Posted on : 18 Mar, 2021, 03:23:45 PM

Top 50 R Programming Interview Questions And Answers

Top 50 R Programming Interview Questions And Answers


R is one of the advanced open-source programming languages used to operate and verify multiple tasks, including statistical analysis, data visualization, predictive modeling, forecast analysis, data manipulations, etc. According to the survey, the R programming language is considered the fastest-growing field in the software or IT industry. It is used in all the major organizations like Google, Facebook, Twitter, etc. 

This blog covers the list of the top 50 frequently asked questions during the R interview that candidates most likely encounter. That’s why Wissenhive decided to target the most important R programming interview questions with answers that candidate must prepare for: 

1. What do you understand by R?

R refers to a programming language and software development platform for statistical graphics and computing that the R foundation supports. The R programming language is broadly used in various areas by data miners, statisticians, and data analytics, to develop statistical software with advanced features.

2. What are the advantages of R Programming Language?

There are many advantages of the R language, and those advantages are 

  • Open-source
  • Representative support for data wrangling
  • The array of packages
  • Quality graphing and plotting 
  • Highly compatible
  • Eye-catching reports
  • Platform independent
  • Statistics
  • Machine learning operations
  • Continuously growing

3. What are the disadvantages of the R programming language?

  • Weak origin
  • Data handling
  • Basic security
  • Complicated language
  • Spread across various packages
  • Lesser speed

4. How many impossible values and missing values are represented in the R programming language?

NA or Not Available is used to represent missing values, whereas NaN or Not a Number represents impossible values. Mentioning deleted missing values is not a good idea as the probable causes for missing values can raise some problems in programming and data collection. That’s why it is important to find the root cause of missing values to take the necessary steps to handle them.

5. Comparison between Python and R programming language for predictive modeling?

Features Python Programming Language R Programming Language
Model Building They both are similar. They both are similar.
Model Interpretability Python is not better than R R is better than Python.
Production Python is better than R R is not better than Python.
Community Support Python is not better than R R is better than Python.
Data Science Libraries They both are similar. They both are similar.
Data Visualizations Python is not better than R R is better than Python
Learning Curve Learning Python is more manageable than R R has a steep learning curve.

6.  What is the difference between Python and R programming languages?

Features Python Programming Language R Programming Language
Scope Used for multiple purposes like data analysis and web application development Primarily used for statistical modeling
Used By
  • Developers
  • Data Engineers
  • Data Scientists
  • Statisticians
  • Analyst
  • Data Scientist
Suitable For Newbie to experienced IT professionals People with no prior experience in programming
  • IPython
  • Sypder
  • Jupyter Notebook
  • Rstudio
  • R GUI
Database Handling Capacity Can handle extensive data easily without any fault Poses problems for handling extensive database
Package Distribution PyPi CRAN
Essential Packages And Library
  • Numpy
  • Pandas
  • Scipy
  • Scikit-learn
  • TensorFlow
  • ggplot 2
  • Tidyverse
  • Caret

7. What is Data Import in R language?

R Commander is used to importing data in R language. To start the GUI R commander, the user must type the Rcmdr command into the console. There are three different alternates to import data in the R programming language.

  • Entering the name of the data set or selecting the data set in the dialog box 
  • Data is accessed directly using R Commander’s editor via Data->New Data Set, but this works well when the data set is too massive in amount.
  • It can also be imported from a plain text file (ASCII) or from a URL, or from the clipboard, or from any other statistical package.


8.  What are the various Data Structures in the R programming language?

Data Structure Detailed Description
Vector It is a sequence of some basic types of data elements and vector members known as components.
List It refers to R objects which include different types of elements such as strings, numbers vectors, or sub-lists.
Matrix It is a two-dimensional structure that is used to bind multiple vectors from the same length. Elements included in the same types are logical, complex, numeric, characters.
Dataframe It is more generic than matrix, i.e., different columns include different types of data such as character, numeric and logical, etc. It also combines the main features of matrix and rectangular list.


9.What are some packages used for Data Mining in R?

  • Data Table
  • TM
  • Rpart and caret
  • Arules
  • GGplot
  • Forecast

10. What are the different elements of the grammar of graphics?

There are different components available in the grammar of graphics.

  • Data layer
  • Themes layer
  • Aesthetics layer
  • Coordinate layer
  • Geometry layer
  • Facet layer

11. What are the differences in Require () and Library () functions in R language?

Require () Library ()
Used for inside function and informs while sending message  whenever particular packages are founded It gives an error message display if the desired package is not loaded.
The checks loaded the package and loaded the unloaded packages. Loads all the packages whether they are ready or not

12. What is R Markdown?

R Markdown refers to documents that provide reproductive and quick reporting from the R. Professionals write documents in markdown to embed executable R code chunks with the advanced knitr syntax. R Markdown allows users to update the document at any time with the help of re-knitting the code chunks. After creating and updating, the user can convert the document into multiple formats.

