Vectors in R Programming Language

The post is about another data structure called Vectors in R Programming. It is in the form of questions and answers with examples. Here we will discuss some important vector functions, recycling of elements, and different types of vectors with examples.

What are Vectors in R Programming?

Vectors in R Programming are basic data structures. It comes in two parts: atomic vectors and lists (recursive vectors). A vector in R language is a fundamental data structure that stores a collection of elements, all of the same data type (like numbers, characters, or logical values). Vectors in R Programming are essentially one-dimensional arrays.

How many types of vectors are in R?

The primary types of vectors in R Programming are

  • Logical Vectors (stores TRUE or FALSE values)
  • Integer Vectors (Stores Whole numbers, i.e., integers only)
  • Double (Numeric) Vectors (Stores decimal numbers)
  • Character Vectors (Stores text strings)

The less common types of vectors are:

  • Complex Vectors
  • Raw Vectors.

How to Create Vectors in R Programming Language?

To create vectors in R Programming Language, the following are few ways:

  • Create a vector using integers, use the colon (:) operator. For Example, typing 2:6 results in a vector with numbers from 2 to 6, and typing 3:-4 creates a vector with the numbers 3 to -4.
  • Create a vector using the seq() Function, Write a command such as seq(from = 4.5, to = 3.0, by = -0.5) to create a vector of numbers from 4.5 to 3.0 by decrementing 0.5 step, that is, 4.5 4.0 3.5 3.0.
  • The seq() function may also be used by specifying the length of the sequence by using the argument out, e.g., seq(from = -2.7, to = 1.3, length.out = 9). It will result in -2.7 -2.2 -1.7 -1.2 -0.7 -0.2 0.3 0.8 1.3.

What are Logical Vectors in R Programming?

In R language, a logical vector contains elements having the values TRUE, FALSE, and NA. Like numerical vectors, R allows the manipulation of logical quantities.

What are Vector Functions?

In R language, some functions are used to perform some computation or operation on vector objects, for example, rep(), seq(), all(), any(), and c(), etc. However, the most common functions that are used in different vector operations are rep(), seq(), and c() functions.

How One Can Repeat Vectors in R?

One can use the rep() function to repeat the vectors. For example, to repeat a vector: c(0, 0, 7), three times, one can use rep(c(0, 0, 7), times = 3).

To repeat a vector several times, each argument can be used, for example, rep(c(2, 4, 2), each = 2).

To repeat each element, and how often it has to repeat, one can use the code, rep(c(0, 0, 7), times = 5)

The length.out argument can be used to repeat the vector until it reaches that length, even if the last repetition is incomplete. For example, rep(1:3, length.out = 9).

rep(c(0, 0, 7), times = 3)

rep(c(2, 4, 2), each = 2)
rep(c(0, 0, 7), times = 5)
rep(1:3, length.out = 9)
Vectors in R Programming Language

What is the Recycling of Elements in R Vectors?

When two vectors of different lengths are involved in an operation then the elements of the shorter vector are reused to complete the operation. This is called the recycling of elements in R vectors. For example,

v1 <- c(4, 1, 0, 6)
v2 <- c(2, 4)
v1 * v2

## Output
8, 4, 0, 24

In the above example, the elements 2 and 4 are repeated.

What do copy-on-change Issues in R?

It is an important feature of R that makes it safer to work with data. Let us create a numeric vector x1 and assign the values of x1 to x2.

x1 <- c(1, 2, 3, 4)
x2 <- x1

Now x1 and x2 vectors have exactly the same values. If one modifies the element(s) in one of the two vectors, the question is do both vectors change?

x1[1] <- 0
x1
## Output
0 2 3 4

x2

## Output
1 2 3 4

The output shows that when x1 is changed, the vector x2 will remain unchanged. It means that the assignment automatically copies the values and makes the new variable point to the copy of the data instead of the original data.

Basic Computer MCQs

Matrices in R Programming

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.

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)
                   )
matrices in r programming language

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.
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    Statistics and Data Analysis

    Questions Data Types in R 2025

    This post is about Data Types in R language. It contains Interview Questions about Data Types. It contains some basic questions that are usually asked in job interviews and examinations vivas.

    What are R data types?

    In programming languages, a data type is a classification that specifies what type of a value variable can have. It also describes what type of relational, mathematical, and logical operations can be applied to it without causing an error. We need to use various variables to store information while coding in any programming language. Variables are nothing but reserved memory locations to store values. This means that when one creates a variable one reserves some space in memory. The variables are assigned with R-Objects. Thus, the data type of the R-object becomes the data type of the variable.

    How Many Data Types in R Language?

    There are 5 types of data types in R language, namely

    • Integer data type
    • Numeric data type
    • Character data type
    • Complex data type
    • Logical data type

    What are the Data Types in R on Which Binary Operators Can Be Applied?

    The binary operators can be applied to the data types (i) Scalars, (ii) Matrices, and (iii) Vectors.

    What are the Types of Objects in R?

    There are 6 types of objects in the R Language.

    • Vectors are the most important data type of object in R. A vector is a sequence of data elements having the same data type.
    • Matrices (and arrays) that are multi-dimensional generalizations of vectors. Matrices are arranged into a fixed number of rows and columns. The matrices (or arrays) are vectors that can be indexed by two or more indices and will be printed in special ways.
    • Factors provide compact ways to handle categorical data.
    • Lists are a general form of vector in which the various elements need not be of the same type and are often themselves vectors or lists. Lists provide a convenient way to return the results of a statistical computation.
    • Data frames are matrix-like (tabular data objects) structures, in which the columns can be of different types. Think of data frames as ‘data matrices’ with one row per observational unit but with (possibly) both numerical and categorical variables. Many experiments are best described by data frames.
    • Functions are themselves objects in R language which can be stored in the project’s workspace. Functions provide a simple and convenient way to extend R.

    Note that vector, matrix, and array are of a homogenous type and the other two list and data frames are of heterogeneous type.

    What is the difference between a Data Frame and a Matrix in R Language?

    In R language data frame may contain heterogeneous data while a matrix cannot. Matrix in R stores only similar data types while data frame can be of different data types like characters, integers, or other data types.

    What is the Factor Variable in R language?

    In language, Factor variables are categorical (qualitative) variables that can have either string or numeric values. Factor variables are used in various types of graphics, particularly for statistical modeling where the correct number of degrees of freedom is assigned to them.

    What is an Atomic Vector and How Many Types of Atomic Vectors are in R?

    The atomic vector is the simplest data type in R. Atomic vectors are linear vectors of a single primitive type. There are four types of atomic vectors are present in R:

    • Numerical
    • Integer
    • Character
    • Logical
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