## STATISTICS Course

# Variables

Variables are the columns of our matrix, basically the properties of the individuals we are observing. They can be **quantitative** or **qualitative**.

A **quantitative** variable is a measurement, for instance, if we took two values, we can subtract them (to compare them).

They usually follow a distribution

- continue: can take an infinite number of values
- discrete: can take a finite number of values

```
ex: age, size, score, ...
```

A **qualitative** variable (`catégorielles`

) is either

- binary: taking values like 0 or 1, true or false
- ordered: the values are sorted like easy < medium < hard
- not ordered: the rest

## Variables in R

Qualitative variables are usually from the **factor** class.

```
# get information about the variable types
str(iris)
# 'data.frame': 150 obs. of 5 variables:
# $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
# $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
# $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
# $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
# $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
```

A factor is a variable that got levels (the unique values), and you can order the levels.

```
levels(iris$Species)
# [1] "setosa" "versicolor" "virginica"
```

Here you can read that as `"setosa" > "versicolor" > "virginica"`

, but that doesn't make sense here, since this does not seem like an ordered qualitative variable.