In this tutorial you will explore matrix in R.You will learn to create a matrix, there are different methods in creating matrices you can learn and choose the most appropriate method for creating a matrix of your choice. Further naming rows and columns in a matrix, checking matrix existence, selecting, modifying, and deleting elements from a matrix.
A matrix is like a sibling of the vector. You know a vector is a data structure with a sequence of data elements that is dimensional and a matrix is also a similar collection of data elements but the difference is elements are arranged into a fixed number of rows and columns. Since matrix data elements are arranged in rows and columns they are called two-dimensional.
| Vector -> 1D | Matrix -> 2D |
|---|---|
|
To create a vector > |
To create a matrix > |
|
Output [1] 3 5 4 8 |
Output [,1] [,2] |
 |
 |
Note: Don’t bother about the syntax just understand the concept.
A matrix is a two-dimensional array (2D) with rows and columns where the matrix contains elements of similar basic data types like numerals, character, logical, etc.
The following is an example of a matrix with 2 rows and 2 columns.
In R, a matrix is created using matrix() function.
The basic Syntax 1 to create a matrix is a matrix() function with arguments such as data, number of rows, number of columns passed inside the parentheses.
matrix(data,nrow,ncol) #Syntax 1
Where the arguments inside matrix() are
> MATRIX <- matrix(1:15, nrow=3, ncol=5)
> MATRIX
Output:
[,1] [,2] [,3] [,4] [,5]
[1,] 1 4 7 10 13
[2,] 2 5 8 11 14
[3,] 3 6 9 12 15
matrix(data, nrow, ncol, byrow =TRUE) #Syntax 2
Where byrow =TRUE distribute the elements row-wise.
> MATRIX <- matrix(1:15, nrow=3, ncol=5,byrow =TRUE)
> MATRIX
Output:
[,1] [,2] [,3] [,4] [,5]
[1,] 1 2 3 4 5
[2,] 6 7 8 9 10
[3,] 11 12 13 14 15
Syntax 1 arranges the data in column-wise by default, in syntax 2 we passed the argument byrow = TRUE which arranges the same data inside the matrix row-wise.
The numbers 1 2 3 4 5 are arranged column-wise in Syntax 1 where the same data(numbers) is arranged as rows by using byrow =TRUE while creating a matrix in Syntax 2.
Note: By default, matrices are created with column-wise data in that case byrow=FALSE.You can either give byrow=FALSE else by default it takes column-wise arrangement of data.
In R language they provide a built-in function rbind() to create a matrix by filling data or elements row-wise.In R programming rbind stands for row binding.
rbind(data)
Example: matrix<- rbind(Item_NO,Item_name)
// Place code hereItem_NO <- c(1:4)
Item_name<-c("MILK","CHEESE","BUTTER","CURD")
matrix<- rbind(Item_NO,Item_name)
print(matrix)
Output produced by the above code
NO "1" "2" "3" "4" Item_name "MILK" "CHEESE" "BUTTER" "CURD"
You can infer from the output that the set of data are distributed along the rows.
In R language they provide a built-in function cbind() to create a matrix by filling data or elements column-wise.R programming cbind stands for column binding.
cbind(data)
Example : matrix<- cbind(Item_NO,Item_name)
Let us see a code
Item_NO <- c(1:4)
Item_name<-c("MILK","CHEESE","BUTTER","CURD")
matrix<- cbind(Item_NO,Item_name)
print(matrix)
Output:
Item_NO Item_name
[1,] "1" "MILK"
[2,] "2" "CHEESE"
[3,] "3" "BUTTER"
[4,] "4" "CURD"
You can infer from the output the data is displayed column-wise with Item_NO corresponding to each Item_name column-wise.
Note in both rbind() and cbind() we can have deparse.level. The deparse.level can be set to 0,1 which constructs labels to the matrix.
Consider the table below to better infer the output of rbind() in different values for deparse.level.
| Function | Code | Output |
|---|---|---|
| rbind() |
matrix<- rbind(Item_NO,Item_name,deparse.level = 0)
matrix<- rbind(Item_NO,tem_name,deparse.level = 1) |
[,1] [,2] [,3] [,4] [,1] [,2] [,3] [,4] |
| cbind() |
matrix<- cbind(Item_NO, item_name,deparse.level = 0)
matrix<- cbind(Item_NO,Item_name, deparse.level = 1) |
[,1] [,2] Item_NO Item_name
|
NOTE : Both cbind() and rbind() are built-in functions in R to create matrices by combining several vectors of the same length.
The deparse.level values 0 or 1 determine the labels to construct (column labels for cbind or row labels for rbind).
Matrices refer to the standards used while creating a Matrix. Using the syntax for creating a matrix, a matrix m is created.
m <- matrix(1:15,
nrow=5,
ncol=3,
byrow=FALSE)
The following are the metrics of a matrix
The matrix m is
[,1] [,2] [,3]
[1,] 1 6 11
[2,] 2 7 12
[3,] 3 8 13
[4,] 4 9 14
[5,] 5 10 15
Examples of matrix functions are shown below
nrow(m)
ncol(m)
dim(m)
It produces an output
> print(nrow(m)) [1] 5 > print(ncol(m)) [1] 3 > print(dim(m)) [1] 5 3
Consider the above-created MATRIX using the matrix() function, let us give names to the rows and columns of MATRIX using the following syntax
To name rows of the matrix
rownames(<MATRIX>) = c(<name1>,<name2>………)
Example: rownames(MATRIX) =c("row_1","row_2","row_3")
To name coloumn of the matrix
colnames(<MATRIX>)=c(<name1>,<name2>…….)
Example: colnames(MATRIX)=c("col_1","col_2","col_3","col_4","col_5")
Let us see a simple program to understand the naming of matrix rows and columns
#created a matrix named MATRIX
#MATRIX is 3 by 5 with data distributed row wise
MATRIX <- matrix(1:15, nrow=3, ncol=5,byrow =TRUE)
#create names to rows
rownames(MATRIX)=c("row_1","row_2","row_3")
#create names to columns
colnames(MATRIX)=c("col_1","col_2","col_3","col_4","col_5")
cat("The MATRIX after naming is \n")
print(MATRIX)
Output:
The MATRIX after naming is
col_1 col_2 col_3 col_4 col_5
row_1 1 2 3 4 5
row_2 6 7 8 9 10
row_3 11 12 13 14 15
Let us see how these code looks in RStudio
In R you can name matrix rows and columns using dimnames() function. It is a built in function in R to set values to row and columns as well as to get names of rows and columns.
dimnames() <- list(c(), c())
Start by creating a matrix using matrix() function
m <- matrix(1:15,
nrow=5,
ncol=3,
byrow=FALSE)
