readr Exercises in R: 30 Real Practice Problems

Thirty practice problems on readr: reading and writing CSVs, custom NAs, column types, locales, parsing helpers. Hidden solutions, runnable code.

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

RRun this once before any exercise

library(readr) library(dplyr) library(tibble) library(purrr)

Section 1. Reading basics (8 problems)

Exercise 1.1: read_csv

Difficulty: Beginner. Write and re-read a small CSV.

Show solution

RInteractive R

write_csv(mtcars, "demo.csv") df <- read_csv("demo.csv") head(df)

Exercise 1.2: read_tsv

Difficulty: Beginner.

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RInteractive R

write_tsv(mtcars, "demo.tsv") read_tsv("demo.tsv")

Exercise 1.3: Read with custom NA strings

Difficulty: Intermediate.

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RInteractive R

read_csv("demo.csv", na = c("","NA","N/A","missing"))

Exercise 1.4: Skip lines

Difficulty: Intermediate. Skip first 2 rows.

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RInteractive R

read_csv("demo.csv", skip = 2)

Exercise 1.5: First N rows only

Difficulty: Beginner. Read 5 rows.

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RInteractive R

read_csv("demo.csv", n_max = 5)

Exercise 1.6: From a string

Difficulty: Intermediate.

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RInteractive R

read_csv("a,b\n1,2\n3,4")

Exercise 1.7: Read without column names

Difficulty: Intermediate.

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RInteractive R

read_csv("demo.csv", col_names = FALSE)

Exercise 1.8: Custom column names

Difficulty: Intermediate.

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RInteractive R

read_csv("demo.csv", col_names = c("v1","v2","v3"), skip = 1)

Section 2. Column types (6 problems)

Exercise 2.1: Force a column to character

Difficulty: Intermediate.

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RInteractive R

read_csv("demo.csv", col_types = cols(mpg = col_character()))

Exercise 2.2: Compact spec string

Difficulty: Intermediate. cidD = char/int/double.

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RInteractive R

read_csv("a,b,c\nx,1,2.5", col_types = "cid")

Exercise 2.3: Skip a column

Difficulty: Intermediate.

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RInteractive R

read_csv("demo.csv", col_types = cols(disp = col_skip()))

Exercise 2.4: Date column

Difficulty: Intermediate. Parse a date column.

Show solution

RInteractive R

csv <- "id,date\n1,2024-01-15" read_csv(csv, col_types = cols(date = col_date()))

Exercise 2.5: Custom date format

Difficulty: Advanced.

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RInteractive R

csv <- "id,date\n1,15/01/2024" read_csv(csv, col_types = cols(date = col_date(format = "%d/%m/%Y")))

Exercise 2.6: Spec problems

Difficulty: Advanced. Inspect parsing problems.

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RInteractive R

df <- read_csv("a\nfoo\n3", col_types = cols(a = col_integer())) problems(df)

Section 3. Parsers (8 problems)

Exercise 3.1: parse_number from currency

Difficulty: Intermediate.

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RInteractive R

parse_number("$1,234.50")

Exercise 3.2: parse_double

Difficulty: Beginner.

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RInteractive R

parse_double(c("1.5","2.7","NA"))

Exercise 3.3: parse_logical

Difficulty: Intermediate.

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RInteractive R

parse_logical(c("TRUE","FALSE","T","F","NA"))

Exercise 3.4: parse_date

Difficulty: Intermediate.

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RInteractive R

parse_date("01/15/2024", format = "%m/%d/%Y")

Exercise 3.5: parse_datetime

Difficulty: Intermediate.

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RInteractive R

parse_datetime("2024-01-15T14:30:00")

Exercise 3.6: parse_number with custom locale

Difficulty: Advanced. Parse "1.234,50" (European).

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RInteractive R

parse_number("1.234,50", locale = locale(decimal_mark = ",", grouping_mark = "."))

Exercise 3.7: parse_factor

Difficulty: Intermediate.

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RInteractive R

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

Exercise 3.8: parse_guess

Difficulty: Advanced.

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RInteractive R

parse_guess(c("1.5","2","TRUE","2024-01-15"))

Section 4. Writing (4 problems)

Exercise 4.1: write_csv

Difficulty: Beginner.

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RInteractive R

write_csv(mtcars, "out.csv")

Exercise 4.2: write_excel_csv

Difficulty: Intermediate. Excel-friendly UTF-8 BOM.

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RInteractive R

write_excel_csv(mtcars, "out_excel.csv")

Exercise 4.3: write_rds for round-trip

Difficulty: Intermediate.

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RInteractive R

write_rds(mtcars, "out.rds") read_rds("out.rds")

Exercise 4.4: Append rows

Difficulty: Advanced.

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RInteractive R

write_csv(mtcars[1:5,], "incr.csv") write_csv(mtcars[6:10,], "incr.csv", append = TRUE)

Section 5. Real-world workflows (4 problems)

Exercise 5.1: Read a messy file

Difficulty: Advanced. Skip lines, custom NA, custom delimiter.

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RInteractive R

read_delim("comment_line\na;b\n1;2\n3;NA", skip = 1, delim = ";", na = "NA")

Exercise 5.2: Read multiple files

Difficulty: Advanced. Use map_dfr.

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RInteractive R

files <- c("demo.csv","demo.csv") # demo df <- purrr::map_dfr(files, read_csv, .id = "source") df

Exercise 5.3: Read large file in chunks

Difficulty: Advanced.

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RInteractive R

read_csv_chunked("demo.csv", callback = DataFrameCallback$new(function(x, pos) head(x, 2)), chunk_size = 5)

Exercise 5.4: Read with progress

Difficulty: Intermediate.

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RInteractive R

read_csv("demo.csv", progress = TRUE)

What to do next

Data-Wrangling-Exercises (shipped), clean data after import.
EDA-Exercises (shipped), explore the imported data.