R programming language is a tool at your disposal used for multiple purposes from data manipulation, statistical analysis, forecast analysis to predictive modeling, data visualization, and so on. This programming language is used by top companies like Google, Facebook, and Twitter. That’s the reason we’re here with a list of 50 R Interview Questions to help you ace your interview game.
Top 50 R Interview Questions
Q1. What is R?
R is an interpreted computer programming language that is used as a software environment to analyze statistical information, reporting, graphical representation, and data modeling. It was created by Ross Ihaka and Robert Gentleman and is the implementation of the S programming language.
Q2. Name the packages which are used for data imputation.
The packages used for data imputation are:
Q3. Explain the Random Walk model.
A random walk has no specified mean or variance and is the simplest example of a stationary process having a strong dependence over time. Moreover, its increments or changes are white noise.
Q4. List a few features of R.
R is a simple, effective, and an interpreted programming language. It is a data analysis software that gives effective storage facility, data handling, and high extensible graphical techniques.
Q5. State the difference between vector and list.
A vector represents a series of data elements of the basic type whose members are known as a component whereas a list is an R object containing elements of different types such as vectors, strings, numbers, or another list inside it.
Q6. State the difference between matrix and data frame.
A matrix is a two-dimensional data structure containing the same number of elements that are used to bind the vectors from the same length. A data frame is a combination of lists and matrices wherein different data columns contain different data types.
Q7. How can we find the mean of one column with respect to another?
We can calculate the mean of Sepal-Length across different species of the iris flower using the function below, which is from the mosaic package:
Q8. Explain the initialize() function in R.
We need the initialize() function in R in order to initialize the private data members while declaring an object.
Q9. Explain the White Noise model?
A white noise model is a basic time series model having a fixed constant variance, a fixed constant mean, and no correlation over time. Thus, it is a simple example of the stationary process.
Q10. State the advantages and disadvantages of R.
- Data Wrangling
- Platform Independent
- Open Source
- Machine Learning Objectives
- Array of Packages
- Basic Security
- Lesser Speed
- Weak Origin
- Data Handling
- complicated Language
Q11. List a few applications of R.
Various applications of R are available in real-time such as Google, Facebook, Twitter, NDAA, HRDAG, etc.
Q12. State the difference between sample() and subset() in R.
In order to choose a random sample of size n from a dataset, we make use of the sample() method whereas the subset() method is made use of to choose variables and observations.
Q13. Differentiate between “%%” and “%/%”.
The former will provide you with the remainder of division of the first vector with the second and the latter will provide you with the quotient of division of the first vector with the second.
Q14. What is the need to use the command – install.packages(file.choose(), repos = NULL)?
The following command helps to install an R package from the local directory after browsing and selecting the file.
Q15. State the purpose behind R and Hadoop integration.
In order to execute Hadoop to be able to execute R code and to use R for accessing the data that is stored in Hadoop.
Q16. Differentiate between R and Python in terms of functionality.
Python is devoid of the data analysis functionalities which are only available by packages like Numpy and Pandas. However, R comes with inbuilt functionality for data analysis.
Q17. Explain RStudio.
RStudio is similar to the standard RGui but is a more friendly version of it, allowing users to interact with R more readily and providing them with various windows with multiple tabs, drop-down menus, and several other customization processes.
Q18. State the Hadoop integration methods.
- R Hadoop
- Hadoop Streaming
Q19. Name the command used to create a histogram and remove a vector from the R workspace.
The functions hist() and rm() are used in order to serve the purpose.
Q20. State the difference between library() and require() functions.
Whenever a particular package cannot be loaded, the library() function displays an error message. On the other hand, the required() function throws a warning message if the particular package is not found.
Q21. What will be the output of the following expression all(NA==NA)?
Q22. State the function of the apply() function in R.
The apply() function is used to apply the same function to each of the elements in an array.
Q23. What is the t-test() in R?
We use the t-test() function to determine whether the mean of the two groups are equal or not.
Q24. Explain the doBy package.
The doBy package makes use of function and model formula to define the desired table.
Q25. State the use of with() and by() functions in R.
The with() function is used to apply an expression to a dataset whereas the by() function is used to apply a function to each level of factors.
Q26. State the use of the table() function.
The table() function is used to create the frequency table in R.
