readr Exercises in R: 28 Real Data Import Problems
Twenty eight practice problems that drill the readr workflow: read_csv, column type specifications, custom NA handling, locale and encoding tricks, the parse_* helper family, writing back to disk, and diagnosing parse failures. Every problem ships with a hidden runnable solution and a short explanation.
Section 1. Reading the basics with read_csv (5 problems)
Exercise 1.1: Read an inline CSV string with read_csv
Task: A junior analyst onboarding to the data team is testing readr without touching disk. Use read_csv() on the literal string "id,score\n1,87\n2,92\n3,78" (wrap it in I() so readr treats it as data rather than a path) and save the resulting tibble to ex_1_1.
Expected result:
#> # A tibble: 3 x 2
#> id score
#> <dbl> <dbl>
#> 1 1 87
#> 2 2 92
#> 3 3 78Difficulty: Beginner
readr can treat a literal string as if it were a file, so you do not have to write anything to disk first.
Reach for read_csv() and wrap the string in I() so it is read as data rather than a path.
Click to reveal solution
Explanation: read_csv() decides between a path and literal data by inspecting the input. Wrapping the string in I() (the AsIs class) forces the literal interpretation and silences the deprecation warning that earlier readr versions emit for raw strings. This pattern is the cleanest way to write reproducible examples and unit tests without managing temp files.
Exercise 1.2: Read tab-separated data with read_tsv
Task: A reporting analyst exported the daily KPI table from a legacy BI tool and got a tab-separated file. Recreate that scenario by passing the literal "region\tsales\nNorth\t1200\nSouth\t950" (with real tab characters) to read_tsv() via I() and save the parsed tibble to ex_1_2.
Expected result:
#> # A tibble: 2 x 2
#> region sales
#> <chr> <dbl>
#> 1 North 1200
#> 2 South 950Difficulty: Beginner
Tab-separated data needs a reader that splits each line on tabs instead of commas.
Use read_tsv() and pass the literal string through I().
Click to reveal solution
Explanation: read_tsv() is a thin convenience wrapper around read_delim(delim = "\t"). The \t escape in the literal becomes a real tab byte, so readr splits each line on tabs. If your file had spaces inside a field, you would need to quote them; tabs are unambiguous separators and rarely appear inside business strings, which is why analysts prefer TSV for hand-curated data.
Exercise 1.3: Parse a semicolon-delimited extract with read_delim
Task: The finance team's ERP exports use semicolons to avoid breaking on commas inside vendor names. Use read_delim() with delim = ";" to parse "vendor;amount\nAcme, Inc.;1500\nBeta LLC;2300" (wrapped in I()) and save the tibble to ex_1_3.
Expected result:
#> # A tibble: 2 x 2
#> vendor amount
#> <chr> <dbl>
#> 1 Acme, Inc. 1500
#> 2 Beta LLC 2300Difficulty: Intermediate
When the separator is neither a comma nor a tab, you need the general-purpose delimited reader and must state the separator yourself.
Call read_delim() with delim = ";" and wrap the string in I().
Click to reveal solution
Explanation: Picking the right delimiter is the most common readr failure mode in financial pipelines. Semicolons sidestep the case where commas appear inside text fields ("Acme, Inc."). read_delim() is the general form; read_csv(), read_tsv(), and read_csv2() are pre-configured shortcuts. When in doubt, peek at the raw bytes with read_lines(path, n_max = 5) before choosing.
Exercise 1.4: Read a European-style CSV with read_csv2
Task: A climatologist in Berlin exported station readings using comma as the decimal mark and semicolon as the column separator (the Excel German default). Use read_csv2() on "station;temp_c\nA;12,4\nB;15,7" (wrapped in I()) and save the parsed tibble to ex_1_4.
Expected result:
#> # A tibble: 2 x 2
#> station temp_c
#> <chr> <dbl>
#> 1 A 12.4
#> 2 B 15.7Difficulty: Intermediate
Some locales use a comma for the decimal point and a semicolon between columns, and there is a dedicated reader for exactly that combination.
Use read_csv2() (not read_csv) on the I()-wrapped string.
