tidyr Data Wrangling Cheat Sheet - Reshape, Tidy & Handle Missing Data

Oct 14, 2026

Cheat sheet for tidyr focused on tidying tabular data into a consistent structure: variables in separate columns and observations in separate rows. Covers tibbles (enhanced data frames) with clearer subsetting behavior, plus core reshaping tools including pivot_longer() and pivot_wider(). Includes expanding combinations, constructing tibbles, and cleaning workflows such as unite(), separate(), drop_na(), fill(), and replace_na(), with examples and tibble display options.

LanguageEnglish

CategoryPresentations

FormatPDF

Pages2

Reading Time1 min

AccessFree to download

Tidy Data & Tibbles

Reshaping Data (Pivot)

Split, Expand, and Construct Tibbles

Handling Missing Values (NA)

Nested Data Frames

Data tidying with tidyr : : CHEAT SHEET Tidy data is a way to organize tabular data in a consistent data structure across packages. A table is tidy if: Each variable is in its own column & Each observation, or case, is in its own row | | A | | | A | | * B
| | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | | | B | C | | | | | | C | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Access variables | | | | | Preserve cases in | | | | | | | as vectors | | | | | vectorized operations | | | | | | Tibbles AN ENHANCED DATA FRAME Tibbles are a table format provided by the tibble package. They inherit the data frame class, but have improved behaviors: • Subset a new tibble with ], a vector with [[ and $. • No partial matching when subsetting columns. • Display concise views of the data on one screen. Reshape Data -Pivot data to reorganize values into a new layout. table4a | country | 1999 | 2000 | | --- | --- | --- | | A | 0.7K | 2K | | B | 37K | 80K | | C | 212K | 213K | | country | year | cases | | --- | --- | --- | | A | 1999 | 0.7K | | B | 1999 | 37K | | C | 1999 | 212K | | A | 2000 | 2K | | B | 2000 | 80K | | C | 2000 | 213K | pivot_longer(data, cols, names_to = "name", values_to = "value", values_drop_na = FALSE)"Lengthen" data by collapsing several columns into two. Column names move to a new names_to column and values to a new values_to column. pivot_longer(table4a, cols = 2:3, names_to ="year", values_to = "cases") | country | year | cases | pop | | --- | --- | --- | --- | | A | 1999 | 0.7K | 19M | | A | 2000 | 2K | 20M | | B | 1999 | 37K | 172M | | B | 2000 | 80K | 174M | | C | 1999 | 212K | 1T | | C | 2000 | 213K | 1T | pivot_wider(data, names_from = "name", values_from = "value") The inverse of pivot_longer(). "Widen" data by expanding two columns into several. One column provides the new column names, the other the values. pivot_wider(table2, names_from = type, values_from = count) Split Cells -Use these functions to split or combine cells into individual, isolated values. Expand Tables Create new combinations of variables or identify implicit missing values (combinations of variables not present in the data) . x expand(data,…) Create anew tibble with all possible combinations ofthe values of the variables listed in… Drop other variables. expand(mtcars, cyl, gear, carb) complete(data,…, fill = list()) Add missing possible combinations of values of variables listed in…Fill remaining variables with NA. complete(mtcars, cyl, gear, carb) Handle Missing Values options(tibble.print_max = n, tibble.print_min = m, tibble.width = Inf) Control default display settings. View() or glimpse() View the entire data set. CONSTRUCT A TIBBLE | A tibble: 3 × 2 | | | --- | --- | | x
1 1 2 2
3 3 | y
a b
c | Both make this tibble tibble(…) Construct by columns. tibble(x = 1:3, y = c("a", "b", "c")) tribble(…) Construct by rows. tribble(~x, ~y, 1, "a", 2, "b", 3, "c") as_tibble(x,…) Convert a data frame to a tibble. enframe(x, name = "name", value = "value") Convert a named vector to a tibble. Also deframe() . is_tibble(x) Test whether x is a tibble. table5 | country | year | cases | pop | | --- | --- | --- | --- | | A | 1999 | 0.7K | 19M | | A | 2000 | 2K | 20M | | B | 1999 | 37K | 172 | | B | 2000 | 80K | 174 | unite(data, col,…, sep = "_ ", remove = TRUE, na. rm = FALSE) Collapse cells across several columns into a single column. unite(table5, century, year, col = "year", sep = "") separate(data, col, into, sep = "[^[:alnum:]]+", remove = TRUE, convert = FALSE, extra = "warn", fill = "warn",…) Separate each cell in a column into several columns. Also extract() . separate(table3, rate, sep = "/", into = c("cases", "pop")) separate_rows(data,…, sep = "[^[:alnum:].]+", convert = FAL

FAQs

What is tidy data in tidyr terms?

A table is tidy when each variable is in its own column and each observation (case) is in its own row.

How do pivot_longer() and pivot_wider() relate?

pivot_longer() “lengthens” data by collapsing multiple columns into two columns (names_to and values_to). pivot_wider() is the inverse, “widening” data by expanding two columns into multiple columns.

Which functions help manage missing values (NA)?

Use drop_na() to remove rows containing NA, fill() to propagate non-missing values into NA cells, and replace_na() to substitute a specified value for NA in selected columns.

Oct 14, 2026

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