---
title: "Additional Functions for Manipulating Data in treedata.table"
author: "Josef Uyeda, Cristian Roman-Palacios, April Wright"
date: "08/08/2020"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Additional Functions for Manipulating Data in treedata.table}
  %\VignetteEngine{knitr::rmarkdown}
  \usepackage[utf8]{inputenc}
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

## Additional functions for manipulating data

`treedata.table` includes additional functions that allow the identification of `discrete` and `continuous` character in a given dataset. We first load the dataset:

```{r}
library(ape)
library(treedata.table)

# Load example data
data(anolis)
#Create treedata.table object with as.treedata.table
td <- as.treedata.table(tree = anolis$phy, data = anolis$dat)
```

The `detectCharacterType()` function can be used to examine whether `SVL` is `discrete` or `continuous`: 

```{r}
detectCharacterType(anolis$dat$SVL)
```

We can further examine the type of characters we have in our dataset by using the `detectAllCharacters()` function:

```{r}
detectAllCharacters(anolis$dat)
```

Summarizing this information in a table, we get:

```{r}
cbind.data.frame(character=colnames(anolis$dat),type=detectAllCharacters(anolis$dat))
```

Finally, we can use the `filterMatrix()` function to subset our dataset to only a certain type of characters. For instance, let's extract all discrete characters in the *Anolis* dataset:

```{r}
filterMatrix(anolis$dat, "discrete")
```

Two additional functions in `treedata.table` are designed to examine and modify column and row names in any dataset. For instance, we can ask if the *Anolis* dataset has column names:

```{r}
hasNames(anolis$dat, "col")
```

It does have column names. Let's just remove the column names and check if `hasNames()` can detect this change. Here's our new dataset:

```{r}
data=anolis$dat
colnames(data)<-NULL
head(data,2)
```

Let's run `hasNames()` on our new dataset:

```{r}
hasNames(data, "col")
```

Now, we can create new column names using the `forceNames()` function:

```{r}
data <- forceNames(data, "col")
```

The new dataset, with column names (n1...), looks like this:

```{r}
head(data,2)
```

And we can finally ask whether our new dataset actually have column names by running the `hasNames()` function again:


```{r}
hasNames(data, "col")
```


We can apply the same procedure for columns (`col`), rows (`row`) or both (`rowcol`).

