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("yellow", "red", "red", "blue", "yellow", "blue")
class(x)

## [1] "character"

x_fact <- factor(x) # factor() is a function
class(x_fact)

## [1] "factor"

Factors have levels (character types do not).

x

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

x_fact

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

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

Extract the levels of a factor vector using levels():

levels(x_fact)

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

Group by each school and aggregate (sum):

dropouts <-
  dropouts %>%
  group_by(CDS_CODE) %>%
  summarize(
    Freshman = sum(D9),
    Sophomore = sum(D10),
    Junior = sum(D11),
    Senior = sum(D12)
  )
dropouts

## # A tibble: 5,507 × 5
##    CDS_CODE       Freshman Sophomore Junior Senior
##    <chr>             <dbl>     <dbl>  <dbl>  <dbl>
##  1 01100170112607        1         1      0      1
##  2 01100170123968        0         0      0      0
##  3 01100170130401        3         5     12     24
##  4 01100170130419        1         2     13     36
##  5 01100170131581        0         0      0      0
##  6 01100176002000        0         0      0      0
##  7 01316090131755        0         0      0      0
##  8 01316170131763        0         0      0      2
##  9 01611190000001        0         1      0      1
## 10 01611190106401        0         0      0      0
## # ℹ 5,497 more rows

Pivot to long format:

dropouts <-
  dropouts %>%
  pivot_longer(
    !CDS_CODE,
    names_to = "grade",
    values_to = "n_dropouts"
  )
dropouts

## # A tibble: 22,028 × 3
##    CDS_CODE       grade     n_dropouts
##    <chr>          <chr>          <dbl>
##  1 01100170112607 Freshman           1
##  2 01100170112607 Sophomore          1
##  3 01100170112607 Junior             0
##  4 01100170112607 Senior             1
##  5 01100170123968 Freshman           0
##  6 01100170123968 Sophomore          0
##  7 01100170123968 Junior             0
##  8 01100170123968 Senior             0
##  9 01100170130401 Freshman           3
## 10 01100170130401 Sophomore          5
## # ℹ 22,018 more rows

head(dropouts)

## # A tibble: 6 × 3
##   CDS_CODE       grade     n_dropouts
##   <chr>          <chr>          <dbl>
## 1 01100170112607 Freshman           1
## 2 01100170112607 Sophomore          1
## 3 01100170112607 Junior             0
## 4 01100170112607 Senior             1
## 5 01100170123968 Freshman           0
## 6 01100170123968 Sophomore          0

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.

set.seed(123) # same random sample each time
dropouts_subset <- slice_sample(dropouts, n = 32)
dropouts_subset

## # A tibble: 32 × 3
##    CDS_CODE       grade     n_dropouts
##    <chr>          <chr>          <dbl>
##  1 45699716050231 Junior             0
##  2 45700036050330 Junior             0
##  3 12630401230069 Sophomore          0
##  4 09100900930131 Sophomore          1
##  5 15633216009179 Junior             0
##  6 33670330113647 Sophomore          0
##  7 19643941931823 Freshman           1
##  8 19647331933381 Sophomore          7
##  9 38684786062053 Senior             0
## 10 11626616007611 Freshman           0
## # ℹ 22 more rows

Let’s make a plot first. We might not like the ordering on the x-axis, though.

dropouts_subset %>%
  ggplot(aes(x = grade, y = n_dropouts)) +
  geom_boxplot() +
  theme_bw(base_size = 16) # make all labels size 16

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.

dropouts_fct <-
  dropouts_subset %>%
  mutate(grade = factor(grade,
    levels = c("Freshman", "Sophomore", "Junior", "Senior")
  ))

dropouts_fct %>%
  pull(grade) %>%
  levels()

## [1] "Freshman"  "Sophomore" "Junior"    "Senior"

head(dropouts_fct)

## # A tibble: 6 × 3
##   CDS_CODE       grade     n_dropouts
##   <chr>          <fct>          <dbl>
## 1 45699716050231 Junior             0
## 2 45700036050330 Junior             0
## 3 12630401230069 Sophomore          0
## 4 09100900930131 Sophomore          1
## 5 15633216009179 Junior             0
## 6 33670330113647 Sophomore          0

Now let’s make our plot again:

dropouts_fct %>%
  ggplot(aes(x = grade, y = n_dropouts)) +
  geom_boxplot() +
  theme_bw(base_size = 16)

The factor data is automatically plotted in the order we would like.

Tables grouped by a character are arranged alphabetically.

dropouts_subset %>%
  group_by(grade) %>%
  summarize(total_dropouts = sum(n_dropouts))

## # A tibble: 4 × 2
##   grade     total_dropouts
##   <chr>              <dbl>
## 1 Freshman               3
## 2 Junior                 2
## 3 Senior                13
## 4 Sophomore             12

Tables grouped by a factor are arranged by level.

dropouts_fct %>%
  group_by(grade) %>%
  summarize(total_dropouts = sum(n_dropouts))

## # A tibble: 4 × 2
##   grade     total_dropouts
##   <fct>              <dbl>
## 1 Freshman               3
## 2 Sophomore             12
## 3 Junior                 2
## 4 Senior                13

A. Meaningful ordering of text data

B. Automatic ordering or numeric data

C. More precise values

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

The fct_count() function of the forcats package is helpful for checking that the proportions of each level for a factor are similar. Need the prop = TRUE argument otherwise just counts are reported.

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

## # A tibble: 4 × 3
##   f             n     p
##   <fct>     <int> <dbl>
## 1 Freshman      7 0.219
## 2 Sophomore     7 0.219
## 3 Junior       12 0.375
## 4 Senior        6 0.188

• the factor class allows us to have a different order from alphanumeric for categorical data
• we can change data to be a factor variable using mutate and a factor creating function like factor() (alphabetical order by default)
• with factor() we can specify the levels with the levels argument if we want a specific order
• the fct_reorder({variable_to_reorder}, {variable_to_order_by}, {summary function}) helps us reorder a variable by the values of another variable
• arranging, tabulating, and plotting the data will reflect the new order

🏠 Class Website
💻 Lab

Image by Gerd Altmann from Pixabay