There are many possible ways one could choose to nest columns inside a data frame. nest() creates a list of data frames containing all the nested variables: this seems to be the most useful form in practice.

nest(data, ..., .key = "data")

Arguments

data

A data frame.

...

A selection of columns. If empty, all variables are selected. You can supply bare variable names, select all variables between x and z with x:z, exclude y with -y. For more options, see the dplyr::select() documentation. See also the section on selection rules below.

.key

The name of the new column, as a string or symbol.

This argument is passed by expression and supports quasiquotation (you can unquote strings and symbols). The name is captured from the expression with rlang::quo_name() (note that this kind of interface where symbols do not represent actual objects is now discouraged in the tidyverse; we support it here for backward compatibility).

Rules for selection

Arguments for selecting columns are passed to tidyselect::vars_select() and are treated specially. Unlike other verbs, selecting functions make a strict distinction between data expressions and context expressions.

  • A data expression is either a bare name like x or an expression like x:y or c(x, y). In a data expression, you can only refer to columns from the data frame.

  • Everything else is a context expression in which you can only refer to objects that you have defined with <-.

For instance, col1:col3 is a data expression that refers to data columns, while seq(start, end) is a context expression that refers to objects from the contexts.

If you really need to refer to contextual objects from a data expression, you can unquote them with the tidy eval operator !!. This operator evaluates its argument in the context and inlines the result in the surrounding function call. For instance, c(x, !! x) selects the x column within the data frame and the column referred to by the object x defined in the context (which can contain either a column name as string or a column position).

See also

unnest() for the inverse operation.

Examples

library(dplyr) as_tibble(iris) %>% nest(-Species)
#> # A tibble: 3 x 2 #> Species data #> <fct> <list> #> 1 setosa <tibble [50 × 4]> #> 2 versicolor <tibble [50 × 4]> #> 3 virginica <tibble [50 × 4]>
as_tibble(chickwts) %>% nest(weight)
#> # A tibble: 6 x 2 #> feed data #> <fct> <list> #> 1 horsebean <tibble [10 × 1]> #> 2 linseed <tibble [12 × 1]> #> 3 soybean <tibble [14 × 1]> #> 4 sunflower <tibble [12 × 1]> #> 5 meatmeal <tibble [11 × 1]> #> 6 casein <tibble [12 × 1]>
if (require("gapminder")) { gapminder %>% group_by(country, continent) %>% nest() gapminder %>% nest(-country, -continent) }
#> # A tibble: 142 x 3 #> country continent data #> <fct> <fct> <list> #> 1 Afghanistan Asia <tibble [12 × 4]> #> 2 Albania Europe <tibble [12 × 4]> #> 3 Algeria Africa <tibble [12 × 4]> #> 4 Angola Africa <tibble [12 × 4]> #> 5 Argentina Americas <tibble [12 × 4]> #> 6 Australia Oceania <tibble [12 × 4]> #> 7 Austria Europe <tibble [12 × 4]> #> 8 Bahrain Asia <tibble [12 × 4]> #> 9 Bangladesh Asia <tibble [12 × 4]> #> 10 Belgium Europe <tibble [12 × 4]> #> # ... with 132 more rows