Merging Multiple DataFrames by a Common Column Using bind_rows and pivot_wider

As data scientists, we often encounter situations where we need to merge multiple dataframes or datasets into one. In R, one of the most commonly used packages for data manipulation is the dplyr package. This post will cover how to use bind_rows and pivot_wider from the dplyr and tidyr packages respectively to merge a list of tables by a common column while suffixing column headings with the list item name.

Introduction

In this example, we’ll be using R as our programming language for data manipulation. We’ll assume you have some basic knowledge of R and its ecosystem.

When working with multiple dataframes, it can be challenging to determine which values belong to which dataframe. In order to solve this problem, we need to find a way to merge the tables while maintaining the information from each table.

The Problem

Suppose we have a list of four dataframes:

list_of_tables <- list(
  number = structure(list(sample_id = structure(1:3, levels = c("CSF_1", "CSF_2", "CSF_4"), class = "factor"), total = c(655, 331, 271), max = c(12, 5, 7)), row.names = c(NA, -3L), class = c("tbl_df", "tbl", "data.frame")),
  concentration_cm_3 = structure(list(sample_id = structure(1:3, levels = c("CSF_1", "CSF_2", "CSF_4"), class = "factor"), total = c(121454697, 90959097, 43080697), max = c(2050000, 2140000, 915500)), row.names = c(NA, -3L), class = c("tbl_df", "tbl", "data.frame")),
  volume_nm_3 = structure(list(sample_id = structure(1:3, levels = c("CSF_1", "CSF_2", "CSF_4"), class = "factor"), total = c(2412783009, 1293649395, 438426087), max = c(103500000, 117400000, 23920000)), row.names = c(NA, -3L), class = c("tbl_df", "tbl", "data.frame")),
  area_nm_2 = structure(list(sample_id = structure(1:3, levels = c("CSF_1", "CSF_2", "CSF_4"), class = "factor"), total = c(15259297.4, 7655352.2, 3775922), max = c(266500, 289900, 100400)), row.names = c(NA, -3L), class = c("tbl_df", "tbl", "data.frame"))
)

list_of_tables

We want to merge these dataframes by the sample_id column and suffix the column headings with the list item name.

The Solution

One way to achieve this is by using bind_rows from the dplyr package. We’ll use the .id = 'id' argument to add a new column containing the original dataframe names, which will be used as a suffix for the merged columns.

Here’s how we can do it:

library(dplyr)
library(tidyr)

merged_tables <- bind_rows(list_of_tables, .id = 'id') %>% 
  pivot_wider(names_from = id,
              values_from = c(total, max))

merged_tables

This will produce the following output:

The output is a new dataframe with the sample_id column, which contains all values from the original dataframes. The id column has been added as a suffix to each value in the total, max columns.

Conclusion

Merging multiple dataframes by a common column can be achieved using the bind_rows and pivot_wider functions from the dplyr and tidyr packages, respectively. By adding the .id = 'id' argument to bind_rows, we can create a new column containing the original dataframe names, which will be used as a suffix for the merged columns. This approach is particularly useful when working with large datasets or complex data manipulation tasks.

Note: The example above uses R’s vectorized operations and dplyr package functions. Depending on your programming language of choice, you may need to use different functions and approaches to achieve the same result.

Last modified on 2024-02-19