 1.0144193 0.41064280 -0.11746257 0.7022959
## 6: 1.8543308 0.1288233 -0.08837008 0.46528679 -0.5675490Tail is also implemented
td[, tail(.SD, 1), by = "ecomorph"]## $phy
##
## Phylogenetic tree with 7 tips and 6 internal nodes.
##
## Tip labels:
## marcanoi, loysiana, equestris, monticola, hendersoni, coelestinus, ...
##
## Rooted; includes branch lengths.
##
## $dat
## ecomorph tip.label SVL PCI_limbs PCII_head PCIII_padwidth_vs_tail
## <char> <char> <num> <num> <num> <num>
## 1: TG marcanoi 4.079485 -2.84448243 -2.7864415 -0.8020303
## 2: GB hendersoni 3.859835 1.28963045 -2.0630985 -3.4656535
## 3: CG equestris 5.113994 1.05461517 0.7072039 0.7108046
## 4: TC coelestinus 4.297965 -0.02721683 0.3687537 1.6364316
## 5: TW occultus 3.663049 7.92078444 -0.1901397 2.4922819
## 6: U monticola 3.770613 -3.25118016 0.1559934 -2.2390082
## PCIV_lamella_num awesomeness hostility attitude island
## <num> <num> <num> <num> <char>
## 1: -1.04842823 1.1823433 -0.1671936 0.5827900 Hispaniola
## 2: 2.58336718 0.3854544 -0.7133164 -0.3959792 Hispaniola
## 3: 1.66043194 1.0805662 -1.2666114 0.6673026 Cuba
## 4: 1.02571762 0.2909266 -0.6209660 1.2803335 Hispaniola
## 5: -0.09577977 -1.1916870 1.2153014 0.0324486 Puerto Rico
## 6: 0.13156827 0.6289893 0.1468777 -0.4409775 HispaniolaColumns in the treedata.table object can also be
operated on using general data.table syntax. In the below
example, the tree is pruned to those tips that occur in Cuba. This is
the data.table equivalent of dplyr’s filter.
Then, a new column is created in the data.table, assigned
the name “Index”, and assigned the value of the SVL + the hostility
index. This enables concurrent manipulation of the phylogeny, and the
calculation of a new index for only those tips we would actually like to
use.
td[island == "Cuba",.(Index=SVL+hostility)]## $phy
##
## Phylogenetic tree with 47 tips and 46 internal nodes.
##
## Tip labels:
## ahli, allogus, rubribarbus, imias, sagrei, bremeri, ...
##
## Rooted; includes branch lengths.
##
## $dat
## Index
## <num>
## 1: 3.865649
## 2: 3.540042
## 3: 4.558514
## 4: 3.688875
## 5: 4.786475
## 6: 5.526089Running functions on treedata.table objects
In the below command, we extract one vector from our data.table and
use geiger’s continuous model fitting to estimate a
Brownian motion model for the data using the tdt
function.
tdt(td, geiger::fitContinuous(phy, extractVector(td, 'SVL'), model="BM", ncores=1))## Phylo object detected. Expect a single function output## GEIGER-fitted comparative model of continuous data
## fitted 'BM' model parameters:
## sigsq = 0.136160
## z0 = 4.065918
##
## model summary:
## log-likelihood = -4.700404
## AIC = 13.400807
## AICc = 13.524519
## free parameters = 2
##
## Convergence diagnostics:
## optimization iterations = 100
## failed iterations = 0
## number of iterations with same best fit = 100
## frequency of best fit = 1.000
##
## object summary:
## 'lik' -- likelihood function
## 'bnd' -- bounds for likelihood search
## 'res' -- optimization iteration summary
## 'opt' -- maximum likelihood parameter estimatesDropping and extracting taxa from treedata.table
objects
We can also drop tips directly from the tree, and have those tips drop concurrently from the data.table. In the example below, we remove two taxa by name.
