tf_nest: Turn "long" tables into tidy data frames with 'tf'-objects

In tidyfun: Tidy Functional Data Wrangling and Visualization

Turn "long" tables into tidy data frames with tf-objects

Description

Similar in spirit to tidyr::nest(). This turns tables in "long" format, where one column (.id) defines the unit of observation, one column (.arg) defines the evaluation grids of the functional observations, and other columns (...) define the values of the functions at those points into a (much shorter) table containing tfd-objects. All other variables are checked for constancy over .id and appended as well.

Usage

tf_nest(
  data,
  ...,
  .id = "id",
  .arg = "arg",
  domain = NULL,
  evaluator = "tf_approx_linear"
)

Arguments

data	a data frame
...	A selection of columns. If empty, all variables except the .id and .arg columns 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.
.id	the (bare or quoted) name of the column defining the different observations. Defaults to "id".
.arg	the (bare or quoted) name of the column defining the arg-values of the observed functions. Defaults to "arg".
domain	optional. Range of possible arg-values. See tf::tfd() for details.
evaluator	optional. A function accepting arguments x, arg, evaluations. See tf::tfd() for details.

Details

domain and evaluator can be specified as lists or vectors if you are nesting multiple functional data columns with different properties. Because this interface captures evaluator names as text, supply the evaluator as a string rather than a bare function name.

Value

a data frame with (at least) .id and tfd columns

See Also

tf::tfd() for details on domain and evaluator.

Other tidyfun data wrangling functions: tf_evaluate.data.frame(), tf_gather(), tf_spread(), tf_unnest()

Examples

d <- dplyr::tibble(id = rep(1:3, each = 5), arg = rep(1:5, 3), value = rnorm(15))
tf_nest(d, .id = id, .arg = arg)

tidyfun documentation built on April 24, 2026, 5:06 p.m.