Creating a DataFrame from a List of DataFrames

Overview

In this article, we’ll explore how to create a single DataFrame by aggregating multiple individual DataFrames in R. We’ll delve into the details of using nested sapply functions and discuss how to handle numeric columns.

Background

R is an excellent language for data analysis and manipulation. Its built-in data.frame structure allows us to easily store and manipulate data. However, sometimes we find ourselves dealing with a collection of individual DataFrames that we want to merge into one cohesive DataFrame. This can be a challenge, especially when working with multiple datasets.

Problem Statement

Given a list of DataFrames (myList) containing 16 variables each, we want to create a single DataFrame where each column represents the mean value from its corresponding column in myList. We’re looking for an efficient and elegant solution that leverages R’s built-in functions.

Step 1: Understanding the Problem

The goal is to apply the mean function to each column of every DataFrame in myList, ignoring any non-numeric values. We’ll then combine these results into a single DataFrame where each row corresponds to a specific variable.

# Load necessary libraries
library(dplyr)

# Create a sample list of DataFrames
my_list <- lapply(1:10, function(i) data.frame(
  Stat = factor(sample(c("a", "b"), i)),
  P10 = rnorm(i),
  R = rnorm(i),
  S = rnorm(i)
))

# Print the list of DataFrames
print(my_list)

Step 2: Using Nested sapply Functions

To achieve our goal, we can use a nested sapply function. The outer loop will iterate over each DataFrame in the list (myList), while the inner loop will apply the mean function to each column of that DataFrame.

# Create a single DataFrame using nested sapply functions
result_df <- do.call(data.frame, lapply(my_list, function(x) {
  x[, which(is.numeric(x))] %>%
    map_dbl(mean, na.rm = TRUE)
}))

Step 3: Explaining the Code

Let’s break down what happens in this code:

• do.call(data.frame, ...): This line tells R to apply the data.frame function to our result vector (...) and create a new DataFrame.
• lapply(my_list, function(x) { ... }): The outer loop iterates over each DataFrame (x) in myList.
• x[, which(is.numeric(x))]: This line extracts only the numeric columns from the current DataFrame (x).
• map_dbl(mean, na.rm = TRUE): For each column, we apply the mean function and remove any missing values with na.rm=TRUE. The map_dbl function is used to ensure all results are numeric.

Example Walkthrough

Suppose our list of DataFrames contains three variables: Stat, P10, and S.

When we apply the sapply function, here’s what happens:

• First loop (outer): We get my_list[[1]], which is a DataFrame with three columns (Stat, P10, and S).
• Second loop (inner): For each column, we apply the mean function:
  • Column Stat: Mean of a (a), b (b) = 2.5
  • Column P10: Mean of 1.23 (1.23), 4.56 (4.56), 6.78 (6.78)
  • Column S: Mean of 3.67 (3.67), 5.67 (5.67), 6.78 (6.78)

After applying the mean function, we get a new DataFrame with three rows.

Step 4: Handling Non-Numeric Columns

To handle columns that are not numeric, we use which(is.numeric(x)). This returns an integer vector indicating which columns are numeric and can be used for calculating the mean. If there’s no match, then we consider those columns as non-numeric.

# Create a sample list of DataFrames with one non-numeric column
my_list <- lapply(1:10, function(i) data.frame(
  Stat = factor(sample(c("a", "b"), i)),
  P10 = rnorm(i),
  R = rnorm(i),
  S = runif(i)
))

# Create a single DataFrame using nested sapply functions with non-numeric columns
result_df <- do.call(data.frame, lapply(my_list, function(x) {
  x[, which(is.numeric(x))] %>%
    map_dbl(mean, na.rm = TRUE)
}))

# Print the result DataFrame
print(result_df)

Step 5: Conclusion

We’ve demonstrated how to use R’s built-in functions and sapply functions to create a single DataFrame where each column represents the mean value from its corresponding column in a list of DataFrames. By using this approach, you can easily combine multiple datasets into one cohesive structure, which is essential for data analysis.

Additional Resources

For more information on R’s built-in functions and sapply, refer to:

• data.frame() for creating DataFrames
• lapply() and [do.call()] for applying functions to vectors
• map_dbl() from the dplyr package for calculating double values

Last modified on 2025-03-14