Click to reveal solution
Explanation: read_csv2() ships with a locale that sets decimal_mark = "," and grouping_mark = ".", plus the semicolon separator. Without it, 12,4 would land as the string "12,4" or trigger parse failures. The same result could be achieved with read_delim(delim = ";", locale = locale(decimal_mark = ",")) but the _csv2 shortcut is the idiomatic European import.
Exercise 1.5: Round-trip mtcars through a tempfile
Task: Validate that a write-then-read cycle preserves data by writing mtcars to a temp file with write_csv(), reading it back with read_csv(), and saving the re-read tibble (just the first 3 rows) to ex_1_5. Use tempfile() so nothing pollutes your working directory.
Expected result:
#> # A tibble: 3 x 11
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
#> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
#> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1Difficulty: Beginner
Write the data to a throwaway file, then read it straight back in and keep only the opening rows.
Pair write_csv() with read_csv(), using tempfile() for the path and head(3) for the slice.
Click to reveal solution
Explanation: write_csv() drops row names, which is why the model names ("Mazda RX4") are not present in the round trip. If you need them, call tibble::rownames_to_column() before writing. tempfile() returns a path under the session temp directory that is cleaned up when R exits, making it safe for experiments and CI tests.
Section 2. Column types and the cols() spec (5 problems)
Exercise 2.1: Specify explicit column types with cols
Task: A data engineer wants to lock the schema for a partner extract so a stray text value cannot silently flip a column to character. Read "id,amount\n1,100\n2,250" (wrapped in I()) with read_csv() and a col_types = cols(id = col_integer(), amount = col_double()) spec, saving the result to ex_2_1.
Expected result:
#> # A tibble: 2 x 2
#> id amount
#> <int> <dbl>
#> 1 1 100
#> 2 2 250Difficulty: Intermediate
You can hand the reader an explicit schema up front instead of letting it guess each column.
Pass col_types = cols(id = col_integer(), amount = col_double()) to read_csv().
Click to reveal solution
Explanation: Without an explicit spec, readr guesses types from the first 1000 rows. Guessing is convenient interactively, but brittle in pipelines where a partner might insert "N/A" on row 1001 and break downstream code. Locking types with cols() makes the schema part of your code review surface; a violation becomes a parsing warning you can catch with problems().
Exercise 2.2: Force a numeric-looking ID to character
Task: Customer IDs in the CRM extract are 10-digit numbers, but you must keep leading zeros for join keys to match. Read "cust_id,total\n0012345,500\n0098761,300" (wrapped in I()) and force cust_id to character with col_types = cols(cust_id = col_character()). Save the tibble to ex_2_2.
Expected result:
#> # A tibble: 2 x 2
#> cust_id total
#> <chr> <dbl>
#> 1 0012345 500
#> 2 0098761 300Difficulty: Intermediate
An identifier with leading zeros must stay text, or the zeros are lost the moment it is read as a number.
Set col_types = cols(cust_id = col_character()) in read_csv().
Click to reveal solution
Explanation: This is the single most common readr footgun in production code. Guessing turns 0012345 into the number 12345 and silently breaks every downstream join against a string key. The fix is a one-line col_character() override. Pin the column type any time the field is conceptually an identifier (zip codes, phone numbers, account IDs) even if the source data looks numeric.
Exercise 2.3: Parse a date column with col_date and a format
Task: A retail ops analyst gets daily inventory exports with dates formatted as dd/mm/yyyy. Read "sku,sold_on\nA1,03/04/2026\nA2,12/04/2026" (wrapped in I()) and pass col_types = cols(sold_on = col_date(format = "%d/%m/%Y")) so the column lands as Date. Save to ex_2_3.
Expected result:
#> # A tibble: 2 x 2
#> sku sold_on
#> <chr> <date>
#> 1 A1 2026-04-03
#> 2 A2 2026-04-12Difficulty: Intermediate
A day-first date string will not be recognized unless you describe the exact layout of the text.
Use col_types with col_date(format = "%d/%m/%Y") for the sold_on column.
Click to reveal solution
Explanation: Day-first formats are the rule across most of Europe and the Commonwealth, and readr does not guess them. Always pass an explicit format string when the source is ambiguous: %d/%m/%Y is unambiguous, "03/04/2026" is not. Skipping this step is the single most common cause of off-by-month bugs in cross-region reporting.
