# How to Combine Two Data Frames in R

Gustavo du Mortier Feb 22, 2024

In data analysis and manipulation in R, the need to merge or combine two separate data frames arises frequently. Whether consolidating information from multiple sources or conducting relational operations, learning how to merge data frames is crucial for efficient data handling.

In this article, we delve into various techniques and methods available in R to seamlessly merge two data frames. From basic functions like `rbind()` to more advanced tools like those offered by packages such as `dplyr`, `plyr`, and the versatile `merge()` function, we’ll explore each approach, providing insights into their syntax, functionality, and practical applications.

## How to Combine Two Data Frames in R Using the `rbind()` Function

The `rbind()` function is a straightforward tool for combining or merging two data frames by stacking the rows of two or more data frames on top of each other.

The syntax of the `rbind()` function is straightforward:

``````result <- rbind(dataframe1, dataframe2, ...)
``````

Here, `dataframe1`, `dataframe2`, etc., are the data frames you want to combine. The function takes one or more data frames as arguments and returns a new data frame with the rows stacked on top of each other.

The `rbind()` function essentially stacks the rows of the specified data frames one after another to create a new data frame. It aligns the columns based on their names, so the data frames must have the same column names and data types.

If the column names are not identical across the data frames, the function will throw an error.

Now, let’s delve into a practical example to illustrate how to use the `rbind()` function in R.

Suppose we have two data frames, `x` and `y`, with some overlapping columns. We aim to combine these data frames into a single one using the `rbind()` function.

``````x <- data.frame(
a = c(218, 415, 339),
b = c(25, 19, 43),
c = c(950, 872, 645)
)
y <- data.frame(
a = c(309, 115),
b = NA, c = c(799, 814)
)

z <- rbind(x, y)

print("Combined dataframe:")
print(z)
``````

In this example, we first define two data frames, `x` and `y`, with differing numbers of rows and some overlapping columns (`a` and `c`). We then use the `rbind()` function to combine these data frames into a new data frame, `z`.

The `rbind()` function seamlessly stacks the rows of `x` and `y`, aligning them based on their column names. Since both `x` and `y` have columns `a` and `c`, the function successfully combines them.

The resulting data frame `z` contains all the rows from both `x` and `y`.

Output:

As shown in the output, the combined data frame `z` consists of all the rows from `x` followed by all the rows from `y`, maintaining the structure and column names of the original data frames.

## How to Combine Two Data Frames in R Using the `dplyr` Package

In addition to base R functions like `rbind()`, the `dplyr` package provides a powerful set of functions for data manipulation, including merging or combining data frames.

The `dplyr` package offers several functions for merging data frames, including `bind_rows()` for row-wise merging. The syntax is as follows:

``````result <- dplyr::bind_rows(dataframe1, dataframe2, ...)
``````

Here, `dataframe1`, `dataframe2`, etc., are the data frames you want to combine. The `dplyr::bind_rows()` function takes one or more data frames as arguments and returns a new data frame with the rows stacked on top of each other.

The `dplyr::bind_rows()` function behaves similarly to `rbind()`, stacking the rows of the specified data frames one after another to create a new data frame. It aligns the columns based on their names, ensuring that the columns with the same names are merged correctly.

Like `rbind()`, the data frames must have identical column names and data types for successful merging.

Now, let’s proceed with a practical example to demonstrate how to use the `dplyr` package for merging data frames in R.

Suppose we have two data frames, `x` and `y`, with some overlapping columns. We aim to combine these data frames into a single one using the `dplyr` package’s `bind_rows()` function.

``````library(dplyr)

x <- data.frame(
a = c(218, 415, 339),
b = c(25, 19, 43),
c = c(950, 872, 645)
)
y <- data.frame(
a = c(309, 115),
c = c(799, 814)
)

z <- dplyr::bind_rows(x, y)

print("Combined dataframe:")
print(z)
``````

In this example, we begin by loading the `dplyr` package using the `library()` function. Next, we define two data frames, `x` and `y`, with differing numbers of rows and some overlapping columns (`a` and `c`).

We then utilize the `dplyr::bind_rows()` function to combine these data frames into a new data frame, `z`.

The `dplyr::bind_rows()` function seamlessly stacks the rows of `x` and `y`, aligning them based on their column names. Since both `x` and `y` have columns `a` and `c`, the function successfully combines them.

The resulting data frame `z` contains all the rows from both `x` and `y`.

Output:

As illustrated in the output, the combined data frame `z` consists of all the rows from `x` followed by all the rows from `y`, maintaining the structure and column names of the original data frames.

## How to Combine Two Data Frames in R Using the `plyr` Package

In addition to the base R functions and the `dplyr` package, the `plyr` package also offers functionality for merging or combining data frames in R. This package is particularly known for its consistent and intuitive syntax, making it popular among R users for data manipulation tasks.

