Nesting creates a list-column of data frames; unnesting flattens it back out into regular columns. Nesting is implicitly a summarising operation: you get one row for each group defined by the non-nested columns. This is useful in conjunction with other summaries that work with whole datasets, most notably models.
Learn more in vignette("nest").
Usage
nest(.data, ..., .names_sep = NULL, .key = deprecated())
unnest(
data,
cols,
...,
keep_empty = FALSE,
ptype = NULL,
names_sep = NULL,
names_repair = "check_unique",
.drop = deprecated(),
.id = deprecated(),
.sep = deprecated(),
.preserve = deprecated()
)Arguments
- .data
A data frame.
- ...
<
tidy-select> Columns to nest, specified using name-variable pairs of the formnew_col = c(col1, col2, col3). The right hand side can be any valid tidy select expression.: previously you could write
df %>% nest(x, y, z)anddf %>% unnest(x, y, z). Convert todf %>% nest(data = c(x, y, z)). anddf %>% unnest(c(x, y, z)).If you previously created new variable in
unnest()you'll now need to do it explicitly withmutate(). Convertdf %>% unnest(y = fun(x, y, z))todf %>% mutate(y = fun(x, y, z)) %>% unnest(y).- .key
: No longer needed because of the new
new_col = c(col1, col2, col3)syntax.- data
A data frame.
- cols
<
tidy-select> Columns to unnest.If you
unnest()multiple columns, parallel entries must be of compatible sizes, i.e. they're either equal or length 1 (following the standard tidyverse recycling rules).- 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
NULLor an empty data frame), that entire row will be dropped from the output. If you want to preserve all rows, usekeep_empty = TRUEto replace size-0 elements with a single row of missing values.- ptype
Optionally, a named list of column name-prototype pairs to coerce
colsto, 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 allcols.- names_sep, .names_sep
If
NULL, the default, the names will be left as is. Innest(), inner names will come from the former outer names; inunnest(), the new outer names will come from the inner names.If a string, the inner and outer names will be used together. In
unnest(), the names of the new outer columns will be formed by pasting together the outer and the inner column names, separated bynames_sep. Innest(), the new inner names will have the outer names +names_sepautomatically stripped. This makesnames_seproughly symmetric between nesting and unnesting.- names_repair
Used to check that output data frame has valid names. Must be one of the following options:
"minimal": no name repair or checks, beyond basic existence,
"unique": make sure names are unique and not empty,
"check_unique": (the default), no name repair, but check they are unique,
"universal": make the names unique and syntactic
a function: apply custom name repair.
tidyr_legacy: use the name repair from tidyr 0.8.
a formula: a purrr-style anonymous function (see
rlang::as_function())
See
vctrs::vec_as_names()for more details on these terms and the strategies used to enforce them.- .drop, .preserve
: all list-columns are now preserved; If there are any that you don't want in the output use
select()to remove them prior to unnesting.- .id
: convert
df %>% unnest(x, .id = "id")todf %>% mutate(id = names(x)) %>% unnest(x)).- .sep
New syntax
tidyr 1.0.0 introduced a new syntax for nest() and unnest() that's
designed to be more similar to other functions. Converting to the new syntax
should be straightforward (guided by the message you'll recieve) but if
you just need to run an old analysis, you can easily revert to the previous
behaviour using nest_legacy() and unnest_legacy() as follows:
Grouped data frames
df %>% nest(data = c(x, y)) specifies the columns to be nested; i.e. the
columns that will appear in the inner data frame. Alternatively, you can
nest() a grouped data frame created by dplyr::group_by(). The grouping
variables remain in the outer data frame and the others are nested. The
result preserves the grouping of the input.
Variables supplied to nest() will override grouping variables so that
df %>% group_by(x, y) %>% nest(data = !z) will be equivalent to
df %>% nest(data = !z).
