---
title: "Loading phylogenetic data into R"
author: "Martin R. Smith <martin.smith@durham.ac.uk>"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
bibliography: ../inst/REFERENCES.bib
csl: ../inst/apa-old-doi-prefix.csl
vignette: >
  %\VignetteIndexEntry{Loading phylogenetic data into R}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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](filesystem-navigation.html).

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
'[readxl](https://readxl.tidyverse.org/)' package.
First you'll have to install it:
```r
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:

```r
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 Nexus file

TreeTools contains an inbuilt Nexus parser:

```r
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](https://morphobank.org/), 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](https://github.com/ms609/TreeTools/issues/new?title=Unsupported+NEXUS+file&body=Please%20attach%20the%20problematic%20NEXUS%20file%20and%20describe%20the%20issue&labels=enhancement)
and I'll try to fix it.

In the meantime, alternative Nexus parsers are available: try 

```R
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](https://morphobank.org) 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](https://github.com/ms609/TreeTools/issues/new?title=Unsupported+TNT+file&body=Please%20attach%20the%20problematic%20TNT%20file%20and%20describe%20the%20issue&labels=enhancement)
and I'll try to fix it.

```r
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 

```r 
my_data <- PhyDat(raw_data)
```

or if that doesn't work, 

```r
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](https://github.com/ms609/TreeTools/issues/new?title=data+to+PhyDat+conversion+issue&body=Please%20describe+the+problem+here:&labels=bug).  


Failing that, you can enlist the help of the 'phangorn' package:

```r
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 [@Brazeau2018]).
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.

```{r Contrast matrix}
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
```

If you need to use a contrast matrix, convert the data using 
```r
my.phyDat <- phyDat(my.data, type = "USER", contrast = contrast.matrix)
```


# What next?

You might want to:

- [Load a phylogenetic tree](load-trees.html) into R.

- Conduct parsimony search using Brazeau, Guillerme & Smith's [-@Brazeau2018]
  [approach to inapplicable data](
   https://ms609.github.io/TreeSearch/articles/tree-search.html),
  or using [Profile parsimony](
   https://ms609.github.io/TreeSearch/articles/profile.html) [@Faith2001].

# References
