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").
Arguments
- .data
A data frame.
- ..., .by, .key
<
tidy-select> Column selections....selects columns to nest using name-variable pairs of the formkey_col = c(col1, col2, col3). The right hand side can be any valid tidyselect expression, and the selected columns will appear in the inner data frames. If not specified, will be derived as all columns not selected by.by, and will use the column name from.key, which is"data"if.keyis not specified..byselects columns to nest by. These will remain in the outer data frame. If not specified, will be derived as all columns not selected by....
Specifying both
...and.bydrops unselected columns from.data.Specifying neither
...nor.bynests all variables, and will use the column name supplied through.key.![[Deprecated]](figures/lifecycle-deprecated.svg)
: previously you could write
df |> nest(x, y, z). Convert todf |> nest(data = c(x, y, z)).- .names_sep
If
NULL, the default, the inner names will come from the former outer names. If a string, the new inner names will use the outer names withnames_sepautomatically stripped. This makesnames_seproughly symmetric between nesting and unnesting.
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 receive) 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. df |> nest(.by = c(x, y)) specifies the columns to nest by; i.e. the columns that will remain in
the outer data frame. An alternative way to achieve the latter is to 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).
You can't supply .by with a grouped data frame, as the groups already
represent what you are nesting by.
Examples
df <- tibble(x = c(1, 1, 1, 2, 2, 3), y = 1:6, z = 6:1)
# Specify variables to nest using name-variable pairs.
# 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]>
# Specify variables to nest by (rather than variables to nest) using `.by`
df |> nest(.by = x)
#> # A tibble: 3 × 2
#> x data
#> <dbl> <list>
#> 1 1 <tibble [3 × 2]>
#> 2 2 <tibble [2 × 2]>
#> 3 3 <tibble [1 × 2]>
# In this case, since `...` isn't used you can specify the resulting column
# name with `.key`
df |> nest(.by = x, .key = "cols")
#> # A tibble: 3 × 2
#> x cols
#> <dbl> <list>
#> 1 1 <tibble [3 × 2]>
#> 2 2 <tibble [2 × 2]>
#> 3 3 <tibble [1 × 2]>
# 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]>
# `...` and `.by` can be used together to drop columns you no longer need,
# or to include the columns you are nesting by in the inner data frame too.
# This drops `z`:
df |> nest(data = y, .by = x)
#> # A tibble: 3 × 2
#> x data
#> <dbl> <list>
#> 1 1 <tibble [3 × 1]>
#> 2 2 <tibble [2 × 1]>
#> 3 3 <tibble [1 × 1]>
# This includes `x` in the inner data frame:
df |> nest(data = everything(), .by = x)
#> # A tibble: 3 × 2
#> x data
#> <dbl> <list>
#> 1 1 <tibble [3 × 3]>
#> 2 2 <tibble [2 × 3]>
#> 3 3 <tibble [1 × 3]>
# Multiple nesting structures can be specified at once
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
fish_encounters |>
dplyr::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]>
# That is similar to `nest(.by = )`, except here the result isn't grouped
fish_encounters |>
nest(.by = fish)
#> # A tibble: 19 × 2
#> 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 |>
nest(.by = cyl) |>
dplyr::mutate(models = lapply(data, function(df) lm(mpg ~ wt, data = df)))
#> # A tibble: 3 × 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>