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## The User-Item Rating Matrix

First thing first, to build a recommender system, it is required to have access on a data set. In rrecsys, we load a user-item rating matrix.
Rows represent the users and columns the items. Please notice that we consider the missing values
of the user-item rating matrix as NA (not available) values.

Currently rrecsys is equipped with two data sets. We have included the 
[MovieLens 100k](https://grouplens.org/datasets/movielens/100k/) and 
the [MovieLens Latest](http://grouplens.org/datasets/movielens/latest/) datasets. 


We will use these datasets for demonstration. 

To load the MovieLens 100k data set:

```{r, eval=FALSE}
data("ml100k")
```
To load the MovieLens Latest data set:

```{r, eval=FALSE}
data("mlLatest100k")
```

rrecsys includes a method to read, define or even change the characteristics of a data set.
For example, to define a rating scale (e.g., 0.5-5), which also includes real number values,
 we can write the following:

```{r, eval=FALSE}
ML <- defineData(mlLatest100k, minimum = .5, maximum = 5, intScale = TRUE)
ML
## Dataset containing 718 users and  8927 items and a total of  100234  scores.
```
A dataset can be as well binarized by the following command:
```{r, eval=FALSE}
binML <- defineData(mlLatest100k, binary = TRUE, positiveThreshold = 3)
binML
## Binary dataset containing 718 users and  8927 items and a total of  84326  scores.
```

In this case all the rating in mlLatest100k with a value larger or equal to _positiveThreshold_ 
are converted to 1 and the rest to NA values. 

A _dataSet_ object can be analysed with the following methods:
```{r, eval=FALSE}
# Number of times an item was rated.
colRatings(ML)
# Number of times a user has rated.
rowRatings(ML)
# Total number of rating in the rating matrix.
numRatings(ML)
# Sparsity.
sparsity(ML)
```
A _dataSet_ object can be cropped to contain a specific number of ratings:
```{r, eval=FALSE}
# Removing users that rated less than 40 items and items that were rated less than 30 times.
subML <- ML[rowRatings(ML)>=40, colRatings(ML)>=30]
sparsity(ML)
sparsity(subML)
```

Please notice that the output of _defineData_ is an S4 object (class _dataSet_). 
This would be the main input to the dispatcher in rrecsys for training a model.