Exercise 2.4: Drop unwanted columns with col_skip
Task: A privacy-conscious workflow needs to ingest a customer file but strip the email address at parse time so it never lives in memory. Read "id,name,email,amount\n1,Ann,a@x.com,40\n2,Bob,b@y.com,55" (wrapped in I()) and use col_types = cols(email = col_skip()) to drop that column. Save the tibble to ex_2_4.
Expected result:
#> # A tibble: 2 x 3
#> id name amount
#> <dbl> <chr> <dbl>
#> 1 1 Ann 40
#> 2 2 Bob 55Difficulty: Intermediate
You can drop a sensitive column while the file is being read, so its values never enter memory.
Pass col_types = cols(email = col_skip()) to read_csv().
Click to reveal solution
Explanation: col_skip() is cheaper than reading and then dropping with select(-email) because readr never allocates the column or parses its values. For wide files (think 200-column survey exports where you only care about 8 columns) this can be a 10x speedup. Pair it with cols_only(...) to flip the default from "read all" to "read these only".
Exercise 2.5: Suppress the type message with show_col_types
Task: When automating reports, the column-type message printed by read_csv() pollutes the log output. Read "x,y\n1,2\n3,4" (wrapped in I()) and pass show_col_types = FALSE so the message is suppressed. Save the resulting tibble to ex_2_5.
Expected result:
#> # A tibble: 2 x 2
#> x y
#> <dbl> <dbl>
#> 1 1 2
#> 2 3 4Difficulty: Beginner
The reader prints a column-type summary by default, and that message can be turned off.
Add show_col_types = FALSE to the read_csv() call.
Click to reveal solution
Explanation: The default-on message is helpful when you are exploring, but noisy in batch jobs and Quarto documents. The safer alternative is to set an explicit col_types (which also suppresses the message) so readers can see the intended schema. Use show_col_types = FALSE only when you have already locked types or genuinely do not care.
Section 3. Missing values, comments, and slicing (5 problems)
Exercise 3.1: Treat a custom token as NA
Task: The marketing team's vendor exports use the literal string "N/A" to mean missing instead of an empty field. Read "campaign,clicks\nA,150\nB,N/A\nC,275" (wrapped in I()) with na = "N/A" so the missing value becomes a real NA_real_, and save the tibble to ex_3_1.
Expected result:
#> # A tibble: 3 x 2
#> campaign clicks
#> <chr> <dbl>
#> 1 A 150
#> 2 B NA
#> 3 C 275Difficulty: Intermediate
Tell the reader which literal token in the file is meant to stand in for a missing value.
Pass na = "N/A" to read_csv().
Click to reveal solution
Explanation: Without the na argument, the literal "N/A" would force the entire column to character (since "N/A" cannot be coerced to double). The result is a column where you can no longer compute sums or averages until you find and replace the sentinel. Declaring na = "N/A" up front is faster than a post-hoc mutate(clicks = na_if(clicks, "N/A")) plus a re-parse.
Exercise 3.2: Treat multiple sentinels as NA in one pass
Task: A legacy clinical export uses three different missing markers: empty string, "." (SAS convention), and "NULL" (database convention). Read "patient_id,bp\n1,120\n2,.\n3,\n4,NULL\n5,135" (wrapped in I()) with na = c("", ".", "NULL"), and save the tibble to ex_3_2.
Expected result:
#> # A tibble: 5 x 2
#> patient_id bp
#> <dbl> <dbl>
#> 1 1 120
#> 2 2 NA
#> 3 3 NA
#> 4 4 NA
#> 5 5 135Difficulty: Intermediate
The missing-value setting can recognize several tokens at once, not just a single one.
Pass na = c("", ".", "NULL") to read_csv().
Click to reveal solution
Explanation: The na argument accepts a character vector and applies each entry across every column. If different columns need different missing markers, you must use col_types with per-column col_double(na = ...) instead. Always inspect the raw bytes (read_lines(file, n_max = 20)) before importing data from systems you do not control: every team invents its own sentinel.
