Additional Functions for Manipulating Data in treedata.table
Josef Uyeda, Cristian Roman-Palacios, April Wright
08/08/2020
Source:vignettes/D_AdditionalFunctions_treedata.table.Rmd
D_AdditionalFunctions_treedata.table.RmdAdditional functions for manipulating data
treedata.table includes additional functions that allow
the identification of discrete and continuous
characters in a given dataset. We first load the dataset:
## 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 preservedThe detectCharacterType() function can be used to
examine whether SVL is discrete or
continuous:
detectCharacterType(anolis$dat$SVL)## [1] "continuous"We can further examine the type of characters we have in our dataset
by using the detectAllCharacters() function:
detectAllCharacters(anolis$dat)## [1] "discrete" "continuous" "continuous" "continuous" "continuous"
## [6] "continuous" "continuous" "continuous" "continuous" "discrete"
## [11] "discrete"Summarizing this information in a table, we get:
cbind.data.frame(character=colnames(anolis$dat),type=detectAllCharacters(anolis$dat))## character type
## 1 X discrete
## 2 SVL continuous
## 3 PCI_limbs continuous
## 4 PCII_head continuous
## 5 PCIII_padwidth_vs_tail continuous
## 6 PCIV_lamella_num continuous
## 7 awesomeness continuous
## 8 hostility continuous
## 9 attitude continuous
## 10 ecomorph discrete
## 11 island discreteFinally, we can use the filterMatrix() function to
subset our dataset to only a certain type of characters. For instance,
let’s extract all discrete characters in the Anolis
dataset:
filterMatrix(anolis$dat, "discrete")## X ecomorph island
## 1 ahli TG Cuba
## 2 alayoni TW Cuba
## 3 alfaroi GB Cuba
## 4 aliniger TC Hispaniola
## 5 allisoni TC Cuba
## 6 allogus TG Cuba
## 7 altitudinalis TC Cuba
## 8 alumina GB Hispaniola
## 9 alutaceus GB Cuba
## 10 angusticeps TW Cuba
## 11 argenteolus U Cuba
## 12 argillaceus U Cuba
## 13 armouri TG Hispaniola
## 14 bahorucoensis GB Hispaniola
## 15 baleatus CG Hispaniola
## 16 baracoae CG Cuba
## 17 barahonae CG Hispaniola
## 18 barbatus U Cuba
## 19 barbouri U Hispaniola
## 20 bartschi U Cuba
## 21 bremeri TG Cuba
## 22 breslini TG Hispaniola
## 23 brevirostris T Hispaniola
## 24 caudalis T Hispaniola
## 25 centralis U Cuba
## 26 chamaeleonides U Cuba
## 27 chlorocyanus TC Hispaniola
## 28 christophei U Hispaniola
## 29 clivicola GB Cuba
## 30 coelestinus TC Hispaniola
## 31 confusus TG Cuba
## 32 cooki TG Puerto Rico
## 33 cristatellus TG Puerto Rico
## 34 cupeyalensis GB Cuba
## 35 cuvieri CG Puerto Rico
## 36 cyanopleurus GB Cuba
## 37 cybotes TG Hispaniola
## 38 darlingtoni TW Hispaniola
## 39 distichus T Hispaniola
## 40 dolichocephalus GB Hispaniola
## 41 equestris CG Cuba
## 42 etheridgei U Hispaniola
## 43 eugenegrahami U Hispaniola
## 44 evermanni TC Puerto Rico
## 45 fowleri U Hispaniola
## 46 garmani CG Puerto Rico
## 47 grahami TC Puerto Rico
## 48 guafe TG Cuba
## 49 guamuhaya U Cuba
## 50 guazuma TW Cuba
## 51 gundlachi TG Puerto Rico
## 52 haetianus TG Hispaniola
## 53 hendersoni GB Hispaniola
## 54 homolechis TG Cuba
## 55 imias TG Cuba
## 56 inexpectatus GB Cuba
## 57 insolitus TW Hispaniola
## 58 isolepis TC Cuba
## 59 jubar TG Cuba
## 60 krugi GB Puerto Rico
## 61 lineatopus TG Jamaica
## 62 longitibialis TG Hispaniola
## 63 loysiana T Cuba
## 64 lucius U Cuba
## 65 luteogularis CG Cuba
## 66 macilentus GB Cuba
## 67 marcanoi TG Hispaniola
## 68 marron T Hispaniola
## 69 mestrei TG Cuba
## 70 monticola U Hispaniola
## 71 noblei CG Cuba
## 72 occultus TW Puerto Rico
## 73 olssoni GB Hispaniola
## 74 opalinus TC Jamaica
## 75 ophiolepis GB Cuba
## 76 oporinus TC Cuba
## 77 paternus TW Cuba
## 78 placidus TW Hispaniola
## 79 poncensis GB Puerto Rico
## 80 porcatus TC Cuba
## 81 porcus U Cuba
## 82 pulchellus GB Puerto Rico
## 83 pumilis U Cuba
## 84 quadriocellifer TG Cuba
## 85 reconditus U Jamaica
## 86 ricordii CG Hispaniola
## 87 rubribarbus TG Cuba
## 88 sagrei TG Cuba
## 89 semilineatus GB Hispaniola
## 90 sheplani TW Hispaniola
## 91 shrevei TG Hispaniola
## 92 singularis TC Hispaniola
## 93 smallwoodi CG Cuba
## 94 strahmi TG Hispaniola
## 95 stratulus TC Puerto Rico
## 96 valencienni TW Jamaica
## 97 vanidicus GB Cuba
## 98 vermiculatus U Cuba
## 99 websteri T Hispaniola
## 100 whitemani TG HispaniolaTwo additional functions in treedata.table are designed
to examine and modify column and row names in any dataset. For instance,
we can ask if the Anolis dataset has column names:
hasNames(anolis$dat, "col")## col
## TRUEIt does have column names. Let’s just remove the column names and
check if hasNames() can detect this change. Here’s our new
dataset:
##
## 1 ahli 4.039125 -3.248286 0.3722519 -1.042219 -2.4147423 -0.2416517
## 2 alayoni 3.815705 3.408886 -1.7833585 2.208451 0.9496969 -0.2590322
##
## 1 -0.1734769 0.6443771 TG Cuba
## 2 0.1273443 0.2959732 TW CubaLet’s run hasNames() on our new dataset:
hasNames(data, "col")## col
## FALSENow, we can create new column names using the
forceNames() function:
data <- forceNames(data, "col")The new dataset, with column names (n1…), looks like this:
head(data,2)## n1 n2 n3 n4 n5 n6 n7
## 1 ahli 4.039125 -3.248286 0.3722519 -1.042219 -2.4147423 -0.2416517
## 2 alayoni 3.815705 3.408886 -1.7833585 2.208451 0.9496969 -0.2590322
## n8 n9 n10 n11
## 1 -0.1734769 0.6443771 TG Cuba
## 2 0.1273443 0.2959732 TW CubaAnd we can finally ask whether our new dataset actually have column
names by running the hasNames() function again:
hasNames(data, "col")## col
## TRUEWe can apply the same procedure for columns (col), rows
(row) or both (rowcol).