Reading in the data

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!

Analyzing the data

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.