R Language Job Interview Questions

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

R Language Job Interview Questions

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

What are the Capabilities of the R Language?

The following are the capabilities of the R Language:

• Data Handling Capabilities: Good data handling capabilities and options for parallel computation.
• Availability/Cost: R languages is an open source, one can use it anywhere and free of cost.
• Advancement in Tool: R gets the latest features and updates frequently.
• Ease of Learning: R language has a steep learning curve. On the other hand, R is a low-level programming language, as a result, simple procedures can take long codes.
• Graphical capabilities: R has the most advanced graphical capabilities.
• Job Scenario: It is a better option for start-ups and companies looking for cost efficiency.
• Customer Service support and community: R is the biggest online growing community.

Explain a Few Features to Write R Code that Runs Faster.

R Language is a popular and famous statistical software for its enormous amount of packages. The syntax of R language is very flexible making it convenient at the cost of performance. R is indeed slow compared to many other scripting languages, but there are a few tricks that can make our R code run faster.

• Use a matrix instead of a data frame whenever possible. Data frames cause problems in many cases. Only use data frame when necessary.
• Use double(n) to create a vector of length n instead of using code rep(0,n), and similar to others.
• Split big data objects (e.g., big data frame or matrix) into smaller ones, and operate on these smaller objects.
• Use vector and matrix operations if possible. These *apply functions are very helpful for this purpose.
• Use for each(i=1:n) %dopar% {} to do parallel computing if applicable. Even if a for loop is not parallelizable, for each(i=1:n) %do% {} is a better alternative.
• Avoid changing the type and size of an object in R language. Though one can use R objects as if they are typeless, they have type actually. In R, changing the type and size of an R object forces it to reallocate a memory space which is of course insufficient.
• Avoid creating too many objects in each working environment. Not having enough memory can not only make the code run slower but also make it fail to run if have to allocate big vectors. One way to do this is to write small functions and run the functions instead of running everything directly in a working environment.

What is Visualization in R?

Visualization is a technique for creating images, diagrams, or animations to communicate a message. Visualization through visual imagery has been an effective way to communicate both abstract and concrete ideas since the dawn of humanity. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.

Why R Language?

R is popular among researchers, data scientists, and statisticians. It is also used in finance, which relies heavily on statistical data. The R programming language is used for many reasons, including:

• Data analysis: R is a statistical programming tool well-suited for data analysis, mining, and modeling. It is also used for data cleaning and importing.
• Open-source and free: R is free and open-source and works on different platforms, including Windows, Mac, and Linux.
• Data visualization: R is a powerful tool for creating publication-ready graphics and visualizations, such as cluster bar charts, pie charts, histograms, box plots, and scatter plots.
• Machine learning: R is an effective tool for machine learning algorithms.
• Specialized focus on analysis: R handles specialized data science projects better than general-purpose software development languages like Python.
• Many packages: R has many packages (libraries of functions) that can be used to solve different problems.
• Large community support: R has a large community support.

What is SAS and SPSS in R?

SAS: SAS stands for Statistical Analysis System. It was primarily developed to be able to analyze large quantities of agriculture data.
SPSS: SPSS stands for Statistical Package for the Social Sciences and was developed for the social sciences.
R Language: R language was the first statistical programming language for the PC.

Computer MCQs Online Test, Chi-Square Distribution $\chi^2$