phylo.to.map with right-facing trees; preliminary t-shirt design for Valdivia workshop

I will be teaching a workshop at Universidad Austral de Chile in Valdivia in December. Right now I'm working on the design for the t-shirt that we have made a tradition in prior similar workshops (e.g., 1, 2).

I decided it might be cool to project a phylogeny onto the geographic outline of Chile. The only problem is that Chile is a very long country, and phylo.to.map previously only permits a projection of the tree from above facing down.

Well - that has now been addressed. I just pushed a phytools update that allows a phylogeny to be plotted to the left of a geographic map facing rightwards. Here is a demo.

First, we need to install from GitHub:

library(devtools)
install_github("liamrevell/phytools")

Load packages:

library(phytools)
library(phangorn) ## we will use phangorn too

For fun, let's use the lat. & long. coordinates of some major Chilean cities:

X<-read.csv("Chile-cities.csv",row.names=1)
X<-as.matrix(X)
X

##                 Lat   Long
## Santiago     -33.45 -70.67
## Valparaiso   -33.05 -71.62
## Antofagasta  -23.65 -70.40
## Temuco       -38.75 -72.67
## Concepcion   -36.82 -73.05
## Rancagua     -34.17 -70.75
## Talca        -35.43 -71.67
## Arica        -18.48 -70.33
## Iquique      -20.22 -70.15
## Puerto_Montt -41.47 -72.93
## La_Serena    -29.90 -71.25
## Chillan      -36.60 -72.12
## Osorno       -40.57 -73.14
## Valdivia     -39.75 -72.50
## Calama       -22.47 -68.93
## Copiapo      -27.36 -70.33
## Los_Angeles  -37.47 -72.35
## Punta_Arenas -53.17 -70.93

(Hopefully I got most of these right!)

Rather than use a random “tree” - I figured why not compute a tree from the latitudinal distances between cities:

D<-dist(X[,1])
D

##              Santiago Valparaiso Antofagasta Temuco Concepcion Rancagua
## Valparaiso       0.40                                                  
## Antofagasta      9.80       9.40                                       
## Temuco           5.30       5.70       15.10                           
## Concepcion       3.37       3.77       13.17   1.93                    
## Rancagua         0.72       1.12       10.52   4.58       2.65         
## Talca            1.98       2.38       11.78   3.32       1.39     1.26
## Arica           14.97      14.57        5.17  20.27      18.34    15.69
## Iquique         13.23      12.83        3.43  18.53      16.60    13.95
## Puerto_Montt     8.02       8.42       17.82   2.72       4.65     7.30
## La_Serena        3.55       3.15        6.25   8.85       6.92     4.27
## Chillan          3.15       3.55       12.95   2.15       0.22     2.43
## Osorno           7.12       7.52       16.92   1.82       3.75     6.40
## Valdivia         6.30       6.70       16.10   1.00       2.93     5.58
## Calama          10.98      10.58        1.18  16.28      14.35    11.70
## Copiapo          6.09       5.69        3.71  11.39       9.46     6.81
## Los_Angeles      4.02       4.42       13.82   1.28       0.65     3.30
## Punta_Arenas    19.72      20.12       29.52  14.42      16.35    19.00
##              Talca Arica Iquique Puerto_Montt La_Serena Chillan Osorno
## Valparaiso                                                            
## Antofagasta                                                           
## Temuco                                                                
## Concepcion                                                            
## Rancagua                                                              
## Talca                                                                 
## Arica        16.95                                                    
## Iquique      15.21  1.74                                              
## Puerto_Montt  6.04 22.99   21.25                                      
## La_Serena     5.53 11.42    9.68        11.57                         
## Chillan       1.17 18.12   16.38         4.87      6.70               
## Osorno        5.14 22.09   20.35         0.90     10.67    3.97       
## Valdivia      4.32 21.27   19.53         1.72      9.85    3.15   0.82
## Calama       12.96  3.99    2.25        19.00      7.43   14.13  18.10
## Copiapo       8.07  8.88    7.14        14.11      2.54    9.24  13.21
## Los_Angeles   2.04 18.99   17.25         4.00      7.57    0.87   3.10
## Punta_Arenas 17.74 34.69   32.95        11.70     23.27   16.57  12.60
##              Valdivia Calama Copiapo Los_Angeles
## Valparaiso                                      
## Antofagasta                                     
## Temuco                                          
## Concepcion                                      
## Rancagua                                        
## Talca                                           
## Arica                                           
## Iquique                                         
## Puerto_Montt                                    
## La_Serena                                       
## Chillan                                         
## Osorno                                          
## Valdivia                                        
## Calama          17.28                           
## Copiapo         12.39   4.89                    
## Los_Angeles      2.28  15.00   10.11            
## Punta_Arenas    13.42  30.70   25.81       15.70

tree<-untangle(upgma(D),"read.tree") ## untangle is hack

(Use of untangle is a hack to ensure that the tree is in good conformation for phylo.to.map.)

Now let's proceed!

obj<-phylo.to.map(tree,X,regions="Chile",direction="rightwards",
    plot=FALSE)

## objective: 32
## objective: 32
## objective: 32
## objective: 26
## objective: 26
## objective: 26
## objective: 8
## objective: 2
## objective: 2
## objective: 2
## objective: 2
## objective: 2
## objective: 2
## objective: 2
## objective: 2
## objective: 2
## objective: 0

obj

## Object of class "phylo.to.map" containing:
## 
## (1) A phylogenetic tree with 18 tips and 17 internal nodes.
## 
## (2) A geographic map with range:
##      -55.89N, -17.51N
##      -109.43W, -66.44W.
## 
## (3) A table containing 18 geographic coordinates.

plot(obj,direction="rightwards",ftype="i")

One issue here is the spacing - which actually turns out to be the result of the inclusion in “Chile” of Chile's pacific island territories, such as Easter Island. Let's unceremoniously chop off those islands!

ii<-which(obj$map$x<(-77))
obj$map$x<-obj$map$x[-ii]
obj$map$y<-obj$map$y[-ii]
obj$map$range[1]<--81
plot(obj,direction="rightwards",split=c(0.6,0.4),ftype="i")

Finally, let's plot it with the tip labels removed & a special star at the location of our upcoming workshop!

plot(obj,direction="rightwards",split=c(0.3,0.7),ftype="off",
    colors=c(grey(0.3),"black"))
points(X["Valdivia",2],X["Valdivia",1],pch=8,cex=1.2)

Not bad! Of course, as always, we can export a higher quality version using pdf:

pdf(file="phylo.to.map-Chile.pdf",width=5,height=8)
plot(obj,direction="rightwards",split=c(0.3,0.7),ftype="off",
    colors=c(grey(0.3),"black"))
points(X["Valdivia",2],X["Valdivia",1],pch=8,cex=1.2)
dev.off()

## windows 
##       2

Here it is!

That's all for now.