R Interview Questions

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If you're looking for R Interview Questions & Answers for Experienced or Freshers, you are at right place. There are lot of opportunities from many reputed companies in the world. According to research Business Analytics with R salary ranges from $30,000 to $182,000. So, You still have opportunity to move ahead in your career in Business Analytics. Mindmajix offers Advanced R Interview Questions 2019 that helps you in cracking your interview & acquire dream career as Business Analytics Developer.

Q1) Describe the R environment (Features).

1. R is termed as an integrated suite which contains various software solutions for data calculation, manipulation, and illustration.
2. An optimum data handling software with data storage facilities.
3. R provides graphical solutions for analyzing data which is displayed either on screen or saved in a hard copy.
4. Acts as a large, coherent repository containing intermediate tools for data analysis.
5. A collection of operators for calculations on arrays, in particular, matrices.

Q2) Who and what are the uses of R software environment?

The statistical programming language is widely used amongst data miners and statistics analysts for the development of data analysis and statistical software respectively.

Q3) What are the statistical features of R?

• This software programming language has implemented an extensive range of statistical and graphical techniques, namely Non-linear and linear modeling, analysis of time-series, classical statistical tests, clustering etc
• Functions and extensions of the language make it very convenient and in addition to this, the R community is also acclaimed for its functioning contribution with respect to packages.
• Most of the R’s fundamental functions are written in R itself to serve the purpose of making it easy for the end-users to adhere the algorithmic choices made.
• Due to the legacy of S, The R language has high potential in object-oriented programming amenities when compared to most of the statistical computing languages.
• Producing publication-quality graphical representation which included mathematical symbols is an essential feature of the language.
• The program has its own LaTeX-like documentation format which is utilized to supply thorough documentation.

Q4) Explain the evolution of the language R.

R is an application of the S programming language with lexical scoping semantics encouraged by Scheme. This computing program was created by Mr. Ross Ihaka and Mr. Robert Gentleman at Auckland University, New Zealand. The language is named “R” after the first names of the authors. Although the project was initiated in the year 1992, its first version was released in 1995 and a stable version after half a decade (2000) respectively.

Q5) Explain the application of R Programming in the real world.

1. Data Science: A data scientist is a statistical analyst with an add-on asset which is i,e. Computer programming skills. R gives a data scientist an edge that authorizes them to conduct statistical and anticipating analysis, collect data, create graphical illustrations etc.

2. Statistical Computing: With a repository of more than 90,000 packages with every statistical function, R program is the fundamental and famous language built and used by statisticians for statisticians. The language’s artistic syntax allows researchers from the non computer science background to instantly import, analyze and clean data from various data sources. Additionally, R has a charting competencies where the user can plot their data and create alluring illustrations from any dataset.

3. Machine Learning: Tasks like Linear and Non-Linear  regression and classification, decision tree are the part of the various package for common machine learning. The R language has been useful in a predictive analytical model as well. Most of the people who are interested in machine learning and researchers use R to initiate algorithms in the finance, retail, marketing and many more fields.

Q6) Please state alternatives to the R programming.

The following are the alternatives which can be replaced in the place of R t obtain statistical computing and graphics:

1. Python: A superior-level, object-oriented programming language with a simple and easy to use syntax. Apart from R, Python is equally famous among data-scientists and researchers. Almost every package in R has proportionate libraries in Python as well. The parameter to choose between R and Python depends on what the user is trying to accomplish with their code, i,e. If the main objective is to analyze the data set and present the finding through a research paper then the former is a better choice. On the other hand, if the motive is to draft data analysis program, running in a  distributed system and interacts with a whole lot of factors, then the latter can be considered.

2. Statistical Analysis System: SAS has always been the first choice for exclusive and independent enterprises for their analytics needs. Extensive documentation and GUI, paired with authentic technical support makes SAS a reliable tool for business organizations. R is considered as the foremost programming tool in the field of academics and research when compared to SAS which is extremely famous in commercial analytics.

3. Software package for statistical analysis: a Software package for statistical analysis (SPSS) is often used in social sciences and is studied as the easiest to learn in comparison to other statistical tools. It is very prominent amongst non-statisticians because of its similarities to excel, making it easy for the users who are priorly aware of excel easy to understand SPSS.

Related Article: Compare R Language with SAS

Q7) Explain the programming features of R.

1. Users commonly approach through a command-line interpreter to run the R language.
2. Similar to other programming languages like MATLAB or APL, matrix arithmetic is supported by R.
3. The language’s object system is inclusive of objects such as time-series, geospatial coordinates, and regression models. 
4. R also backs productional programming along functions and for few functions, it supports object-oriented programming with generic functions.
5. Apart from being the first choice in the field of statistical computing and research, R is capable of operating as a general matrix calculation toolbox.

Q8) Explain the role of packages in R.