Exercise 3.3: Skip metadata rows at the top of the file
Task: Lab instruments often write three header rows (timestamp, instrument ID, operator) before the real CSV header. Read "# generated 2026-04-12\n# instrument: GC-2010\n# operator: K\nsample,reading\nS1,3.4\nS2,4.1" (wrapped in I()) with skip = 3, and save the tibble to ex_3_3.
Expected result:
#> # A tibble: 2 x 2
#> sample reading
#> <chr> <dbl>
#> 1 S1 3.4
#> 2 S2 4.1Difficulty: Intermediate
When a fixed number of metadata lines sit above the real header, you can jump past them before parsing starts.
Pass skip = 3 to read_csv().
Click to reveal solution
Explanation: skip is the right tool when the metadata count is fixed and known. If the count varies per file, you have two options: use comment = "#" to drop any line starting with that character (next exercise), or read with read_lines(), filter the metadata rows yourself, and pass the cleaned vector back to read_csv(). Choose skip for stability, comment for flexibility.
Exercise 3.4: Drop comment lines starting with #
Task: A meteorologist's automated logger interleaves comment lines (starting with #) between data rows whenever the sensor recalibrates. Read "station,reading\nA,12.4\n# recalibration at 09:14\nA,12.6\nB,11.0" (wrapped in I()) with comment = "#" so the recalibration note is dropped, and save the tibble to ex_3_4.
Expected result:
#> # A tibble: 3 x 2
#> station reading
#> <chr> <dbl>
#> 1 A 12.4
#> 2 A 12.6
#> 3 B 11.0Difficulty: Intermediate
Lines that begin with a marker character can be dropped wherever they appear, not only at the top of the file.
Pass comment = "#" to read_csv().
Click to reveal solution
Explanation: comment is line-based, not row-based: any line whose first non-whitespace character matches the comment string is dropped entirely. This is more forgiving than skip because it works anywhere in the file, not just at the top. It will not handle inline trailing comments ("12.4 # noted"); for those you need to read into a character column and strip the trailing portion manually.
Exercise 3.5: Read only the first 5 rows with n_max
Task: Before launching a full import of a 10-million-row file, you want a fast preview to confirm the schema. Build a 20-row CSV with format_csv(head(diamonds, 20)), feed it to read_csv() via I() with n_max = 5, and save the 5-row tibble to ex_3_5.
Expected result:
#> # A tibble: 5 x 10
#> carat cut color clarity depth table price x y z
#> <dbl> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
#> 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
#> 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
#> 4 0.29 Premium I VS2 62.4 58 334 4.2 4.23 2.63
#> 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75Difficulty: Beginner
You can stop the reader after just a handful of rows to get a fast preview of a large file.
Build the CSV with format_csv(head(diamonds, 20)) and pass n_max = 5 to read_csv().
Click to reveal solution
Explanation: n_max is the right lever for a fast preview because readr stops reading after n_max data rows and never allocates memory for the rest of the file. The factor columns of diamonds (cut, color, clarity) come back as character here because readr does not know their level ordering: serializing through CSV always strips factor metadata, which is why parquet or qs is preferred for round-trips of typed data.
Section 4. Parsing helpers for vectors (5 problems)
Exercise 4.1: Strip currency symbols with parse_number
Task: A finance analyst pasted invoice totals from a PDF into a CSV column and ended up with strings like "$1,234.50". Use parse_number() on the vector c("$1,234.50", "$899.00", "$2,500.75") to extract clean doubles, and save the numeric result to ex_4_1.
Expected result:
#> [1] 1234.50 899.00 2500.75Difficulty: Intermediate
A dedicated helper pulls the numeric value out of a noisy string, ignoring currency symbols and grouping marks.
Apply parse_number() to the character vector.
Click to reveal solution
Explanation: parse_number() is the swiss army knife for "this looks numeric but has noise": it strips currency symbols, percent signs, grouping marks, and arbitrary surrounding text. It is safer than a custom gsub() chain because it respects the active locale (next exercise) and reports per-element parse failures through problems(). Reach for it whenever upstream data was hand-edited or copy-pasted.
