Breve Introducción al Paquete treedata.table

Breve Introducción al Paquete `treedata.table`

El paquete treedata.table tiene como objetivo permitir a investigadores acceder y manipular datos filogenéticos usando herramientas del paquete data.table. data.table tiene diferentes funciones para manipular rápidamente datos en una forma eficiente.

El primer paso para usar treedata.table consiste en crear un objeto treedata.table. El objeto treedata.table empareja los tip.labels de la filogenia con una columna en el data.frame que contiene los nombres de los taxones. Este paso inicial permite la manipulación subsecuente y coordinada de los datos en el árbol y la matriz de caracteres dentro de treedata.table.

Para este tutorial vamos a usar los datos de Anolis creados en treeplyr. Estos caracteres fueron generados aleatoriamente. Es importante resaltar tres aspectos. Primero, el árbol tiene que ser en formato phylo (o multiPhylo en caso de múltiples árboles). Segundo, la matriz de caracteres tiene que estár en formato data.frame. Tercero, la matriz de caracteres tiene que contener una columna con los nombres de los taxones coincidiendo con los tip.labels del árbol (o árboles).

El objeto treedata.table se crea usando la función as.treedata.table.

library(ape)
library(treedata.table)

# Cargamos los datos del ejemplo
data(anolis)
# Creamos el objecto treedata.table con 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 preserved

Podemos revisar el objeto resultante simplemente llamando el nombre del objeto en la consola. La matriz de caracteres, antes en data.frame, es ahora un data.table

td

## $phy 
## 
## Phylogenetic tree with 100 tips and 99 internal nodes.
## 
## Tip labels:
##   ahli, allogus, rubribarbus, imias, sagrei, bremeri, ...
## 
## Rooted; includes branch length(s).
## 
## $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   Cuba

Adicionalmente, la matriz de caracteres en el nuevo formato data.table ha sido re-ordenado para tener las filas en el mismo orden que los tip.labels en el árbol.

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 TRUE

Manipulando Datos

La matriz de datos en el objeto treedata.table puede ser indexada como cualquier otro objeto data.table. Por ejemplo, podemos hacer lo siguiente para extraer la columna con información de longitud hocico-cloaca (SVL) para cada especie.

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
##           SVL

También podemos usar los brackets dobles para extraer directamente la misma columna como un vector con nombres.

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        3.663049

El mismo resultado puede también ser logrado usando la función extractVector. Al igual que con los brackets dobles, el resultado de la función extractVector es un vector con nombres.

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        3.663049

Múltiples columnas pueden tambien ser extraídas usando 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"

Hay un par de aspectos que son únicos a [[.treedata.table() y extractVector(). Primero, [[.treedata.table() tiene un argumento adicional que permite un correspondencia parcial del nombre de la columna (i.e. cuando el nombre objetivo tiene una superposicion parcial con los elementos en el objeto). Segundo, extractVector() puede extraer múltiples columnas y permite una evaluación no estandard (i.e. los nombres son tratados como cadenas de texto literal).

El poder real de treedata.table está en coindexar el árbol con la matriz de caracteres. Por ejemplo, en el siguiente comando usamos la sintaxis de data.table para extraer el primer representante de cada ecomorfo y retener todas las columnas.

 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 length(s).
## 
## $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     Jamaica

Podemos hacer la misma operación con múltiples columnas.

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 length(s).
## 
## $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 -0.5675490

También implementamos la función tail.

 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 length(s).
## 
## $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  Hispaniola

Las columnas en treedata.table pueden ser operadas usando la misma sintaxis de data.table. En el siguiente ejemplo, los árboles solo incluirán especies distribuidas en Cuba. Este es el equivalente a filtrar usando dplyr. Después, una nueva columna llamada “Index” es creada en el objeto data.table dentro del objeto treedata.table con los valores de SVL+hostility. En resumen, la siguiente línea permite en forma simultánea crear una nueva columna y reducir el número de taxones en la filogenia a las especies de interés.

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 length(s).
## 
## $dat 
##       Index
##       <num>
## 1: 3.865649
## 2: 3.540042
## 3: 4.558514
## 4: 3.688875
## 5: 4.786475
## 6: 5.526089

treedata.table permite aplicar funciones directamente en nuestros datos de interés. En el siguiente ejemplo, evaluamos un modelo de evolución browniano sobre los datos de SVL en nuestro set de datos. Usamos una combinación de tdt, extractVector y geiger::fitContinuous para correr funciones en nuestros datos, extraer un vector de caracteres y ajustar el model en cuestión, respectivamente.

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 estimates

Los terminales en el árbol también pueden ser removidos fácilmente, con los cambios también reflejados sobre la matriz de caracteres. En el siguiente ejemplo, removemos dos taxones por sus nombres.

dt <- droptreedata.table(tdObject=td, taxa=c("chamaeleonides" ,"eugenegrahami" ))

## 2 taxa were dropped from the treedata.table object

Revisamos si A. chamaeleonides y A. eugenegrahami aún están en el árbol.

c("chamaeleonides" ,"eugenegrahami" ) %in% dt$phy$tip.label

## [1] FALSE FALSE

Y podemos hacer lo mismo con la matriz de caracteres en el nuevo objeto treedata.table.

c("chamaeleonides" ,"eugenegrahami" ) %in% dt$dat$X

## [1] FALSE FALSE

Por último, el árbol y la matriz de caracteres pueden ser extraídos de el objeto treedata.table fácilmente usando la función pulltreedata.table.

df <- pulltreedata.table(td, "dat")
tree <- pulltreedata.table(td, "phy")

La tabla

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
##           island

Y el árbol

tree

## 
## Phylogenetic tree with 100 tips and 99 internal nodes.
## 
## Tip labels:
##   ahli, allogus, rubribarbus, imias, sagrei, bremeri, ...
## 
## Rooted; includes branch length(s).

La misma funcionalidad explicada en este tutorial sobre objetos phylo aplica directamente a objetos multiPhylo.

- Breve Introducción al Paquete treedata.table
- Manipulando Datos