---
title: "Basic usage"
author: "Doug Friedman"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Basic usage}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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

## Introduction
There are two ways to use the topic model diagnostics included `topicdoc`. You can calculate all the topic diagnostics at once using `topic_diagnostics` or use the other functions to calculate the diagnostics individually.

The only prerequisite for using `topicdoc` is that your topic model is fit using the `topicmodels` package and that your document-term matrix (DTM) is `slam` coercible. This includes DTMs created through popular text mining packages like `tm` and `quanteda`.

## Example
For this example, the Associated Press Dataset from topicmodels is used. It contains a DTM created a series of AP articles from 1988.

```{r example_setup}
library(topicdoc)
library(topicmodels)

data("AssociatedPress")

lda_ap4 <- LDA(AssociatedPress,
               control = list(seed = 33), k = 4)

# See the top 10 terms associated with each of the topics
terms(lda_ap4, 10)
```

Here's how you would run all the diagnostics at once.

```{r all_at_once}
topic_diagnostics(lda_ap4, AssociatedPress)
```

Here's how you would run a few of them individually.

```{r one_at_a_time}
topic_size(lda_ap4)
mean_token_length(lda_ap4)
```

## Diagnostics Included
A full list of the diagnostics included are provided below.

| Diagnostic/Metric                               |      Function       |  Description                                |
|:-----------------------------------------------:|:-------------------:|:-------------------------------------------:|
| topic size                                      | `topic_size`        | Total (weighted) number of tokens per topic |
| mean token length                               | `mean_token_length` | Average number of characters for the top tokens per topic |
| distance from corpus distribution               | `dist_from_corpus`  | Distance of a topic's token distribution from the overall corpus token distribution |
| distance between token and document frequencies | `tf_df_dist`        | Distance between a topic's token and document distributions |
| document prominence                             | `doc_prominence`    | Number of unique documents where a topic appears
| topic coherence                                 | `topic_coherence`   | Measure of how often the top tokens in each topic appear together in the same document |
| topic exclusivity                               | `topic_exclusivity` | Measure of how unique the top tokens in each topic are compared to the other topics |
