Introduction

Merging multiple data frames is a pivotal skill in data manipulation. Whether you’re handling small-scale datasets or large-scale ones, mastering the art of merging can significantly enhance your efficiency. In this tutorial, we’ll delve into various methods of merging data frames in R, using straightforward examples to demystify the process.

Understanding the Data

Before we dive into merging data frames, let’s familiarize ourselves with the data at hand. We have a list named random_list, which comprises three samples (sample1, sample2, and sample3). Each sample consists of 50 random numbers generated from a normal distribution using the rnorm() function.

random_list <- list(
  sample1 = rnorm(50), 
  sample2 = rnorm(50), 
  sample3 = rnorm(50)
  )

Method 1: Using `cbind()` and `rbind()`

One approach to merge data frames is by combining them column-wise using cbind() or row-wise using rbind().

# Creating data frames from the list
df1 <- data.frame(ID = 1:50, Value = random_list$sample1)
df2 <- data.frame(ID = 1:50, Value = random_list$sample2)
df3 <- data.frame(ID = 1:50, Value = random_list$sample3)

# Merging data frames column-wise
cbined_df <- cbind(df1, df2$Value, df3$Value)
head(cbined_df)

  ID      Value  df2$Value  df3$Value
1  1 -0.8828435 -1.5116620  1.4729716
2  2  0.7371127  0.1140000  0.6455959
3  3  0.7611256  0.9740632 -0.2355084
4  4  2.0613462 -1.0748615 -0.4654242
5  5  0.1966095 -0.2415080  0.1059656
6  6  0.3217213 -1.3252347  0.9432906

# Merging data frames row-wise
rbined_df <- rbind(df1, df2, df3)
head(rbined_df)

  ID      Value
1  1 -0.8828435
2  2  0.7371127
3  3  0.7611256
4  4  2.0613462
5  5  0.1966095
6  6  0.3217213

In the first example, cbind() combines df1, df2, and df3 column-wise, creating a new data frame combined_df. In the second example, rbind() stacks df1, df2, and df3 row-wise, appending the rows to create combined_df.

Method 2: Using `purrr::map()` and `data.frame()`

With the purrr package, you can efficiently merge data frames within a list using map() and data.frame().

library(purrr)

# Merging data frames within the list
merged_list <- map(random_list, data.frame)

# Combining data frames row-wise
combined_df <- do.call(rbind, merged_list)
head(combined_df)

             .x..i..
sample1.1 -0.8828435
sample1.2  0.7371127
sample1.3  0.7611256
sample1.4  2.0613462
sample1.5  0.1966095
sample1.6  0.3217213

Here, map() iterates over each element of random_list and converts them into data frames using data.frame(). Then, do.call(rbind, merged_list) combines the data frames row-wise, creating combined_df.

Method 3: Using `purrr::map_df()`

Another purrr function, map_df(), directly merges data frames within a list, producing a single combined data frame.

# Merging data frames within the list
combined_df <- map_df(random_list, cbind)
head(combined_df)

# A tibble: 6 × 3
  sample1[,1] sample2[,1] sample3[,1]
        <dbl>       <dbl>       <dbl>
1      -0.883      -1.51        1.47 
2       0.737       0.114       0.646
3       0.761       0.974      -0.236
4       2.06       -1.07       -0.465
5       0.197      -0.242       0.106
6       0.322      -1.33        0.943

By employing map_df() with cbind, we merge data frames within random_list, resulting in combined_df, which is a single merged data frame.

Encouragement to Try on Your Own

Now that you’ve explored different methods of merging data frames in R, I encourage you to experiment with your datasets. Practice merging data frames using various columns and explore how different merge methods influence the resulting data frame. The more hands-on experience you gain, the more proficient you’ll become in data manipulation with R.

In conclusion, merging multiple data frames in R is a foundational skill for any data analyst or scientist. By understanding the principles behind various merge methods and experimenting with real datasets, you’ll enhance your data manipulation capabilities and streamline your workflow.

Happy coding!

Bonus Section

One more method of this for you and I think I like this one the best. It’s very simple and adds the name of the list item as a value in a column.

stacked_list <- utils::stack(random_list)
head(stacked_list)

      values     ind
1 -0.8828435 sample1
2  0.7371127 sample1
3  0.7611256 sample1
4  2.0613462 sample1
5  0.1966095 sample1
6  0.3217213 sample1

Here is yet another method to merge data frames in R. This method is simple and effective, providing a straightforward way to combine data frames within a list.

# Merging data frames within the list
mapped_list <- map(random_list, \(x) data.frame(x)) |>
  list_rbind()
head(mapped_list)

           x
1 -0.8828435
2  0.7371127
3  0.7611256
4  2.0613462
5  0.1966095
6  0.3217213

This next method comes courtesy of a reader who suggested using the list2DF function from base R. This method is concise and efficient, making it a valuable addition to your data manipulation toolkit.

list2DF(random_list) |> head()

     sample1    sample2    sample3
1 -0.8828435 -1.5116620  1.4729716
2  0.7371127  0.1140000  0.6455959
3  0.7611256  0.9740632 -0.2355084
4  2.0613462 -1.0748615 -0.4654242
5  0.1966095 -0.2415080  0.1059656
6  0.3217213 -1.3252347  0.9432906