index.Rmd

---
title: "slowraker"
output: github_document
---

```{r, echo = FALSE, message = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  progress = FALSE,
  error = FALSE, 
  message = FALSE,
  warning = FALSE
)
```

<br>

## Installation

You can get the stable version from CRAN:

```r
install.packages("slowraker")
```

Or the development version from GitHub:

```r
if (!"devtools" %in% rownames(installed.packages())) 
  install.packages("devtools")

devtools::install_github("crew102/slowraker")
```

## Basic usage

There is one main function in the `slowraker` package - `slowrake()`. `slowrake()` extracts keywords from a vector of documents using the RAKE algorithm. This algorithm doesn't require any training data, so it's super easy to use:

```{r, eval = FALSE}
library(slowraker)

data("dog_pubs")
rakelist <- slowrake(txt = dog_pubs$abstract[1:5])
```

```{r, eval = TRUE, results = 'hide', echo = FALSE}
# Note, we have to split the README.Rmd up like this so that it doesn't print 
# the progress bar.
library(slowraker)
options(width = 100, digits = 2)

data("dog_pubs")
rakelist <- slowrake(txt = dog_pubs$abstract[1:5])
```

`slowrake()` outputs a list of data frames. Each data frame contains the keywords that were extracted for a given document:

```{r}
rakelist
```

You can bind these data frames together using `rbind_rakelist()`:

```{r}
rbind_rakelist(rakelist, doc_id = dog_pubs$doi[1:5])
```

## Learning more

One this site you will find:

* A [getting started](articles/getting-started.html) vignette that explains how RAKE works as well as the basics of `slowrake()`
* A few example [applications](articles/applications.html)
* An introduction to a faster version of `slowraker` called [rapidraker](articles/rapidraker.html)
* Answers to [frequently asked questions](articles/faqs.html)