library("RNeXML") library("dplyr") library("geiger") knitr::opts_chunk$set(message = FALSE, warning=FALSE, comment = NA)
Let's generate a NeXML
file using the tree and trait data from the geiger
package's "primates" data:
data("primates") add_trees(primates$phy) %>% add_characters(primates$dat, ., append=TRUE) %>% taxize_nexml() -> nex
(Note that we've used dplyr
's cute pipe syntax, but unfortunately our add_
methods take the nexml
object as the second
argument instead of the first, so this isn't as elegant since we need the stupid .
to show where the piped output should go...)
We now read in the three tables of interest. Note that we tell get_characters
to give us species labels as there own column, rather than as rownames. The latter is the default only because this plays more nicely with the default format for character matrices that is expected by geiger
and other phylogenetics packages, but is in general a silly choice for data manipulation.
otu_meta <- get_metadata(nex, "otus/otu") taxa <- get_taxa(nex) char <- get_characters(nex, rownames_as_col = TRUE)
We can take a peek at what the tables look like, just to orient ourselves:
otu_meta
taxa
head(char)
Now that we have nice data.frame
objects for all our data, it's easy to join them into the desired table with a few obvious dplyr
commands:
taxa %>% left_join(char, by = c("label" = "taxa")) %>% left_join(otu_meta, by = c("id" = "otu")) %>% select(id, label, x, href)
Because these are all from the same otus block anyway, we haven't selected that column, but were it of interest it is also available in the taxa table.
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