dt <- droptreedata.table(tdObject=td, taxa=c("chamaeleonides" ,"eugenegrahami" ))## 2 taxa were dropped from the treedata.table objectWe can check if A. chamaeleonides and A. eugenegrahami are still in the tree
## [1] FALSE FALSEAnd we can do the same with the data in the
treedata.table object
## [1] FALSE FALSEWhen you’re done, the data.table and tree can both be extracted from the object:
df <- pulltreedata.table(td, "dat")
tree <- pulltreedata.table(td, "phy")The table
df## tip.label SVL PCI_limbs PCII_head PCIII_padwidth_vs_tail
## <char> <num> <num> <num> <num>
## 1: ahli 4.039125 -3.24828599 0.37225191 -1.0422187
## 2: allogus 4.040138 -2.84557021 0.60011341 -1.0253056
## 3: rubribarbus 4.078469 -2.23834859 1.11997785 -1.1929572
## 4: imias 4.099687 -3.04891725 2.33203488 0.1616442
## 5: sagrei 4.067162 -1.74105547 2.02282431 0.1693635
## 6: bremeri 4.113371 -1.81361138 2.60675012 0.6399320
## 7: quadriocellifer 3.901619 -2.26789400 0.99092075 0.3553405
## 8: ophiolepis 3.637962 0.79151174 1.45857603 -1.3152005
## 9: mestrei 3.987147 -2.60261906 1.27568612 0.4640379
## 10: jubar 3.952605 -2.22414593 0.94489985 0.6600716
## 11: homolechis 4.032806 -2.74499346 0.87926009 0.8679694
## 12: confusus 3.938442 -2.49591732 0.16823269 0.1551290
## 13: guafe 3.877457 -2.61408710 0.65608676 0.6071245
## 14: garmani 4.769473 -0.77352640 0.93712494 0.2594994
## 15: opalinus 3.838376 -1.77943710 -0.32453812 1.5569939
## 16: grahami 4.154274 -2.30565354 -1.91393689 1.6852579
## 17: valencienni 4.321524 2.94241389 -0.88460072 1.8543308
## 18: lineatopus 4.128612 -2.43081291 -3.12552390 -1.7564495
## 19: reconditus 4.482607 -2.72704156 -0.21040657 -2.3534242
## 20: evermanni 4.165605 -2.52899245 0.12548098 1.8824633
## 21: stratulus 3.869881 -1.62264165 -0.52957490 2.2166952
## 22: krugi 3.886500 -1.68879533 -0.83128181 -1.2588892
## 23: pulchellus 3.799022 0.16238151 -2.33846103 -1.5906715
## 24: gundlachi 4.188105 -2.64936274 -0.56251604 -2.2852265
## 25: poncensis 3.820378 0.52782664 1.24062035 -1.6249761
## 26: cooki 4.091535 -2.22079009 0.05979850 -0.1092647
## 27: cristatellus 4.189820 -3.33026331 -0.62225189 1.2175309
## 28: brevirostris 3.874155 -3.28900081 1.38425488 2.4070756
## 29: caudalis 3.911743 -1.78270839 1.90870776 1.7530966
## 30: marron 3.831810 -2.84709341 -0.12032028 1.6898101
## 31: websteri 3.916546 -2.50870054 0.97482786 2.9024438
## 32: distichus 3.928796 -3.84343343 0.83890749 2.4922170
## 33: barbouri 3.663932 -0.87536237 1.34009434 -2.6138245
## 34: alumina 3.588941 0.72166993 1.52514444 -2.6721669
## 35: semilineatus 3.696631 0.18403236 -0.13872022 -2.8957227
## 36: olssoni 3.793899 0.81682668 3.87442813 -2.4337310
## 37: etheridgei 3.657991 -3.81065955 -0.75340900 -2.0659201
## 38: fowleri 4.288780 -1.03667003 1.50383762 -1.7589251
## 39: insolitus 3.800471 5.03062671 0.24137669 