To read in the data, you can use read.csv and read.nexus.
raw_data<-read.csv("~/Documents/teaching/comparativeMethods/fall2017UI/exercises/6_freeplay/Caudata_PhyloComp_Data.csv")
cd<-raw_data[,-1]
rownames(cd)<-raw_data[,1]
ct<-read.nexus("~/Documents/teaching/comparativeMethods/fall2017UI/exercises/6_freeplay/caud.ultra.nexus")
The first challenge is matching species names.
require(geiger)
## Loading required package: geiger
name.check(ct, cd)
## $tree_not_data
## [1] "Ambystoma_tigrinum" "Desmognathus_conanti"
## [3] "Desmognathus_planiceps" "Eurycea_aquatica"
## [5] "Haideotriton_wallacei" "Hydromantes_ambrosii"
## [7] "Hydromantes_flavus" "Hydromantes_genei"
## [9] "Hydromantes_imperialis" "Hydromantes_italicus"
## [11] "Hydromantes_strinatii" "Hydromantes_supramontis"
## [13] "Hynobius_fuca" "Hynobius_glacialis"
## [15] "Ixalotriton_niger" "Ixalotriton_parvus"
## [17] "Lineatriton_lineolus" "Lineatriton_orchileucos"
## [19] "Oedipina_kasios" "Oedipina_leptopoda"
## [21] "Oedipina_quadra" "Pachytriton_labiatus"
## [23] "Paradactylodon_gorganensis" "Paradactylodon_mustersi"
## [25] "Paradactylodon_persicus" "Paramesotriton_guangxiensis"
## [27] "Paramesotriton_zhijinensis" "Plethodon_chattahoochee"
## [29] "Plethodon_chlorobryonis" "Plethodon_grobmani"
## [31] "Plethodon_mississippi" "Plethodon_ocmulgee"
## [33] "Plethodon_savannah" "Plethodon_variolatus"
## [35] "Pleurodeles_nebulosus"
##
## $data_not_tree
## character(0)
pruning<-treedata(ct, cd)
## Warning in treedata(ct, cd): The following tips were not found in 'data' and were dropped from 'phy':
## Ambystoma_tigrinum
## Desmognathus_conanti
## Desmognathus_planiceps
## Eurycea_aquatica
## Haideotriton_wallacei
## Hydromantes_ambrosii
## Hydromantes_flavus
## Hydromantes_genei
## Hydromantes_imperialis
## Hydromantes_italicus
## Hydromantes_strinatii
## Hydromantes_supramontis
## Hynobius_fuca
## Hynobius_glacialis
## Ixalotriton_niger
## Ixalotriton_parvus
## Lineatriton_lineolus
## Lineatriton_orchileucos
## Oedipina_kasios
## Oedipina_leptopoda
## Oedipina_quadra
## Pachytriton_labiatus
## Paradactylodon_gorganensis
## Paradactylodon_mustersi
## Paradactylodon_persicus
## Paramesotriton_guangxiensis
## Paramesotriton_zhijinensis
## Plethodon_chattahoochee
## Plethodon_chlorobryonis
## Plethodon_grobmani
## Plethodon_mississippi
## Plethodon_ocmulgee
## Plethodon_savannah
## Plethodon_variolatus
## Pleurodeles_nebulosus
pct<-pruning$phy
# Note that you may have to prune more out if your data includes NAs!
Let’s see what we have.
head(cd)
## iucn_status.category population_trend.category
## Ambystoma_andersoni CR decreasing
## Ambystoma_barbouri NT decreasing
## Ambystoma_californiense VU decreasing
## Ambystoma_cingulatum VU decreasing
## Ambystoma_dumerilii CR decreasing
## Ambystoma_gracile LC stable
## TOTL_mm.totallength.quant SnoutVentLength.Mean
## Ambystoma_andersoni 235 140
## Ambystoma_barbouri 170 75.267
## Ambystoma_californiense 220 102.5
## Ambystoma_cingulatum 135 51.415
## Ambystoma_dumerilii 205 122
## Ambystoma_gracile 220 79.9335
## BW_mm.bodywidth.quant
## Ambystoma_andersoni NA
## Ambystoma_barbouri 8.133
## Ambystoma_californiense 14.100
## Ambystoma_cingulatum 5.900
## Ambystoma_dumerilii NA
## Ambystoma_gracile 11.900
## elevation_m.maxaltitude.quant cvalue.quant
## Ambystoma_andersoni 2000 NA
## Ambystoma_barbouri 300 NA
## Ambystoma_californiense 1200 32.0
## Ambystoma_cingulatum 100 29.5
## Ambystoma_dumerilii 1920 NA
## Ambystoma_gracile 3110 42.0
## ndiploid.count area frag lon
## Ambystoma_andersoni NA 5.257860e-04 1.0000000 -102.20972
## Ambystoma_barbouri NA 2.769358e+00 0.4486713 -85.56420
## Ambystoma_californiense NA 6.792483e-01 0.2377771 -121.45388
## Ambystoma_cingulatum NA 3.119059e+00 0.6656023 -82.52289
## Ambystoma_dumerilii NA 1.539524e-04 1.0000000 -101.63012
## Ambystoma_gracile 28 1.939970e+01 0.4550897 -126.88784
## lat DR Abs.Mol.Rate Volume
## Ambystoma_andersoni 19.75502 0.13946406 0.0004762878 NA
## Ambystoma_barbouri 37.47118 0.04704303 0.0016358247 3910.174
## Ambystoma_californiense 37.15783 0.03472722 0.0015071699 16004.863
## Ambystoma_cingulatum 31.21996 0.01019219 0.0011079862 1405.671
## Ambystoma_dumerilii 19.57350 0.06944087 0.0013591474 NA
## Ambystoma_gracile 50.96355 0.01492638 0.0014371805 8890.223
Since we are focused on continuous character evolution, you might focus on the following columns: (these are the ones that jump out at me)
Your assignment: use a combination of uni- and multivariate analyses to analyze these data. Come up with some results that you can present to all of us at the end of class.