The rbind() function in R is “used to combine vectors, matrices, and data frames by rows“.
Syntax
rbind(input_data, data_to_bind)Parameters
- input_data: It is input data.
- data_to_bind: It is the data that needs to be bound.
Visual representation
Example 1: Rbind vectors into a matrix
You can use the rbind() function in R to “combine vectors into a matrix by rows”.
# Create two vectors
a <- c(11, 31, 32, 41, 51)
b <- c(71, 72, 81, 31, 21)
# Rbind the two vectors into a matrix
rv_matrix <- rbind(a, b)
# View matrix
print(rv_matrix)
Output
[,1] [,2] [,3] [,4] [,5]
a 11 31 32 41 51
b 71 72 81 31 21
Example 2: Rbind Vector to a Data Frame
You can row-bind a single vector to a data frame using the “rbind()” function.
# Create a data frame
df <- data.frame(col1 = c("a", "b", "c"), col2 = c("A", "B", "C"))
# Create a vector
new_row <- c("new1", "new2")
# Append the vector as a new row to the data frame
df_new <- rbind(df, new_row)
# View the updated data frame
df_new
Output
col1 col2
1 a A
2 b B
3 c C
4 new1 new2
Example 3: Rbind multiple vectors to a Data Frame
Use the “rbind()” function to row-bind multiple vectors to an existing data frame.
df <- data.frame(col1 = c("a", "b", "c"), col2 = c("A", "B", "C"))
# Create vectors
new_row1 <- c("new1", "new2")
new_row2 <- c("new3", "new4")
# Append the vectors as new rows to the data frame
df_new <- rbind(df, new_row1, new_row2)
# View the updated data frame
df_new
Output
col1 col2
1 a A
2 b B
3 c C
4 new1 new2
5 new3 new4
Example 4: Rbind Two Data Frames
You can use the “rbind()” function to row-bind “two data frames”.
# Create two data frames
df1 <- data.frame(col1 = c("a", "b", "c"), col2 = c("A", "B", "C"))
df2 <- data.frame(col1 = c("d", "e", "f"), col2 = c("D", "E", "F"))
# Row-bind the two data frames
df_new <- rbind(df1, df2)
# View the updated data frame
df_new
Output
col1 col2
1 a A
2 b B
3 c C
4 d D
5 e E
6 f F
That’s it!
Krunal Lathiya is a seasoned Computer Science expert with over eight years in the tech industry. He boasts deep knowledge in Data Science and Machine Learning. Versed in Python, JavaScript, PHP, R, and Golang. Skilled in frameworks like Angular and React and platforms such as Node.js. His expertise spans both front-end and back-end development. His proficiency in the Python language stands as a testament to his versatility and commitment to the craft.