Data Cleaning Exercises in R: 50 Real Practice Problems
Fifty practice problems on data cleaning in R: handling missing values, duplicates, type coercion, outliers, validation, and end-to-end cleanup pipelines.
RRun this once before any exercise
library(dplyr)
library(tidyr)
library(stringr)
library(lubridate)
library(tibble)
library(readr)
Section 1. Missing values (8 problems)
Exercise 1.1: Total NAs
Difficulty: Beginner.
Show solution
RInteractive R
sum(is.na(airquality))
Exercise 1.2: NAs per column
Difficulty: Intermediate.
Show solution
RInteractive R
airquality |>
summarise(across(everything(), ~ sum(is.na(.x))))
Exercise 1.3: Drop rows with any NA
Difficulty: Beginner.
Show solution
RInteractive R
drop_na(airquality)
Exercise 1.4: Drop rows with NA in target
Difficulty: Intermediate.
Show solution
RInteractive R
drop_na(airquality, Ozone)
Exercise 1.5: Replace NA with 0
Difficulty: Beginner.
Show solution
RInteractive R
airquality |> mutate(Ozone = replace_na(Ozone, 0))
Exercise 1.6: Mean impute
Difficulty: Intermediate.
Show solution
RInteractive R
airquality |>
mutate(Ozone = if_else(is.na(Ozone), mean(Ozone, na.rm = TRUE), Ozone))
Exercise 1.7: Median impute per group
Difficulty: Advanced.
Show solution
RInteractive R
airquality |>
group_by(Month) |>
mutate(Ozone = if_else(is.na(Ozone), median(Ozone, na.rm = TRUE), Ozone)) |>
ungroup()
Exercise 1.8: Forward fill
Difficulty: Intermediate.
Show solution
RInteractive R
tibble(x = c(1, NA, NA, 4, NA, 6)) |> fill(x)
Section 2. Duplicates (6 problems)
Exercise 2.1: Count duplicate rows
Difficulty: Beginner.
Show solution
RInteractive R
sum(duplicated(diamonds))
Exercise 2.2: Drop full duplicates
Difficulty: Beginner.
Show solution
RInteractive R
diamonds |> distinct()
Exercise 2.3: Drop dupes by key
Difficulty: Intermediate.
Show solution
RInteractive R
df <- tibble(email = c("a@x","b@x","a@x"), name = c("A","B","A2"))
df |> distinct(email, .keep_all = TRUE)
Exercise 2.4: Detect duplicate keys
Difficulty: Intermediate.
Show solution
RInteractive R
df <- tibble(email = c("a@x","b@x","a@x","c@x"), n = 1:4)
df |> group_by(email) |> filter(n() > 1)
Exercise 2.5: Dedupe with priority rule
Difficulty: Advanced. Keep most recent per email.
Show solution
RInteractive R
df <- tibble(email = c("a@x","b@x","a@x"),
date = as.Date(c("2024-01-01","2024-02-01","2024-03-01")))
df |> arrange(desc(date)) |> distinct(email, .keep_all = TRUE)
Exercise 2.6: Fuzzy duplicates by normalized key
Difficulty: Advanced.
Show solution
RInteractive R
df <- tibble(name = c(" Alice ","alice","BOB","bob "))
df |> mutate(key = str_to_lower(str_trim(name))) |> distinct(key, .keep_all = TRUE)
Section 3. Type coercion (8 problems)
Exercise 3.1: Character to numeric
Difficulty: Beginner.
Show solution
RInteractive R
as.numeric(c("1.5","2.7","3"))
Exercise 3.2: Strip currency before parsing
Difficulty: Intermediate.
Show solution
RInteractive R
readr::parse_number("$1,234.50")
Exercise 3.3: Logical from yes/no
Difficulty: Intermediate.
Show solution
RInteractive R
v <- c("yes","no","y","n")
v %in% c("yes","y")
Exercise 3.4: Date from string
Difficulty: Intermediate.
Show solution
RInteractive R
as.Date(c("2024-01-15","2024-02-20"))
Exercise 3.5: Mixed-format dates
Difficulty: Advanced.
