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"

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

factor() is from base R and as_factor() is from forcats

Both can change a variable to be of class factor.

• factor() will order alphabetically unless told otherwise.
• as_factor() will order by first appearance unless told otherwise.

If you are assigning your levels manually either function is fine!

x <- c("yellow", "red", "red", "blue", "yellow", "blue")
x_fact_2 <- as_factor(x)
x_fact_2

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

# Compare to factor() method:
x_fact

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

head(er_visits_age)

## # A tibble: 6 × 7
##    YEAR GENDER       AGE              RATE L95CL U95CL VISITS
##   <dbl> <chr>        <chr>           <dbl> <dbl> <dbl>  <dbl>
## 1  2011 Both genders 0-4 years old    3.52  1.82  6.16     12
## 2  2011 Both genders 15-34 years old  7.34  5.95  8.74    106
## 3  2011 Both genders 35-64 years old  5.84  4.80  6.88    121
## 4  2011 Both genders 5-14 years old   5.20  3.50  6.90     36
## 5  2011 Both genders 65+ years old    8.34  5.98 10.7      48
## 6  2011 Both genders All ages         6.30  5.62  6.99    323

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

R does not realize that there is any order related to the AGE values. It will assume that it is alphabetical (for numbers, this means ascending order).

However, we know that the order is: 0-4 years old, 5-14 years old, 15-34 years old, 35-64 years old, 65+ years old, and All ages.

Use set.seed() to take the same random sample each time.

set.seed(123)
er_visits_age_subset <- slice_sample(er_visits_age, n = 32)

Let’s make a plot first.

er_visits_age_subset %>%
  ggplot(mapping = aes(x = AGE, y = RATE)) +
  geom_boxplot() +
  theme_bw(base_size = 12) # make all labels size 12

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

er_visits_age_fct <-
  er_visits_age_subset %>%
  mutate(AGE = factor(AGE,
    levels = c("0-4 years old", "5-14 years old", "15-34 years old", "35-64 years old", "65+ years old", "All ages")
  ))

er_visits_age_fct %>%
  pull(AGE) %>%
  levels()

## [1] "0-4 years old"   "5-14 years old"  "15-34 years old" "35-64 years old"
## [5] "65+ years old"   "All ages"

head(er_visits_age_fct)

## # A tibble: 6 × 7
##    YEAR GENDER       AGE              RATE L95CL U95CL VISITS
##   <dbl> <chr>        <fct>           <dbl> <dbl> <dbl>  <dbl>
## 1  2016 Both genders 0-4 years old    4.19  2.29  7.03     14
## 2  2019 Both genders 35-64 years old  7.19  6.07  8.30    159
## 3  2013 Both genders 15-34 years old  8.13  6.69  9.58    121
## 4  2022 Both genders 0-4 years old   NA    NA    NA        NA
## 5  2017 Both genders All ages         5.77  5.14  6.40    323
## 6  2019 Both genders 15-34 years old  8.34  6.94  9.73    137

Now let’s make our plot again:

er_visits_age_fct %>%
  ggplot(mapping = aes(x = AGE, y = RATE)) +
  geom_boxplot() +
  theme_bw(base_size = 12)

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

Tables grouped by a character are arranged alphabetically (if letters) or by ascending first number (if numbers).

er_visits_age_subset %>%
  group_by(AGE) %>%
  summarize(total_visits = sum(VISITS, na.rm = T))

## # A tibble: 6 × 2
##   AGE             total_visits
##   <chr>                  <dbl>
## 1 0-4 years old             39
## 2 15-34 years old          831
## 3 35-64 years old          649
## 4 5-14 years old            62
## 5 65+ years old            389
## 6 All ages                1943

Tables grouped by a factor are arranged by level.

er_visits_age_fct %>%
  group_by(AGE) %>%
  summarize(total_visits = sum(VISITS, na.rm = T))

## # A tibble: 6 × 2
##   AGE             total_visits
##   <fct>                  <dbl>
## 1 0-4 years old             39
## 2 5-14 years old            62
## 3 15-34 years old          831
## 4 35-64 years old          649
## 5 65+ years old            389
## 6 All ages                1943

er_visits_age_fct %>% pull(AGE) %>% levels() # By year order

## [1] "0-4 years old"   "5-14 years old"  "15-34 years old" "35-64 years old"
## [5] "65+ years old"   "All ages"

er_visits_age_fct <- er_visits_age_fct %>%
  mutate(
    AGE = fct_reorder(AGE, RATE, mean)
  )

er_visits_age_fct %>% pull(AGE) %>% levels() # by increasing mean dropouts

## [1] "0-4 years old"   "5-14 years old"  "35-64 years old" "All ages"       
## [5] "65+ years old"   "15-34 years old"

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.

er_visits_age_fct %>%
  pull(AGE) %>%
  fct_count(prop = TRUE)

## # A tibble: 6 × 3
##   f                   n      p
##   <fct>           <int>  <dbl>
## 1 0-4 years old       8 0.25  
## 2 5-14 years old      2 0.0625
## 3 35-64 years old     5 0.156 
## 4 All ages            5 0.156 
## 5 65+ years old       6 0.188 
## 6 15-34 years old     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() or as_factor
• the as_factor() is from the forcats package (first appearance order by default)
• the factor() base R function (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