R FAQs Interview Questions

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

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

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.

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.

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.

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.

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.

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.

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.

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