Now, we will discuss each of these R-objects in brief.<\/p>\n
1. R Vector<\/h3>\n
Vector is the most basic data structure in R programming language.\u00a0It comes in two parts: Atomic vectors<\/b> and Lists.<\/b> They have three common properties:<\/p>\n These elements have different types. For instance, atomic vectors must share the same type. On the contrary, elements that are present in a list can have different data types. We have discussed\u00a0every concept of R List<\/a><\/strong> in our previous article, here we are going to understand only Atomic Vectors.<\/em><\/p>\n Atomic Vectors<\/strong><\/p>\n There are four common types of R Atomic Vectors:<\/p>\n First of all, we will discuss what exactly matrices in data structures in R mean. A matrix is a two-dimensional rectangular data set and thus it can be created using vector input to the matrix function.<\/em> In addition, a matrix is a collection of numbers arranged into a fixed number of rows and columns. Usually, the numbers are the real numbers. By using a matrix function, we can reproduce a memory representation of the matrix in R. Hence, the data elements must be of the same basic type.<\/p>\n Output:<\/strong><\/p>\n We can access the element present at the mth row and nth column as follows:<\/p>\n Output:<\/strong><\/p>\n We can also extract the entire mth row and nth row separately as follows:<\/p>\n Output:<\/strong><\/p>\n Before proceeding ahead, please confirm that you have completed – R Matrix Operations<\/a><\/strong><\/em><\/p>\n Method to solve the matrices:<\/p>\n In addition of two matrices, we add the numbers in matching positions:<\/p>\n Output:<\/strong><\/p>\n In subtraction of two matrices, we subtract the numbers in matching positions:<\/p>\n Output:<\/strong><\/p>\n We can multiply by some constant value like so:<\/p>\n Output:<\/strong><\/p>\n In division of two matrices, divides the numbers in matching positions<\/p>\n Output:<\/strong><\/p>\n The transpose of a matrix is a matrix with the rows and columns swapped, we can find the transpose of a matrix with the t() function:<\/p>\n Output:<\/strong><\/p>\n We can find the nxn identity matrix using the diag(n) function.<\/p>\n Output:<\/strong><\/p>\n A must learn concept to ease your R programming journey – R Factor Functions<\/a><\/strong><\/em><\/p>\n In R Programming, arrays are multi-dimensional Data structures. In an array, data is stored in the form of matrices, row, and as well as in columns. We can use the matrix level, row index, and column index<\/em> to access the matrix elements.<\/p>\n Arrays in R are the data objects which can store data in more than two dimensions. An array is created using the array()<\/em> function. We can use vectors as input. To create an array, we can use these values in the dim parameter.<\/p>\n For example:<\/b><\/p>\n In this following example, we will create an array in R of two 3\u00d73 matrices each with 3 rows and 3 columns. # Create two vectors of different lengths.<\/b><\/p>\n Output:<\/strong><\/p>\n We can give names to the rows, columns, and matrices<\/em> in the array by using the dimnames parameter. # Create two vectors of different lengths.<\/b><\/p>\n # Take these vectors as input to the array.<\/b><\/p>\n Output:<\/strong><\/p>\n # We will create two vectors of different lengths.<\/b><\/p>\n Output:<\/strong><\/p>\n Output:<\/strong><\/p>\n As the array is made up of matrices in multiple dimensions, the operations on elements of an array are carried out by accessing elements of the matrices.<\/p>\n Output:<\/strong><\/p>\n We will be using the apply()<\/em> function for calculations in an array in R<\/a>. Syntax:<\/b><\/p>\n Following is the description of the parameters used:<\/b><\/p>\n For example<\/b>:<\/p>\n We use the apply() function below in different ways to calculate the sum of the elements in the rows of an array across all the matrices. Output:<\/strong><\/p>\n <\/b>Explore a complete tutorial on R Array Function<\/a><\/strong><\/em><\/p>\n Lists are the objects which contain elements of different types \u2013 like strings, numbers, vectors and another list inside them.<\/em> A list can also contain a matrix or a function as its elements. In other words, a list is a generic vector containing other objects. A list is created using the list() function.<\/p>\n For example:<\/strong><\/p>\n The variable x is containing copies of three vectors n, s, b and a numeric value 3.<\/p>\n\n
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2. R Matrix<\/h3>\n
> mat1 <- matrix(1:4, nrow = 2, ncol = 2) #Author DataFlair\r\n> mat1\r\n> mat2 <- matrix(4:7, nrow = 2, ncol = 2)\r\n> mat2<\/pre>\n
![\"mat1](\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/01\/mat1-matrix.jpg\")
