R FAQs Interview Questions

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The post is about R FAQs Interview Questions. It contains some basic questions that are usually asked in interviews.

R FAQs Interview Questions

The following are R FAQs Interview Questions with their detailed answers:

Why Should One Adopt the R Programming Language?

  • R programming language is the best software for statistical data analysis and machine learning. By using R language software, one can create objects, functions, and R packages.
  • R is an open-source programming language.
  • Using R one can create any form of statistical analysis and data manipulation.
  • It can be used in almost every field of finance, marketing, sports, etc.
  • R Programming is extensible and hence, R contributor groups are noted for their energetic contributions.
  • A lot of R’s typical features can be written in R Language itself and hence, R has gotten faster over time and serves as a glue language.

What are the programming features of R?

  • Packages are part of R programming. R Packages are useful in collecting sets of R functions into a single unit.
  • R’s programming features include database input, exporting data, viewing data, variable labels, missing data, etc.
  • R is an interpreted language, so one can access it through a command line interpreter.
  • R supports matrix arithmetic.
  • R supports procedural programming with functions and object-oriented programming with generic functions.
  • Procedural programming includes procedures, records, modules, and procedure calls while object-oriented programming language includes classes, objects, and functions.

Is R is a slow language?

  • R programs can be slow, however, well-written R code/programs are usually fast enough.
  • In R language, Speed was not the primary design criterion.
  • R language is designed to make programming easier.
  • Slow programs are often a result of bad programming practices or not understanding how R works.
  • There are various options for calling C or C++ functions from R.

Why is R important for data science?

  • One can run the R code without any Compiler because R language is an interpreted language. Hence one can run Code without any compiler.
  • R interprets the Code and makes the development of code easier.
  • Many calculations are done with vectors because R is a vector language, so anyone can add functions to a single Vector without putting it in a loop. Hence, the R language is more powerful and faster than other languages.
  • R language is a Language widely used in biology, genetics as well as in Statistics. R is to a turning complete language where any type of task can be performed.

Why is R Good for Business?

  • The most important reason why R is good for business is that it is open-source and Free. R language is great for data visualization. As per new research, R has far more capabilities as compared to earlier tools and computing languages.
  • For data-driven decisions in businesses, data science talent shortage is a very big problem. Companies are using R programming as their platform and recruit trained users of R.

What are the statistical and programming features of the R Language?

  1. Statistical Features
  • Basic Statistics: Measures of central tendencies (Mean, variance, median, etc.), measures of dispersion (range, standard deviation, variance), Quartiles, etc.
  • Static graphics: Basic plots, graphic maps, scatter plots, line charts, etc.
  • Probability distributions: Normal, Poisson, Binomial, t, F, Beta, Gamma, etc.
  • Inferential Statistics: Comparison tests (one sample, two samples, ANOVA, etc.), correlation and regression analysis, non-parametric tests, etc.
  • Multivariate Analysis: Principal Component Analysis (PCA), Factor Analysis, Canonical Correlation, etc.
  1. Programming Features
  • Distributed Computing: Distributed computing is an open-source, high-performance platform for the R language. It splits tasks between multiple processing nodes to reduce execution time and analyze large datasets.
  • R packages: R packages are a collection of R functions, compiled code, documentation, and sample data. By default, R installs a set of packages during installation.
  • R is an interpreted language: R language does not need a compiler to make a program from the code. R directly interprets provided code into lower-level calls and pre-compiled code.
  • Compatible Programming Language: Most R language functions are written in R itself, C, C++, or FORTRAN, and can be used for computationally heavy tasks. Java, .NET, Python, C, C++, and FORTRAN can also be used to manipulate objects directly.
R FAQS Interview Questions Frequently Asked Questions About R

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Interview Questions about R Language

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The post is about Interview Questions about R Language. It contains some basic questions that are usually asked in interviews.

What is R?

R is a programming language and environment for statistical computing and graphics. It is an open-source language that provides a wide variety of statistical and graphical techniques and is highly extensible. The strength of R is the ease with which well-designed publication-quality plots can be produced, including mathematical/statistical symbols and formulae where needed.

Learn R Language and FAQS, Interview Questions about R Language

R language is available as Free Software under the terms of the Free Software Foundation’s GNU General Public License in source code form. It compiles and runs on a wide variety of UNIX platforms and similar systems (including FreeBSD and Linux), Windows, and Mac OS. The R command line interface (CLI) consists of a prompt, usually the > character. Data miners use it for developing statistical software and data analysis.

What is CLI in R?

CLI stands for Command Line Interface. In a command line interface, the user types the command that they want to execute and presses the Return key. For example, if you type the line 2+2 and press the return key, R will give you the result [1] 4.

Interview Questions about R Language

What is GUI in R?

GUI stands for Graphical User Interface. R Language is a command line-driven program. The user enters instructions at the command prompt ( > by default ) and each command is executed one at a time. There have been a number of attempts to create a more graphical interface, ranging from code editors that interact with R, to full-blown GUIs that present the user with menus and dialog boxes.

