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 alphanumerically 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 × 6
##    year age          rate lower95cl upper95cl visits
##   <dbl> <chr>       <dbl>     <dbl>     <dbl>  <dbl>
## 1  2011 0-4 years    3.52      1.82      6.16     12
## 2  2011 15-34 years  7.34      5.95      8.74    106
## 3  2011 35-64 years  5.84      4.80      6.88    121
## 4  2011 5-14 years   5.20      3.50      6.90     36
## 5  2011 65+ years    8.34      5.98     10.7      48
## 6  2012 0-4 years    3.58      1.85      6.25     12

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 alphanumeric (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, and 65+ years old.

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", "5-14 years", "15-34 years", "35-64 years", "65+ years")
  ))

er_visits_age_fct %>%
  pull(age) %>%
  levels()

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

head(er_visits_age_fct)

## # A tibble: 6 × 6
##    year age          rate lower95cl upper95cl visits
##   <dbl> <fct>       <dbl>     <dbl>     <dbl>  <dbl>
## 1  2017 0-4 years    3.29      1.64      5.89     11
## 2  2013 65+ years    4.50      2.86      6.14     29
## 3  2021 0-4 years   NA        NA        NA        NA
## 4  2013 5-14 years   5.51      3.78      7.23     39
## 5  2011 35-64 years  5.84      4.80      6.88    121
## 6  2019 15-34 years  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: 5 × 2
##   age         total_visits
##   <chr>              <dbl>
## 1 0-4 years             38
## 2 15-34 years          986
## 3 35-64 years          983
## 4 5-14 years           215
## 5 65+ years            296

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: 5 × 2
##   age         total_visits
##   <fct>              <dbl>
## 1 0-4 years             38
## 2 5-14 years           215
## 3 15-34 years          986
## 4 35-64 years          983
## 5 65+ years            296

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

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

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 visits

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

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: 5 × 3
##   f               n     p
##   <fct>       <int> <dbl>
## 1 0-4 years       4 0.125
## 2 5-14 years      8 0.25 
## 3 35-64 years     7 0.219
## 4 65+ years       6 0.188
## 5 15-34 years     7 0.219

A. It helps us check the factual accuracy of our datasets.

B. It helps us change the order of variables in case the order has meaning.

A. It helps us reorder a factor based on the values of another variable.

B. It helps us reorder a factor based on a random change in the order.

• 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 (alphanumeric 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. 📃Day 6 Cheatsheet 📃Posit’s forcats cheatsheet

Image by Gerd Altmann from Pixabay