# nest

0th

Percentile

##### Nest repeated values in a list-variable.

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.

##### Usage
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).

unnest() for the inverse operation.

• nest
##### Examples
# NOT RUN {
library(dplyr)
as_tibble(iris) %>% nest(-Species)
as_tibble(chickwts) %>% nest(weight)

if (require("gapminder")) {
gapminder %>%
group_by(country, continent) %>%
nest()

gapminder %>%
nest(-country, -continent)
}
# }

Documentation reproduced from package tidyr, version 0.8.0, License: MIT + file LICENSE

### Community examples

oia8@txstate.edu at Jun 2, 2019 tidyr v0.8.3

# Simple example of nest function usage **#make simple dataframe of 2 columns** df <- data.frame(name=c("a","b","b","c","c","c"), count=c(1,2,3,1,2,3)) **#nest second column within dataframe** df %>% nest(count)

matthewchangkit@gmail.com at Aug 20, 2018 tidyr v0.8.1

## This example is to demonstrate the nest function on the iris dataset  # Load tidyverse library library(tidyverse) # Load native iris dataset df <- iris # Check names of columns to find out which column you want to subset on names(df) # Since we want to "group by" the species type we can either group all other variables we want in a list, or just exclude the species column if we want the remaining variables new_df <- df %>% nest(-Species) # Check that the new dataframe is what we expected new_df