Nesting and unnesting in R Programming - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of nested list unnesting with dplyr

What is the output of the following R code using dplyr and tidyr to unnest a nested list column?

R Programming

library(dplyr)
library(tidyr)
df <- tibble(id = 1:2, data = list(tibble(x = 1:2), tibble(x = 3:4)))
unnest(df, data)

A A tibble with 2 rows: id 1 and 2, x column with list of vectors

B A tibble with 2 rows: id 1 and 2, data column still nested

C Error: object 'unnest' not found

D A tibble with 4 rows: id repeated twice for each nested tibble, x values 1,2,3,4

💡 Hint

Think about how unnest() expands nested tibbles into rows.

The unnest() function expands the nested data column, which contains tibbles, into multiple rows. Each row in the nested tibble becomes a separate row in the output, repeating the id accordingly.

❓ Predict Output

intermediate

Result of nesting and unnesting with multiple columns

Given this R code, what is the output after nesting and then unnesting?

R Programming

library(dplyr)
library(tidyr)
df <- tibble(group = c('A', 'A', 'B'), value = 1:3)
nested <- df %>% group_by(group) %>% nest()
unnest(nested, data)

A Dataframe with 3 rows, group column repeated, value column expanded

B Dataframe with 2 rows, group column and nested data column

C Original dataframe with columns group and value, 3 rows

D Error: object 'nest' not found

💡 Hint

Nesting groups rows, unnesting expands them back.

After nesting, the data is grouped by group with nested tibbles. Unnesting expands these nested tibbles back into rows, repeating the group values accordingly.

🔧 Debug

advanced

Identify the error in unnesting a list-column

What error does this R code produce and why?

R Programming

library(tidyr)
df <- tibble(id = 1:2, vals = list(1:3, 4:5))
unnest(df, vals, keep_empty = TRUE)

A Error: object 'unnest' not found

B No error, returns 5 rows with id repeated accordingly

C Error: unused argument (keep_empty = TRUE)

D Error: vals must be a data frame or list of data frames

💡 Hint

Check the arguments supported by the current unnest() function version.

The unnest() function in tidyr does not have a keep_empty argument, so passing it causes an unused argument error.

📝 Syntax

advanced

Correct syntax to unnest a nested list-column

Which option correctly unnests the nested list-column data in this tibble?

R Programming

library(tidyr)
df <- tibble(id = 1:2, data = list(tibble(a = 1:2), tibble(a = 3:4)))

A unnest(df, data = data)

B unnest(df, cols = data)

C unnest(df, c(data))

D unnest(df, data)

💡 Hint

unnest() uses tidyselect syntax (like select()) to specify columns.

The unnest() function uses tidyselect syntax so unnest(df, cols = data) correctly specifies the column to unnest. Other options use incorrect or deprecated syntax.

🚀 Application

expert

How many rows after nesting and unnesting complex data?

Consider this R code that nests and unnests a dataframe with multiple grouping variables and nested tibbles. How many rows does the final dataframe have?

R Programming

library(dplyr)
library(tidyr)
df <- tibble(
  group1 = c('X', 'X', 'Y', 'Y'),
  group2 = c('A', 'B', 'A', 'B'),
  val = 1:4
)
nested <- df %>% group_by(group1) %>% nest()
unnested <- nested %>% unnest(data)

A 4 rows, same as original dataframe

B 2 rows, one per group1 value

C 8 rows, double the original rows

D Error due to ambiguous grouping

💡 Hint

Nesting groups by group1 nests all rows per group1, unnesting expands them back.

Nesting by group1 creates two nested tibbles, each containing rows for that group. Unnesting expands these back, resulting in the original 4 rows.

Practice

1. What does nest() do in R when applied to a grouped data frame?

easy

A. It sorts the data frame by a column.

B. It separates a list-column into multiple rows.

C. It removes grouping from the data frame.

D. It groups specified columns into a list-column inside the data frame.

Solution

1. Step 1: Understand the purpose of nest()

   The nest() function groups specified columns into a list-column, keeping other columns as keys.

2. Step 2: Identify the effect on data frame structure

   This creates a nested data frame where each row contains a key and a list of grouped data.

3. Final Answer:

   It groups specified columns into a list-column inside the data frame. -> Option D

4. Quick Check:

   nest() = groups columns into list-column [OK]

Hint: nest() groups columns into list-columns quickly [OK]

Common Mistakes:

• Confusing nest() with unnest()
• Thinking nest() sorts data
• Assuming nest() removes grouping

2. Which of the following is the correct syntax to unnest a list-column named data in a data frame df using tidyr?

easy

A. unnest(df, cols)

B. df %>% unnest()

C. df %>% unnest(cols = data)

D. unnest(df)

Solution

1. Step 1: Recall unnest() syntax in tidyr

   The correct syntax uses unnest() with the argument cols = specifying the list-column to expand.

