A factor is a special character vector where the elements have pre-defined groups or ‘levels’. You can think of these as qualitative or categorical variables:

x <- c("red", "red", "blue", "yellow", "blue")
class(x)

## [1] "character"

x_fact = factor(x) # factor() is a function
class(x_fact)

## [1] "factor"

x_fact

## [1] red    red    blue   yellow blue  
## Levels: blue red yellow

Note that levels are, by default, in alphanumerical order.

You can learn what are the unique levels of a factor vector

levels(x_fact)

## [1] "blue"   "red"    "yellow"

A package called forcats is really helpful for working with factors.

First we will create some data about absences of students. Each row is a different student. We have information about the number of days absent and the grade for the individual students. We will use the tibble() function to create the data. We will use the sample() function to create a random sequence of numbers from 0 to 7 with replacements for 32 hypothetical students. Since there are four grades and 8*4 is 32, we will repeat the grade values 8 times. We use the set.seed() function so that the random sample from 0 to 7 is the same each time the code is run.

set.seed(123)
data_highschool <- tibble(absences = 
  sample(0:7, size = 32, replace = TRUE), 
    grade = 
      rep(c("Sophomore",
            "Freshman", 
             "Junior", 
             "Senior"), 8))

head(data_highschool)

## # A tibble: 6 × 2
##   absences grade    
##      <int> <chr>    
## 1        6 Sophomore
## 2        6 Freshman 
## 3        2 Junior   
## 4        5 Senior   
## 5        2 Sophomore
## 6        1 Freshman

Notice that grade is a chr variable. This indicates that the values are character strings.

R does not realize that there is any order related to the grade values. It will assume that it is alphabetical.

However, we know that the order is: freshman, sophomore, junior, senior.

Let’s make a plot first:

data_highschool %>%
  ggplot(mapping = aes(x = grade, y = absences)) +
  geom_boxplot()

OK this is very useful, but it is a bit difficult to read. We expect the values to be plotted by the order that we know, not by alphabetical order.

Currently grade is class character but let’s change that to class factor which allows us to specify the levels or order of the values.

class(data_highschool$grade)

## [1] "character"

data_highschool_fct <- data_highschool %>% 
 mutate(grade = factor(grade, levels = 
              c("Freshman", 
                "Sophomore", 
                "Junior", 
                "Senior")))
head(data_highschool_fct)

## # A tibble: 6 × 2
##   absences grade    
##      <int> <fct>    
## 1        6 Sophomore
## 2        6 Freshman 
## 3        2 Junior   
## 4        5 Senior   
## 5        2 Sophomore
## 6        1 Freshman

Now let’s make our plot again:

data_highschool_fct %>%
  ggplot(mapping = aes(x = grade, y = absences)) +
  geom_boxplot()

Now that’s more like it! Notice how the data is automatically plotted in the order we would like.

data_highschool %>% group_by(grade) %>% tally(absences)

## # A tibble: 4 × 2
##   grade         n
##   <chr>     <int>
## 1 Freshman     21
## 2 Junior       16
## 3 Senior       25
## 4 Sophomore    32

data_highschool_fct %>% group_by(grade) %>% tally(absences)

## # A tibble: 4 × 2
##   grade         n
##   <fct>     <int>
## 1 Freshman     21
## 2 Sophomore    32
## 3 Junior       16
## 4 Senior       25

Let’s say that we also want to assess which grade has the most incidences of being tardy (another word for late) to class. Now we will add another simulated variable of random values from 0 to 7 and of 32 values total. We set a seed again so that our results will be consistent each time we run this code.

set.seed(1956)
data_highschool_fct <- 
  data_highschool_fct %>% 
  mutate("tardy" = sample(0:7, size = 32, replace = TRUE))

data_highschool_fct

## # A tibble: 32 × 3
##    absences grade     tardy
##       <int> <fct>     <int>
##  1        6 Sophomore     1
##  2        6 Freshman      3
##  3        2 Junior        5
##  4        5 Senior        1
##  5        2 Sophomore     4
##  6        1 Freshman      0
##  7        1 Junior        7
##  8        5 Senior        0
##  9        2 Sophomore     0
## 10        4 Freshman      3
## # … with 22 more rows

Now let’s plot each of our variables of interest (absences and tardy) on the y axis and grade on the x axis. Let’s arrange grade by the amount of each.

The last plot is informative, but what if we are mostly interested in absences and we are secondarily interested in tardiness. Then it might help to order grade for both plots by the amount of absences for each grade.

data_highschool_fct %>% pull(grade) %>% fct_count(prop= TRUE)

## # A tibble: 4 × 3
##   f             n     p
##   <fct>     <int> <dbl>
## 1 Freshman      8  0.25
## 2 Sophomore     8  0.25
## 3 Junior        8  0.25
## 4 Senior        8  0.25