R initially appeared in 1993, which is more than 28 years ago! Right now, the fourteenth most used programming language is R. it is trendy and is utilised by many firms for various projects.
Let's go ahead and hit on 25 important R programming Interview questions. We will cover the most vital and likely R Programming interview questions in this blog.
Be interview ready by brushing your concepts and practising these R programming Interview questions.
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Easy level
This section will get the basic R programming interview questions to build a strong foundation. This part is essential because it creates a firm basis for your R programming interview questions.

What are the different data structures present in R?
Ans: In general, R includes the following data structures:
Vector: The most common data structure in R. A vector consists of a series of data elements(ordered) of the same data type. It is a oneD data structure. The data elements in the vector are known as components.
List: The data structure that stores elements of various types, such as numbers, strings, vectors, or another list. It also consists ordered collection of elements.
Matrix: It is a twoD data structure. Vectors of the same length are bound together using matrices. A matrix's elements must all be the same type (numbers, strings, characters).
Dataframe: Unlike a matrix, a data frame allows various columns to include various data types (numeric, character, logical, etc.). It combines aspects of rectangular lists and matrices.

List any five features of R.
Ans: The features of R:
Quick and simple programming language.

It is a programme for data analysis.

It provides efficient data handling and storage.

High visual approaches are provided.

It is an interpreted language.

Quick and simple programming language.

What functional differences exist between R and Python?
Ans: R comes with builtin functionality for data analysis. However, Python does not have these features. They can be found in packages such as Pandas and Numpy.

What distinguishes R's sample() and subset() functions?
Ans: The subset( ) method chooses observations and variables, whereas the sample() method selects a random sample of size n from a dataset.
Also see, Servicenow Interview Questions

Why do we employ R's apply() function?
Ans: The apply() function enables us to apply a function to a matrix's or data frame's rows or columns. This function accepts as arguments a matrix or data frame. It returns the result as a vector, array, or list of values obtained.

What is the use of ttest() in R?
Ans: One of the most used statistical tests is the ttest in R. To determine whether the means of the two groups are equal, use the ttest() function.

Distinguish between the require() and library() functions.
Ans: If the packages are not being loaded inside the function, there isn't any significant difference between the two. The require() function is utilised inside the function and throws a warning whenever a specific package is not found. The library() function displays an error message if the system cannot load the desired package.

How to create corelations and covariances in R?
Ans: The cor() function can be used to create corelations, and You can use the cov() function to create covariances.

What are the goals of R's with() and by() functions?
Ans: The by() function assigns a function to each factor level.
The with() function delivers an expression to a dataset.

What are some disadvantages of R?
Ans: An interviewer can also ask this kind of R Programming Interview Questions.
Some of the cons of R:
Data Handling.

Difficult Language.

Simple Security.

Slower Speed.
 Weak Origin.

Data Handling.
Medium Level
We discussed some of the simple R programming Interview Questions. Let's now discuss a few of the mediumlevel questions.

What does R's confusion matrix mean?
Ans: A confusion or error matrix handles the common classification problem. It is possible to assess the accuracy of the created model using a confusion matrix. A crosstabulation of observed and anticipated classes is calculated.
You can use the "confusionmatrix()" method from the "caTools" package to accomplish this. It uses a specific table design that makes it easier for data analysts to see how an algorithm works.

How to create a custom function in R?
Ans: The syntax for creating a custom function in R is as follows:
Name of the function = function(Arguments) {
Statement 1
Statement 2
Statement 3
â€¦â€¦â€¦â€¦â€¦.
}
Here is an example of how to write a custom function in R.
CodingNinjas = function (a) {
ifelse(a > 15, 20, 0)
}
X = c(1, 2, 3, 4, 5, 6, 7, 8, 9)
CodingNinjas(X) = X

What do you mean by Data Imputation?
Ans: Most datasets may contain missing values due to a mistake or because they weren't input. Data imputation is the process of replacing these missing values with a different value. You can replace the column's missing value in R in various ways, including by setting the column's missing value to zero, the average, the median, and so on.

Which R function is used to combine datasets?
Ans: The rbind() function can combine two data frames by rows. The same variables must be present in both data frames, although they need to be in a different order.
The syntax is as follows:
rbind(object1, object2,.....)

What is the use of the cbind() function?
Ans: To combine a specified Vector, Matrix, or Data Frame by columns, use the cbind() function.
The syntax is as follows:
cbind(object1, object2,.....)

Which R packages are available for data imputation?
Ans: The following R packages can be used for data imputation.
Amelia

Hmisc

imputeR

Mi

MICE

missForest

Amelia

List some of the functions in the "dplyr" package.
Ans: The following R functions are in the "dplyr" package.
Arrange

Count

Filter

Mutate
 Select

Arrange