Top 50 R Interview Questions & Answers


If you are here, you must be looking for a concrete guide that can help you in your R interview. This post covers the most important top R interview questions and answers to give you an idea of the relevant and probable questions that you may expect in your R interview. 

These R interview questions will provide you with a competitive edge in the expanding analytics sector where big and small local and international businesses are looking for experts with verified R proficiency.

R is a programming language that you can use for whichever purpose you like. You have a tool at your disposal that can be utilized for a variety of tasks, including forecast analysis, predictive modeling, data manipulation, and statistical analysis. Top businesses like Twitter, Google,  and Facebook, use R.

1. What are the various data structures in R. Explain each of them briefly?

The various data structures that are available with R are discussed below:

  • Vector:  A vector is a series of data elements that belong to a similar basic type. The members present in a vector are known as components. 
  • List: A list can be referred to as the R objects consisting of different types of elements like strings, numbers, vectors, or a variant of a list inside it. 
  • Matrix: A matrix is a data structure that is two-dimensional. Matrices are utilized for binding vectors from the same length. Matrix elements belong to the same type such as numeric, complex, character, and logical. 
  • Datafraame: In comparison to Matrix Data Frame are more generic for instance the varied columns in the matrix consist of elements of different types. It also merges features of both lists and matrices like a rectangular list.

2. What are the various grammar for graphics?

Below is a list of the parts of grammar for graphics:

  • Data layer
  • Aesthetics layer
  • Geometry layer
  • Facet layer
  • Co-ordinate layer
  • Themes layer

3. Describe Rmarkdown. What purpose does it serve?

R has a reporting tool called Rmarkdown. You can produce excellent reports using Rmarkdown using R code. 

Rmarkdown's output format options include:

  • HTML
  • PDF
  • WORD

4. What are the packages that are utilized for data imputation?

The packages that are utilized in data imputation are as follows:

  • MICE
  • missFores 
  • Mi
  • Hmisc
  • Amelia
  • imputeR

5. State the function that R() perform.

When an object is declared, this function is used to initialize the private data members.

6. How do we calculate the average of One column in relation to another?

There are five columns in the iris dataset: 

  • Species
  • Sepal.Length
  • Sepal.Width 
  • Petal. width
  • Petal.Length.

Using the mean() function from the mosaic package, we will determine the average Sepal-Length across many species of Iris flowers.

7. Define a Random Walk Model.

A Random Walk model can be referred to as the simplest illustration of a non-stationary process is a random walk. A random walk has an undefined mean and variance, substantial temporal dependence, and changes or increments that are nothing more than white noise.

8. Define White Noise Model? 

It is a straightforward illustration of a stationary process and a basic time series model. A white noise model has no temporal correlation, a fixed constant mean, and a fixed variance. 

9. Name any 5 characteristics of R.

The following are the major 5 characteristics of R:

  • Effective and simple programming language.
  • It is a program for data analysis.
  • It provides efficient data handling and storage.
  • High-extensibility graphical approaches are provided.
  • It is a language that is interpreted.

10. Describe the functional differences between Python and R?

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.

11. Describe RStudio.

We can interact with R more easily with the help of RStudio, an integrated programming environment. While RGui is still used, RStudio is thought to be more user-friendly. There are numerous drop-down menus, windows with multiple tabs, and customization options in this IDE. Three Windows will appear when we initially launch RStudio. The fourth Window will by default be hidden.

12. Can you name some of the applications of R?

Numerous applications are available in real-time. Some of the R applications are:

  • Facebook
  • Twitter
  • Googl
  • NDAA

13. Briefly list out the merits and demerits of R.

The following are some of the advantages and disadvantages of R:


  • Open Source
  • Data Wrangling
  • Array of Packages
  • Platform Independent
  • Machine Learning Operations
  • Disadvantages
  • Weak origin
  • Data Handling
  • Basic Security
  • Complicated Language
  • Lesser Speed

14. What is the goal of integrating R and Hadoop?

R code is executed in order to run Hadoop.

for accessing data stored in Hadoop using R.

15. What are the Hadoop integration techniques?

The following are the techniques of Hadoop Integration: 

  • R Hadoop
  • Hadoop Streaming
  • ORCH

16. Can you tell the output of the given expression all(NA==NA)?

The output of the given expression will be NA

17. What distinguishes R's sample() and subset() functions?

The subset method is used to choose variables and observations, whereas the sample() method is used to select a random sample of size n from a dataset.

18. State the purpose of using the command install.packages(file.choose(), repos=NULL) 

By navigating and choosing the file, this command can be used to install an R package from the local directory.

19. What is the procedure to give the R workspace the commands to produce a histogram and remove a vector?

To produce a histogram and remove a vector from the R workspace, use the hist() and rm() functions.

20. Distinguish between "%%" and "%/%."

The "%%" serves as a reminder of the division of the first and second vectors, and the "%/%" represents the quotient of that division.

21. Why do we employ the apply() function in R?

This is used to provide each element in an array with the same function. Take the average of each row's rows as an example.

22. Explain the differences between the require() and library() functions.

The necessary () function is utilized inside the function and throws a warning whenever a specific package is not found. If the desired package cannot be loaded, the library() method displays an error message.

23. What does R's t-test() do?

To determine whether the means of the two groups are equal or not, use the t-test() function.

24. In R, what are the purposes of the with() and by() functions?

The by() function applies a function to each level of components, whereas the with() function applies an expression to a dataset.

