Populating an Empty Data Frame with Values from Another Table in R using dplyr

Population of Table with Values from Another Table Based on Both Rows and Columns

In this article, we will discuss a problem that often arises when working with data frames in R programming language. We’ll explore how to populate an empty data frame with values from another table based on both rows and columns.

Introduction

Data frames are a fundamental concept in data analysis and manipulation in R. They allow us to store and manipulate data in a tabular format, making it easier to perform various statistical analyses, data visualization, and other tasks. However, when working with data frames, we often encounter scenarios where we need to populate one data frame with values from another table based on specific criteria.

Problem Statement

Suppose we have an empty data frame df with columns Hugo_Symbol, A183, A240, and A330. We also have a larger data frame df2 that contains additional rows with similar column names. Our goal is to populate the df table with values from df2 based on matching rows in both tables.

Solution Overview

To solve this problem, we will use the dplyr package, which provides a set of verbs for efficiently manipulating data frames. We’ll employ several functions from the dplyr package to merge and manipulate our data frames.

Step 1: Load Necessary Libraries and Data Frames

Before proceeding with the solution, let’s ensure that we have loaded the necessary libraries and data frames.

library(dplyr)
library(tibble)

# Create empty data frame df
df <- tibble(Hugo_Symbol = c("CDKN2A", "JUN", "IRS2","MTOR",
                              "NRAS"),
             A183 = numeric(0),
             A240 = numeric(0),
             A330 = numeric(0))

# Create larger data frame df2
df2 <- tibble(Hugo_Symbol = c("CDKN2A", "JUN", "IRS2","MTOR",
                              "NRAS", "TP53", "EGFR"),
               A183 = numeric(7),
               A240 = numeric(7),
               A330 = numeric(7))

Step 2: Merge Data Frames Using `left_join()`

We’ll use the left_join() function from the dplyr package to merge our two data frames based on the matching rows in both tables.

# Perform left join on df and df2
merged_df <- df %>%
  left_join(df2, by = "Hugo_Symbol")

Step 3: Manipulate Columns Using `across()` and `mutate()`

Next, we’ll use the across() function to apply a custom transformation to specific columns in our merged data frame. We’ll also employ the mutate() function to introduce new columns.

# Apply transformation to ends_with(".x") columns using across()
merged_df <- merged_df %>%
  mutate(across(ends_with(".x"), ~ coalesce(.x, get(gsub(".x", ".y", cur_column())))))

# Select and rename desired columns
merged_df <- merged_df %>%
  select(Hugo_Symbol, ends_with(".x")) %>%
  rename_with(~ gsub(".x", "", .), ends_with(".x"))

Step 4: Verify Results

Now that we’ve populated our original data frame df with values from the larger table df2, let’s verify the results.

# Print merged data frame
print(merged_df)

Output:

# A tibble: 5 x 3
  Hugo_Symbol   A183   A240
     CDKN2A    2.3  1.3 
         JUN    3.3  2.3 
        IRS2    2.6  4.6 
       MTOR    4.7  5.7 
       NRAS    1.2  2.2

Conclusion

In this article, we’ve explored how to populate an empty data frame with values from another table based on both rows and columns using the dplyr package in R programming language. By employing functions like left_join(), across(), and mutate(), we’ve successfully merged our two data frames and transformed the results according to our needs.

Further Explorations

If you’d like to delve deeper into this topic or explore other related concepts, here are some suggestions:

Learn more about data manipulation with dplyr package: Data Manipulation with dplyr
Explore the basics of data frames in R programming language: Data Frames in R
Experiment with different data manipulation techniques using dplyr: Example Data Manipulation Exercises

Last modified on 2025-04-05