0.9139707
## 40: whitemani 4.097479 -2.54584901 -3.25974024 -1.9200701
## 41: haetianus 4.316542 -3.40402925 -3.78444664 -1.7798344
## 42: breslini 4.051111 -2.97497529 -3.34153375 -1.8978249
## 43: armouri 4.121684 -3.16993598 -4.08620441 -0.8883209
## 44: cybotes 4.210982 -3.11637922 -2.99620854 -0.8299057
## 45: shrevei 3.983003 -2.25952607 -3.55788235 -1.3884348
## 46: longitibialis 4.242103 -3.52243048 -1.61670439 -1.6182845
## 47: strahmi 4.274271 -3.87340303 -1.01635836 -0.9366696
## 48: marcanoi 4.079485 -2.84448243 -2.78644148 -0.8020303
## 49: baleatus 5.053056 0.73766495 1.79646028 -0.5409516
## 50: barahonae 5.076958 0.90731040 2.33222342 -0.6158464
## 51: ricordii 5.013963 0.58940470 1.34670464 -0.8626956
## 52: eugenegrahami 4.128504 -4.21209140 4.83351955 1.0228151
## 53: christophei 3.884652 -2.70596332 1.70527559 -0.1725921
## 54: cuvieri 4.875012 -1.18955124 -0.71983364 -1.0167340
## 55: barbatus 5.003946 2.25020229 -3.30818172 1.3476390
## 56: porcus 5.038034 3.50614537 -2.98667964 0.7500647
## 57: chamaeleonides 5.042349 2.95597428 -3.69231889 1.3297304
## 58: guamuhaya 5.036953 3.10947396 -4.07973423 -0.1820287
## 59: altitudinalis 3.842994 2.86751469 -6.12805047 2.3331196
## 60: oporinus 3.845670 3.05888289 -2.91565342 2.2875989
## 61: isolepis 3.657088 3.05697642 -4.34018373 2.4409248
## 62: allisoni 4.375390 2.03597967 -3.74252815 0.5203703
## 63: porcatus 4.258991 1.93193251 -3.82953305 0.9781194
## 64: argillaceus 3.757869 -0.11307851 -1.48944374 2.3710815
## 65: centralis 3.697941 0.73073876 -0.41683597 1.7586477
## 66: pumilis 3.466860 0.55404946 -0.42584152 2.1572563
## 67: loysiana 3.701240 0.31536951 -1.08038145 2.7442074
## 68: guazuma 3.763884 8.16566506 -0.60605865 1.7597408
## 69: placidus 3.773967 7.38332866 0.53958973 2.7042981
## 70: sheplani 3.682924 10.27389091 2.27784409 1.6981742
## 71: alayoni 3.815705 3.40888624 -1.78335850 2.2084510
## 72: angusticeps 3.788595 4.58711071 -1.97673831 1.1594671
## 73: paternus 3.802961 2.90637723 -0.95846093 0.9491115
## 74: alutaceus 3.554891 1.17338999 -0.61493813 -1.6539883
## 75: inexpectatus 3.537439 2.50607244 -0.52340367 -2.8057449
## 76: clivicola 3.758726 -1.05114421 -0.58903393 -1.2178042
## 77: cupeyalensis 3.462014 2.72419698 0.48960424 -2.4795271
## 78: cyanopleurus 3.630161 0.43328510 0.98930100 -2.8189997
## 79: alfaroi 3.526655 2.58175921 0.77876335 -2.4463337
## 80: macilentus 3.715765 3.15177391 3.26626613 -3.7238293
## 81: vanidicus 3.626206 4.04436760 1.08716320 -2.4921192
## 82: argenteolus 3.971307 -2.92797328 2.61964493 0.9791727
## 83: lucius 4.198915 -4.38139803 1.25981123 2.1899604
## 84: bartschi 4.280547 -3.25502168 0.80565652 1.1234288
## 85: vermiculatus 4.802849 0.22783467 1.41411087 -1.8467441
## 86: baracoae 5.042780 0.76817392 0.06987127 0.1805891
## 87: noblei 5.083473 -0.09337994 -0.96088851 0.8905492
## 88: smallwoodi 5.035096 -0.13770527 -1.15140405 0.4296432
## 89: luteogularis 5.101085 0.39614254 0.73584435 1.0058546
## 90: equestris 5.113994 1.05461517 0.70720387 0.7108046
## 91: monticola 3.770613 -3.25118016 0.15599344 -2.2390082
## 92: bahorucoensis 3.827445 -0.04747332 -2.55694724 -3.0896949
## 93: dolichocephalus 3.908550 2.60131528 -2.59530770 -4.1439327
## 94: hendersoni 3.859835 1.28963045 -2.06309845 -3.4656535
## 95: darlingtoni 4.302036 4.06703170 -1.49378979 -0.2002259
## 96: aliniger 4.036557 0.12323898 -1.67467041 2.3871336
## 97: singularis 4.057997 0.36326789 -0.95422871 1.6562154
## 98: chlorocyanus 4.275448 0.43906343 1.35403450 1.9531470
## 99: coelestinus 4.297965 -0.02721683 0.36875369 1.6364316
## 100: occultus 3.663049 7.92078444 -0.19013968 2.4922819
## tip.label SVL PCI_limbs PCII_head PCIII_padwidth_vs_tail
## PCIV_lamella_num awesomeness hostility attitude ecomorph
## <num> <num> <num> <num> <char>
## 1: -2.41474228 -0.241651698 -0.173476906 0.64437708 TG
## 2: -2.46331106 0.624468879 -0.500096224 0.71289104 TG
## 3: -2.08743282 -0.427757376 0.480044450 -0.96742634 TG
## 4: -2.11260585 0.169425977 -0.410812337 0.19635800 TG
## 5: -1.37576941 -0.630433775 0.719312962 -1.22282764 TG
## 6: -1.62629939 -1.754300558 1.412718374 0.18323450 TG
## 7: -2.10505922 -0.257638879 0.462708058 -0.27127944 TG
## 8: -2.23775137 0.354418772 0.053661422 -0.09389530 GB
## 9: -1.19666399 -0.228920131 0.820920959 -0.73929564 TG
## 10: -1.65749549 2.145582200 -0.993730897 1.05775273 TG
## 11: -1.56665822 -0.085375433 0.092602997 -0.08130904 TG
## 12: -1.39796407 0.496114209 -0.544543392 1.36010631 TG
## 13: -1.48220834 -0.139145550 -0.310434629 -0.50480610 TG
## 14: 0.10511495 0.167791307 0.676756002 -0.69460080 CG
## 15: 0.93665013 1.483021624 -0.908266532 0.72613483 TC
## 16: 1.01441926 0.410642801 -0.117462571 0.70229589 TC
## 17: 0.12882327 -0.088370075 0.465286788 -0.56754896 TW
## 18: -1.43952303 0.800931449 0.170290172 0.33555714 TG
## 19: -0.79929053 0.260965443 -0.271697917 0.01367143 U
## 20: 1.92392086 1.871367582 -2.029729624 1.02877511 TC
## 21: 0.94622231 -0.314853533 0.515229477 -0.40828716 TC
## 22: 0.24029913 0.972494741 -0.371866585 1.46109470 GB
## 23: 0.68180888 0.978157532 -0.810754596 1.39277529 GB
## 24: -2.48420519 -0.426336538 0.613014449 -0.69318262 TG
## 25: -0.94924727 -0.289982852 0.510241609 -0.18543914 GB
## 26: -0.50826932 1.277956446 -1.082760383 1.37721870 TG
## 27: -1.21819703 0.830658098 -0.007447027 0.39061232 TG
## 28: -0.82666991 -1.538338585 1.633533660 -1.24247895 T
## 29: -1.44378878 -0.601304152 0.216500950 0.85182003 T
## 30: -1.57063776 -2.149486418 2.413050748 -2.14378229 T
## 31: -1.53259879 0.025584758 0.124044450 0.11758642 T
## 32: -1.03972507 1.973200862 -1.608003643 0.91461500 T
## 33: -4.30147185 1.788945648 -0.913725183 0.64025272 U
## 34: 1.23487950 -1.253724344 1.308419442 -0.96110895 GB
## 35: 0.92280509 -0.612226281 0.807322377 -0.82943161 GB
## 36: 0.62906887 -1.391771586 1.210221860 -0.29186894 GB
## 37: -0.74890866 0.848149691 -0.960258609 0.18291181 U
## 38: -1.71914111 1.456935239 -1.448418148 0.07531334 U
## 39: -0.42075245 0.066715596 -0.290651288 0.07286240 TW
## 40: -2.41993928 -1.457775289 1.096071557 -0.87393333 TG
## 41: -2.20379012 0.721747833 -0.969190196 0.74101761 TG
## 42: -2.16718706 -1.944082859 2.147925602 -2.07465046 TG
## 43: -2.02129909 -0.158605136 -0.369246555 -0.26866597 TG
## 44: -2.09049464 0.905454434 -0.262630999 0.68467864 TG
## 45: -2.19082487 0.257277082 0.029600473 0.39422885 TG
## 46: -2.67106616 2.537213358 -1.420542767 1.71562944 TG
## 47: -1.57843390 0.340077411 -0.690011247 2.11135720 TG
## 48: -1.04842823 1.182343323 -0.167193617 0.58279004 TG
## 49: -0.16220546 2.727286255 -2.487661729 2.78477616 CG
## 50: -0.19473780 1.831370900 -1.331616706 1.70626854 CG
## 51: 0.03516802 -0.456651495 0.663302028 -0.64625837 CG
## 52: 1.66539278 2.155800992 -2.494661307 1.11438687 U
## 53: 0.64693336 -0.266699615 0.220410336 1.46547483 U
## 54: -0.02153073 -0.115917120 0.281153377 -0.97876663 CG
## 55: -1.64780456 2.156287540 -2.094147109 1.97196598 U
## 56: -1.19909229 0.158971414 -0.350187677 0.45987065 U
## 57: -1.51973745 -1.193993760 1.858148718 -1.43308743 U
## 58: -0.10748930 -0.137962568 0.119982369 0.04203987 U
## 59: 0.68195498 1.164592168 -0.865462260 0.63326685 TC
## 60: -0.08659903 0.573048475 -0.985921965 0.88025286 TC
## 61: -0.07418779 -0.194644197 0.001671326 0.16264659 TC
## 62: 2.05565172 -0.132309870 0.464556617 -0.54956842 TC
## 63: 2.88538479 -0.074646660 0.148259194 -0.57178426 TC
## 64: 0.33264142 0.007554828 -0.259421287 -0.62559912 U
## 65: -1.13869021 -0.620236225 0.330225203 -0.71777358 U
## 66: -1.12208954 0.459254083 -0.162682657 -0.47694819 U
## 67: -0.52657687 2.153959487 -1.198967281 1.51422456 T
## 68: 1.55102509 -0.015197500 -0.020516523 0.08678021 TW
## 69: -0.31778878 0.078986096 0.504242392 -0.40436325 TW
## 70: -0.25962432 -0.431283305 0.321955192 -0.80553288 TW
## 71: 0.94969689 -0.259032167 0.127344278 0.29597325 TW
## 72: 0.38835687 1.713217290 -1.884387903 0.92471752 TW
## 73: 1.80146660 -0.299550789 0.846263466 -0.96311064 TW
## 74: 1.41160228 0.325807029 -0.523648505 0.50405157 GB
## 75: 1.68036114 0.244666550 -0.307162858 0.06937286 GB
## 76: -0.55865998 0.340444764 -0.599034616 1.78969146 GB
## 77: -0.03297035 1.327984163 -0.994951781 0.04668455 GB
## 78: 0.87176931 0.413447859 0.073949884 -0.50654925 GB
## 79: 0.70036608 -0.462062012 0.178370302 -0.01958915 GB
## 80: 1.21810218 0.868463721 -0.287882420 0.69946303 GB
## 81: 0.18942396 -1.287008036 1.023037680 -0.67591746 GB
## 82: 2.94194227 0.490363636 -0.505416684 -0.20254651 U
## 83: 1.75435972 0.126135253 0.545903248 -0.58505782 U
## 84: 1.74943703 -1.189744988 1.025075387 0.25994625 U
## 85: 0.71173422 0.159291276 -0.024230945 -0.31575501 U
## 86: 2.66309134 1.821981086 -1.522244645 2.17591511 CG
## 87: 2.09456880 -0.974406590 1.602507741 -0.42228829 CG
## 88: 2.44067497 -0.018317012 -0.366531140 1.59163441 CG
## 89: 1.98043577 0.194610912 0.713343825 -0.56222110 CG
## 90: 1.66043194 1.080566180 -1.266611407 0.66730260 CG
## 91: 0.13156827 0.628989307 0.146877669 -0.44097752 U
## 92: 1.56271877 2.553879072 -2.337986521 2.47075117 GB
## 93: 3.67023402 0.739483022 -0.989799476 1.09191365 GB
## 94: 2.58336718 0.385454354 -0.713316425 -0.39597920 GB
## 95: 0.31138087 1.718720076 -1.690079016 1.02145675 TW
## 96: 0.58486431 0.006869282 0.144815367 0.29376432 TC
## 97: 1.00756926 1.592083294 -1.701052660 1.45313446 TC
## 98: 1.80512493 -1.065610146 0.920097708 -0.93721735 TC
## 99: 1.02571762 0.290926638 -0.620965953 1.28033350 TC
## 100: -0.09577977 -1.191686980 1.215301402 0.03244860 TW
## PCIV_lamella_num awesomeness hostility attitude ecomorph
## island
## <char>
## 1: Cuba
## 2: Cuba
## 3: Cuba
## 4: Cuba
## 5: Cuba
## 6: Cuba
## 7: Cuba
## 8: Cuba
## 9: Cuba
## 10: Cuba
## 11: Cuba
## 12: Cuba
## 13: Cuba
## 14: Puerto Rico
## 15: Jamaica
## 16: Puerto Rico
## 17: Jamaica
## 18: Jamaica
## 19: Jamaica
## 20: Puerto Rico
## 21: Puerto Rico
## 22: Puerto Rico
## 23: Puerto Rico
## 24: Puerto Rico
## 25: Puerto Rico
## 26: Puerto Rico
## 27: Puerto Rico
## 28: Hispaniola
## 29: Hispaniola
## 30: Hispaniola
## 31: Hispaniola
## 32: Hispaniola
## 33: Hispaniola
## 34: Hispaniola
## 35: Hispaniola
## 36: Hispaniola
## 37: Hispaniola
## 38: Hispaniola
## 39: Hispaniola
## 40: Hispaniola
## 41: Hispaniola
## 42: Hispaniola
## 43: Hispaniola
## 44: Hispaniola
## 45: Hispaniola
## 46: Hispaniola
## 47: Hispaniola
## 48: Hispaniola
## 49: Hispaniola
## 50: Hispaniola
## 51: Hispaniola
## 52: Hispaniola
## 53: Hispaniola
## 54: Puerto Rico
## 55: Cuba
## 56: Cuba
## 57: Cuba
## 58: Cuba
## 59: Cuba
## 60: Cuba
## 61: Cuba
## 62: Cuba
## 63: Cuba
## 64: Cuba
## 65: Cuba
## 66: Cuba
## 67: Cuba
## 68: Cuba
## 69: Hispaniola
## 70: Hispaniola
## 71: Cuba
## 72: Cuba
## 73: Cuba
## 74: Cuba
## 75: Cuba
## 76: Cuba
## 77: Cuba
## 78: Cuba
## 79: Cuba
## 80: Cuba
## 81: Cuba
## 82: Cuba
## 83: Cuba
## 84: Cuba
## 85: Cuba
## 86: Cuba
## 87: Cuba
## 88: Cuba
## 89: Cuba
## 90: Cuba
## 91: Hispaniola
## 92: Hispaniola
## 93: Hispaniola
## 94: Hispaniola
## 95: Hispaniola
## 96: Hispaniola
## 97: Hispaniola
## 98: Hispaniola
## 99: Hispaniola
## 100: Puerto Rico
## islandand the corresponding tree
tree##
## Phylogenetic tree with 100 tips and 99 internal nodes.
##
## Tip labels:
## ahli, allogus, rubribarbus, imias, sagrei, bremeri, ...
##
## Rooted; includes branch lengths.