25. Distinguish between Lapply and Sapply.

The lapply is used to display output as a list, whereas the sapply is used to display output as a vector or data frame.

26. What does aggregate() function do?

In R the aggregate() function is utilized for aggregatingg data. Two methods of collapsing data - One is by employing one or more By variable and Aggregate () is the other wherein By variable must be in the list. 

27. What does doBy package mean?

The desired table is defined using this package's functions and model formula.

28. Describe how to use the table() function.

The frequency table in R is created using this function.

29. What is the function of fitdistr()?

This function, which is part of the MASS package, is used to provide the maximum likelihood fitting of a univariate distribution.

30. Explain GGobi and iPlots.

The iPlots package includes bar plots, mosaic plots, box plots, parallel plots, histograms, and scatter plots. The GGobi is an open-source tool for visualisation to explore high dimensional typed data.

31. Describe the lattice package.

By providing better defaults and the capability to depict multivariate connections simply, the lattice package aims to enhance the base R graphics.

32. Describe the function anova().

The hierarchical models are compared using the anova() algorithm.

33. Describe the functions stepAIC() and cv.lm().

While the stepAIC() function is defined under the MASS package and performs stepwise model selection under exactAIC, the cv.lm() function is defined under the DAAG package and is used for k-fold validation.

34. Describe the leap() function.

The all-subsets regression is carried out using the leaps() function, which is part of the leaps package.

35. Describe the sturdy packaging and relaimpo.

This package provides a library of robust methods, including regression, and is used to assess the relative weights of each predictor in the model.

36. Describe MANOVA and explain how it works.

Multivariate Analysis of Variance, or MANOVA, is a technique used to assess multiple dependent variables at once.

37. Describe mashapiro.test() and barlett.test in detail ().

The Shapiro-Wilk test for multivariate normality is produced by this function, which is defined in the mvnormtest package. To give a parametric k-sample test of the equality of variances, the barlett.test() is utilized.

38. Describe how to utilize the forecasting package.

The forecasting program provides the tools for selecting exponential and ARIMA models automatically.

39. Describe how the qda() and lda() functions differ.

While lda() writes discriminant functions based on the centered variable, qda() prints a quadratic discriminant function.

40. Describe the principal() and auto.arima() functions.

Both the seasonal and non-seasonal ARIMA models, as well as the rotation and extracting of the primary components, are handled by the auto.arima() function.

41. Describe FactoMineR.

A software called FactoMineR has both qualitative and quantitative variables. These packages also contain the observations and auxiliary variables.

42. What are SEM and CFA's complete names?

Confirmatory factor analysis (CFA) and structural equation modeling (SEM) are two related statistical techniques.

43. Describe the functions cluster () stats() and pvclust().

The pvclust() function is defined in the pvclust package and offers p-values for hierarchical clustering. The cluster.stats() function in the fpc package provides a way for assessing the similarity of two cluster solutions using different validation criteria.

44. Define party packages and MATLAB.

To replicate Matlab function calls, this package offers wrapper functions and variables.

45. Describe the S4 and S3 systems.

The S3 is employed in oops to overload any function. The S4 is the most significant attribute of oops, and it allows us to call the functions by multiple names depending on the type of input parameter or the number of parameters. This is a drawback, too, as it is challenging to debug. For S4, there is a reference class that is optional.

46. List the visualization software programs.

The following R packages are available for visualization:

  • Plotly
  • ggplot2
  • tidyquant
  • geofacet
  • googleVis
  • Shiny

47. Can you explain Chi-Square Test?

The frequency table, or contingency table, is analyzed using the Chi-Square Test and is made up of two categorical variables. The chi-square test determines whether there is a meaningful correlation between the two variables' categories.

48. Describe the Random Forest.

Decision Tree Forest is another name for the Random Forest. It is one of the widely used ensemble models based on decision trees. These models in comparison with other decision trees have higher accuracy. Both classification and regression applications employ this approach.

49. Describe Time series analysis.

A time series is produced by any metric that is measured throughout time at regular intervals. Due to its industrial significance and necessity, time series analysis is commercially significant, particularly in terms of forecasting (demand, supply, sale, etc.). Time series refers to a collection of data points where each data point has a timestamp.

50. Describe shinyR in detail.

An R tool called Shiny makes it simple to create dynamic web applications directly from R. You can create dashboards, embed standalone apps in Rmarkdown pages, or host them on a website. Additionally, you may add CSS themes, HTML widgets, and JavaScript actions to your Shiny apps.

Those are the best R interview questions and answers covering the fundamental concepts and practices. This brief guide on R interview questions and answers should provide you with the idea of what all R Interview questions you can expect in your interview, and come out successful.

Read More:

Top 80 Python Interview Questions & Answers

Top 50 React Interview Questions and Answers in 2022

Top 50 Blockchain Interview Questions and Answers

Investment Banking Interview Questions and Answers

Top 50 Project Management (PMP) Interview Questions & Answers

Top 50 Agile Interview Questions And Answers

Top 30 Data Engineer Interview Questions & Answers

Top 50 Network Security Interview Questions and Answers

Top 80 Data Science Interview Questions & Answers

Cyber Security Architect Interview Questions and Answers

Top 120 Cyber Security Interview Questions & Answers in 2022

Top Project Manager Interview Questions and Answers

Top 50 Angular Interview Questions & Answers

Top 50 Tableau Interview Questions and Answers

Top 50 Artificial Intelligence Interview Questions and Answers

Post a Comment