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")
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 
.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

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).
unnest()
for the inverse operation.
#> # A tibble: 3 x 2 #> Species data #> <fct> <list> #> 1 setosa <tibble [50 × 4]> #> 2 versicolor <tibble [50 × 4]> #> 3 virginica <tibble [50 × 4]>#> # 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