Converting List to Data Frame in R

The fastest and easiest way to convert a list to a data frame in R is to use the “as.data.frame()” function. This function checks whether an input object is a data frame and, if not, tries to convert it.

main_list <- list(
  col1 = c(1, 2, 3),
  col2 = c(4, 5, 6),
  col3 = c(7, 8, 9)
)

main_list

cat("After converting list to data frame", "\n")

as.data.frame(main_list)

Output

You can see from the above output that each list vector became a data frame column.

Nested lists

While as.data.frame() method is not ideal for converting nested lists into data frames, but it can be used with do.call() and rbind() for simpler nesting structures.

Using the do.call() and rbind() approach, we can iterate through the nested list, combine the inner lists into rows, and create a DataFrame.

nested_list <- list(
  list(1, "apple"),
  list(2, "banana")
)

df <- as.data.frame(do.call(rbind, nested_list))

print(df)

Output

   V1    V2
1  1    apple
2  2    banana

Pros

1. It converts directly in one step. No intermediary steps are required.
2. If a list has named elements, it becomes column names. You don’t have to assign it manually.
3. It is very easy to use and understand.

Cons

1. If the list structure is complex, it may result in unexpected results. Ensure the structure is compatible with the output data frame.

Here are four other ways:

1. Using t() and do.call()
2. Using do.call() with rbind() or cbind()
3. Using data.table()
4. Using as_tibble()

Alternate approach 1: Using the t() and do.call()

The t() function transposes a matrix or a data frame, but it can’t be directly used to convert a list to a data frame.

However, you can first convert the list to a matrix and then transpose it. It still depends on the structure of your list.

main_list <- list(
  col1 = c(1, 2, 3),
  col2 = c(4, 5, 6),
  col3 = c(7, 8, 9)
)

main_list

# convert the list to a matrix
mat <- do.call(rbind, main_list)

# transpose the matrix
mat <- t(mat)

# convert the matrix to a data frame
df <- as.data.frame(mat)

cat("After converting list to data frame", "\n")

print(df)

Output

Pros

1. It allows more control over the data frame creation process.
2. It is more adaptable in complex lists than as.data.frame().

Cons

1. The process is complex compared to as.data.frame(). It requires knowledge of the matrix as well.
2. It is not one-step solution. It requires multiple steps, depending on the structure of the list.

Alternate Approach 2: Using do.call() with rbind()

The do.call(rbind, …) approach combines data frames vertically (by rows). This approach is helpful when you have a list of data frames and want to stack one on top of another to create a single, larger data structure.

# Example list of data frames
main_list <- list(
  df1 = data.frame(x = 1:3, y = 4:6),
  df2 = data.frame(x = 7:9, y = 10:12)
)

# Row-bind the data frames
df <- do.call(rbind, main_list)
df

Output

       x   y
df1.1  1   4
df1.2  2   5
df1.3  3   6
df2.1  7  10
df2.2  8  11
df2.3  9  12

Pros

1. It provides flexibility in combining multiple data frames of a list.

Cons

1. This is a very complex approach that requires a thorough understanding of the matrix, list, and data frame. You also need to have a basic knowledge of how rbind() works.

Alternate approach 3: Using data.table()

Using the data. table () function, we can easily convert a nested list to a data table (an improved version of the data frame). The data table is different from the data frame and is more efficient.

If you have not installed the “data.table” library, then you need to install it first and then import it using the library() function like the code below:

library(data.table)

nested_list <- list(
  list(1, "apple"),
  list(2, "banana")
)
data_table <- data.table(do.call(rbind, nested_list))

print(data_table)

Output

     V1     V2
1:   1    apple
2:   2    banana

Pros

1. data.table is known for its speed in large datasets.
2. It requires less code compared to other approaches.

Cons

1. You need to install a separate package.
2. It demands a learning curve to use all of its features.

Alternate approach 4: Using tibble() and sapply()

The tibble is a modern take on the base data frame in R. Using tibble() and sapply() functions, we can convert a nested list into a tibble.

First, you need to install a “tibble” package in your R environment, and then you can import and use it.

library(tibble)

nested_list <- list(
  list(1, "apple"),
  list(2, "banana")
)

# Create a tibble with proper column names
tibble_df <- tibble(
  id = sapply(nested_list, `[[`, 1),
  fruit = sapply(nested_list, `[[`, 2)
)

print(tibble_df)

Output

 # A tibble: 2 × 2

  id      fruit
  <dbl>   <chr>
1  1      apple
2  2      banana

Pros

1. Tibbles have more informative output than data frames, which results in more clarity about the dataset.

Cons

1. You have to install a separate package to work with tibbles.
2. It requires a steep learning curve to understand tibble and its operations.

That’s all!