R Programming Interview Questions & Answers- Part 3

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R Programming Interview Questions & Answers- Part 3

Starting a career in data science? R is a great tool to showcase your statistical skills and data knowledge. Many entry-level roles still prefer candidates who can work comfortably in R, especially when dealing with exploratory data analysis, linear models, or data cleaning.

This page brings you commonly asked R interview questions and answers to help you prepare for your first tech or analytics interview. These questions cover basics like variable types, control structures, and package usage, as well as more advanced topics like dplyr functions, data visualization with ggplot2, and model building.

Practice with this guide to build confidence, improve communication, and understand how to respond effectively to technical questions. With proper prep, you’ll be able to explain your skills and land that first big opportunity in the data field.

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To create scatterplot matrices in R, you can use the pairs() function from the base graphics package or the ggpairs() function from the GGally package.

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The “npmc” refers to a package that gives nonparametric multiple comparisons.

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This package is used to measure the relative importance of each of the predictors in the model.

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The “robustbase” refers to a statistical package or library in the R programming language called “robustbase.” R is a popular computing language used for statistical computing and graphics. It offers several packages that provide specialized functions for different statistical tasks.

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Numerical variables are represented by lower-case letters.

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In R, categorical factors are represented by upper-case letters.

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The survfit() function is typically used in survival analysis, a statistical method for analysing  time-to-event data. Specifically, it estimates the survival probabilities or curves based on a survival object in the R programming language.

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In machine learning, hyperparameter is the parameters that determine and control the complete training process. The examples of these parameters are Learning rate, Hidden Layers, Hidden units, Activation functions, etc. These parameters are external from the model. The selection of good hyperparameters makes a better algorithm.

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coxph() is a function used to model the hazard function on the set of a predictor variable.

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MASS functions have functions that perform linear & quadratic discriminant function analysis.

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In R, the principal() function is not a built-in function. Therefore, it is likely that you are referring to a specific function from a package or code that you have encountered. Without additional context or information about the package or code you are working with, I cannot provide specific details about the principal() function.

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CFA in R programming stands for Confirmatory Factor Analysis.

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FactoMineR is an R package for multivariate exploratory data analysis (EDA) and data mining. It provides a wide range of statistical methods and visualization techniques for analyzing complex data sets. The package is designed to perform various tasks such as data visualization, dimensionality reduction, clustering, and classification.

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In R, the fundamental data structure is the vector, which is divided into two categories: atomic vectors and lists. Both types of vectors possess three key characteristics:

  • Type function: Specifies the nature of the vector.
  • Attribute function: Allows for the inclusion of additional arbitrary metadata.
  • Length function: Indicates the number of elements contained within the vector.

The distinction between atomic vectors and lists lies in the types of elements they can accommodate. Atomic vectors mandate that all elements be of the same type, while lists permit elements of different types.

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  • Combining Vector
  •  Atomic Vector
  • Vector arithmetic
  • Logical index vector
  • Numeric index
  • Range Indexes
  • named Vectors Members
  • Out-of-order Indexes

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In the context of R programming, sockets refer to the mechanisms that allow for network communication between different processes or systems. Sockets provide a programming interface for establishing communication channels, known as network sockets, which facilitate the exchange of data between a client and a server over a network.

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Debugging refers to the process of identifying and fixing errors, bugs, or issues in your code. Debugging is an essential skill for programmers as it helps them understand and resolve problems that may arise during the execution of their R programs or scripts.

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Following five tools present for debugging in R respectively:

  • trace()                                                                                                                                  
  • debug()
  • browser()
  • traceback()
  • recover()

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A graphic device is a software or hardware component that allows you to create and display graphical output. R provides various graphic devices that enable you to generate plots, charts, and other visual representations of data. The default graphic device in R is the interactive graphics device, which typically opens a new window where plots are displayed. This device is suitable for creating and interacting with graphics in an interactive manner.

R also supports other graphic devices, such as:

  • PDF
  • PostScript
  • SVG
  • Windows-specific devices
  • PNG, JPEG, and other bitmap devices

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In R, data aggregation refers to the process of summarizing data based on certain variables or groups. There are several functions and techniques available in R for data aggregation. Here are some common methods:

  • Base R functions:
    • aggregate(): This function allows you to aggregate data using a formula interface. You can specify the variables to aggregate and the aggregation functions to apply.
    • tapply(): It applies a function to subsets of a vector based on specified factors or groups.
    • by(): It splits a data frame into subsets based on factors or groups and applies a function to each subset.
    • ave(): It calculates the average or other functions for a vector or variables within groups defined by factors.
  • The dplyrpackage: The dplyr package provides a set of functions that offer a more intuitive and efficient way to aggregate data.
  • The tablepackage: It provides fast and efficient methods for data manipulation and aggregation.