Q27. What are GGobi and iPlots?
GGobi is an open source program that is used to explore high dimensional typed data and iPlots is a complete package providing parallel plots, mosaic plots, bar plots, box plots, scatter plots, and histogram.
Q28. Explain the aggregate() function.
In R, collapsing the data can be achieved in two ways, one is by using one or more BY variables or by using an aggregate() function wherein the BY variable should be in the list.
Q29. State the difference between lapply and sapply.
Using lapply, you can show the output in the form of a list while sapply will help you show the output in the form of a data frame or a vector.
Q30. What is the fitdistr() function?
This function is defined under the MASS package that gives the maximum likelihood fitting of univariate distribution.
Q31. Explain cv.Im() and stepAIC() function.
The cv.Im() function is used for k-fold validation and is defined under the DAAG package whereas the stepAIC() function is used to perform stepwise model selection under exactAIC and is defined under the MASS package.
Q32. What is the lattice package?
The lattice package is used for the purpose of improving the base R graphics by providing better defaults and displaying multivariate relationships easily.
Q33. Explain the leaps() function.
The leaps() function is defined under the leaps package and is used to perform the all-subsets regression.
Q34. State the use of the forecast package.
The forecast package provides you with functions that are useful for the automatic selection of exponential and ARIMA models.
Q35. Explain the auto.arima() and principal() function.
The auto.arima() function is used to handle the seasonal and non-seasonal ARIMA models. The principal() function is helpful in rotating and extracting the principal components.
Q36. Explain relaimpo and robust package.
The package measures the relative importance of each predictor in the model with the robust package providing a library of robust methods that also includes regression.
Q37. What is the anova() function?
We use the anova() function in order to compare the nested models.
Q38. State the full form of MANOVA.
MANOVA stands for Multivariate Analysis of Variance.
Q39. What is the use of MANOVA?
MANOVA is used to test more than one dependent variable simultaneously.
Q40. State the difference between qda() and lda() function.
The qda() function is explicitly used to print a quadratic discriminant function whereas the lda() function is used to print those discriminant functions that are based on the centered variable.
Q41. Explain barlett.test() and mashapiro.test().
The mashapiro.test() function is defined in the mvnormtest package which produces the Shapiro-wilk test to multivariate normality whereas the barlett.test() function provides a parametric k-sample test of the equality of variances.
Q42. State the full form of SEM and CFA.
SEM stands for Structural Equation Modeling whereas CFA stands for Confirmatory Factor Analysis.
Q43. Explain S3 and S4 systems.
The S3 system in oops helps in overloading any function, thus allowing us to call the functions with different names, depending on the type of input parameters. On the other hand, the S4 system is the most important characteristic of oops, however, it comes with a limitation of being too long. Though, you’re provided with an optional reference class for S4.
Q44. What is the Chi-Square test?
The chi-square test is used to evaluate whether there exists a significant relationship between the categories of the two variables or not by analyzing the frequency table, also known as the contingency table, formed by two categorical variables.
Q45. State the visualization packages.
Q46. Explain cluster.stats() and pvclust() function.
The cluster.stats() provides methods to compare the similarity between two cluster solutions using the different validation criteria and is defined in the fpc package whereas the pvclust() function provides p-values for hierarchical clustering and is defined in the pvclust package.
Q47. What is FactoMineR?
FactoMineR is a package including qualitative and quantitative variables and observations and supplementary variables as well.
Q48. Explain Pie chart in R.
R programming supports various libraries for creating graphs and charts, one of which is a pie-chart, which is a pictorial representation of values with different colors in the form of slices of a circle.
Q49. Explain Histogram in R.
Histogram is a type of bar chart used for the distribution, showing the frequency of the number of values that are compared with a set of value ranges where each bar represents the height of the number of values present in that particular range.
Q50. Explain MATLAB and party packages.
This package is used for replicating MATLAB function calls that include wrapper functions and variables.
So, these were some of the most probable questions you could get in an R programming interview. The list will provide you with a thorough understanding of R programming language, which interviewers are seeking from the apt candidates. If you want to fall into this category then make your core concepts stronger by reviewing yourself with the above questions. If you’re a beginner, no worries! Because now you know where you need to focus to get into your dream company. All the best!