The `plyr` package provides the `join()` function, which can be used to merge two data frames based on common variables. The syntax is as follows:

``````result <- join(dataframe1, dataframe2, by = "common_column", type = "join_type")
``````

Where:

• `x` and `y` : The data frames to be merged.
• `by`: The common column(s) based on which the merge should be performed.
• `type`: The type of join to be performed (e.g., `inner`, `left`, `right`, `full`, `semi`, `anti`).

The `plyr::join()` function performs an SQL-style join operation between the two data frames based on the specified common column(s).

It combines rows from both data frames where the common column(s) have matching values. The type of join determines how unmatched rows are handled.

Before we begin, ensure that the `plyr` package is installed. If not, you can install it from CRAN using the following command:

``````install.packages("plyr")
``````

Now, let’s proceed with a practical example to demonstrate how to use the `plyr` package for merging data frames in R.

``````library(plyr)

df1 <- data.frame(
ID = c(1, 2, 3),
Name = c("John", "Ray", "Dan")
)
df2 <- data.frame(
ID = c(2, 3, 4),
Age = c(25, 30, 35)
)

inner_merged <- join(df1, df2, by = "ID", type = "inner")

print("Combined dataframe:")
print(inner_merged)
``````

In this example, we first load the `plyr` package using the `library()` function. Then, we create two sample data frames, `df1` and `df2`, representing individuals’ information with some overlapping columns.

We aim to merge these data frames based on the common column `ID`.

We then use the `plyr::join()` function to perform an inner join between `df1` and `df2`, specifying `"ID"` as the common column to merge on and `"inner"` as the type of join. This ensures that only rows with matching `ID` values in both data frames are retained in the merged result.

Finally, we print the merged data frame `inner_merged` to view the result of the inner join operation.

Output:

As shown in the output, the inner join operation using the `plyr` package combines rows from `df1` and `df2` where the `ID` values match, resulting in a merged data frame containing columns from both data frames.

## How to Combine Two Data Frames in R Using the `merge()` Function

In addition to the functions and packages discussed above, the `merge()` function is also a powerful tool for combining or merging two data frames based on common columns.

The basic syntax for merging data frames using the `merge()` function is as follows:

``````merge(x, y, by = "common_column", all.x = FALSE, all.y = FALSE, ...)
``````

Where:

• `x` and `y` are the data frames or data tables to be merged.
• `by` specifies the common column(s) based on which the merge should be performed.
• `all.x` and `all.y` are logical values indicating whether to include all observations from `x` or `y`, respectively.
• Additional arguments (`...`) can be used to specify additional options such as the type of join (`all`, `all.x`, `all.y`, `intersect`), sorting, or other parameters.

The `merge()` function performs an SQL-style join operation between the two data frames based on the specified common column(s). It combines rows from both data frames where the common column(s) have matching values.

Now, let’s proceed with a practical example to demonstrate how to use the `merge()` function for merging data frames in R.

``````df1 <- data.frame(
ID = c(1, 2, 3),
Name = c("John", "Ray", "Dan")
)
df2 <- data.frame(
ID = c(2, 3, 4),
Age = c(25, 30, 35)
)

inner_merged <- merge(df1, df2, by = "ID")

print("Combined dataframe:")
print(inner_merged)
``````

In this example, we create two sample data frames, `df1` and `df2`, representing individuals’ information with some overlapping columns. We aim to merge these data frames based on the common column `ID`.

We then use the `merge()` function to perform an inner join between `df1` and `df2`, specifying `"ID"` as the common column to merge on. By default, `merge()` performs an inner join, so only rows with matching `ID` values in both data frames are retained in the merged result.

Finally, we print the merged data frame `inner_merged` to view the result of the inner join operation.

Output:

As shown in the output, the inner join operation using the `merge()` function combines rows from `df1` and `df2` where the `ID` values match, resulting in a merged data frame containing columns from both data frames.

## Conclusion

Combining or merging two data frames in R is a fundamental operation in data analysis and manipulation. We explored several methods to accomplish this task, including using base R functions like `rbind()`, as well as leveraging packages such as `dplyr`, `plyr`, and `merge()`.

The `rbind()` function provides a straightforward approach for combining data frames by stacking their rows, while packages like `dplyr` and `plyr` offer more advanced functionalities for data manipulation, including various types of joins and efficient row-wise operations. Additionally, the `merge()` function provides fast and efficient methods for merging data frames, making it suitable for handling large datasets.

Ultimately, the choice of method depends on factors such as the complexity of the data, the desired output, and performance considerations. By understanding the syntax and functionality of these methods, you can effectively merge data frames to streamline your data analysis workflows and derive meaningful insights from your data.