tidyr: chop – R documentation

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chop

Chop and unchop

Description

Chopping and unchopping preserve the width of a data frame, changing its length. chop() makes df shorter by converting rows within each group into list-columns. unchop() makes df longer by expanding list-columns so that each element of the list-column gets its own row in the output. chop() and unchop() are building blocks for more complicated functions (like unnest(), unnest_longer(), and unnest_wider()) and are generally more suitable for programming than interactive data analysis.

Usage

chop(data, cols)

unchop(data, cols, keep_empty = FALSE, ptype = NULL)

Arguments

`data`	A data frame.
`cols`	<`tidy-select`> Columns to chop or unchop (automatically quoted). For `unchop()`, each column should be a list-column containing generalised vectors (e.g. any mix of `NULL`s, atomic vector, S3 vectors, a lists, or data frames).
`keep_empty`	By default, you get one row of output for each element of the list your unchopping/unnesting. This means that if there's a size-0 element (like `NULL` or an empty data frame), that entire row will be dropped from the output. If you want to preserve all rows, use `keep_empty = TRUE` to replace size-0 elements with a single row of missing values.
`ptype`	Optionally, supply a data frame prototype for the output `cols`, overriding the default that will be guessed from the combination of individual values.

Details

Generally, unchopping is more useful than chopping because it simplifies a complex data structure, and nest()ing is usually more appropriate that chop()ing' since it better preserves the connections between observations.

chop() creates list-columns of class vctrs::list_of() to ensure consistent behaviour when the chopped data frame is emptied. For instance this helps getting back the original column types after the roundtrip chop and unchop. Because <list_of> keeps tracks of the type of its elements, unchop() is able to reconstitute the correct vector type even for empty list-columns.

Examples

# Chop ==============================================================
df <- tibble(x = c(1, 1, 1, 2, 2, 3), y = 1:6, z = 6:1)
# Note that we get one row of output for each unique combination of
# non-chopped variables
df %>% chop(c(y, z))
# cf nest
df %>% nest(data = c(y, z))

# Unchop ============================================================
df <- tibble(x = 1:4, y = list(integer(), 1L, 1:2, 1:3))
df %>% unchop(y)
df %>% unchop(y, keep_empty = TRUE)

# Incompatible types -------------------------------------------------
# If the list-col contains types that can not be natively
df <- tibble(x = 1:2, y = list("1", 1:3))
try(df %>% unchop(y))

# Unchopping data frames -----------------------------------------------------
# Unchopping a list-col of data frames must generate a df-col because
# unchop leaves the column names unchanged
df <- tibble(x = 1:3, y = list(NULL, tibble(x = 1), tibble(y = 1:2)))
df %>% unchop(y)
df %>% unchop(y, keep_empty = TRUE)

tidyr

Tidy Messy Data

v1.1.3

MIT + file LICENSE

Authors

Hadley Wickham [aut, cre], RStudio [cph]

Initial release