---
title: CA Step 4 Corpus Analytics
subtitle: corpus_analytics()
author: Jamie Reilly, Ben Sacks, Ginny Ulichney, Gus Cooney, Chelsea Helion
date: "`r format(Sys.Date(), '%B %d, %Y')`"
show_toc: true
slug: ConversationAlign Analytics
output:
  rmarkdown::html_vignette:
    toc: yes
vignette: >
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteIndexEntry{CA Step 4 Corpus Analytics}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{r, message=FALSE, warning=F, echo=F}
# Load SemanticDistance
library(ConversationAlign)
```

# Generate corpus analytics
## `corpus_analytics()`
This is a helpful addition to `ConversationAlign` that will generate a variety of corpus analytics (e.g., word count, type-token-ratio) for your conversation corpus. The output is in a summary table that is readily exportable to to the specific journal format of your choice using any number of packages such as `flextable` or `tinytable`. <br>

Generate your corpus analytics on the dataframe you created with `prep_dyads`.

<span style="color: darkred;">Arguments to `corpus_analytics` include:</span> <br>
1) **dat_prep**= dataframe created by ``prep_dyads()``function <br>

```{r, eval=T, warning=F, message=F}
NurseryRhymes_Analytics <-  corpus_analytics(dat_prep=NurseryRhymes_Prepped)
knitr::kable(head(NurseryRhymes_Analytics, 15), format = "simple", digits = 2)
```



`