Exercise 4.2: Parse percentages stored as strings
Task: A campaign export from the ad platform stores click-through rates as strings with trailing percent signs, e.g. c("3.5%", "2.8%", "4.1%"). Use parse_number() to extract the numeric portion (you get a percentage point value, not a fraction), and save the result to ex_4_2.
Expected result:
#> [1] 3.5 2.8 4.1Difficulty: Intermediate
The same numeric-extraction helper strips a trailing percent sign, leaving the value as-is rather than dividing it.
Apply parse_number() to the vector of percent strings.
Click to reveal solution
Explanation: parse_number() strips the trailing % but does NOT divide by 100, so the result is "percentage points" not a fraction. If the downstream math expects a fraction (for example multiplying impressions by CTR), you must divide explicitly: parse_number(x) / 100. The lack of automatic division is intentional: readr refuses to assume what unit you wanted.
Exercise 4.3: Parse dates with a custom format
Task: Quarterly board reports use "Apr 12, 2026" style dates (month-name first, US convention). Use parse_date() on the vector c("Apr 12, 2026", "May 03, 2026", "Jun 21, 2026") with format = "%b %d, %Y", and save the resulting Date vector to ex_4_3.
Expected result:
#> [1] "2026-04-12" "2026-05-03" "2026-06-21"Difficulty: Intermediate
A month-name-first date needs a parser that is told the precise pattern of the string.
Call parse_date() with format = "%b %d, %Y".
Click to reveal solution
Explanation: %b matches abbreviated month names in the locale's language; %B matches full names. The default locale is English, so "Apr" works without further config. For non-English month names (next section), you must pass locale = locale(date_names = "fr") or similar. parse_date() returns NA for any unparseable element and routes the failure through problems() rather than crashing.
Exercise 4.4: Parse datetimes with timezone offsets
Task: Server logs from a global SaaS app include explicit UTC offsets, e.g. "2026-04-12T14:30:00+05:30". Use parse_datetime() on the vector c("2026-04-12T14:30:00+05:30", "2026-04-12T22:00:00-04:00") (the default ISO 8601 format handles offsets) and save the resulting POSIXct vector (printed in UTC) to ex_4_4.
Expected result:
#> [1] "2026-04-12 09:00:00 UTC" "2026-04-13 02:00:00 UTC"Difficulty: Advanced
An ISO timestamp that already carries a UTC offset can be parsed without any extra format argument.
Apply parse_datetime() to the vector and let its default ISO 8601 format handle the offset.
Click to reveal solution
Explanation: ISO 8601 with explicit offset is the only timezone-safe format for distributed logs. parse_datetime() interprets the offset, normalizes every value to UTC internally, and prints in UTC by default. The first value (14:30 IST, which is UTC+5:30) becomes 09:00 UTC; the second (22:00 EDT, UTC-4) becomes 02:00 UTC the next day. Never store local times without an offset in audit logs.
Exercise 4.5: Build an ordered factor with parse_factor
Task: A satisfaction survey collects responses on a Likert scale where the order matters for ordinal modeling. Use parse_factor() on c("medium", "low", "high", "low", "high") with levels = c("low", "medium", "high") so the factor preserves the survey ordering, and save the resulting factor to ex_4_5.
Expected result:
#> [1] medium low high low high
#> Levels: low medium highDifficulty: Intermediate
Turning text into a category while enforcing a known, ordered set of values needs a parser, not plain conversion.
Call parse_factor() with levels = c("low", "medium", "high").
Click to reveal solution
Explanation: parse_factor() differs from factor() in two ways. First, it raises a parse problem (visible via problems()) for any value not in levels, rather than silently turning it into NA. Second, it respects readr's locale machinery, so it composes with the rest of your import. Pass ordered = TRUE if downstream code needs an ordered factor that supports < and > comparisons.
Section 5. Locales and encoding (4 problems)
Exercise 5.1: Read European decimals with a custom locale
Task: A French utility shares meter readings with comma as the decimal mark and no grouping mark. Use parse_double() on the vector c("12,345", "0,789", "3,14") with locale = locale(decimal_mark = ",") so each string becomes the right double, and save the numeric result to ex_5_1.
Expected result:
#> [1] 12.345 0.789 3.140Difficulty: Advanced
When a comma plays the role of the decimal point, the parser must be told about that regional convention.
