## Introduction

R programming has become one of the most popular languages in the fields of data analysis, statistical computing, and machine learning. Known for its versatility, R provides a comprehensive environment for statistical modeling and data visualization, making it a go-to tool for data scientists, statisticians, and researchers across industries. As the demand for data-driven insights grows, companies seek professionals skilled in R programming. In this blog, we will discuss the R Programming Interview Questions and Answers.

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## Entry-Level R Programming Interview Questions

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.

**1. 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 one-D 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 two-D 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.

**2. 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.

**3. What functional differences exist between R and Python?**

**Ans: **R comes with built-in functionality for data analysis. However, Python does not have these features. They can be found in packages such as Pandas and Numpy.

**4. 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.

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**5. 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.

**6. What is the use of t-test() in R?**

**Ans: **One of the most used statistical tests is the t-test in R. To determine whether the means of the two groups are equal, use the t-test() function.

**7. 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.

**8. How to create co-relations and covariances in R?**

**Ans: **The** ****cor() **function can be used to create co-relations, and You can use the cov() function to create covariances.

**9. 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.

**10. 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.