Examples
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-nested variables
df %>% nest(data = c(y, z))
#> # A tibble: 3 × 2
#> x data
#> <dbl> <list>
#> 1 1 <tibble [3 × 2]>
#> 2 2 <tibble [2 × 2]>
#> 3 3 <tibble [1 × 2]>
# chop does something similar, but retains individual columns
df %>% chop(c(y, z))
#> # A tibble: 3 × 3
#> x y z
#> <dbl> <list<int>> <list<int>>
#> 1 1 [3] [3]
#> 2 2 [2] [2]
#> 3 3 [1] [1]
# use tidyselect syntax and helpers, just like in dplyr::select()
df %>% nest(data = any_of(c("y", "z")))
#> # A tibble: 3 × 2
#> x data
#> <dbl> <list>
#> 1 1 <tibble [3 × 2]>
#> 2 2 <tibble [2 × 2]>
#> 3 3 <tibble [1 × 2]>
iris %>% nest(data = !Species)
#> # A tibble: 3 × 2
#> Species data
#> <fct> <list>
#> 1 setosa <tibble [50 × 4]>
#> 2 versicolor <tibble [50 × 4]>
#> 3 virginica <tibble [50 × 4]>
nest_vars <- names(iris)[1:4]
iris %>% nest(data = any_of(nest_vars))
#> # A tibble: 3 × 2
#> Species data
#> <fct> <list>
#> 1 setosa <tibble [50 × 4]>
#> 2 versicolor <tibble [50 × 4]>
#> 3 virginica <tibble [50 × 4]>
iris %>%
nest(petal = starts_with("Petal"), sepal = starts_with("Sepal"))
#> # A tibble: 3 × 3
#> Species petal sepal
#> <fct> <list> <list>
#> 1 setosa <tibble [50 × 2]> <tibble [50 × 2]>
#> 2 versicolor <tibble [50 × 2]> <tibble [50 × 2]>
#> 3 virginica <tibble [50 × 2]> <tibble [50 × 2]>
iris %>%
nest(width = contains("Width"), length = contains("Length"))
#> # A tibble: 3 × 3
#> Species width length
#> <fct> <list> <list>
#> 1 setosa <tibble [50 × 2]> <tibble [50 × 2]>
#> 2 versicolor <tibble [50 × 2]> <tibble [50 × 2]>
#> 3 virginica <tibble [50 × 2]> <tibble [50 × 2]>
# Nesting a grouped data frame nests all variables apart from the group vars
library(dplyr)
fish_encounters %>%
group_by(fish) %>%
nest()
#> # A tibble: 19 × 2
#> # Groups: fish [19]
#> fish data
#> <fct> <list>
#> 1 4842 <tibble [11 × 2]>
#> 2 4843 <tibble [11 × 2]>
#> 3 4844 <tibble [11 × 2]>
#> 4 4845 <tibble [5 × 2]>
#> 5 4847 <tibble [3 × 2]>
#> 6 4848 <tibble [4 × 2]>
#> 7 4849 <tibble [2 × 2]>
#> 8 4850 <tibble [6 × 2]>
#> 9 4851 <tibble [2 × 2]>
#> 10 4854 <tibble [2 × 2]>
#> 11 4855 <tibble [5 × 2]>
#> 12 4857 <tibble [9 × 2]>
#> 13 4858 <tibble [11 × 2]>
#> 14 4859 <tibble [5 × 2]>
#> 15 4861 <tibble [11 × 2]>
#> 16 4862 <tibble [9 × 2]>
#> 17 4863 <tibble [2 × 2]>
#> 18 4864 <tibble [2 × 2]>
#> 19 4865 <tibble [3 × 2]>
# Nesting is often useful for creating per group models
mtcars %>%
group_by(cyl) %>%
nest() %>%
mutate(models = lapply(data, function(df) lm(mpg ~ wt, data = df)))
#> # A tibble: 3 × 3
#> # Groups: cyl [3]
#> cyl data models
#> <dbl> <list> <list>
#> 1 6 <tibble [7 × 10]> <lm>
#> 2 4 <tibble [11 × 10]> <lm>
#> 3 8 <tibble [14 × 10]> <lm>
# unnest() is primarily designed to work with lists of data frames
df <- tibble(
x = 1:3,
y = list(
NULL,
tibble(a = 1, b = 2),
tibble(a = 1:3, b = 3:1)
)
)
df %>% unnest(y)
#> # A tibble: 4 × 3
#> x a b
#> <int> <dbl> <dbl>
#> 1 2 1 2
#> 2 3 1 3
#> 3 3 2 2
#> 4 3 3 1
df %>% unnest(y, keep_empty = TRUE)
#> # A tibble: 5 × 3
#> x a b
#> <int> <dbl> <dbl>
#> 1 1 NA NA
#> 2 2 1 2
#> 3 3 1 3
#> 4 3 2 2
#> 5 3 3 1
# If you have lists of lists, or lists of atomic vectors, instead
# see hoist(), unnest_wider(), and unnest_longer()
#' # You can unnest multiple columns simultaneously
df <- tibble(
a = list(c("a", "b"), "c"),
b = list(1:2, 3),
c = c(11, 22)
)
df %>% unnest(c(a, b))
#> # A tibble: 3 × 3
#> a b c
#> <chr> <dbl> <dbl>
#> 1 a 1 11
#> 2 b 2 11
#> 3 c 3 22
# Compare with unnesting one column at a time, which generates
# the Cartesian product
df %>% unnest(a) %>% unnest(b)
#> # A tibble: 5 × 3
#> a b c
#> <chr> <dbl> <dbl>
#> 1 a 1 11
#> 2 a 2 11
#> 3 b 1 11
#> 4 b 2 11
#> 5 c 3 22