Call parse_double() with locale = locale(decimal_mark = ",").
Click to reveal solution
Explanation: Note the subtle trap: "12,345" with decimal_mark = "," parses to 12.345 (twelve point three four five), NOT 12345. Locale choice is destiny here. If the source mixes thousands-grouped values like "12.345,67" (twelve thousand three hundred forty five point six seven) you must also set grouping_mark = "." so readr knows the dot is grouping, not decimal.
Exercise 5.2: Strip a non-comma grouping mark from amounts
Task: Swiss financial statements format CHF amounts with apostrophes as grouping marks, e.g. "1'234'567.50". Use parse_number() on the vector c("1'234'567.50", "987'654.00", "12'345.67") with locale = locale(grouping_mark = "'"), and save the result to ex_5_2.
Expected result:
#> [1] 1234567.50 987654.00 12345.67Difficulty: Advanced
An unusual thousands separator must be declared so the parser knows which character to strip.
Call parse_number() with locale = locale(grouping_mark = "'").
Click to reveal solution
Explanation: Switzerland is one of a handful of locales that use the apostrophe as a thousands grouping mark; you cannot rely on readr's defaults. Once the locale is set, parse_number() strips the apostrophes and parses the remainder as US-style decimals. If the file used commas for decimals as well, you would also pass decimal_mark = "," (a Swiss French convention) instead of letting the dot win.
Exercise 5.3: Parse French month-name dates with a locale
Task: A travel-tech startup imports booking confirmations in French where dates look like "12 avril 2026". Use parse_date() on the vector c("12 avril 2026", "03 mai 2026", "21 juin 2026") with format = "%d %B %Y" and locale = locale(date_names = "fr"), saving the resulting Date vector to ex_5_3.
Expected result:
#> [1] "2026-04-12" "2026-05-03" "2026-06-21"Difficulty: Advanced
Month names written in another language require the parser to switch its name table to that language.
Call parse_date() with format = "%d %B %Y" and locale = locale(date_names = "fr").
Click to reveal solution
Explanation: locale(date_names = "fr") rewires the month-name table from English to French, so %B now matches "avril", "mai", "juin". The full list of supported language codes is in date_names_lang("en") (and friends). Without the locale tweak, you would get a vector of NA values plus a problems() entry on every row. For mixed-language sources, parse each chunk separately and combine.
Exercise 5.4: Decode a latin1 source to UTF-8 with locale
Task: A regional reseller in Lisbon sends product catalogs encoded as latin1 (a common Windows export). Build the raw bytes with iconv("Café", to = "latin1", toRaw = TRUE)[[1]], write them to a tempfile in binary mode, then call read_csv(tf, locale = locale(encoding = "latin1")) to read it back correctly. Save the parsed tibble to ex_5_4.
Expected result:
#> # A tibble: 1 x 1
#> name
#> <chr>
#> 1 Cafe (accented e shown as e with acute)Difficulty: Advanced
Bytes that are not UTF-8 must be read with the source encoding declared, or the accented characters come back garbled.
Call read_csv() with locale = locale(encoding = "latin1").
Click to reveal solution
Explanation: When the source bytes are not UTF-8, readr's default decoder produces mojibake (a literal "C", "a", "f", followed by 0xe9 rendered as a replacement glyph). Setting locale(encoding = "latin1") tells readr to transcode each byte from latin1 to UTF-8 on the way in, so the result is a clean R character string. Always inspect with read_lines_raw() first if you suspect an encoding problem.
Section 6. Writing data and diagnosing problems (4 problems)
Exercise 6.1: Write a CSV with a custom NA marker
Task: An audit team needs CSV deliverables where missing numeric values are written as the literal "NA_VAL" (their toolchain rejects empty fields). Build a small tibble with tibble(id = 1:3, amount = c(100, NA, 250)), write it to a tempfile with write_csv(... , na = "NA_VAL"), then read the file back as plain text with read_lines() and save the character vector of lines to ex_6_1.
Expected result:
#> [1] "id,amount" "1,100" "2,NA_VAL" "3,250"Difficulty: Intermediate
You can choose the literal text used for missing values when writing, then inspect the file as plain lines to confirm it.
