{"id":4293,"date":"2017-09-21T09:49:00","date_gmt":"2017-09-21T04:19:00","guid":{"rendered":"http:\/\/data-flair.training\/blogs\/?p=4293"},"modified":"2021-08-25T17:26:54","modified_gmt":"2021-08-25T11:56:54","slug":"r-matrix-operations-applications","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/r-matrix-operations-applications\/","title":{"rendered":"How to Create & Access R Matrix – 5 Operations that you Must Check!"},"content":{"rendered":"

A matrix\u00a0in R is a two-dimensional rectangular data set and thus it can be created using vector input to the matrix function. <\/em>R is a tool for expressing statistical and mathematical operations from which beginners will learn how to create and access the R matrix. And, by the end of this article, you will be able to perform addition, subtraction, multiplication, and division operations<\/em> on R matrices.<\/p>\n

Before diving into R matrix, brush up your skills for Vectors in R<\/a><\/strong><\/em><\/p>\n

What is R Matrix?<\/h2>\n

In a matrix, numbers are arranged in a fixed number of rows and columns and usually, the numbers are the real numbers. With the help of a matrix function, a memory representation of the matrix can be easily reproduced.<\/p>\n

Therefore, all the data elements must share a common basic type.<\/p>\n

mat <- matrix (\r\nc(2 , 4, 3, 1, 5, 7) \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0# the data elements\r\nnrow =2,\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0# no. of rows\r\nncol =3,\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0# no. of columns\r\nbyrow\u00a0=\u00a0TRUE)<\/pre>\n
Output:<\/strong><\/p>\n
\"R<\/a><\/p>\n
An element at the m<\/strong>th row and\u00a0n<\/strong>th column of our matrix ‘mat’\u00a0can be created using this expression mat[m, n].<\/p>\n
mat[2, 3]<\/pre>\n
To extract only the m<\/strong>th row of our matrix ‘mat’, we can use the expression, mat[m, ].<\/p>\n
mat[2, ]<\/pre>\n
And, to extract\u00a0only the n<\/strong>th column of our matrix ‘mat’, we use the expression mat[, n].<\/p>\n
mat[ ,3]<\/pre>\n
Output:<\/strong><\/p>\n
\"How<\/a><\/p>\n
History of Matrices in R<\/h3>\n
We can trace back the origins of matrices to ancient times! However, it was not until 1850, when the concept of the matrix was actually applied.<\/p>\n
\u201cMatrix\u201d is the Latin word for womb<\/em>. Generally, it can also mean any place in which something is formed or produced. The word has been used in unusual ways by at least two authors of historical importance. They proposed this axiom as a means to reduce any function to one of the lower types so that at the \u201cbottom\u201d (0order) the function is identical to its extension.<\/p>\n
By using the process of generalisation, any possible function other than a matrix from the matrix holds true. However, it is only true if the proposition which asserts function in question is considered. Furthermore, it holds true for all or one of the value of argument when other argument(s) is undetermined.<\/p>\n
Wait! Have you checked – R List Tutorial<\/a><\/strong><\/em><\/p>\n
How to Create Matrix in R?<\/h2>\n
Using the matrix()<\/em> function, we will create our first matrix in R. The basic syntax for creating a matrix in R is as follows:<\/p>\n
matrix(data, nrow, ncol, byrow, dimnames)<\/em><\/p>\n
Where,<\/p>\n
    \n
  1. Data<\/strong> is the input vector. This can also include a list or an expression.<\/li>\n
  2. Nrow<\/strong> is the number of rows that we wish to create in our matrix.<\/li>\n
  3. Ncol<\/strong> is the specification of the number of columns in our matrix.<\/li>\n
  4. Byrow<\/strong> is a logical attribute which is FALSE by default. Setting it true will arrange the input vectors by row.<\/li>\n
  5. Dimnames<\/strong> allows you to name rows and columns in a matrix.<\/li>\n<\/ol>\n
    Creating R matrix based on the variations in the attributes<\/h4>\n