Show solution
RInteractive R
parse_date_time(c("2024-01-15","01/15/2024"), orders = c("ymd","mdy"))
Exercise 3.6: Factor from character
Difficulty: Beginner.
Show solution
RInteractive R
factor(c("low","high","med"), levels = c("low","med","high"))
Exercise 3.7: Cleanup a column with mixed garbage
Difficulty: Advanced.
Show solution
RInteractive R
v <- c("1","2.5","abc","NA","")
suppressWarnings(as.numeric(v)) # NAs for non-numeric
Exercise 3.8: Coerce all numeric-like in a tibble
Difficulty: Advanced.
Show solution
RInteractive R
df <- tibble(a = c("1","2","3"), b = c("x","y","z"), c = c("1.5","2.5","NA"))
df |> mutate(across(c(a, c), ~ suppressWarnings(as.numeric(.x))))
Section 4. Strings cleanup (8 problems)
Exercise 4.1: Trim whitespace
Difficulty: Beginner.
Show solution
RInteractive R
str_trim(c(" Alice ","Bob "))
Exercise 4.2: Squish multiple spaces
Difficulty: Intermediate.
Show solution
RInteractive R
str_squish(" hello world ")
Exercise 4.3: Standardize case
Difficulty: Beginner.
Show solution
RInteractive R
str_to_lower(c("ALICE","Bob","carol"))
Exercise 4.4: Remove punctuation
Difficulty: Intermediate.
Show solution
RInteractive R
str_replace_all("Hello, world!", "[[:punct:]]", "")
Exercise 4.5: Standardize categorical
Difficulty: Intermediate. Map "USA","us","United States" -> "US".
Show solution
RInteractive R
v <- c("USA","us","United States","Canada")
case_when(v %in% c("USA","us","United States","U.S.A.") ~ "US",
TRUE ~ v)
Exercise 4.6: Remove stopwords (basic)
Difficulty: Advanced.
Show solution
RInteractive R
stop <- c("the","a","is","to","and")
clean <- function(s) {
words <- str_split(s, " ", simplify = TRUE)
paste(words[!words %in% stop], collapse = " ")
}
clean("the cat is on the mat")
Exercise 4.7: Detect non-ASCII
Difficulty: Advanced.
Show solution
RInteractive R
str_detect("café", "[^[:ascii:]]")
Exercise 4.8: Normalize encoding
Difficulty: Advanced.
Show solution
RInteractive R
iconv("café", from = "UTF-8", to = "ASCII//TRANSLIT")
Section 5. Outliers (6 problems)
Exercise 5.1: IQR rule
Difficulty: Intermediate.
Show solution
RInteractive R
mtcars |>
mutate(out = {
q <- quantile(mpg, c(0.25, 0.75))
mpg < q[1] - 1.5*IQR(mpg) | mpg > q[2] + 1.5*IQR(mpg)
})
Exercise 5.2: Z-score rule
Difficulty: Intermediate.
Show solution
RInteractive R
mtcars |> mutate(z = scale(mpg)[,1], out = abs(z) > 3)
Exercise 5.3: Per-group outliers
Difficulty: Advanced.
Show solution
RInteractive R
mtcars |>
group_by(cyl) |>
mutate(z = scale(mpg)[,1], out = abs(z) > 2) |>
ungroup()
Exercise 5.4: Winsorize 5/95
Difficulty: Intermediate.
Show solution
RInteractive R
q <- quantile(mtcars$mpg, c(0.05, 0.95))
mtcars |> mutate(mpg = pmin(pmax(mpg, q[1]), q[2]))
Exercise 5.5: Cap at 99th percentile
Difficulty: Intermediate.
Show solution
RInteractive R
cap <- quantile(diamonds$price, 0.99)
diamonds |> mutate(price = pmin(price, cap))
Exercise 5.6: Drop outliers in target column
Difficulty: Advanced.
Show solution
RInteractive R
mtcars |> filter({
q <- quantile(mpg, c(0.25, 0.75))
mpg >= q[1] - 1.5*IQR(mpg) & mpg <= q[2] + 1.5*IQR(mpg)
})
Section 6. Validation (6 problems)
Exercise 6.1: Range check
Difficulty: Beginner. Age 0-120.