2. Step 2: Identify the pipe-friendly syntax

   Using the pipe operator, the syntax is df %>% unnest(cols = data), which is the modern recommended style.

3. Final Answer:

   df %>% unnest(cols = data) -> Option C

4. Quick Check:

   unnest() needs cols= and pipe for data frame [OK]

Hint: Use pipe and cols= to unnest list-columns [OK]

Common Mistakes:

• Omitting cols= argument
• Passing data frame as first argument without pipe
• Using unnest(df, data) without cols=

3. Given the code:

library(tidyr)
library(dplyr)
df <- tibble(group = c('A', 'A', 'B', 'B'), value = 1:4)
nested_df <- df %>% group_by(group) %>% nest()
unnested_df <- nested_df %>% unnest(cols = data)

What will be the output of unnested_df?

medium

A. A data frame identical to the original df with columns group and value.

B. A data frame with columns group and a list-column data.

C. An error because unnest() cannot be applied here.

D. A data frame with only the group column.

Solution

1. Step 1: Understand the nesting step

   The nest() groups rows by group and stores the remaining columns in a list-column named data.

2. Step 2: Understand the unnesting step

   The unnest() expands the list-column data back into regular rows, restoring the original structure.

3. Final Answer:

   A data frame identical to the original df with columns group and value. -> Option A

4. Quick Check:

   nest() then unnest() returns original data [OK]

Hint: nest() then unnest() restores original data frame [OK]

Common Mistakes:

• Thinking unnest() returns list-column
• Expecting error from unnest()
• Assuming columns are dropped

4. Consider this code snippet:

library(tidyr)
df <- tibble(id = 1:2, data = list(tibble(x = 1:2), tibble(x = 3:4)))
unnested <- unnest(df)

It throws an error. What is the likely cause?

medium

A. The list-column data contains data frames, which cannot be unnested.

B. The unnest() function requires the cols argument to specify the list-column.

C. The tibble package is not loaded.

D. The unnest() function cannot unnest list-columns with numeric data.

Solution

1. Step 1: Check unnest() argument requirements

   Modern unnest() requires specifying which columns to unnest using the cols = argument.

2. Step 2: Identify missing argument causing error

   Omitting cols = data causes the function to fail because it doesn't know which list-column to expand.

3. Final Answer:

   The unnest() function requires the cols argument to specify the list-column. -> Option B

4. Quick Check:

   unnest() needs cols= argument [OK]

Hint: Always specify cols= when using unnest() [OK]

Common Mistakes:

• Forgetting cols= argument
• Assuming unnest() works without specifying columns
• Confusing list-column content type

5. You have a data frame df with columns category, subcat, and value. You want to nest subcat and value by category, then filter out groups where the sum of value is less than 10, and finally unnest the filtered data. Which sequence of commands correctly achieves this?

hard

A. df %>% group_by(category) %>% nest(data = c(subcat, value)) %>% filter(map_dbl(data, ~sum(.x$value)) >= 10) %>% unnest(cols = data)

B. df %>% nest(data = c(subcat, value)) %>% filter(sum(value) >= 10) %>% unnest(data)

C. df %>% group_by(category) %>% nest(cols = c(subcat, value)) %>% filter(sum(value) >= 10) %>% unnest(cols = data)

D. df %>% group_by(category) %>% nest(data = c(subcat, value)) %>% filter(sum(data$value) >= 10) %>% unnest(cols = data)

Solution

1. Step 1: Nest subcat and value by category

   Use group_by(category) then nest(data = c(subcat, value)) to create a list-column named data with those columns.

2. Step 2: Filter groups by sum of value

   Use filter(map_dbl(data, ~sum(.x$value)) >= 10) to keep only groups where sum of value in nested data is at least 10.

3. Step 3: Unnest the filtered data

   Finally, use unnest(cols = data) to expand the filtered nested data back into rows.

4. Final Answer:

   df %>% group_by(category) %>% nest(data = c(subcat, value)) %>% filter(map_dbl(data, ~sum(.x$value)) >= 10) %>% unnest(cols = data) -> Option A

5. Quick Check:

   nest(), filter with map_dbl(), then unnest() [OK]

Hint: Use map_dbl() on nested data to filter before unnesting [OK]

Common Mistakes:

• Filtering before nesting
• Using sum() directly on list-column
• Omitting cols= in unnest()