How to Add Row to DataFrame in R?

One of the fundamental data structures in R is a DataFrame. DataFrames are similar to tables in a database, where the columns are variables and the rows are observations. Often, it is required to add new observations or rows to a DataFrame, either manually or by combining different DataFrames. This article discusses various methods to add a row to a DataFrame in R, exploring functions and packages that allow seamless integration of new data.

Create Sample DataFrame:

Let’s create a sample dataframe for illustration.

# Create a DataFrame
df <- data.frame(
  Name = c("John", "Jane"),
  Age = c(21, 22),
  Grade = c("A", "B")
)
print(df)

Output:

  Name Age Grade
1 John  21     A
2 Jane  22     B

1. Adding Rows Using rbind():

The rbind() function is the simplest and most common way to add a row to a DataFrame. It binds the rows of two DataFrames together.

Syntax:

new_df <- rbind(old_df, new_row)

Example:

# Adding a new row using rbind()
new_row <- data.frame(Name = "Mike", Age = 23, Grade = "C")
df <- rbind(df, new_row)
print(df)

Output:

  Name Age Grade
1 John  21     A
2 Jane  22     B
3 Mike  23     C

Here, new_row is added to the existing DataFrame df, and the modified DataFrame is reassigned to df.

Constraints:

• The column names of the DataFrame and the new row must match.
• The corresponding columns must be of the same type.

2. Using the add_row() Function from the tibble Package:

The tibble package provides an add_row() function that enables the easy addition of new rows.

Syntax:

library(tibble)
new_df <- add_row(old_df, ...)

Example:

# Using add_row() to add a new row
library(tibble)
df <- add_row(df, Name = "Sara", Age = 25, Grade = "A")
print(df)

Output:

  Name Age Grade
1 John  21     A
2 Jane  22     B
3 Mike  23     C
4 Sara  25     A

3. Adding Rows with dplyr:

The dplyr package is part of the tidyverse family of packages, providing versatile data manipulation capabilities. To add a row, use the bind_rows() function.

Syntax:

library(dplyr)
new_df <- bind_rows(old_df, new_row)

Example:

# Using bind_rows() to add a new row
library(dplyr)
new_row <- data.frame(Name = "Robert", Age = 24, Grade = "B")
df <- bind_rows(df, new_row)
print(df)

Output:

    Name Age Grade
1   John  21     A
2   Jane  22     B
3   Mike  23     C
4   Sara  25     A
5 Robert  24     B

4. Using do.call() and rbind():

do.call() can be used with rbind() to add multiple rows from a list of rows.

Syntax:

new_df <- do.call(rbind, list(old_df, row1, row2, ...))

Example:

# Adding multiple rows using do.call() and rbind()
row1 <- data.frame(Name = "Emma", Age = 22, Grade = "B")
row2 <- data.frame(Name = "Chris", Age = 23, Grade = "C")
df <- do.call(rbind, list(df, row1, row2))
print(df)

Output:

    Name Age Grade
1   John  21     A
2   Jane  22     B
3   Mike  23     C
4   Sara  25     A
5 Robert  24     B
6   Emma  22     B
7  Chris  23     C

Constraints and Considerations:

• When using rbind(), bind_rows(), and do.call() with rbind(), ensure column names and types match between the DataFrames.
• add_row() is more flexible and can automatically coerce types and partially match column names, making it suitable for adding single rows.

5. Adding Rows by Indexing:

You can add a new row by directly indexing the row after the last row in the DataFrame and assigning the new row’s values.

Syntax:

old_df[nrow(old_df) + 1, ] <- new_row

Example:

# Adding a new row using indexing
df[nrow(df) + 1, ] <- data.frame(Name = "Olivia", Age = 23, Grade = "B")
print(df)

Output:

    Name Age Grade
1   John  21     A
2   Jane  22     B
3   Mike  23     C
4   Sara  25     A
5 Robert  24     B
6   Emma  22     B
7  Chris  23     C
8 Olivia  23     B

Conclusion

Adding rows to a DataFrame is a common operation in R programming, which can be achieved using various methods such as rbind(), add_row(), bind_rows(), do.call() with rbind(), and direct indexing. The choice of method depends on the specific requirements, such as whether you are adding a single row or multiple rows, and whether you need to manage different column names or types.

When performing any data manipulation tasks, including adding rows to a DataFrame, be mindful of the data’s integrity and ensure that the new data complies with the existing data structure, particularly in terms of column names and data types.