forcats Exercises in R: 40 Real Practice Problems

Forty practice problems on forcats: ordering, reordering, recoding, lumping, releveling, dropping unused levels. Hidden solutions, runnable code.

By Selva Prabhakaran · Published May 11, 2026 · Last updated May 11, 2026

RRun this once before any exercise

library(forcats) library(dplyr) library(ggplot2) library(tibble)

Section 1. Creating and inspecting (6 problems)

Exercise 1.1: Create a factor

Difficulty: Beginner.

Show solution

RInteractive R

factor(c("low","high","med","low"), levels = c("low","med","high"))

Exercise 1.2: Inspect levels

Difficulty: Beginner.

Show solution

RInteractive R

levels(iris$Species)

Exercise 1.3: Count by level

Difficulty: Beginner.

Show solution

RInteractive R

fct_count(iris$Species)

Exercise 1.4: Unique levels (factor)

Difficulty: Beginner.

Show solution

RInteractive R

fct_unique(iris$Species)

Exercise 1.5: Identify unused levels

Difficulty: Intermediate.

Show solution

RInteractive R

f <- factor(c("a","b"), levels = c("a","b","c")) levels(f)[!(levels(f) %in% f)]

Exercise 1.6: Check factor type

Difficulty: Beginner.

Show solution

RInteractive R

is.factor(iris$Species)

Section 2. Reordering (10 problems)

Exercise 2.1: fct_relevel, explicit order

Difficulty: Beginner.

Show solution

RInteractive R

fct_relevel(factor(c("a","b","c","a")), "c","a","b")

Exercise 2.2: fct_relevel, move to first

Difficulty: Intermediate. Move "virginica" first.

Show solution

RInteractive R

fct_relevel(iris$Species, "virginica") |> levels()

Exercise 2.3: fct_relevel, move to last

Difficulty: Intermediate.

Show solution

RInteractive R

fct_relevel(iris$Species, "setosa", after = Inf) |> levels()

Exercise 2.4: fct_infreq, by frequency

Difficulty: Intermediate. Reorder diamonds$cut by descending frequency.

Show solution

RInteractive R

fct_infreq(diamonds$cut) |> levels()

Exercise 2.5: fct_inorder, by appearance

Difficulty: Intermediate.

Show solution

RInteractive R

fct_inorder(c("a","c","b","a")) |> levels()

Exercise 2.6: fct_reorder, by another variable

Difficulty: Intermediate. Reorder by mean.

Show solution

RInteractive R

ggplot(diamonds, aes(fct_reorder(cut, price), price)) + geom_boxplot()

Exercise 2.7: fct_reorder2 for line plots

Difficulty: Advanced. Reorder by last value of y.

Show solution

RInteractive R

df <- tibble(time = rep(1:3, 2), grp = rep(c("A","B"), each = 3), val = c(1,2,5, 3,4,2)) ggplot(df, aes(time, val, color = fct_reorder2(grp, time, val))) + geom_line()

Exercise 2.8: fct_rev

Difficulty: Beginner. Reverse level order.

Show solution

RInteractive R

fct_rev(iris$Species) |> levels()

Exercise 2.9: fct_shift

Difficulty: Intermediate. Shift levels cyclically by 1.

Show solution

RInteractive R

fct_shift(factor(c("a","b","c","d"))) |> levels()

Exercise 2.10: fct_shuffle

Difficulty: Intermediate. Random level order.

Show solution

RInteractive R

set.seed(1) fct_shuffle(iris$Species) |> levels()

Section 3. Recoding and grouping (8 problems)

Exercise 3.1: fct_recode

Difficulty: Intermediate. Rename "setosa" -> "S".

Show solution

RInteractive R

fct_recode(iris$Species, S = "setosa", V = "versicolor", VG = "virginica") |> levels()

Exercise 3.2: fct_collapse, multi to one

Difficulty: Intermediate. Lump levels.

Show solution

RInteractive R

fct_collapse(iris$Species, big = c("versicolor","virginica")) |> levels()

Exercise 3.3: fct_lump_n, keep top N

Difficulty: Intermediate. Keep top 3 cut categories, lump rest into "Other".

Show solution

RInteractive R

fct_lump_n(diamonds$cut, n = 3) |> levels()

Exercise 3.4: fct_lump_prop, by proportion

Difficulty: Advanced.

Show solution

RInteractive R

fct_lump_prop(diamonds$cut, prop = 0.1) |> levels()

Exercise 3.5: fct_lump_min, by min count

Difficulty: Intermediate.

