# A tibble: 344 × 9
species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
<fct> <fct> <dbl> <dbl> <int> <int>
1 Adelie Torgersen 39.1 18.7 181 3750
2 Adelie Torgersen 39.5 17.4 186 3800
3 Adelie Torgersen 40.3 18 195 3250
4 Adelie Torgersen NA NA NA NA
5 Adelie Torgersen 36.7 19.3 193 3450
6 Adelie Torgersen 39.3 20.6 190 3650
7 Adelie Torgersen 38.9 17.8 181 3625
8 Adelie Torgersen 39.2 19.6 195 4675
9 Adelie Torgersen 34.1 18.1 193 3475
10 Adelie Torgersen 42 20.2 190 4250
# ℹ 334 more rows
# ℹ 3 more variables: sex <fct>, year <int>, year_factor <fct>
Photo by James Eades on Unsplash
forcats: info
forcats helps us work with categorical variables or factors
These are variables that have a fixed and known set of possible values, like species, island, and sex in our penguins dataset
forcats: exercise
Currently the year variable in penguins is continuous from 2007 to 2009
Usually this isn’t what we want and we might want to turn it into a categorical variable instead
The factor() function is perfect for this
The result is a new variable year_factor with factor levels 2007, 2008, and 2009
penguins_new <- penguins |> mutate(year_factor = factor(year, levels = unique(year)))
penguins_new |> head()# A tibble: 6 × 9
species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
<fct> <fct> <dbl> <dbl> <int> <int>
1 Adelie Torgersen 39.1 18.7 181 3750
2 Adelie Torgersen 39.5 17.4 186 3800
3 Adelie Torgersen 40.3 18 195 3250
4 Adelie Torgersen NA NA NA NA
5 Adelie Torgersen 36.7 19.3 193 3450
6 Adelie Torgersen 39.3 20.6 190 3650
# ℹ 3 more variables: sex <fct>, year <int>, year_factor <fct>Photo by James Eades on Unsplash