<\/a><\/p>\n> mat1[1,2] #Author DataFlair\r\n>\u00a0mat2[2,1]<\/pre>\n
![\"mat](\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/01\/mat-1-12.jpg\")
<\/a><\/p>\n> mat1[2, ] #Author DataFlair\r\n> mat1[, 2] #Author DataFlair<\/pre>\n
![\"mat](\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/01\/mat-12.jpg\")
<\/a><\/p>\nUses of Matrices<\/h3>\n
1.<\/strong> Adding<\/h4>\n
> mat1 + mat2 #Author DataFlair\r\n<\/pre>\n
![\"Sum](\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/01\/Sum.jpg\")
<\/a><\/p>\n2. Subtracting<\/strong><\/h4>\n
> mat1 - mat2 #Author DataFlair\r\n<\/pre>\n
![\"subtraction](\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/01\/subtraction.jpg\")
<\/a><\/p>\n3.<\/strong> Multiply by a constant<\/strong><\/h4>\n
> 4 * mat1 #Author DataFlair<\/pre>\n
![\"multiply](\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/01\/mult.jpg\")
<\/a><\/p>\n4. Divid<\/strong>ing<\/strong><\/h4>\n
> (mat1\/mat2) #Author DataFlair\r\n<\/pre>\n
![\"matrix](\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/01\/matrix-division.png\")
<\/a><\/p>\n5.<\/strong> Transposing<\/h4>\n
> t(mat1) #DataFlair\r\n<\/pre>\n
![\"transposing\"](\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/01\/transposing.jpg\")
<\/a><\/p>\n6. Identity<\/strong> Matrix<\/h4>\n
> diag(4) #Author DataFlair\r\n<\/pre>\n
![\"identity](\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/01\/identity-matrix.jpg\")
<\/a><\/p>\nApplications of Matrices<\/h3>\n
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3. R Array<\/h3>\n
\n<\/b><\/p>\n> vec1 <- c(1,2,4) #Author DataFlair\r\n> vec2 <- c(15,17,27,3,10,11)\r\n> output <- array(c(vec1,vec2),dim = c(3,3,2))\r\n> output<\/pre>\n
![\"Data](\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/01\/Data-Structures-in-R-Array.jpg\")
<\/a><\/p>\nDifferent Operations on Rows and Columns<\/h3>\n
1. Naming Columns And Rows<\/h4>\n
\n<\/b><\/p>\nvec1 <- c(1,2,4)\r\nvec2 <- c(15,17,27,3,10,11)\r\ncolumn_names <- c(\u201ccol1\u2033,\u201dcol2\u2033,\u201dcol3\u201d)\r\nrow_names <- c(\u201crow1\u2033,\u201drow2\u2033,\u201drow3\u201d)\r\nmatrix_names <- c(\u201cMat1\u2033,\u201dMat2\u201d)\r\n\r\n<\/pre>\n
output <- array(c(vec1,vec2),dim = c(3,3,2),dimnames = list(row_names,column_names,\r\nmatrix_names))\r\noutput<\/pre>\n
![\"Naming](\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/01\/Naming-Columns-and-Rows-of-R-Array.jpg\")
<\/a><\/p>\n2. Accessing Array Elements
\n<\/b><\/h4>\nvec1 <- c(1,2,4)\r\nvec2 <- c(15,17,27,3,10,11)\r\nrow_names <- c(\u201crow1\u2033,\u201drow2\u2033,\u201drow3\u201d)\r\ncol_names <- c(\u201ccol1\u2033,\u201dcol2\u2033,\u201dcol3\u201d)\r\nmatrix_names <- c(\u201cMat1\u2033,\u201dMat2\u201d)\r\noutput\u00a0<-\u00a0array(c(vec1,vec2),dim\u00a0=\u00a0c(3,3,2),dimnames\u00a0=\u00a0list(row_names,col_names,matrix_names))\r\noutput<\/b><\/pre>\n
![\"Accessing](\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/01\/Accessing-Array-Elements.jpg\")
<\/a><\/p>\n> output[3,,2] #Author DataFlair\r\n> output[1,3,1] #Print the element in the 1st row and 3rd column of the 1st matrix\r\n> output[,,2] #Print the 2nd Matrix<\/pre>\n
![\"Accessing](\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/01\/Accessing-Array-Elements-2.jpg\")
<\/a><\/p>\n3. Manipulating Array Elements<\/h4>\n
> vec1 <- c(1,2,3) #Author DataFlair \r\n> vec2 <- c(4,5,6,7,8,9)\r\n> arr1 <- array(c(vec1,vec2),dim = c(3,3,2))\r\n> vec3 <- c(3,2,1)\r\n> vec4 <- c(9,8,7,6,5,4)\r\n> arr2 <- array(c(vec3,vec4),dim = c(3,3,2))\r\n> mat1 <- arr1[,,2] #Creating Matrix out out array\r\n> mat2 <- arr2[,,2]\r\n> output <- mat1 + mat2\r\n> output<\/b><\/pre>\n
![\"Manipulating](\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/01\/Manipulating-Array-Elements.jpg\")
<\/a><\/p>\n4. Calculations across R Array Elements<\/h4>\n
\n<\/b><\/p>\napply(x, margin, fun)<\/pre>\n
\n
\n<\/b><\/p>\n> vec1 <- c(1,2,3) #Author DataFlair \r\n> vec2 <- c(4,5,6,7,8,9)\r\n> array_new <- array(c(vec1,vec2),dim = c(3,3,2))\r\n> array_new\r\n\r\n> output <- apply(array_new, c(1), sum)\r\n> output<\/pre>\n
![\"Calculations](\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/01\/Calculations-Across-R-Array-Elements.jpg\")
<\/a><\/p>\n4. List in R<\/h3>\n
> num_list = c(3,4,5) #Author DataFlair\r\n> char_list = c(\"a\", \"b\", \"c\", \"d\", \"e\")\r\n> logic_list = c(TRUE, TRUE, FALSE, TRUE)\r\n> out_list = list(num_list, char_list, logic_list, 3)\r\n> out_list<\/pre>\n
![\"num_list](\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/01\/num_list.jpg\")
<\/a><\/p>\n![\"num_list](\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/01\/num_list-c-3-4-5.jpg\")
<\/a><\/p>\n