Show solution

RInteractive R

fct_lump_min(diamonds$clarity, min = 1000) |> levels()

Exercise 3.6: fct_other, keep specific

Difficulty: Intermediate. Keep "Ideal", others lump.

Show solution

RInteractive R

fct_other(diamonds$cut, keep = "Ideal") |> levels()

Exercise 3.7: fct_drop, remove unused

Difficulty: Intermediate.

Show solution

RInteractive R

f <- factor(c("a","b"), levels = c("a","b","c")) fct_drop(f) |> levels()

Exercise 3.8: fct_expand, add new levels

Difficulty: Advanced.

Show solution

RInteractive R

fct_expand(iris$Species, "newone") |> levels()

Section 4. Conversion and applying (6 problems)

Exercise 4.1: as_factor (preserves order of appearance)

Difficulty: Intermediate.

Show solution

RInteractive R

as_factor(c("c","a","b","a")) |> levels()

Exercise 4.2: factor() vs as.factor() vs as_factor()

Difficulty: Advanced. Compare ordering.

Show solution

RInteractive R

v <- c("c","a","b") list(base = factor(v), as_factor = as_factor(v))

Exercise 4.3: To character

Difficulty: Beginner.

Show solution

RInteractive R

as.character(iris$Species) |> head()

Exercise 4.4: Numeric from factor

Difficulty: Advanced. Pitfall: as.numeric returns level codes.

Show solution

RInteractive R

f <- factor(c("10","20","30")) as.numeric(as.character(f)) # right way as.numeric(f) # WRONG: returns 1, 2, 3

Exercise 4.5: Reorder by aggregate function

Difficulty: Advanced.

Show solution

RInteractive R

diamonds |> mutate(cut2 = fct_reorder(cut, price, .fun = median)) |> count(cut2)

Exercise 4.6: Apply ordering to a ggplot

Difficulty: Intermediate.

Show solution

RInteractive R

ggplot(diamonds, aes(fct_infreq(cut))) + geom_bar()

Section 5. Real workflows (10 problems)

Exercise 5.1: Bar chart ordered by frequency

Difficulty: Intermediate.

Show solution

RInteractive R

ggplot(diamonds, aes(fct_infreq(cut))) + geom_bar() + labs(x = "cut")

Exercise 5.2: Bar chart with rare lumped

Difficulty: Advanced.

Show solution

RInteractive R

diamonds |> mutate(cut_l = fct_lump_n(cut, 3)) |> ggplot(aes(cut_l)) + geom_bar()

Exercise 5.3: Boxplot ordered by median

Difficulty: Advanced.

Show solution

RInteractive R

ggplot(diamonds, aes(fct_reorder(cut, price, median), price)) + geom_boxplot()

Exercise 5.4: Combine recode + lump

Difficulty: Advanced.

Show solution

RInteractive R

diamonds |> mutate(cut2 = fct_recode(cut, Best = "Ideal", Worst = "Fair") |> fct_lump_n(3)) |> count(cut2)

Exercise 5.5: Drop unused after filter

Difficulty: Intermediate.

Show solution

RInteractive R

df <- iris |> filter(Species != "setosa") |> mutate(Species = fct_drop(Species)) levels(df$Species)

Exercise 5.6: Reorder by sum with .fun

Difficulty: Advanced.

Show solution

RInteractive R

diamonds |> mutate(cut2 = fct_reorder(cut, price, .fun = sum)) |> count(cut2)

Exercise 5.7: Manual interleave of two factors

Difficulty: Advanced.

Show solution

RInteractive R

fct_cross(factor(c("a","b")), factor(c("1","2","3"))) |> levels()

Exercise 5.8: Map to ordinal

Difficulty: Intermediate.

Show solution

RInteractive R

factor(c("low","high","med"), levels = c("low","med","high"), ordered = TRUE) |> levels()

Exercise 5.9: Visual reorder for ggplot dotplot

Difficulty: Advanced.

Show solution

RInteractive R

mtcars |> tibble::rownames_to_column("car") |> mutate(car = fct_reorder(car, mpg)) |> ggplot(aes(mpg, car)) + geom_point()

Exercise 5.10: Stable lump with explicit "Other" position

Difficulty: Advanced.

Show solution

RInteractive R

diamonds |> mutate(cut2 = fct_lump_n(cut, 3) |> fct_relevel("Other", after = Inf)) |> count(cut2)

What to do next

dplyr-Exercises (shipped), factors in larger wrangling pipelines.
ggplot2-Exercises (shipped), factor ordering and viz.