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My good friend Andrew recently posted this gist:

It shows how to solve a simple programming exercise know as `FizzBuzz` in R, but also is clearly showing off his already amazing proficiency in the very new R package `magrittr`. Those are some piping hot coding skillz (or should I say (not-a-)piping hot?)

Clearly this was a challenge to see who can come up with the most ridiculously long and complicated expression that uses only a single string of functions piped together with the magic of `%>%`. In that spirit, I have decided to try and replicate one of the more complicated analyses and figures in my first paper—a phylogenetic ordination—using no intermediate variables and lots of `%>%`-ing. Actually, I am going to use a different method for the phylogenetic ordination from the paper, and I think this one is better (I will post more on this later).

I download the data directly from the supplemental material on my paper, so you should be able to run this code on your computer if you want (as long as you have an internet connection). This requires the following packages:

• `magrittr`
• `ape`
• `plyr`
• `dplyr`
• `vegan`
• `ggplot2`
```library(ape)
library(plyr)
library(dplyr)
library(magrittr)
library(vegan)
library(ggplot2)
read.tree(text=.) %>% #make it a phylo object
cophenetic %>% # turn it into a phylogenetic distance matrix
l(.,x -> cmdscale(x,nrow(x)-1)) %>% # get phylogenetic principle components
l(.,x -> x[order(rownames(x)), ]) %>% # order by rownames
aaply("http://www.plosone.org/article/fetchSingleRepresentation.action?uri=info:doi/10.1371/journal.pone.0007071.s001" %>%
l(x -> x %>% set_rownames(paste(rownames(x),x[,2],sep="_"))) %>% #save treatment names for later in the rownames
extract(c(-1,-2)) %>% # toss non-species data
l(x -> x[,order(colnames(x))]) %>% # order by column names
as.matrix, # make it a matrix
1,function(x, y) (y*x) %>% colSums, y=.) %>% #make a function to pipe in
# community data as x, and phylo data (.) as y, then multiple each community abundance
# by phylo principle components. now we have a phylogenetic feature vector for each
# site!
metaMDS("euclidean") %>% # use non-metric multidimensional scaling on phylogenetic features
extract("points") %>% # pull out the ordinated points
data.frame %>% # make it  data.frame
l(., x -> mutate(x,treatment=rownames(x) %>%
strsplit("_") %>%
laply(function(y) y[2]))) %>% #extract treatment names from rownames
ggplot(.,aes(x=points.MDS1,y=points.MDS2)) + geom_point(aes(color=factor(treatment)),size=5) #make a ggplot

## Run 0 stress 0.1055
## Run 1 stress 0.1055
## ... procrustes: rmse 0.01079  max resid 0.05315
## Run 2 stress 0.1199
## Run 3 stress 0.1198
## Run 4 stress 0.1057
## ... procrustes: rmse 0.006186  max resid 0.03488
## Run 5 stress 0.1055
## ... procrustes: rmse 0.01088  max resid 0.05316
## Run 6 stress 0.121
## Run 7 stress 0.1408
## Run 8 stress 0.1055
## ... procrustes: rmse 0.01081  max resid 0.05317
## Run 9 stress 0.1055
## ... New best solution
## ... procrustes: rmse 0.000166  max resid 0.0007816
## *** Solution reached

dat #plot ordination!!
```

You can also find the above as a gist.

We can see that the main difference between the disturbed and undisturbed sites is that there is a much larger variance in phylogenetic composition between disturbed sites. This has interesting implications which has inspired me to do a follow-up to my old paper, on
this blog. Look for that soon.

So that’s a phylogenetic ordination, done in a single line of code (well, it would be a single line if I removed all the linebreaks). Not a single intermediate variable was used. Note that if you do try this code, you will get some warnings, they don’t affect the outcome. It’s not nearly as elegant as Andrew’s, but I would hate to see what this would look like if I tried nesting all of these functions!

Did I mention that I love `magrittr` (you could probably guess that from the gif that I made, below).

Happy piping!