A Detailed Guide to R Programming Interview Questions and Answers

Ans:  R is a Programming Language and environment for statistical computing and graphics. It is an open-source programming Language.

Ans: R is an open-source tool, while other statistical software like SAS, SPSS, and Stata are licensed products.

Ans: The workspace is your current R working environment and includes any user-defined objects (vectors, matrices, data frames, lists, functions).

Ans: There are vectors, matrices, lists, data frames, factors, and arrays in R.

Ans: A factor is a data object used for fields that take a limited number of levels, such as yes/no or levels of a factor.

Ans: A matrix is a two-dimensional array where every individual element has the same mode (numeric, character, or logical).

Ans: Data frames are tabular data objects. Unlike a matrix in data frame, each column can contain different modes of data.

Ans: The tapply() function applies a function over subsets of a vector as defined by some other vector, usually a factor

Ans: The apply() function can be used to apply a function to the rows or columns of a matrix.

Ans: Data in R can be sorted with the help of the function order().

Ans: with() is used to apply an expression to a dataset. It is used to avoid redundant mentioning of the data frame name when we call each column.

Ans: Packages in R are collections of functions, compiled code, sample data, and documentation that can be used to add on to R's functionality.

Ans: ggplot2 is a data visualisation package for the R Programming Language. It helps in creating visually appealing plots.

Ans: Reshaping of data is transforming the structure of a data set to make it suitable for certain analyses. Functions like melt() and cast() are used for data reshaping.

Ans: We can merge two data frames using the merge() function in R.

Ans: The best way to present the results is through visualisation. R provides various tools for creating charts, plots, and maps.

Ans: While both are popular in data science, R is used for statistical analysis and visualisation, and Python provides a more general approach to data science.

Ans: The split() function can be used to divide a data frame.

Ans: Arrays are used to store data in multiple dimensions, making them important for mathematical and scientific computations.

Ans: Factors are categorical variables that hold either string or integer, while variables are common data elements.

Ans: R supports several types of loops, including 'for', 'while', and 'repeat'.

Ans: The ls() function lists the objects in the specified environment. If no environment is specified, it lists the objects in the current environment.

Ans: The str() function displays the internal structure of an R object.

Ans: The cor() function is used to calculate the correlation between different variables in R.

Ans: The subset() function is used for selecting variables and observations, while the sample() function is used for generating a random sample from a large dataset.

Ans: dplyr is an R package for data manipulation, while tidyverse is a collection of R packages for data science.

Ans: The reshape() function is used to change the structure of a dataset.

Ans: Sorting algorithms include bubble sort, quicksort, and merge sort, all of which can be implemented in R.

Ans: knitr is an R package that allows you to intertwine R code and its output with the narrative of a report.

Ans: Rmarkdown documents are fully reproducible documents that replace the traditional statistical report.

Ans: The Next statement is used when we want the current iteration of the loop without ending it.

Ans: Type conversion refers to changing an object of one data type to another.

Ans: Type conversion refers to changing an object of one data type to another.

Ans: set.seed() function sets the starting number used to produce a sequence of random numbers, ensuring that the same random datasets can be reproduced.

Ans: A CSV is a comma-separated values file which saves the data to be saved in tabular format. We can read a CSV file in R using the read.csv() function.

Ans: Some libraries for data visualisation include ggplot2, lattice, and RGL.

Ans: S3 and S4 are two systems for creating classes and methods in R Programming. They allow for object-oriented programming.

Ans: The predict() function can be used for predictions. Some methods for prediction include linear regression, logistic regression, and decision trees.

Ans: Overfitting is a modelling error in statistics when a function corresponds too closely to a dataset. It might not fit well with new data.

Ans: Overfitting can be avoided by using techniques like cross-validation, pruning, regularisation, and keeping the model simple.

Ans: A list is a data structure having components of mixed data types. A vector having all elements of the same type is a list itself.

Ans: The rbind() function combines vector, matrix or data frame by rows, and the cbind() function combines vector, matrix or data frame by columns.

Ans: Some commonly used R Programming packages include dplyr for data manipulation, ggplot2 for data visualisation, and caret for machine learning.

Ans: The data.table package in R provides an enhanced version of data.frame that allows for fast data manipulations.

Ans: Scatter plots are used to visualise the relationship between two quantitative variables, which is particularly useful for linear regression analysis.

Ans: There are generally three types of errors: syntax errors when we do not follow the correct sequence of the Language grammar, runtime errors when R environment detects an illegal operation, and semantic errors when our expectation does not match the output.

Ans: The plyr package is a set of tools for manipulating datasets and making data cleaning more efficient.

Ans: Data cleaning is the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset. In R, it can be done using various packages like dplyr, tidyr, or data.table.

Ans: Missing values in R can be replaced using functions like replace(), na.omit() or using package Amelia that can handle multiple imputations.

Ans: Cluster sampling is a probability sampling technique where researchers divide the entire population of samples into individual groups, called clusters. Then, a random set of clusters is selected from the population.

Ans: The six types of data objects are: vector, factor, list, matrix, data frame, and array.

Ans: Coercion refers to the conversion of an object from one class to another.

Ans: Random Forest is a powerful machine learning technique that is used for regression, classification and other tasks using decision trees. Random Forest can be implemented in R using the randomForest() function.

Ans: seq(4) will create a sequence of 4 numbers from 1 to 4, whereas seq_along(4) treats 4 as a single sequence and returns 1 as the output.

Ans: The sink() function is used to redirect the output to a file rather than the R console.

Ans: Bar plots can be created using the barplot() function in R.

Ans: A Boxplot is a graphical representation of data that summarises sample data using 25th, 50th and 75th percentiles.

Ans: Deep learning is a part of AI that imitates the working of the human brain in processing data for decision-making. Deep learning can be performed in R using packages like MXNet, darch, deepnet, and H2O.

Ans: Datasets can be added in R using the cbind() or rbind() function, or the data.frame() function to combine objects.

Ans: Some best practices include: using vectorised operations, not growing objects in a loop, avoiding unnecessary memory usage, and writing clean and modular code.

These 60 questions are some of the Interview Questions that you can expect to face in your next interview. However, your aim should always be to build a strong fundamental base so that it is easy for you to answer even the advanced level questions.

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