R matrix is a two-dimensional, rectangular data structure that consists of elements of the same atomic type (most commonly numeric, but can be logical, character, or complex).
Matrices are suited for mathematical and statistical operations, where linear algebra (like matrix multiplication, transpose, etc.) plays a key role.
Creating a Matrix
To create a matrix in R, you can use the matrix() function. The nrow and ncol properties define the dimension of the Matrix.
Syntax
matrix(values, nrow=3, ncol=3)Parameters
- values: The first parameter values are required, containing the values that must be filled in the Matrix.
- nrow and ncol: The nrow and ncol parameters can be optional if one is provided. For example, if your Matrix’s length is 9 and if you give the nrow =3 then you don’t have to provide ncol. It will take ncol = 3 automatically.
Return value
It returns the Matrix of provided values and dimensions.
Visual representation
Example
vec <- 1:9
mtrx <- matrix(vec, 3, 3)
mtrx
You will see the following (3 * 3) matrix.
[,1] [,2] [,3]
[1,] 1 4 7
[2,] 2 5 8
[3,] 3 6 9It is not necessary to provide both dimension values. However, if one of the values of the dimension is provided, the other will take from the length of the data.
mtrx <- matrix(1:9, nrow = 3)
print(mtrx)Output
[,1] [,2] [,3]
[1,] 1 4 7
[2,] 2 5 8
[3,] 3 6 9We can change this order and fill the matrix row-wise.
vec <- 1:9
mtrx <- matrix(vec, nrow = 3, byrow = TRUE)
print(mtrx) Output
[,1][,2][,3]
[1,] 1 2 3
[2,] 4 5 6
[3,] 7 8 9Assigning columnnames and rownames in Matrix
To assign columnnames and rownames, use the dimnames property. For example, we can name the columns and rows by passing a two-element list to the argument dimnames.
vec <- 1:9
mtrx <- matrix(vec,
nrow = 3, ncol = 3,
dimnames = list(c("R1", "R2", "R3"), c("C1", "C2", "C3"))
)
mtrx
Output
C1 C2 C3
R1 1 4 7
R2 2 5 8
R3 3 6 9
Use the rownames(), colnames(), and column names in Matrix to access row names.
vec <- 1:9
mtrx <- matrix(vec,
nrow = 3, ncol = 3,
dimnames = list(c("R1", "R2", "R3"), c("C1", "C2", "C3"))
)
print(colnames(mtrx))
print(rownames(mtrx))
Output
[1] "C1" "C2" "C3"
[1] "R1" "R2" "R3"
To change the column names and row names, we can reassign the values using the following syntax.
colnames(x) <- c("C1","C2","C3")
rownames(x) <- c("R1","R2","R3")See the following example.
vec <- 1:9
mtrx <- matrix(vec,
nrow = 3, ncol = 3,
dimnames = list(c("R1", "R2", "R3"), c("C1", "C2", "C3"))
)
cat("Before changing the names of row and column\n")
print(colnames(mtrx))
print(rownames(mtrx))
cat("After changing the names of row and column\n")
colnames(mtrx) <- c("K1", "K2", "K3")
rownames(mtrx) <- c("B1", "B2", "B3")
print(colnames(mtrx))
print(rownames(mtrx))
Output
Before changing the names of row and column
[1] "C1" "C2" "C3"
[1] "R1" "R2" "R3"
After changing the names of row and column
[1] "K1" "K2" "K3"
[1] "B1" "B2" "B3"
Creating Matrix using cbind() and rbind()
You can also create a matrix by using the two functions cbind() and rbind(), as in column bind and row bind.
matrix_column_binded <- cbind(1:3, 4:6)
cat("Column-binded Matrix\n")
print(matrix_column_binded)
matrix_row_binded <- rbind(1:3, 4:6)
cat("Row-binded Matrix\n")
print(matrix_row_binded)Output
Column-binded Matrix
[,1] [,2]
[1,] 1 4
[2,] 2 5
[3,] 3 6
Row-binded Matrix
[,1] [,2] [,3]
[1,] 1 2 3
[2,] 4 5 6
Accessing elements of a matrix
You can access the elements of the matrix by using [ ] brackets. The first “1” in the bracket specifies the row position, while the second “2” specifies the column position.
vec <- 1:9
mtrx <- matrix(vec, nrow = 3, ncol = 3)
mtrx[1, 2]
Output
[1] 4You can also access the whole row by specifying a comma after the number in the bracket:
vec <- 1:9
mtrx <- matrix(vec, nrow = 3, ncol = 3)
mtrx[2, ]
Output
[1] 2 5 8Accessing more than one row
You can access more than one row using the c() function.
vec <- 1:9
mtrx <- matrix(vec, nrow = 3, ncol = 3)
mtrx[, c(1, 2)]Output
[,1] [,2]
[1,] 1 4
[2,] 2 5
[3,] 3 6Adding a column to the matrix
You can use the cbind() function to add new columns to the matrix.
vec <- 1:9
mtrx <- matrix(vec, nrow = 3, ncol = 3)
new_mtrx <- cbind(mtrx, c(4, 5, 6))
new_mtrx
Output
Adding a row to the matrix
You can use the rbind() function to add new rows in a Matrix.
vec <- 1:9
mtrx <- matrix(vec, nrow = 3, ncol = 3)
new_mtrx <- rbind(mtrx, c(4, 5, 6))
new_mtrxOutput
Removing columns and rows
You can use the c() function to remove rows and columns from a matrix.
Let’s remove the first row and first column from the matrix.
vec <- 1:9
mtrx <- matrix(vec, nrow = 3, ncol = 3)
cat("Before removing first column and row", "\n")
mtrx
cat("After removing first column and row", "\n")
new_mtrx <- mtrx[-c(1), -c(1)]
new_mtrx
Output
Check if an element exists in a matrix
To check if an element exists in a matrix, use the “%in% operator”.
The %in operator% returns TRUE if an element exists and if it does not then it returns FALSE.
vec <- 1:9
mtrx <- matrix(vec, nrow = 3, ncol = 3)
5 %in% mtrxOutput
[1] TRUE
Let’s write an example where an element does not exist in a matrix.
vec <- 1:9
mtrx <- matrix(vec, nrow = 3, ncol = 3)
11 %in% mtrx
Output
[1] FALSE
Matrix length
To find the length of a matrix, use the “length()” function.
vec <- 1:9
mtrx <- matrix(vec, nrow = 3, ncol = 3)
length(mtrx)Output
[1] 9Finding the Number of Rows and Columns of the Matrix
You can use the “dim()” function to find the number of rows and columns in a Matrix.
vec <- 1:9
mtrx <- matrix(vec, nrow = 3, ncol = 3)
dim(mtrx)Output
[1] 3 3
Combining two matrices using rbind() and cbind()
Use the rbind() or cbind() functions to combine two or more matrices together:
vec1 <- 1:9
vec2 <- 10:18
mtrx1 <- matrix(vec1, nrow = 3, ncol = 3)
mtrx2 <- matrix(vec2, nrow = 3, ncol = 3)
# Adding it as a rows
combined_matrix_by_rows <- rbind(mtrx1, mtrx2)
cat("Combining matrix by rows", "\n")
combined_matrix_by_rows
# Adding it as a columns
combined_matrix_by_cols <- cbind(mtrx1, mtrx2)
cat("Combining matrix by columns", "\n")
combined_matrix_by_colsOutput
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.