• The programming language can be extended through user-constituted packages. It allows unique statistical techniques, export/import capabilities, reporting tools, graphical devices etc. These packages are primitively developed in R but in some circumstances, it is written in Java, C, C++ etc.
• In order to create compendia to organize researched information, code and report files in a standardized way, researchers utilize the packing system.
• A core set of a package is comprehended with the process of installation of the language R.
• A prominent set of R package “Tidyverse”, where the users are allowed to manipulate and visualize data with consistent manipulation of data and graphic language.
• The subject line page also is known as “ Taks Views”  on the website of CRAN, catalogs a wide range of activities to which application of R has taken place and shows the availability of the package. 
• R has also been recognized as equipped and apt for interpreting data for clinical research purposes by the FDA.
• Various other resources of R package are inclusive of Crantastic, A section of the community which rates and reviews all packages present in CRAN, and a fundamental platform for collaborative development of packages in R, its related software and projects  - R-Forge. It also hosts unpublished beta packages.

Q9) Explain RStudio.

RStudio is defined as the open-source, cost-free, integrated development environment for the computing language R. This integrated environment is written in C++ programming language and for graphical user-interface, adopts Qt framework. RStudio runs on the platform as a desktop application and RStudio server at the same time are available in both open source and commercial editions.

Q10) What is the role of variables in R programming language?

While working in a programming language, the user must adopt variables to store various information. These variables are in other words, reversed memory locations which serve the purpose to store data. Based on the data type of the variable which can either be a character, wide character, integer, floating point etc, the system accommodates memory and concludes what can be reserved in the memory. Unlike C and Java in R, the variables are not termed as any data-type. They are assigned with R-Objects and become the data type of the variable.

Checkout Business Analytics With R Tutorials

Q11) Explain different R-objects.

The following are the R-object variables frequently used in R.

• Vector: This is considered as one of the simplest objects, there are six different data types of these vectors which are also known as six classes of vectors namely: Logical, numeric, integers, complex, character, raw. The rest of the R-objects are built on these atomic vectors.
• Lists: This type of R-object stores numerous types of elements like vectors, functions or other lists internally. 
• Matrices: Defined as a two-dimensional, rectangular dataset, a matrix can be designed with the help of vector input to the concerned function.
• Arrays: Unlike matrices, arrays are multidimensional, A dim attribute is taken by the array function in order to create a required number of dimensions.
• Factors: Vector plays the fundamental role in creating factor R-objects. It stores the vector as well as the values of the elements in the vector which are defined as labels. These are classified as characters irrespective of numeric or Boolean etc. in the input vector. They are also considered useful in the process of statistical modeling.
• Data Frames: These are the tabular data objects when compared to the matrix, data frames can store information in different models of information in each column of the frame.

Q12) Explain R-Variables

Atomic vector, group of atomic vectors or a combination of many R- Objects can be stored in a variable of R.A variable is defined valid if it contains letters, numbers and the dot or underlines characters. The name of the variable either starts with a letter or a dot which is not followed by a number.

Q13) Provide examples of various invalid and valid variables and state the reason for validation.

Q14) What is a variable assignment?

Variable assignment is derived through the assigned values with the help of right, left or equivalent to the operator. These  can either be printed using print() or cat() function respectively. The later option combines various items into a continuous print output.

Q15) Explain the functionality of data type of variable.

The statistical computing program R, the variable itself is not entitled to any data type, it rather acquires the data type of the R- objects assigned to it. That is the reason behind terming R as dynamically typed language. We can conclude that a user can constantly change data of a variable when being used in a program.

Q16) What are the functions of finding and deleting variables?

The function Is() is used in order to know all the currently available variables in the concerned workplace. This function can at the same time use patterns to match the names of the variables.
The variables can be deleted by executing rm() function. Both Is() and rm() function used together can also delete the required variables.

Q17) List various graphical representations present in the R programming language.

The R programming and computing language contain numerous libraries in order to create charts and graphical representations. The following are the types of illustrations available in the program:

1. Pie charts
2. Bar charts
3. Box Plots
4. Histograms
5. Line graphs
6. Scatterplots

Q18) Explain statistical functions performed by R.

The statistical analysis takes place by using numerous inbuilt functions, most of the concerned functions are a part of R basic package, The R vector is considered as the input along with the arguments in order to produce results.

• Mean: The function mean “mean()” is calculated by dividing the sum of all the values with the number of values in the data presented.  
• Median: Median function “Median()” is the middle most value present in the entire data series.
• Mode: the value of highest occurrence which takes place in the data set is termed as a mode. When compared with mean and median, a mode can either be a character or a numerical. R language does not have any fundamental in-built function to calculate mode. This statistical function takes the vector as prime input and provides mode solution as an output.

Q19) Which data object in the language R is utilized to store and process categorized data.

The factor data object is used in the programming language to store and process categorized data. 

Q20) Explain R base package.

R base package is the package which is stored by default when R programming language is installed primarily. It offers core functionalities such as input / output, calculations of arithmetics and many more in the R environment.

About The Author

Ravindra Savaram is a Content Lead at Mindmajix.com. His passion lies in writing articles on the most popular IT platforms including Machine learning, DevOps, Data Science, Artificial Intelligence, RPA, Deep Learning, and so on. You can stay up to date on all these technologies by following him on LinkedIn and Twitter.