chop: Chop and unchop

In hadley/tidyr: Tidy Messy Data

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, ..., error_call = current_env())

unchop(
  data,
  cols,
  ...,
  keep_empty = FALSE,
  ptype = NULL,
  error_call = current_env()
)

Arguments

data	A data frame.
cols	<tidy-select> Columns to chop or unchop. For unchop(), each column should be a list-column containing generalised vectors (e.g. any mix of NULLs, atomic vector, S3 vectors, a lists, or data frames).
...	These dots are for future extensions and must be empty.
error_call	The execution environment of a currently running function, e.g. caller_env(). The function will be mentioned in error messages as the source of the error. See the call argument of abort() for more information.
keep_empty	By default, you get one row of output for each element of the list that you are unchopping/unnesting. This means that if there's a size-0 element (like NULL or an empty data frame or vector), then 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, a named list of column name-prototype pairs to coerce cols to, overriding the default that will be guessed from combining the individual values. Alternatively, a single empty ptype can be supplied, which will be applied to all cols.

Details

Generally, unchopping is more useful than chopping because it simplifies a complex data structure, and nest()ing is usually more appropriate than 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)

# unchop will error if the types are not compatible:
df <- tibble(x = 1:2, y = list("1", 1:3))
try(df %>% unchop(y))

# 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)

hadley/tidyr documentation built on Feb. 3, 2024, 10:45 a.m.