Show solution
RInteractive R
df <- tibble(age = c(25, -5, 130, 40))
df |> mutate(valid_age = age >= 0 & age <= 120)
Exercise 6.2: Email contains "@"
Difficulty: Beginner.
Show solution
RInteractive R
df <- tibble(email = c("a@x.com","not_an_email","b@y.com"))
df |> mutate(valid = str_detect(email, "@"))
Exercise 6.3: Multi-rule validation
Difficulty: Intermediate.
Show solution
RInteractive R
df <- tibble(age = c(25, -5, 30), email = c("a@x","b","c@y"))
df |>
mutate(valid = age >= 0 & age <= 120 & str_detect(email, "@"))
Exercise 6.4: Required-non-NA check
Difficulty: Intermediate.
Show solution
RInteractive R
df <- tibble(id = c(1, 2, NA), name = c("A","B","C"))
df |> mutate(valid = !is.na(id))
Exercise 6.5: Cross-column rule
Difficulty: Advanced. start <= end.
Show solution
RInteractive R
df <- tibble(start = as.Date(c("2024-01-01","2024-03-01")),
end = as.Date(c("2024-02-01","2024-02-15")))
df |> mutate(valid = start <= end)
Exercise 6.6: Schema-style validation report
Difficulty: Advanced.
Show solution
RInteractive R
df <- tibble(age = c(25, -5, 130), email = c("a@x","b","c@y"))
report <- df |>
mutate(invalid_age = age < 0 | age > 120,
invalid_email = !str_detect(email, "@")) |>
filter(invalid_age | invalid_email)
report
Section 7. End-to-end cleaning (8 problems)
Exercise 7.1: Clean phone numbers
Difficulty: Intermediate.
Show solution
RInteractive R
phones <- c("(415) 555-1234","415.555.1234","415 555 1234")
str_replace_all(phones, "\\D", "")
Exercise 7.2: Standardize country names
Difficulty: Intermediate.
Show solution
RInteractive R
v <- c("USA","us","United States","UK","United Kingdom")
case_when(v %in% c("USA","us","United States") ~ "US",
v %in% c("UK","United Kingdom") ~ "GB",
TRUE ~ v)
Exercise 7.3: Parse currency strings
Difficulty: Intermediate.
Show solution
RInteractive R
readr::parse_number(c("$1,234.50","€999.99","£12.34"))
Exercise 7.4: Pivot then clean
Difficulty: Advanced. Wide -> long -> drop NAs.
Show solution
RInteractive R
wide <- tibble(id = 1:2, a = c(1, NA), b = c(2, 3))
wide |> pivot_longer(-id, values_drop_na = TRUE)
Exercise 7.5: Trim and lowercase a key column
Difficulty: Beginner.
Show solution
RInteractive R
df <- tibble(name = c(" Alice ","BOB","carol"))
df |> mutate(name = str_to_lower(str_trim(name)))
Exercise 7.6: Multi-step pipeline
Difficulty: Advanced.
Show solution
RInteractive R
raw <- tibble(name = c(" Alice ","BOB","alice"),
date = c("01/15/2024","02/20/2024","03/05/2024"),
amount = c("$50","$80","$30"))
raw |>
mutate(name = str_to_lower(str_trim(name)),
date = mdy(date),
amount = readr::parse_number(amount)) |>
distinct(name, .keep_all = TRUE)
Exercise 7.7: Validate then split valid/invalid
Difficulty: Advanced.
Show solution
RInteractive R
df <- tibble(age = c(25, -5, 30, 200), email = c("a@x","b","c@y","d@z"))
df <- df |> mutate(valid = age >= 0 & age <= 120 & str_detect(email, "@"))
list(valid = filter(df, valid), invalid = filter(df, !valid))
Exercise 7.8: Reusable cleaning function
Difficulty: Advanced.
Show solution
RInteractive R
clean_text <- function(x) {
x |> str_trim() |> str_squish() |> str_to_lower()
}
clean_text(c(" Alice ","BOB "," carol "))
What to do next
- Data-Wrangling-Exercises (shipped), broader wrangling lifecycle.
- EDA-Exercises (shipped), explore the now-clean data.