Data tidying with tidyr :: Cheatsheet

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Tidy data is a way to organize tabular data in a consistent data structure across packages. A table is tidy if:

library(tidyr)
library(tibble)

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.

  • 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

  • tibble(...): Construct by columns.

    tibble(
  x = 1:3, 
  y = c("a", "b", "c")
)
    # A tibble: 3 × 2
      x y    
  <int> <chr>
1     1 a    
2     2 b    
3     3 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.

Reshape Data

Pivot data to reorganize values into a new layout.

  • pivot_longer(data, cols, name_to = "name", values_to = "value", values_drop_na = FALSE): “Lengthen” data by collapsing several columns into two.

    • The initial table4a looks like the following:
    table4a
    # A tibble: 3 × 3
  country     `1999` `2000`
  <chr>        <dbl>  <dbl>
1 Afghanistan    745   2666
2 Brazil       37737  80488
3 China       212258 213766
    • Column names move to a new names_to column and values to a new values_to column. The output of pivot_longer() will look like the following:
    pivot_longer(table4a, cols = 2:3, names_to = "year", values_to = "cases")
    # A tibble: 6 × 3
  country     year   cases
  <chr>       <chr>  <dbl>
1 Afghanistan 1999     745
2 Afghanistan 2000    2666
3 Brazil      1999   37737
4 Brazil      2000   80488
5 China       1999  212258
6 China       2000  213766

  • pivot_wider(data, name_from = "name", values_from = "value"): The inverse of pivot_longer(). “Widen” data by expanding two columns into several.

    • The initial table2 looks like the following:
    table2
    # A tibble: 12 × 4
   country      year type            count
   <chr>       <dbl> <chr>           <dbl>
 1 Afghanistan  1999 cases             745
 2 Afghanistan  1999 population   19987071
 3 Afghanistan  2000 cases            2666
 4 Afghanistan  2000 population   20595360
 5 Brazil       1999 cases           37737
 6 Brazil       1999 population  172006362
 7 Brazil       2000 cases           80488
 8 Brazil       2000 population  174504898
 9 China        1999 cases          212258
10 China        1999 population 1272915272
11 China        2000 cases          213766
12 China        2000 population 1280428583
    • One column provides the new column names, the other the values. The output of pivot_wider() will look like the following:
    pivot_wider(table2, names_from = type, values_from = count)
    # A tibble: 6 × 4
  country      year  cases population
  <chr>       <dbl>  <dbl>      <dbl>
1 Afghanistan  1999    745   19987071
2 Afghanistan  2000   2666   20595360
3 Brazil       1999  37737  172006362
4 Brazil       2000  80488  174504898
5 China        1999 212258 1272915272
6 China        2000 213766 1280428583

Split Cells

Use these functions to split or combine cells into individual, isolated values.

  • unite(data, col, ..., sep = "_", remove = TRUE, na.rm = FALSE): Collapse cells across several columns into a single column.

    • The initial table5 looks like the following:
    table5
    # A tibble: 6 × 4
  country     century year  rate             
  <chr>       <chr>   <chr> <chr>            
1 Afghanistan 19      99    745/19987071     
2 Afghanistan 20      00    2666/20595360    
3 Brazil      19      99    37737/172006362  
4 Brazil      20      00    80488/174504898  
5 China       19      99    212258/1272915272
6 China       20      00    213766/1280428583
    • The output of unite() will look like the following:
    unite(table5, century, year, col = "year", sep = "")
    # A tibble: 6 × 3
  country     year  rate             
  <chr>       <chr> <chr>            
1 Afghanistan 1999  745/19987071     
2 Afghanistan 2000  2666/20595360    
3 Brazil      1999  37737/172006362  
4 Brazil      2000  80488/174504898  
5 China       1999  212258/1272915272
6 China       2000  213766/1280428583

  • separate_wider_delim(data, cols, delim, ..., names = NULL, names_sep = NULL, names_repair = "check unique", too_few, too_many, cols_remove = TRUE): Separate each cell in a column into several columns. Also extract().

    • The initial table3 looks like the following:
    table3
    # A tibble: 6 × 3
  country      year rate             
  <chr>       <dbl> <chr>            
1 Afghanistan  1999 745/19987071     
2 Afghanistan  2000 2666/20595360    
3 Brazil       1999 37737/172006362  
4 Brazil       2000 80488/174504898  
5 China        1999 212258/1272915272
6 China        2000 213766/1280428583
    • The output of separate_wider_delim() will look like the following:
    separate_wider_delim(table3, rate, delim = "/", names = c("cases", "pop"))
    # A tibble: 6 × 4
  country      year cases  pop       
  <chr>       <dbl> <chr>  <chr>     
1 Afghanistan  1999 745    19987071  
2 Afghanistan  2000 2666   20595360  
3 Brazil       1999 37737  172006362 
4 Brazil       2000 80488  174504898 
5 China        1999 212258 1272915272
6 China        2000 213766 1280428583

  • separate_longer_delim(data, cols, delim, .., width, keep_empty): Separate each cell in a column into several rows.