13. What are the three different output formats of R Markdown?

There are three different types of the output format of R markdown, and those are 

  • HTML
  • PDF
  • WORD

14. How to combine and merge datasets In R programming?

There are three popular and effective steps to merge and combine dataset in R, and those strategies are 

  • By adding columns
  • By adding rows
  • By combining data with different shapes

15. What are some packages available in the R for Data Imputation?

There are some of the packages available in R that is used for data imputation, and those are 

  • MICE
  • imputeR
  • Amelia
  • Mi
  • missForest
  • Hmisc

16. What is the Confusion Matrix In R?

A confusion matrix refers to the procedure that evaluates the accuracy of the building model. The confusion matrix calculates a cross-tabulation of predicted and observed classes. This procedure can be done by using the “confusion matrix()” function from the “caTools” package.

It gives you a tabular representation of lists that is divided into two values, and those are 

  • Actual Values
  • Predicted Values

17. What is the difference between Dataframe and Matrix?

Dataframe Matrix
Dataframe stores data tables that include multiple data types in various columns called fields. Matrix refers to the collection of the dataset that arranges rectangular organization into two dimensional.
It refers to a vector list of equal lengths that is the generalized form of the matrix It refers to the m*n array with a similar data type.
It has a variable number of columns and rows It has a fixed number of columns and rows.
The data stored must be a numeric, factor, or character type. The data stored in columns should be the same data type.
DataFrames are heterogeneous. The matrix is homogeneous.

 18. What is the dplyr in R programming language?

Dplyr is a primary collection of functions that are designed to enable manipulative data frames in a user-friendly and intuitive way. It is one of the key packages of the tidyverse in the R language. Data investigators or analysts prefer using dplyr to transform or convert existing datasets into a better-suited format for some particular type of data visualization and analysis.

19. What are some of the functions available in the "dplyr" package?

There are some of the particular function provided in dplyr packages, which includes

  • Selecting
  • Filtering
  • Mutating
  • Arranging
  • Counting

20. What is the use of the By () and With () function In R?

Use Of The By () Functions - The by( ) function applies a function to each level of factor or factor, which is similar to BY processing in SAS.

Use Of The With () Functions - The with( ) function applies an expression to a dataset, which is similar to DATA= in SAS.

21. What is R Packages?

All the packages available in R include the collection of data. R packages functions and compiles code in a well-defined and organized format that is usually stored in the library. One of the strong strengths of R is the user-written function in the R programming language.

22. What is a workspace in the R programming language?

Workspace refers to a current R working environment that includes many user-friendly objects such as data frames, functions, vectors, lists, matrices. At the end of the R session, the working user can save the current workspace image that automatically reloaded R the next time when R is started.

23. What is the R6 Package?

In Object-Oriented Programming, encapsulation refers to the binding of method and data inside the class. The R6 package provides an implementation of encapsulated OOP systems for the R language. The R6 package presents an R6 class that is similar to the R reference class, but they are independent of the S4 classes. Along with the public and private members, R6 classes support inheritance even if the classes are defined in various packages.

24. How to create a new R6 Class?

To create a new R6 class, following specific steps are important. building an object template is the first step that consists of the ‘’Class Functions’’ and ‘’Data Members’’ presents in the class

An R6 object template includes three parts, and those are 

  • Class Name
  • Private Data Members
  • Public Member Functions

25. How to install packages in R?

To install packages in R following and applying a specific command and that command is 


26. What do you understand by ShinyR?

ShinyR refers to the R package, which makes things easy in building interactive web applications straight from R. Professionals can host standalone applications on a webpage or Build dashboards or embed them in Rmarkdown documents. It can be extended with the Shiny applications with htmlwidgets, CSS themes, and JavaScript actions.

27. How many types of sorting algorithms are available?

There are five different types of sorting algorithms are available, and those are 

  • Bubble Sort
  • Quick Sort
  • Bucket Sort
  • Selection Sort
  • Merge Sort

28. Name some of the functions used for debugging In R?

  • Debug()
  • Traceback()
  • Browser()
  • Trace()
  • Recover()

29. How to load a .csv file in R Language?

Loading a .csv file in R language is quite an easy process to achieve. You just need to follow one simple step to load the file using the “read.csv()” function and just specifying the file’s path.

For an example - house<-read.csv("C:/Users/John/Desktop/house.csv")

30. What is transpose in R Programming Language?

Transpose refers to a process of reshaping the data that will be used for analysis. It is performed by the t() function. Transposing in R, reverse the columns and the rows, which is considered one of the simple reshaping methods in a dataset.

