Good job! You might remember the `Levels: ` line while outputting some factors. What if you are interested in all the possible values of your factor values?

To output all the levels of a vector containing categorical values, use the `levels()` function, passing the vector as the parameter.

# Vector of currencies as factor
curr_f <- factor(c('USD', 'EUR', 'AUD', 'NOK', 
              'CHF', 'EUR', 'AUD', 'EUR'))

# Output all the levels
levels(curr_f)

The output is:
```
[1] "AUD" "CHF" "EUR" "NOK" "USD"
```
Well, these levels can be shuffled, and nothing will change. However, we may face factor variables that can be ordered. For example, we can call a person tall, medium, or short in terms of height. In that case, tall > medium > short. In R, you can consider this moment. You need to specify the `ordered` parameter to `T`. This will order variables alphabetically (if values are textual) or according to math laws (if numerical). The math laws are most likely acceptable for us, but alphabetical order surely not. To set specific order, you need to also set the `labels` parameter to the vector of values in the *ascending* order. Watch out for the example.

# Factors with no ordering
factor(c('USD', 'EUR', 'AUD', 'NOK', 
        'CHF', 'EUR', 'AUD', 'EUR'))

# Factors with ordering without labels parameter
factor(c('USD', 'EUR', 'AUD', 'NOK', 
        'CHF', 'EUR', 'AUD', 'EUR'), ordered = T)

# Factors with ordering with labels parameter
factor(c('USD', 'EUR', 'AUD', 'NOK', 
        'CHF', 'EUR', 'AUD', 'EUR'), ordered = T,
        labels = c('USD', 'EUR', 'CHF', 'AUD', 'NOK'))

The outputs will be:
```
[1] USD EUR AUD NOK CHF EUR AUD EUR
Levels: AUD CHF EUR NOK USD

[1] USD EUR AUD NOK CHF EUR AUD EUR
Levels: AUD < CHF < EUR < NOK < USD

[1] NOK CHF USD AUD EUR CHF USD CHF
Levels: USD < EUR < CHF < AUD < NOK
```
You definitely can notice the difference. Practice by yourself!

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.