    • The initial table3 looks like the following:
    table3
    # A tibble: 6 × 3
  country      year rate             
  <chr>       <dbl> <chr>            
1 Afghanistan  1999 745/19987071     
2 Afghanistan  2000 2666/20595360    
3 Brazil       1999 37737/172006362  
4 Brazil       2000 80488/174504898  
5 China        1999 212258/1272915272
6 China        2000 213766/1280428583
    • The output of separate_longer_delim() will look like the following:
    separate_longer_delim(table3, rate, delim = "/")
    # A tibble: 12 × 3
   country      year rate      
   <chr>       <dbl> <chr>     
 1 Afghanistan  1999 745       
 2 Afghanistan  1999 19987071  
 3 Afghanistan  2000 2666      
 4 Afghanistan  2000 20595360  
 5 Brazil       1999 37737     
 6 Brazil       1999 172006362 
 7 Brazil       2000 80488     
 8 Brazil       2000 174504898 
 9 China        1999 212258    
10 China        1999 1272915272
11 China        2000 213766    
12 China        2000 1280428583

Expand Tables

Create new combinations of variables or identify implicit missing values (combinations of variables not present in the data).

  • expand(data, ...): Create a new tibble with all possible combinations of the 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

Drop or replace explicit missing values (NA).

x <- tribble(
  ~x1, ~x2,
  "A",   1,
  "B",  NA,
  "C",  NA,
  "D",   3,
  "E",  NA
)

  • drop_na(data, ...): Drop rows containing NAs in … columns.

    drop_na(x, x2)

  • fill(data, ..., .direction = "down"): Fill in NAs in … columns using the next or previous value.

    fill(x, x2)

  • replace_na(data, replace): Specify a value to replace NA in selected columns.

    replace_na(x, list(x2 = 2))

Nested Data

A nested data frame stores individual tables as a list-column of data frames within a larger organizing data frame. List-columns can also be lists of vectors or lists of varying data types. Use a nested data frame to:

  • Preserve relationships between observations and subsets of data. Preserve the type of the variables being nested (factors and datetimes aren’t coerced to character).
  • Manipulate many sub-tables are once with purrr functions like map(), map2(), or pmap() or with dplyr rowwise() grouping.

Create Nested Data

  • nest(data, ...): Moves groups of cells into a list-column of a data frame. Use alone or with dplyr::group_by().

  1. Group the data frame with group_by() and use nest() to move the groups into a list-column.

    n_storms <- storms |>
  group_by(name) |>
  nest()

  2. Use nest(new_col = c(x,y)) to specify the columns to group using dplyr::select() syntax.

    n_storms <- storms |>
  nest(data = c(year:long))

  • Index list-columns with [[]].

    n_storms$data[[1]]

Create Tibbles With List-Columns

  • tibble::tribble(...): Makes list-columns when needed.

    tribble(
  ~max, ~seq,
     3,  1:3,
     4,  1:4,
     5,  1:5
)

  • tibble::tibble(...): Saves list input as list-columns.

    tibble(
  max = c(3,4,5),
  seq = list(1:3, 1:4, 1:5)
)

  • tibble::enframe(x, name = "name", value = "value"): Convert multi-level list to a tibble with list-cols.

    enframe(list("3" = 1:3, "4" = 1:4, "5" = 1:5), "max", "seq")

Output List-Columns From Other Functions

  • dplyr::mutate(), transmute(), and summarise() will output list-columns if they return a list.

    mtcars |>
  group_by(cyl) |>
  summarise(q = list(quantile(mpg)))

Reshape Nested Data

  • unnest(data, cols, ..., keep_empty = FALSE): Flatten nested columns back to regular columns. The inverse of nest().

    n_storms |> unnest(data)

  • unnest_longer(data, col, values_to = NULL, indices_to = NULL): Turn each element of a list-column into a row.

    starwars |>
  select(name, films) |>
  unnest_longer(films)

  • unnest_wider(data, col): Turn each element of a list-column into a regular column.

    starwars |>
  select(name, films) |>
  unnest_wider(films, names_sep = "_")

  • hoist(.data, .col, ..., remove = TRUE): Selectively pull list components out into their own top-level columns. Uses purrr::pluck() syntax for selecting from lists.

    starwars |>
  select(name, films) |>
  hoist(films, first_film = 1, second_film = 2)

Transform Nested Data

A vectorized function takes a vector, transforms each element in parallel, and returns a vector of the same length. By themselves vectorized functions cannot work with lists, such as list-columns.

  • dplyr::rowwise(.data, ...): Group data so that each row is one group, and within the groups, elements of list-columns appear directly (accessed with [[), not as lists of length one. When you use rowwise(), dplyr functions will seem to apply functions to list-columns in a vectorized fashion.

  • Apply a function to a list-column and create a new list-column. In this example, dim() returns two values per row and so is wrapped with list() to tell mutate() to create a list-column.

    n_storms |>
  rowwise() |>
  mutate(n = list(dim(data))) # dim() returns two values per row, wrap with list to tell mutate to create a list-column

  • Apply a function to a list-column and create a regular column. In this example, nrow() returns one integer per row.

    n_storms |>
  rowwise() |>
  mutate(n = nrow(data)) # nrow() returns one integer per row

  • Collapse multiple list-columns into a single list-column. In this example, append() returns a list for each row, so col type must be list.

    starwars |>
  rowwise() |>
  mutate(transport = list(append(vehicles, starships))) # append() returns a list for each row, so col type must be list

  • Apply a function to multiple list-columns. In this example, length() returns one integer per row.

    starwars |>
  rowwise() |>
  mutate(n_transports = length(c(vehicles, starships)))
  # length() returns one integer per row

  • See purrr package for more list functions.

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Learn more at tidyr.tidyverse.org.

Updated: 2024-05.

packageVersion("tidyr")
[1] '1.3.1'
packageVersion("tibble")
[1] '3.2.1'