In packages

Fixed column names

If you know the column names, this code works in the same way regardless of whether its inside or outside of a package:

mini_iris %>% nest(
  petal = c(Petal.Length, Petal.Width),
  sepal = c(Sepal.Length, Sepal.Width)
)
#> # A tibble: 3 × 3
#>   Species    petal            sepal           
#>   <fct>      <list>           <list>          
#> 1 setosa     <tibble [2 × 2]> <tibble [2 × 2]>
#> 2 versicolor <tibble [2 × 2]> <tibble [2 × 2]>
#> 3 virginica  <tibble [2 × 2]> <tibble [2 × 2]>

But R CMD check will warn about undefined global variables (Petal.Length, Petal.Width, Sepal.Length, and Sepal.Width), because it doesn’t know that nest() is looking for the variables inside of mini_iris (i.e. Petal.Length and friends are data-variables, not env-variables).

The easiest way to silence this note is to use all_of(). all_of() is a tidyselect helper (like starts_with(), ends_with(), etc.) that takes column names stored as strings:

mini_iris %>% nest(
  petal = all_of(c("Petal.Length", "Petal.Width")),
  sepal = all_of(c("Sepal.Length", "Sepal.Width"))
)
#> # A tibble: 3 × 3
#>   Species    petal            sepal           
#>   <fct>      <list>           <list>          
#> 1 setosa     <tibble [2 × 2]> <tibble [2 × 2]>
#> 2 versicolor <tibble [2 × 2]> <tibble [2 × 2]>
#> 3 virginica  <tibble [2 × 2]> <tibble [2 × 2]>

Alternatively, you may want to use any_of() if it is OK that some of the specified variables cannot be found in the input data.

The tidyselect package offers an entire family of select helpers. You are probably already familiar with them from using dplyr::select().

Continuous integration

Hopefully you’ve already adopted continuous integration for your package, in which R CMD check (which includes your own tests) is run on a regular basis, e.g. every time you push changes to your package’s source on GitHub or similar. The tidyverse team currently relies most heavily on GitHub Actions, so that will be our example. usethis::use_github_action() can help you get started.

We recommend adding a workflow that targets the devel version of tidyr. When should you do this?

• Always? If your package is tightly coupled to tidyr, consider leaving this in place all the time, so you know if changes in tidyr affect your package.

• Right before a tidyr release? For everyone else, you could add (or re-activate an existing) tidyr-devel workflow during the period preceding a major tidyr release that has the potential for breaking changes, especially if you’ve been contacted during our reverse dependency checks.

Example of a GitHub Actions workflow that tests your package against the development version of tidyr:

on:
  push:
    branches:
      - main
  pull_request:
    branches:
      - main

name: R-CMD-check-tidyr-devel

jobs:
  R-CMD-check:
    runs-on: macOS-latest
    steps:
      - uses: actions/checkout@v4
      - uses: r-lib/actions/setup-r@v2
      - name: Install dependencies
        run: |
          install.packages(c("remotes", "rcmdcheck"))
          remotes::install_deps(dependencies = TRUE)
          remotes::install_github("tidyverse/tidyr")
        shell: Rscript {0}
      - name: Check
        run: rcmdcheck::rcmdcheck(args = "--no-manual", error_on = "error")
        shell: Rscript {0}

GitHub Actions are an evolving landscape, so you can always mine the workflows for tidyr itself (tidyverse/tidyr/.github/workflows) or the main r-lib/actions repo for ideas.

Conditional code

Sometimes you’ll be able to write code that works with v0.8.3 and v1.0.0. But this often requires code that’s not particularly natural for either version and you’d be better off to (temporarily) have separate code paths, each containing non-contrived code. You get to re-use your existing code in the “old” branch, which will eventually be phased out, and write clean, forward-looking code in the “new” branch.

The basic approach looks like this. First you define a function that returns TRUE for new versions of tidyr:

tidyr_new_interface <- function() {
  packageVersion("tidyr") > "0.8.99"
}

We highly recommend keeping this as a function because it provides an obvious place to jot any transition notes for your package, and it makes it easier to remove transitional code later on. Another benefit is that the tidyr version is determined at run time, not at build time, and will therefore detect your user’s current tidyr version.

Then in your functions, you use an if statement to call different code for different versions:

my_function_inside_a_package <- function(...)
  # my code here

  if (tidyr_new_interface()) {
    # Freshly written code for v1.0.0
    out <- tidyr::nest(df, data = any_of(c("x", "y", "z")))
  } else {
    # Existing code for v0.8.3
    out <- tidyr::nest(df, x, y, z)
  }

  # more code here
}

If your new code uses a function that only exists in tidyr 1.0.0, you will get a NOTE from R CMD check: this is one of the few notes that you can explain in your CRAN submission comments. Just mention that it’s for forward compatibility with tidyr 1.0.0, and CRAN will let your package through.

New syntax for nest()

What changed:

• The to-be-nested columns are no longer accepted as “loose parts”.
• The new list-column’s name is no longer provided via the .key argument.
• Now we use a construct like this: new_col = <something about existing cols>.