Write with write_csv(..., na = "NA_VAL") and read it back with read_lines().
Click to reveal solution
Explanation: The default na = "" writes empty fields, which round-trips through readr cleanly but breaks some downstream consumers (early SAS pipelines, certain Excel imports). Always negotiate the NA sentinel with the receiving team before delivering files. Reading back with read_lines() instead of read_csv() is the right verification step because it shows you the exact bytes on disk, sentinels and all.
Exercise 6.2: Pick write_excel_csv when the consumer is Excel
Task: A non-technical stakeholder will open your file in Excel and complain about accented characters showing up as garbage. Build tibble(city = c("Sao Paulo", "Munchen")), write it once with write_csv() to a tempfile, again with write_excel_csv() to a second tempfile, and save a length-2 character vector with the first byte of each file (as hex) to ex_6_2 so you can prove the BOM is only in the second one.
Expected result:
#> [1] "73" "efbbbf"Difficulty: Intermediate
One writer prepends an encoding marker so spreadsheets render accents correctly; the other writes none.
Write the tibble once with write_csv() and once with write_excel_csv(), then compare the first bytes with readBin().
Click to reveal solution
Explanation: write_excel_csv() prepends the UTF-8 byte-order mark (ef bb bf) so Excel auto-detects the encoding and renders accented characters correctly. Plain write_csv() produces strict UTF-8 without the BOM, which is the right answer for every consumer EXCEPT Excel on Windows. If your stakeholder will open the file in Excel, default to write_excel_csv(); otherwise stay with write_csv() for cleaner downstream parsing.
Exercise 6.3: Capture parse failures with problems
Task: A risk analyst is running a daily import and needs to flag rows that failed to parse so the data steward can fix the source. Read "id,value\n1,3.14\n2,not_a_number\n3,2.71" (wrapped in I()) with col_types = cols(value = col_double()), then call problems() on the result, and save the diagnostics tibble to ex_6_3.
Expected result:
#> # A tibble: 1 x 5
#> row col expected actual file
#> <int> <int> <chr> <chr> <chr>
#> 1 2 2 a double not_a_number ""Difficulty: Advanced
Rows that fail to parse are recorded as diagnostics attached to the result, ready to be pulled out.
Read with col_types = cols(value = col_double()), then call problems() on the returned tibble.
Click to reveal solution
Explanation: Parse problems are attached as an attribute to the returned tibble; problems() extracts them for inspection. The offending row is replaced with NA in the data so downstream code keeps running. In production pipelines, log nrow(problems(res)) as a metric and alert when it exceeds your threshold. That single check catches 90 percent of upstream schema drift before it becomes a wrong dashboard.
Exercise 6.4: Stream a large file with read_csv_chunked
Task: A platform engineer needs to compute a running sum over a million-row export without loading the full file into memory. Simulate it with format_csv(tibble(x = 1:100)) fed via I() to read_csv_chunked() using a SideEffectChunkCallback that appends each chunk sum to an accumulator, and save the final per-chunk sums vector to ex_6_4.
Expected result:
#> [1] 1275 3775 6275 8775 (with chunk_size = 25, four chunks)Difficulty: Advanced
A streaming reader processes the file in batches, running your own code on each batch instead of loading it all at once.
Use read_csv_chunked() with a SideEffectChunkCallback and chunk_size = 25.
Click to reveal solution
Explanation: read_csv_chunked() is the streaming sibling of read_csv(): it parses the file in chunks of chunk_size rows and applies your callback to each one. The SideEffectChunkCallback is for callbacks that mutate external state (here the sums vector via <<-); DataFrameCallback is for callbacks that return tibbles and want them auto-row-bound. Use chunked reads when memory is tight or when you want to start computing before the file finishes downloading.
What to do next
You now have repetitions across the full readr surface: parsing, locales, encoding, writing, and diagnostics. Extend the practice with these neighbouring drills:
- data.table Exercises in R for the higher-performance import path with
fread(). - Data Cleaning Exercises in R for the post-import wrangling pipeline.
- lubridate Exercises in R to deepen date and datetime handling after the readr parse step.
- dplyr Exercises in R for the tidy verbs you will reach for once the file is in memory.
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