Author: Martin R. Smith martin.smith@durham.ac.uk
Date: 2026-06-02

# Loading phylogenetic data into R

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

#### 2026-06-02

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:

# 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 ‘readxl’ package. First you’ll have to install it:

    install.packages("readxl") # 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("readxl")
    raw_data <- as.matrix(read_excel(
      filename,
      sheet = 1,           # Loads sheet number 1 from the excel file
      range = "B1:AA21",   # Extracts columns B to AA, rows 1 to 21
      # Note that the first row is interpreted as column (character) names
      col_types = "text"   # Read all columns as character strings
    ))

    # Read row (taxon) names from column A
    # Again, the first cell will be interpreted as a column name
    taxon_names <- unlist(read_excel(filename, sheet = 1, range = "A1:A21"))

    rownames(raw_data) <- taxon_names

## From a text or CSV (comma separated values) file

Characters can be read from a text file in a similar manner to Excel. You may need to adjust the R commands to match the particular format of your input file.

    raw_data <- read.table(
      filename,            # Path to your input file
      sep = ",",           # What character separates columns?
      header = TRUE,       # Does the data contain a header row?
      row.names = 1,       # Which column contains the row names?
      na.strings = "",
      stringsAsFactors = FALSE
    )

## 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

Next, we need the raw data in the R-friendly `phyDat` format. 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:

    install.packages("phangorn")
    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 et al., 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 corresponds 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)

# Store processed data

To simplify subsequent analysis, or to allow the data to be read into other contexts (e.g. via the `TreeSearch::EasyTrees()` interface), you could save your processed data in Nexus format:

    ape::write.nexus.data(my.phyDat, "my_data.nex", format = "standard")

# 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, 619–631. https://doi.org/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. https://doi.org/10.1080/10635150118627