31. What is Clustering?

A cluster refers to a collection of objects that always belongs to a similar class. Clustering is the process of making a group of abstract objects or unlabeled examples into classes of similar objects. It includes two different types of clustering, and those are 

  • Hard clustering
  • Soft clustering

32. What are the various types of Clustering?

There are more than 100 types of clustering algorithms, but some of the important algorithms are very popular, and those are 

  • Centroids-based Clustering
  • Connectivity-based Clustering
  • Density-based Clustering
  • Distribution-based Clustering
  • Constraint-based 
  • Fuzzy Clustering

33. What are the different types of clustering algorithms?

  •  k-Means Clustering
  • Hierarchical Clustering Algorithm
    • DIANA or Divisive Analysis
    • Agglomerative Nesting or AGNES
  • Fuzzy C Means Algorithm
  • Mean Shift Clustering
  • Density-based Spatial Clustering
  • Maximization Clustering

34. What are the advantages of Clustering Algorithms?

  • Identifying Fake News
  • Spam filter
  • Marketing and Sales
  • Classifying network traffic
  • Identifying criminal or fraudulent activity 
  • looked into various business problems

35. What is t-tests () in R?

The t-test is a process, which is used to determine whether two different groups are equal or not. It is one of the common tests in statistics to check that both the groups are normally distributed with equal variances or not by using the t-tests function.

36. How to produce Covariances and Correlations?

There are separate functions to produce covariance and correlation, and that can be produced by functions such as 

  • Covariances are produced by cov() function
  • Correlations are produced by cor() functions

37. What is the difference between Correlations and Covariances?

Correlations Covariances
Indicate both the strength and direction of the linear relationship between two variables Indicate the direction of the linear relationship between variables
Correlation values are standardized. Covariance values are not standard
Either it brings a strong positive correlation, or it brings a negative correlation. A positive number brings a positive relationship, and a negative number brings a negative relationship.
Value remains strictly between -1 to 1. Values between positive infinity to negative infinity

38. What is the difference between Lapply And Supply?

There is not a huge difference when it comes to differentiating these two terms. Both are used to show inputs but in different forms. 

  • Supply is used to present the output in the form of the data frame and vector.
  • Lapply is used to present the outfit in the form of lists.

39. What do you understand by R Commander GUI?

The R Commander provides an open-source, and free user interface for R software, focusing on helping learners learn R commands by point-and-clicking their way through analyses. The R Commander is available for various devices such as Linux, Windows, and Mac as there is no server version.

40. What is the memory limit of R?

Memory limit totally depends on a bit system. A large-bit system will provide a better memory limit. Mostly it comes in two different bit systems, and those are 32-bit system and 64cbit system.

  • 32-bit system memory limit is 3Gb, but most versions are limited to 2Gb
  • 64-bit system memory limit is 8Tb

41. How to aggregate data in R?

To aggregate the data, professionals need to specify three points in the code.

  • Collect data that are going to be aggregate
  • Variable to group by within the data
  • The right calculation to apply to the groups

Then there are two methods to collapse all the data that should be aggregated, and those two methods are by using. 

  • One or more BY variable 
  • Aggregate() function with listed BY variables

42. Named the functions that are used to merge data frames vertically and horizontally in R?

The functions that are used to merge two horizontal data frame or two vertical data frame are 

  • rbind() function for the horizontal merging of two data frame
  • Merge()function for the horizontal merging of two data frame

43. What do you understand by the term Power Analysis?

Power analysis refers to a process where multiple statistical parameters are calculated. It is used to define experimental designs used to determine the actual effect of provided sample data size with given sample size or expected size, alpha, and power.

44. What is the name of the package used for power analysis in R?

The package name that is used for power analytics in R is known as the Pwr package.

45. Which method and package are used to export data to R?

There are several ways to export the data into various formats, such as 

  • SPSS
  • Excel Spreadsheet
  • SAS 
  • Stata 

The package used to export the data in R is the xlsReadWrite package which is used for formats that include.

  • SPSS
  • SAS 
  • Stata

46. Which command is used to store and restore R objects into a file?

  • For storing R objects into a file, the command is the Save command.
  • For restoring R objects into a file, the command is the load command.

47. What is GGobi in R?

GGobi is made for inactive data visualization, which is free statistical software, allows users to explore extensive data with interactive dynamic graphics. It is also known as a multivariate data tool. R uses this in sync through rggobi with GGobi. This software can be embedded as a library in program packages and other packages using API or as an add-on to scripting environments and existing languages.

48. Why are the library () function and search () function used?

  • The library() function is used to show the installed packages in the R
  • The search() function  is used to show the loaded packages in R

49. What is Robust Package and Robustbase?

  • A robust package refers to a library or collection of robust methods, including regression.
  • A robust base refers to a package that provides robust basic statistics, including model selection methods.

50. What is the full form of MANOVA and the uses of MANOVA?

The full form of MANOVA is a multivariate analysis of variance, which is used to test more than one dependent variable simultaneously.

Here, we Wissenhive covered the top 50 questions with answers from beginner to advanced level to give the candidate a strong idea about interview questions that they might encounter during the R interview. 

If you find this article helpful and looking for some platform to learn or enhance the R programming skills from industry professionals, then enroll yourself in a R programming certification course. Let us know your query and doubts in the comment box on the R Programming Interview Questions if any, and we will get back to you within 24 hours.


The Pulse of Wissenhive

Upgrade Your Skills with Our Advanced Courses

Speak with

Our Advisor

Mail Us

Contact Us

Drop a query