R Programming Interview Questions and Answers

Here are some frequently asked R programming interview questions and their answers.

1. What is R?

R is an interpreted programming language and software environment for statistical computing, graphics, reporting, and data modeling. It's based on the S programming language and is widely used for data analysis.

2. Differentiate Between Vector, List, Matrix, and Data Frame.

• Vector: A sequence of elements of the same data type.
• List: Can contain elements of different data types (numbers, strings, other lists, etc.).
• Matrix: A two-dimensional array with elements of the same data type.
• Data Frame: Similar to a matrix, but columns can have different data types (like a spreadsheet).

3. Packages for Data Imputation.

Several R packages handle missing data (imputation): MICE, missForest, amelia, imputeR, Hmisc, mi.

4. Explain the initialize() Function.

In object-oriented programming (OOP) contexts in R (using S3 or S4 classes), the initialize() function sets up the object's internal state (data members) when the object is created.

5. Finding the Mean of One Column with Respect to Another.

mean(iris$Sepal.Length ~ iris$Species)

This calculates the mean of Sepal.Length for each species in the iris dataset.

6. What is a Random Walk Model?

A random walk is a non-stationary time series model where the next value is the previous value plus some random noise. It has no fixed mean or variance.

arima.sim(model = list(order = c(0, 1, 0)), n = 40) -> rw
ts.plot(rw)

7. What is a White Noise Model?

A white noise model is a stationary time series where values are independent and identically distributed (i.i.d.) random variables with a constant mean and variance. There's no correlation between values at different time points.

arima.sim(model = list(order = c(0, 0, 0)), n = 50) -> wn

8. Five Features of R.

• Simple and effective syntax
• Powerful data analysis capabilities
• Efficient data handling and storage
• Extensive graphics tools
• Interpreted language (code is executed line by line)

9. R vs. Python for Data Analysis.

R has built-in functions for data analysis, while Python relies on packages like Pandas and NumPy.

10. Applications of R.

R is used in various fields, including data science, statistics, finance, and bioinformatics. Examples of companies using R include Facebook, Google, and Twitter.

11. Explain RStudio.

RStudio is an integrated development environment (IDE) for R, providing a user-friendly interface with features like code editing, debugging, and plotting tools.

12. Advantages and Disadvantages of R.

13. Purpose of R and Hadoop Integration.

Integrating R and Hadoop allows you to run R code on Hadoop clusters and access data stored in Hadoop using R.

14. Hadoop Integration Methods.

Several methods exist for integrating R and Hadoop, including RHadoop, Hadoop Streaming, RHIPE, and ORCH.

15. Output of all(NA == NA).

The output is NA. R treats NA (Not Available) values specially in logical comparisons.

16. Difference Between sample() and subset().

• sample(): Takes a random sample.
• subset(): Selects specific rows and/or columns based on conditions.

17. Purpose of install.packages(file.choose(), repos = NULL).

This command installs an R package from a local file instead of a remote repository.

18. Commands for Histogram and Vector Removal.

• Histogram: hist()
• Vector Removal: rm()

19. Difference Between %% and %/%.

• %%: Modulus operator (remainder after division).
• %/%: Integer division operator (quotient).

20. Use of the apply() Function.

apply() applies a function to rows or columns of a matrix or data frame.

21. Difference Between library() and require().

library() stops execution with an error if the package is not found; require() gives a warning and continues execution.

22. What is t.test()?

t.test() performs a t-test to compare means of two groups, checking for significant differences.

23. Use of with() and by() Functions.

• with(): Evaluates an expression within a specified data frame.
• by(): Applies a function to subsets of a data frame based on grouping variables.

24. Difference Between lapply() and sapply().

Both apply functions to list elements, but lapply() returns a list, while sapply() attempts to simplify the result to a vector or matrix if possible.

25. Explain the aggregate() Function.

aggregate() computes summary statistics (like mean, sum) for groups of data.

26. Explain the doBy Package.

doBy provides functions for creating summary tables and applying functions to subsets of data based on grouping variables.

27. Use of the table() Function.

table() creates frequency tables (contingency tables) from categorical data.

28. Explain the fitdistr() Function.

fitdistr() (in the MASS package) fits probability distributions to data using maximum likelihood estimation.

29. What are GGobi and iPlots?

GGobi is a software package for visualizing high-dimensional data. iplots is an R package providing various plotting functions.

30. Explain the lattice Package.

lattice provides enhanced graphics capabilities in R, particularly useful for multivariate data visualization.

31. Explain the anova() Function.

anova() performs analysis of variance (ANOVA) to compare statistical models.

32. Explain cv.lm() and stepAIC().

• cv.lm() (in DAAG): Performs k-fold cross-validation for linear models.
• stepAIC() (in MASS): Performs stepwise model selection based on AIC (Akaike Information Criterion).

33. Explain the leaps() Function.

leaps() (in the leaps package) performs all-subsets regression, exploring all possible combinations of predictor variables.

34. Explain relaimpo and robust Packages.

• relaimpo: Measures the relative importance of predictor variables in a regression model.
• robust: Provides robust statistical methods.

35. MANOVA and its Use.

MANOVA (Multivariate Analysis of Variance) tests for differences in means of multiple dependent variables simultaneously.

36. shapiro.test() and bartlett.test().

• shapiro.test(): Tests for normality.
• bartlett.test(): Tests for equality of variances.

37. Use of the forecast Package.

The forecast package provides tools for time series forecasting, including automatic model selection.

38. Difference Between qda() and lda().

• qda(): Quadratic discriminant analysis.
• lda(): Linear discriminant analysis.

39. auto.arima() and principal() Functions.

• auto.arima(): Automatic ARIMA model selection.
• principal(): Principal component analysis.

40. Explain FactoMineR.

FactoMineR is a package for multivariate data analysis, including principal component analysis and multiple factor analysis.

41. Full Forms of SEM and CFA.

• SEM: Structural Equation Modeling
• CFA: Confirmatory Factor Analysis

42. cluster.stats() and pvclust() Functions.

• cluster.stats(): Compares clustering results.
• pvclust(): Performs hierarchical clustering with p-values.

43. matlab and party Packages.

• matlab: Provides functions to replicate some MATLAB functionalities.
• party: Used for building and visualizing recursive partitioning models (like decision trees).

44. S3 and S4 Systems.

S3 and S4 are object-oriented programming systems in R. S3 is simpler but less formal; S4 is more structured and robust but more complex.

45. Visualization Packages.

R has numerous visualization packages, including: ggplot2, plotly, lattice, and many more.

46. Chi-Square Test.

The Chi-square test assesses the independence of two categorical variables.

47. Explain Random Forest.

A Random Forest is an ensemble learning method that combines multiple decision trees to improve prediction accuracy. It's used for both classification and regression problems.

48. Explain Time Series Analysis.

Time series analysis involves analyzing data points collected over time. It's widely used in forecasting, where patterns and trends from past data are used to predict future values. Time series data has a time component associated with each data point.

49. Explain Pie Charts in R.

A pie chart is a circular statistical graphic, which is divided into slices to illustrate numerical proportion. Each slice represents a proportion of the whole.

50. Explain Histograms.

A histogram is a bar chart showing the distribution of numerical data. Each bar represents a range of values, and the bar's height indicates the frequency of data points within that range.