# Loading phylogenetic data into R

#### Martin R. Smith martin.smith@durham.ac.uk

#### 2020-08-03

It can be a bit fiddly to get a phylogenetic dataset into R, particularly if you are not used to working with files in the Nexus format.

First off, make sure that you are comfortable telling R where to find a file.

Then you are ready to load raw data:

## From an Excel spreadsheet

If your data is in an Excel spreadsheet, one way to load it into R is using the `xlsx` package. First you’ll have to install it:

    install.packages('xlsx') # You only need to do this once

Then you should prepare your Excel spreadsheet such that each row corresponds to a taxon, and each column to a character.

Then you can read the data from the Excel file by telling R which sheet, rows and columns contain your data:

    library('xlsx')
    raw_data <- as.matrix(read.xlsx(filename,
                          sheetIndex = 1, # Loads sheet number 1 from the excel file
                          rowIndex = 2:21, # Extracts rows 2 to 21
                          colIndex = 2:26, # Extracts columns B to Z
                          header = FALSE
                          ))

    # In this example, the names of taxa are in column 1
    taxon_names <- read.xlsx(filename, sheetIndex = 1, rowIndex = 2:21,
                             colIndex = 1, as.data.frame=FALSE)

    rownames(raw_data) <- taxon_names

## From a Nexus file

`TreeTools` contains an inbuilt Nexus parser:

    raw_data <- ReadCharacters(filename)
    # Or, to go straight to PhyDat format:
    as_phydat <- ReadAsPhyDat(filename)

This will extract character names and codings from a dataset. It’s been written to work with datasets downloaded from MorphoBank, but my aim is for this function to handle most valid (and many invalid) NEXUS files. If you find a file that this function can’t handle, please let me know and I’ll try to fix it.

In the meantime, alternative Nexus parsers are available: try

    raw_data <- ape::read.nexus.data(filename)

Non-standard elements of a Nexus file might be beyond the capabilities of ape’s parser. In particular, you will need to replace spaces in taxon names with an underscore, and to arrange all data into a single block starting `BEGIN DATA`. You’ll need to strip out comments, character definitions and separate taxon blocks.

The function `readNexus` in package `phylobase` uses the NCL library and promises to be more powerful, but I’ve not been able to get it to work.

## From a TNT file

A TNT format dataset downloaded from MorphoBank can be parsed with `ReadTntCharacters`, which might also handle other TNT-compatible files. If there’s a file that’s not being read correctly, please let me know and I’ll try to fix it.

    raw_data <- ReadTntCharacters(filename)
    # Or, to go straight to PhyDat format:
    my_data <- ReadTntAsPhyDat(filename)

# Processing raw data

The next stage is to get the raw data into a format that most R packages can understand. If you’ve used the `ReadAsPhyDat` or `ReadTntAsPhyDat` functions, then you can skip this step – you’re already there.

Otherwise, you can try

    my_data <- PhyDat(raw_data)

or if that doesn’t work,

    my_data <- MatrixToPhyDat(raw_data)

These functions are pretty robust, but might return an error when they encounter an unexpected dataset format – if they don’t work on your dataset, please
let me know.

Failing that, you can enlist the help of the ‘phangorn’ package, which was installed when you installed ‘TreeTools’:

    library('phangorn')
    my_data <- phyDat(raw_data, type='USER', levels=c(0:9, '-'))

`type='USER'` tells the parser to expect morphological data.

The `levels` parameter simply lists all the states that any character might take. `0:9` includes all the integer digits from 0 to 9. If you have inapplicable data in your matrix, you should list `-` as a separate level as it represents an additional state (as handled by the Morphy implementation of (Brazeau, Guillerme, & Smith, 2019)). If you have more complicated ambiguities, you may need to use a contrast matrix to decode your matrix.

A contrast matrix translates the tokens used in your dataset to the character states to which they correspond: for example decoding ‘A’ to {01}. For more details, see the ‘phangorn-specials’ vignette in the phangorn package, accessible by typing ‘?phangorn’ in the R prompt and navigating to index > package vignettes.

    contrast.matrix <- matrix(data=c(
    # 0 1 -  # Each column corresponds to a character-state
      1,0,0, # Each row corresponds to a token, here 0, denoting the
             # character-state set {0}
      0,1,0, # 1 | {1}
      0,0,1, # - | {-}
      1,1,0, # A | {01}
      1,1,0, # + | {01}
      1,1,1  # ? | {01-}
    ), ncol=3, # ncol should correspond to the number of columns in the matrix
    byrow=TRUE);
    dimnames(contrast.matrix) <- list(
      c(0, 1, '-', 'A', '+', '?'), # A list of the tokens corresponding to each row
                                   # in the contrast matrix
      c(0, 1, '-') # A list of the character-states corresponding to the columns
                   # in the contrast matrix
    )

    contrast.matrix

    ##   0 1 -
    ## 0 1 0 0
    ## 1 0 1 0
    ## - 0 0 1
    ## A 1 1 0
    ## + 1 1 0
    ## ? 1 1 1

If you need to use a contrast matrix, convert the data using

    my.phyDat <- phyDat(my.data, type='USER', contrast=contrast.matrix)

# What next?

You might want to:

- Load a phylogenetic tree into R.

- Conduct parsimony search using Brazeau, Guillerme & Smith’s (2019) approach to inapplicable data, or using Profile parsimony (Faith & Trueman, 2001).

# References

Brazeau, M. D., Guillerme, T., & Smith, M. R. (2019). An algorithm for morphological phylogenetic analysis with inapplicable data. Systematic Biology, 68(4), 619–631. doi:10.1093/sysbio/syy083

Faith, D. P., & Trueman, J. W. H. (2001). Towards an inclusive philosophy for phylogenetic inference. Systematic Biology, 50(3), 331–350. doi:10.1080/10635150118627