How to Combine Two Columns into One in R

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The R programming language is widely used for data manipulation, statistical analysis, and visualization. One common data manipulation task is combining two columns into one in a data frame. This article aims to provide an in-depth guide on various ways to accomplish this task, each with its advantages, disadvantages, and appropriate use-cases.

Introduction to Data Frames

Data frames are a type of list, but with an additional structure that makes it two-dimensional (like a table) and hence more convenient for statistical analysis. Data frames are an essential data structure in R and are frequently used in various analyses. Learning how to manipulate them effectively is vital for data science professionals and R enthusiasts alike.

To follow along with some examples, you can create a simple data frame as shown below:

# Create a simple data frame
my_data <- data.frame(
  Name = c('Alice', 'Bob', 'Charlie', 'David'),
  Age = c(29, 35, 37, 22),
  Occupation = c('Engineer', 'Doctor', 'Artist', 'Student')

Methods to Combine Columns

Method 1: Using paste( ) or paste0( )


The simplest way to combine two columns into a single column is to use the paste() or paste0() functions. Here is the syntax for these functions:

paste(..., sep = " ", collapse = NULL)
paste0(..., collapse = NULL)


For example, let’s combine the Name and Occupation columns:

my_data$Combined <- paste(my_data$Name, my_data$Occupation, sep = ", ")

Or using paste0() if you don’t need a separator:

my_data$Combined <- paste0(my_data$Name, my_data$Occupation)

Advantages and Disadvantages

  • Advantages: Simple and easy to use.
  • Disadvantages: Lacks flexibility for more complex operations.

Method 2: Using mutate( ) with dplyr


The mutate() function from the dplyr package can be used to add new variables to a data frame:

mutate(.data, ...)


First, install and load the dplyr package:


Now, use mutate to combine columns:

my_data <- my_data %>% mutate(Combined = paste(Name, Occupation, sep = ", "))

Advantages and Disadvantages

  • Advantages: Provides more flexibility when combined with other dplyr functions.
  • Disadvantages: Requires an additional package; may be overkill for simple tasks.

Method 3: Using cbind( ) and subset( )


You can also use the cbind() function to bind columns together and subset() to remove the originals:

cbind(x, ...)
subset(x, select, ...)


new_data <- cbind(my_data, Combined = paste(my_data$Name, my_data$Occupation, sep = ", "))
new_data <- subset(new_data, select = -c(Name, Occupation))

Advantages and Disadvantages

  • Advantages: Good for creating a new data frame without altering the original.
  • Disadvantages: Verbose and may consume extra memory.

Method 4: Using interaction( )


For factors, the interaction() function can be useful:

interaction(..., drop = TRUE, lex.order = FALSE, sep = “:”, collapse = NULL)


my_data$Combined <- interaction(my_data$Name, my_data$Occupation, sep = ", ")

Advantages and Disadvantages

  • Advantages: Ideal for combining factors; maintains factor levels.
  • Disadvantages: Only applicable for factor columns.

Method 5: Using sprintf( )


The sprintf() function provides more control over the format of the new column:

sprintf(fmt, ...)


my_data$Combined <- sprintf("%s, %s", my_data$Name, my_data$Occupation)

Advantages and Disadvantages

  • Advantages: Offers fine-grained control over formatting.
  • Disadvantages: More complex to use for simple concatenation tasks.

Case Studies

  1. Combining Numerical and Character Columns: When combining such columns, converting the numerical column to character first is advisable.
  2. Creating an Address Column: Consider a scenario where you need to form an address by combining house number, street, city, and state. This could involve using paste() with multiple separators.
  3. Creating Interaction Terms: In statistical models, interaction terms can be created using the interaction() function.


R provides various methods to combine columns in a data frame, each with its own advantages and disadvantages. Depending on your specific requirements and the size of your dataset, you can choose the method that best suits your needs. From simple functions like paste() to more advanced methods using dplyr, there is a way to accomplish your objective efficiently and effectively.

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