R Academy · Lesson

Nesting and Unnesting Data Frames

Use nest() and unnest() to work with list-columns in tidy workflows.

Lesson 4 of 413 steps

Nesting and Unnesting Data Frames is a free R Academy lesson on CoddyKit. This is lesson 4 of 4. Any 3 lessons of this learning path are free to read in full — after that, CoddyKit PRO unlocks every lesson, plus hands-on practice with a built-in code editor and a 24/7 AI tutor. It is part of the R Academy learning path, and your progress syncs across the web and the CoddyKit app. The R Academy course includes 4 lessons in total.

List-Columns and Nested Data

A list-column is a column in a data frame where each cell contains an R object (a vector, data frame, model, or list). This makes it possible to store structured sub-data alongside other columns, enabling powerful group-wise workflows.

library(tidyr)
library(dplyr)

# A simple data frame with a list-column
df <- data.frame(
  group = c('A','B','C'),
  n = c(3, 2, 4)
)
df$values <- list(c(1,2,3), c(4,5), c(6,7,8,9))

print(df)
print(df$values[[1]])  # Access the first list element

nest() — Creating Nested Data Frames

nest(df, data = c(col1, col2)) groups rows by the non-nested columns and packs the specified columns into a list-column of data frames. One row per group, each containing a mini data frame.

library(tidyr)
library(dplyr)

df <- data.frame(
  region = c('East','East','East','West','West'),
  month = c(1,2,3,1,2),
  sales = c(100,120,110,200,190)
)

nested <- df %>%
  nest(data = c(month, sales))

print(nested)
print(nested$data[[1]])  # East region's data

nest() with group_by()

The most common pattern: use group_by() then nest() to create one row per group, with all remaining columns packed into a data list-column. Equivalent to nest(.by = group_col) in newer tidyr versions.

library(tidyr)
library(dplyr)

df <- data.frame(
  category = c('A','A','A','B','B','C','C','C','C'),
  x = c(1,2,3,4,5,6,7,8,9),
  y = c(2,4,3,8,7,5,6,9,8)
)

nested <- df %>%
  group_by(category) %>%
  nest()

print(nested)
cat('Rows per category B:', nrow(nested$data[[2]]))

map() with Nested Data Frames

Once data is nested, use purrr::map() to apply a function to each mini data frame. The function receives one data frame at a time, and the results become a new list-column — often a fitted model.

library(tidyr)
library(dplyr)
library(purrr)

df <- data.frame(
  group = c('A','A','A','B','B','B'),
  x = c(1,2,3,1,2,3),
  y = c(2.1,4.0,5.9,3.2,5.8,9.1)
)

models <- df %>%
  group_by(group) %>%
  nest() %>%
  mutate(model = map(data, ~lm(y ~ x, data = .x)))

print(models)

Extracting Model Results with map()

After fitting models per group, use map() with functions like broom::tidy() or coef() to extract results from each model. The output is another list-column that can later be unnested.

library(tidyr)
library(dplyr)
library(purrr)

df <- data.frame(
  group = c('A','A','A','B','B','B'),
  x = c(1,2,3,1,2,3),
  y = c(2.1,4.0,5.9,3.2,5.8,9.1)
)

df %>%
  group_by(group) %>%
  nest() %>%
  mutate(
    model = map(data, ~lm(y ~ x, data = .x)),
    r_squared = map_dbl(model, ~summary(.x)$r.squared),
    intercept = map_dbl(model, ~coef(.x)[1]),
    slope = map_dbl(model, ~coef(.x)[2])
  ) %>%
  select(group, r_squared, intercept, slope)

unnest() — Expanding List-Columns

unnest(df, cols = data) is the inverse of nest(). It expands the list-column back into regular columns, repeating the id columns as needed. Use cols to specify which list-column to unnest.

library(tidyr)
library(dplyr)

nested <- data.frame(
  region = c('East','West')
)
nested$data <- list(
  data.frame(month=1:3, sales=c(100,120,110)),
  data.frame(month=1:2, sales=c(200,190))
)

unnested <- unnest(nested, cols = data)
print(unnested)

unnest_longer() — Expand a List to Rows

unnest_longer(df, col) expands a list-column so each element of the list becomes a row. Unlike unnest() which expects data frames, unnest_longer() works with lists of vectors or scalars.

library(tidyr)

df <- data.frame(
  person = c('Alice','Bob','Carol')
)
df$skills <- list(
  c('R','Python','SQL'),
  c('Excel','Tableau'),
  c('R','Julia','Stan','BUGS')
)

unnest_longer(df, skills)

unnest_wider() — Expand a List to Columns

unnest_wider(df, col) expands each list element into a set of new columns. The list must have named elements — the names become column names. Useful for nested JSON-like structures.

