How to Combine Data Frames with the Same Column Names in R Using Dplyr Library

Binding Data Frames within a List that Have Same Column Headers using R Functions

In this article, we will discuss how to create a combined data frame from multiple data frames within a list that have the same column headers. We will use R functions and techniques to achieve this.

Introduction

Data manipulation is an essential part of any data analysis task. When working with data in R, it’s not uncommon to encounter multiple data frames that need to be combined into one. However, when these data frames have the same column headers, things can get tricky. In this article, we will explore ways to bind data frames within a list that have the same column headers using R functions.

Problem Statement

The problem statement is as follows:

Suppose we have a list of data frames TL_JSON_TEXT1, TL_JSON_text2, etc., where each data frame has the same column headers. We want to create a combined data frame from these individual data frames.

Here’s an example:

# Create some sample data frames
tank1 <- data.frame(TankName = c("tank1", "tank1", "tank1"), 
                    Capacity = c(100, 100, 100), 
                    PercentFull = c(10, 13, 20), 
                    Date = c("1/2/22", "1/3/22", "1/5/22"))

tank2 <- data.frame(TankName = c("tank2"), 
                    Capacity = c(200), 
                    PercentFull = c(50), 
                    Date = c("2/7/22"))

tank3 <- data.frame(TankName = c("tank3", "tank3"), 
                    Capacity = c(300, 300), 
                    PercentFull = c(80, 60), 
                    Date = c("1/3/22", "1/6/22"))

Nested_DF <- list(tank1, tank2, tank3)

We want to create a combined data frame from Nested_DF.

Approach 1: Using do.call(rbind, ...)

One way to achieve this is by using the do.call function in combination with rbind. Here’s how we can do it:

# Create a list of data frames
TL_JSON_TEXT1 <- tank1

# Use do.call and rbind to create a combined data frame
Binded_TL <- do.call(rbind, TL_JSON_TEXT1)

This code creates a new data frame Binded_TL by binding together the rows from tank1.

However, this approach has some limitations. For example, if we want to add more data frames to our list, we would need to modify the code accordingly.

Approach 2: Using purrr::map and jsonlite::fromJSON

Another way to achieve this is by using the purrr::map function in combination with jsonlite::fromJSON. Here’s how we can do it:

# Create a list of data frames
TL_JSON <- purrr::map(Binded_TL, jsonlite::fromJSON)

# Extract the $Data column from each data frame
TL_JSON2 <- TL_JSON[[1]]$Data

# Use for loop to create a combined data frame
for (i in 1:length(TL_JSON)) {
  TL_JSON2[[i]] <- as.data.frame(TL_JSON[[i]]$Data)
}

This code creates a new list TL_JSON by mapping over the elements of Binded_TL, extracting the $Data column from each data frame, and then using a for loop to create a combined data frame.

However, this approach also has some limitations. For example, if we want to add more data frames to our list, we would need to modify the code accordingly.

Approach 3: Using dplyr::bind_rows

The most efficient way to bind multiple data frames together is by using the dplyr::bind_rows function. Here’s how we can do it:

# Load dplyr library
library(dplyr)

# Create a list of data frames
TL_JSON <- list(tank1, tank2, tank3)

# Use bind_rows to create a combined data frame
Combined_DF <- bind_rows(TL_JSON)

This code creates a new data frame Combined_DF by binding together the rows from all three data frames.

Conclusion

In this article, we explored ways to bind data frames within a list that have the same column headers using R functions. We discussed three approaches: using do.call(rbind, ...), purrr::map and jsonlite::fromJSON, and dplyr::bind_rows. While each approach has its own limitations, we recommend using dplyr::bind_rows for the most efficient solution.

Additional Resources

R documentation: bind_rows
Stack Overflow: How to combine data frames with the same column names in R?

Code Snippets

# Create some sample data frames
tank1 <- data.frame(TankName = c("tank1", "tank1", "tank1"), 
                    Capacity = c(100, 100, 100), 
                    PercentFull = c(10, 13, 20), 
                    Date = c("1/2/22", "1/3/22", "1/5/22"))

# Create a list of data frames
TL_JSON <- list(tank1, tank2, tank3)

# Use bind_rows to create a combined data frame
Combined_DF <- bind_rows(TL_JSON)

# Load dplyr library
library(dplyr)

# Create some sample data frames
tank1 <- data.frame(TankName = c("tank1", "tank1", "tank1"), 
                    Capacity = c(100, 100, 100), 
                    PercentFull = c(10, 13, 20), 
                    Date = c("1/2/22", "1/3/22", "1/5/22"))

# Create a list of data frames
TL_JSON <- list(tank1)

# Use bind_rows to create a combined data frame
Combined_DF <- bind_rows(TL_JSON)

# Load dplyr library
library(dplyr)

# Create some sample data frames
tank1 <- data.frame(TankName = c("tank1", "tank1", "tank1"), 
                    Capacity = c(100, 100, 100), 
                    PercentFull = c(10, 13, 20), 
                    Date = c("1/2/22", "1/3/22", "1/5/22"))

# Create a list of data frames
TL_JSON <- list(tank1, tank2)

# Use bind_rows to create a combined data frame
Combined_DF <- bind_rows(TL_JSON)

Last modified on 2023-07-12