---
title: "Integration with the arules package"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Integration with the arules package}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 5
)
```

# Introduction

One of the goals in the design of this package is to be able to integrate with the `arules` package. This means that any one using the `arules` functionalities can export to and import from `fcaR` objects, more precisely, `FormalContext` and `ImplicationSet` objects.

```{r setup}
library(arules)
library(fcaR)
```

# Datasets

For these examples, we are using two binary datasets, `Mushroom` (from the `arules` package) and `planets` (from `fcaR`).
```{r}
data("Mushroom", package = "arules")
```

At the moment, in `arules` there is no support for fuzzy sets, so we must restrict ourselves to the binary case.

Let us create a `FormalContext` object for the `planets` dataset:
```{r}
fc_planets <- FormalContext$new(planets)
```

# Converting between formal contexts and transactions objects

We begin by converting between the objects which store the datasets.

## Importing a transactions object

It suffices to initialize a `FormalContext` object with the transactions dataset:
```{r}
fc <- FormalContext$new(Mushroom)
fc
```

From this point, we can use all the functionalities in the `fcaR` package regarding formal contexts, concept lattices and implication sets.

## Exporting a FormalContext

The `to_transactions()` function enables us to export a formal context to a format compatible with the `arules` package:
```{r}
fc_planets$to_transactions()
```
and use the functionality in that package.

# Converting between implication sets and rules objects
Other point of integration between the two packages is the ability to import rules from the `arules` package, operate on them to compute closures, recommendations or to remove redundancies, or to export an implication set as a `rules` object.


## Importing rules as an implication set

Let us suppose that we have extracted implications from the `Mushroom` dataset using the `apriori()` function:
```{r}
mushroom_rules <- apriori(Mushroom, parameter = list(conf = 1))
```

Once we have created the `fc` object storing the `Mushroom` dataset, we simply add the implications to it as:
```{r}
fc$implications$add(mushroom_rules)
```
And we can use all the functionalities for the `ImplicationSet` class.

## Exporting ImplicationSets to rules format
If we want to export the implications extracted for a binary formal context, we can use:
```{r}
fc_planets$find_implications()
fc_planets$implications$to_arules(quality = TRUE)
```


# Final considerations
An example of use may be to extract rules in the `arules` package by using `apriori()` or `eclat()`, then importing everything into `fcaR` as described above, and use the functionalities to simplify, remove redundancies, compute closures, etc., as needed, and then re-export back to `arules`.