Who is the Creator of R Language?

R language was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand. It is currently developed by the R Development Core Team, of which Chambers is a member. R is named partly after the first names of the first two R authors and partly as a play on the name of S. The project was conceived in 1992, with an initial version released in 1995 and a stable beta version in 2000.

What are the Applications of the R Language?

  • Many data analysts and researchers use R because R language is the most prevalent language. Hence, R is used as a fundamental tool for data analysis in various disciplines such as mathematics, economics, social sciences, natural sciences, technology and engineering, business and finance, etc.
  • Many quantitative analysts use R as their programming tool. R helps in data importing and cleaning, depending on what manner of strategy the researchers are using.
  • R is best for data Science because it gives a broad variety of statistics and data manipulation tools. In addition, R provides the environment for statistical computing and design. Rather R is considered as an alternate execution of S.

Why R is Important?

R language is a programming language and a leading tool for machine learning, artificial intelligence, data mining, natural language processing, statistics, and data analysis. By using R one can create objects, functions, and packages. R language is a platform independent, so one can use it on any operating system. The downloading and installation of R language is free, therefore, one can use it without purchasing a license.

R is open-source which means anyone can examine the source code to see what exactly is doing on screen. Anyone can add a feature and fix bugs without waiting for the vendor to do this. It also allows the user to integrate with other languages (such as C and C++). It also enables the user to interact with many data sources and statistical packages (such as SAS and SPSS). R has a large growing community of users working day by day to enhance its working and powers.

General Knowledge Quiz, MCQS Statistics with Answers

Packages in R Language

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Packages in R Language store all the functions, datasets, and help files that significantly expand the language’s functionality beyond its core capabilities. When a package is loaded, its contents are available to work with. It makes the packages more efficient (as the full list takes more memory and time to search than a subset). The packages are also protected from name clashes with other codes.

Why Use R Packages?

  • Specialized Functionality: R packages offer tailored solutions for various domains, such as,
    • Biostatistics
    • Data mining
    • Machine learning
    • Financial analysis
    • Geospatial analysis
    • Text mining
  • Efficient Code and Algorithms: Many packages incorporate highly optimized C or C++ code, boosting performance and enabling complex computations.
  • Community-Driven Innovation: The R community actively develops and shares packages, ensuring a constant stream of new tools and techniques.
  • Standardized Data Formats: R Packages often include standard data formats, making it easier to work with diverse data sources.
  • Reproducibility: By using R packages, one can share his/her code and analyses more easily, making them reproducible for others

Seeing the Installed Packages

To see what packages are installed in your computer system, use the following command without arguments.

library()

To load particular packages in R (for example, mctest (https://CRAN.R-project.org/package=mctest) package containing functions to compute the multicollinearity diagnostics), use the command like:

library(mctest)
Packages in R Language

Installing and Updating Packages in R

One can install an R package if a system is connected to the internet using install.packages(). A package can also be updated by using the update.packages() command. (The installation of a package is also available through the Packages menu in the Windows and OS X GUIs.

# Installing a package
install.packages("mctest")

# Install Multiple Packages in R
install.packages(c("mctest", "lmridge", "liureg"))

# Updating a package
update.packages("mctest")

Currently Loaded Packages

One can see the packages that are currently loaded in the more by using the command

search()

Note that some packages may be loaded but not available on the search list, such packages may be seen by using

loadedNamespaces()
Packages in R

One can see a list of all available help topics in an installed package, by using the command

help.start()

An HTML help system will start. One can easily navigate to the package listing in the reference section.

Help System in R

Standard/ Base Packages in R

The base or standard packages are considered part of the R source code. The base packages contain the basic functions that allow R to work, and the datasets, standard statistical, and graphical functions that are described in this manual. These packages are automatically available in any R installation.

Contributed Packages and CRAN

There are thousands of contributed/ customized/ user-defined packages for R, written by many different authors. Some of these packages implement specialized statistical methods, some give access to data or hardware, and others are designed to complement textbooks. Most of the R packages are available for download from CRAN (https://CRAN.R-project.org/ and its mirrors).

Key R Package Repositories

  • CRAN: The primary repository for R packages, offering a vast array of options.
  • Bioconductor: Specializes in bioinformatics and computational biology tools.
  • GitHub: Hosts user-contributed packages and open-source projects.

Commonly Used Packages

  • Data Manipulation:
    • dplyr: For data manipulation and transformation.
    • tidyr: For tidying data.
  • Data Visualization:
    • ggplot2: For creating elegant and customizable plots.
    • plotly: For interactive visualizations.
  • Statistical Computing:
    • stats: The Base R package for statistical computations.
    • MASS: For more advanced statistical methods.
  • Machine Learning:
    • caret: For a unified interface to various machine learning algorithms.
    • randomForest: For random forest models.
    • xgboost: For gradient boosting machines.
  • Text Mining:
    • tidytext: For text mining and analysis.
  • Web Scraping:
    • rvest: For extracting data from websites.

Data Analysis and Statistics