library(tidyr)

df <- data.frame(id = 1:3)
df$info <- list(
  list(name='Alice', age=30, city='NYC'),
  list(name='Bob', age=25, city='LA'),
  list(name='Carol', age=35, city='Chicago')
)

unnest_wider(df, info)

Full Nested Workflow Pattern

The complete nested data frame workflow: group_by() + nest() → apply functions with map() → extract results → unnest() to get back a flat data frame. This pattern enables running any analysis per group.

library(tidyr)
library(dplyr)
library(purrr)

df <- data.frame(
  region = c('East','East','East','West','West','West'),
  month = c(1,2,3,1,2,3),
  revenue = c(100,120,115,200,195,210)
)

df %>%
  group_by(region) %>%
  nest() %>%
  mutate(
    mean_rev = map_dbl(data, ~mean(.x$revenue)),
    trend = map_dbl(data, ~coef(lm(revenue~month, data=.x))[2])
  ) %>%
  select(region, mean_rev, trend)

Nesting Multiple Columns

You can be selective about which columns go into the nested data frame. Specify them explicitly in nest(data = c(...)). Columns not specified stay as regular columns in the outer data frame, alongside the list-column.

library(tidyr)
library(dplyr)

df <- data.frame(
  region = c('East','East','West','West'),
  year = c(2023,2024,2023,2024),
  q1 = c(100,110,200,210),
  q2 = c(105,115,195,215),
  target = c(105,112,198,212)
)

# Only nest quarterly data, keep target as outer column
nested <- df %>%
  group_by(region, year) %>%
  nest(quarters = c(q1, q2))

print(nested)

When to Use Nested Data Frames

Nested data frames excel when you need to: fit the same model across groups, run per-group simulations, apply complex transformations to subsets, or work with JSON-like hierarchical data. They keep group-specific data organized alongside group-level metadata.

library(tidyr)
library(dplyr)
library(purrr)

# Bootstrap confidence interval per group
df <- data.frame(
  group = c('A','A','A','A','B','B','B','B'),
  value = c(10,12,11,13,20,22,19,21)
)

set.seed(42)
df %>%
  group_by(group) %>%
  nest() %>%
  mutate(
    boot_means = map(data, function(d) {
      replicate(100, mean(sample(d$value, replace=TRUE)))
    }),
    ci_low = map_dbl(boot_means, ~quantile(.x, 0.025)),
    ci_high = map_dbl(boot_means, ~quantile(.x, 0.975))
  ) %>%
  select(group, ci_low, ci_high)

Quick Check

What is the correct way to expand a list-column of data frames back into a flat data frame?

Answer it in the interactive lesson

Recap: Nesting and Unnesting

Key takeaways for nested data frame workflows:

nest(data = c(cols)) — packs columns into a list-column of data frames, one per group
group_by() + nest() — the standard pattern to nest by group
map(nested$data, fn) — apply any function to each nested data frame
unnest(cols = data) — expand list-column of data frames back to flat format
unnest_longer(col) — expand a list of vectors/scalars to rows
unnest_wider(col) — expand a named list to columns
Ideal for per-group modeling, bootstrapping, and JSON data processing

library(tidyr)
library(dplyr)
library(purrr)

data.frame(
  team = c('A','A','B','B'),
  x = c(1,2,1,2),
  y = c(2,4,3,6)
) %>%
  group_by(team) %>%
  nest() %>%
  mutate(
    model = map(data, ~lm(y ~ x, data = .x)),
    slope = map_dbl(model, ~coef(.x)['x'])
  ) %>%
  select(team, slope)

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Frequently Asked Questions

Is the “Nesting and Unnesting Data Frames” lesson free?

Yes — any 3 lessons of the R Academy learning path, including “Nesting and Unnesting Data Frames”, are free to read in full here on the web. After that, CoddyKit PRO unlocks every lesson, plus interactive practice with a built-in code editor and a 24/7 AI tutor. The R Academy course includes 4 lessons in total.

What will I learn in “Nesting and Unnesting Data Frames”?

Use nest() and unnest() to work with list-columns in tidy workflows. You practise R Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

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No prior experience is required. R Academy on CoddyKit is structured for beginners through advanced learners, so you can start here or from the beginning and move at your own pace. This is lesson 4 of 4.

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All lessons in this course

Wide to Long with pivot_longer()
Long to Wide with pivot_wider()
separate() and unite() for String Columns
Nesting and Unnesting Data Frames

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