The post is about matrices in the R Programming Language. These questions are about basic concepts and will improve the understanding of R programming-related job interviews or educational examinations.
Table of Contents
Question 1: Write the general format of Matrices in R Programming Language.
Answer: The general format of matrices in R Programming Language is
Mymatrix <- matrix (vector, nrow = r , ncol = c , byrow = FALSE,
dimnames = list (char_vector_for_rowname, char_vector_for_colnames)
)
Question 2: Explain what is transpose.
Answer: The transpose is used to re-shape data. Before performing any analysis, R language provides various methods such as the transpose method for reshaping a dataset. To transpose a matrix or a data frame t() function is used.
Question 3: What is the main difference between an Array and a Matrix?
Answer: A matrix in R language is always a two-dimensional rectangular data set as it has rows and columns. However, an array can be of any number of dimensions, while each dimension of an array is a matrix. For example, a $3\times3\times2$ array represents 2 matrices each of dimension $3\times3$.
Question 4: What are R matrices and R matrices functions?
As discussed earlier, a matrix is a two-dimensional rectangular data set. The matrices in R Programming language can be created using vector input to the matrix() function. Also, a matrix is a collection of numbers or elements that are arranged into a fixed number of rows and columns. Usually, the numbers or elements of the matrix are the real numbers, therefore, the data elements must be of the same basic type. Two types of matrix functions can be used to perform different computations on matrices in R Programming:
- apply()
- apply()
Question 5: How many methods are available to use the matrices?
Answer: There are many methods to solve the matrices like adding, subtraction, negative, etc.
Question 6: What is the difference between matrix and data frames?
Answer: A data frame can contain different types of data but a matrix can contain only similar types of data.
Question 7: What is apply() function in R?
Answer: The apply() function in R returns a vector (or array or list of values) obtained by applying a function to the margins of an array or matrix. the general syntax of the apply() function in R language is:
apply(X, MARGIN, FUN, …)
A short description of the arguments for the apply() functions are
- X is an array, including a matrix.
- MARGIN is a vector giving the subscripts to which the function will be applied.
- FUN is the function to be applied.
- … is optional arguments to FUN
Question 8: What is the apply() family in R?
Answer: The apply() functions in the R language are a family of functions in the base R. The family of these functions allows the users to act on many chunks of data. An apply() function is a loop, but runs faster than loops and often must less code. There are many different apply functions.
- There is some aggregating function. They include mean, or the sum (includes return a number or scalar);
- Other transforming or subsetting functions.
- There are some vectorized functions. They return more complex structures like lists, vectors, matrices, and arrays.
- One can perform operations with very few lines of code in
apply().
Question 9: What is sapply() in R?
Answer: A Dimension Preserving Variant of “sapply” and “lapply”. The sapply is a user-friendly version. It is a wrapper of lapply. By default sapply returns a vector, matrix, or array. The general syntax of sapply() and lapply() is
Sapply(X, FUN, ..., simplify = TRUE, USE.NAMES = TRUE) Lapply(X, FUN, ...)
A short description related to arguments of the above functions are:
- X is a vector or list to call sapply.
- FUN is a function.
- … is optional arguments to FUN.
- simplify is a logical value that defines whether a result is been simplified to a vector or matrix if possible.
- USE.NAMES is logical; if TRUE and if X is a character, use X as the name for the result unless it had names already.
