Do you remember the key difference between numerical and factor variables? It is the limitation on number of the possible values. Therefore the height of a person is a numerical variable. But in one of the previous chapters, we mentioned such a classification as *tall, medium, small*. These are categorical values. So, how can we 'split' numerical values into factor values?

You can do this by using the `cut()` function. This function has several important parameters.
```
cut(x, breaks, labels = NULL, right = TRUE, 
    ordered_result = FALSE, ...)
```
These are not the complete list of parameters, but they are essential for us.
- `x` - is a vector of numerical values we want to transform.
- `breaks` - either integer number (greater than or equal to 2) of intervals or a numeric vector with cut points.
- `labels` - vector of names for categories.
- `right` - logical, should the interval be closed on the right.
- `ordered_result` - logical, should the result be ordered.

If you want to cut the data into three intervals, then the `breaks` parameter should be either `3` or a vector with `4` values (let them be a, b, c, d; you need to build 3 intervals, (a,b], (b,c], (c,d])
For example, we can call a person tall if their height is greater than 190 cm, small if less than 160 cm, and medium otherwise. Assume we have data on people's heights. Let's represent how the function works.

# Vector of heights
heights <- c(170, 165, 195, 172, 189, 156, 178, 198,
            157, 182, 171, 184, 163, 176, 169, 153)

# Convert into factor by cutting into intervals
heights_f <- cut(heights, breaks = c(0, 160, 190, 250), 
                labels = c('small', 'medium', 'tall'), ordered_result = T)
heights_f # Output the factor variable

The output of the script above is
```
[1] medium medium tall   medium medium small  medium tall   small  medium medium medium
[13] medium medium medium small 
Levels: small < medium < tall
```
Note, that we set `c(0, 160, 190, 250)` as `breaks` parameter, since we want to cut all the values into three groups: (0, 160], (160, 190], and (190, 250]. Also, we set the `ordered_result` parameter to `T` to establish a relationship between factor values (i.e., small is less than medium and less than tall).

In the first section, you will consider the very basics of R - outputting and performing basic operations. You will finish the section with the variables theory.

In the second section, you will learn the vector class in R, which represents single values too. You will consider the main data types for single values and how to work with multiple values of a kind at once.

Factors (or categorical variables) are widely used in real life. These are blood types, currencies, nationalities, brands, etc. Unlike numerical variables, factors are limited to a finite number of values.