Why it changed:

• The use of ... for metadata is a problematic pattern we’re moving away from. https://design.tidyverse.org/dots-data.html

• The new_col = <something about existing cols> construct lets us create multiple nested list-columns at once (“multi-nest”).

  mini_iris %>%
    nest(petal = matches("Petal"), sepal = matches("Sepal"))
  #> # A tibble: 3 × 3
  #>   Species    petal            sepal           
  #>   <fct>      <list>           <list>          
  #> 1 setosa     <tibble [2 × 2]> <tibble [2 × 2]>
  #> 2 versicolor <tibble [2 × 2]> <tibble [2 × 2]>
  #> 3 virginica  <tibble [2 × 2]> <tibble [2 × 2]>

Before and after examples:

# v0.8.3
mini_iris %>%
  nest(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width, .key = "my_data")

# v1.0.0
mini_iris %>%
  nest(my_data = c(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width))

# v1.0.0 avoiding R CMD check NOTE
mini_iris %>%
  nest(my_data = any_of(c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width")))

# or equivalently:
mini_iris %>%
  nest(my_data = !any_of("Species"))

If you need a quick and dirty fix without having to think, just call nest_legacy() instead of nest(). It’s the same as nest() in v0.8.3:

if (tidyr_new_interface()) {
  out <- tidyr::nest_legacy(df, x, y, z)
} else {
  out <- tidyr::nest(df, x, y, z)
}

New syntax for unnest()

What changed:

• The to-be-unnested columns must now be specified explicitly, instead of defaulting to all list-columns. This also deprecates .drop and .preserve.

• .sep has been deprecated and replaced with names_sep.

• unnest() uses the emerging tidyverse standard to disambiguate duplicated names. Use names_repair = tidyr_legacy to request the previous approach.

• .id has been deprecated because it can be easily replaced by creating the column of names prior to unnest(), e.g. with an upstream call to mutate().

  # v0.8.3
  df %>% unnest(x, .id = "id")
  
  # v1.0.0
  df %>% mutate(id = names(x)) %>% unnest(x))

Why it changed:

• The use of ... for metadata is a problematic pattern we’re moving away from. https://design.tidyverse.org/dots-data.html

• The changes to details arguments relate to features rolling out across multiple packages in the tidyverse. For example, ptype exposes prototype support from the new vctrs package. names_repair specifies what to do about duplicated or non-syntactic names, consistent with tibble and readxl.

Before and after:

nested <- mini_iris %>%
  nest(my_data = c(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width))

# v0.8.3 automatically unnests list-cols
nested %>% unnest()

# v1.0.0 must be told which columns to unnest
nested %>% unnest(any_of("my_data"))

If you need a quick and dirty fix without having to think, just call unnest_legacy() instead of unnest(). It’s the same as unnest() in v0.8.3:

if (tidyr_new_interface()) {
  out <- tidyr::unnest_legacy(df)
} else {
  out <- tidyr::unnest(df)
}

nest() preserves groups

What changed:

• nest() now preserves the groups present in the input.

Why it changed:

• To reflect the growing support for grouped data frames, especially in recent releases of dplyr. See, for example, dplyr::group_modify(), group_map(), and friends.

If the fact that nest() now preserves groups is problematic downstream, you have a few choices:

• Apply ungroup() to the result. This level of pragmatism suggests, however, you should at least consider the next two options.

• You should never have grouped in the first place. Eliminate the group_by() call and specify which columns should be nested versus not nested directly in nest().

• Adjust the downstream code to accommodate grouping.

Imagine we used group_by() then nest() on mini_iris, then we computed on the list-column outside the data frame.

(df <- mini_iris %>%
   group_by(Species) %>%
   nest())
#> # A tibble: 3 × 2
#> # Groups:   Species [3]
#>   Species    data            
#>   <fct>      <list>          
#> 1 setosa     <tibble [2 × 4]>
#> 2 versicolor <tibble [2 × 4]>
#> 3 virginica  <tibble [2 × 4]>
(external_variable <- map_int(df$data, nrow))
#> [1] 2 2 2

And now we try to add that back to the data post hoc:

df %>%
  mutate(n_rows = external_variable)
#> Error in `mutate()`:
#> ℹ In argument: `n_rows = external_variable`.
#> ℹ In group 1: `Species = setosa`.
#> Caused by error:
#> ! `n_rows` must be size 1, not 3.

This fails because df is grouped and mutate() is group-aware, so it’s hard to add a completely external variable. Other than pragmatically ungroup()ing, what can we do? One option is to work inside the data frame, i.e. bring the map() inside the mutate(), and design the problem away:

df %>%
  mutate(n_rows = map_int(data, nrow))
#> # A tibble: 3 × 3
#> # Groups:   Species [3]
#>   Species    data             n_rows
#>   <fct>      <list>            <int>
#> 1 setosa     <tibble [2 × 4]>      2
#> 2 versicolor <tibble [2 × 4]>      2
#> 3 virginica  <tibble [2 × 4]>      2

If, somehow, the grouping seems appropriate AND working inside the data frame is not an option, tibble::add_column() is group-unaware. It lets you add external data to a grouped data frame.

df %>%
  tibble::add_column(n_rows = external_variable)
#> # A tibble: 3 × 3
#> # Groups:   Species [3]
#>   Species    data             n_rows
#>   <fct>      <list>            <int>
#> 1 setosa     <tibble [2 × 4]>      2
#> 2 versicolor <tibble [2 × 4]>      2
#> 3 virginica  <tibble [2 × 4]>      2

nest_() and unnest_() are defunct

What changed:

• nest_() and unnest_() no longer work

Why it changed:

• We are transitioning the whole tidyverse to the powerful tidy eval framework. Therefore, we are gradually removing all previous solutions:
  • Specialized standard evaluation versions of functions, e.g., foo_() as a complement to foo().
  • The older lazyeval framework.

Before and after:

# v0.8.3
mini_iris %>%
  nest_(
    key_col = "my_data",
    nest_cols = c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width")
  )

nested %>% unnest_(~ my_data)

# v1.0.0
mini_iris %>%
  nest(my_data = any_of(c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width")))

nested %>% unnest(any_of("my_data"))