R interviews are highly technical and competitive. However, if you have a solid grasp of the concepts and the various tools associated with the R programming language, you can easily tackle all the questions and crack an R developer job interview.
While R has several applications, the most prominent use of the programming language is for data science . Usually, many aspirants learn R to get into different career paths, such as data scientist, data analyst, and statistician.
Nonetheless, if you are preparing for a job interview that requires knowledge and expertise in R, then you need to refresh your knowledge of various concepts of the R programming language.
For the same reason, in this article, we are going to discuss the top R interview questions and answers that could help to brush your skills.
Top R Interview Questions
The following are the commonly asked R interview questions divided into three levels: basic R interview questions, intermediate R interview questions, and advanced R interview questions.
Basic R Interview Questions
1. Define R.
Answer: R is a programming language that can be used for data visualization, statistical analysis, forecast analysis, data manipulation, and various other new-age purposes. It is among the most popular and widely used programming languages, and some prominent companies that use R include Twitter, Google, and Meta (Facebook).
2. What is the latest version of R?
Answer: The ongoing series of R programming is 4.x, and the latest version of R is 4.1.2, which was released in November 2021.
3. How many data structures are in the R programming language?
Answer: There are data structures in the R programming language, namely vector, list, matrix, and dataframe.
4. Define vector in R.
Answer: A vector is a sequence of a variety of data elements of the same basic types. The members in vectors are called components.
5. Define a list in R.
Answer: A list is an object in the R programming language that consists of different kinds of elements, such as vectors, strings, numbers, and other lists.
6. What is a matrix in R?
Answer: Matrix is a data structure in the R programming language that is two-dimensional. A matrix makes it possible to bind vectors from the same length. In general, all the elements in a matrix are of a single type, such as character, numeric, logical, or complex.
7. Define dataframe in R?
Answer: The dataframe combines features of lists and matrices to make a rectangular list that can have different columns and data numbers.
8. What are the different components of the grammar of graphics?
Answer: The different components of the grammar of graphics are:
- Themes layer
- Facet layer
- Data layer
- Aesthetics layer
- Geometry layer
- Coordinate layer
9. Define Rmarkdown, and what is its purpose?
Answer: Rmarkdown is a tool of R programming language that is used for reporting. The tool facilitates the creation of high-quality reports of R code.
10. What is the output format of Rmarkdown?
Answer: The output formats of Rmarkdown are as follows:
- WORD
- HTML
11. Identify different packages in the R programming language that can be used for data imputation.
Answer: The different packages in the R programming language for data imputation are:
- ImputeR
- Mi
- Hmisc
- missForest
- MICE
- Amelia
12. What is a confusion matrix?
Answer: A confusion matrix is used in the R programming language for evaluating the accuracy of a classification model. It calculates the cross-tabulation of predicted classes and observed classes.
13. Identify some functions in the "dplyr” package.
Answer: The different functions in the dplyr package are:
- Count
- Arrange
- Filter
- Mutate
- Select
14. What does the R6 object template consist of? Give an example.
Answer: R6 object template consists of a class name, private data members, and public member functions. For example:
- The class name can be “Employee”.
- Private data members can be “Employee name and designation.”
- Public member function can be “Set_name() and Set_designation().”
15. Define Random forest.
Answer: Random forest is an ensemble classifier that is made using the decision tree models. It collects the outcomes from the various decision tree models and offers better results than other individual models.
16. What is ShinyR?
Answer: ShinyR is an R programming package that helps in creating interactive web applications using the R language. This package helps in creating standalone applications on a webpage and embedding them in Rmarkdown documents or building dashboards.
17. Can we extend ShinyR?
Answer: Yes, we can extend ShinyR using JavaScript actions, HTMLwidgets, and CSS themes.
18. What are the apply functions and their advantages?
Answer: The apply functions support making entry-by-entry changes in the data frames and matrices. Its advantage includes the following:
- Editing every entry of a data frame with a single command.
- No waste CPU cycles.
- No auto-filling.
19. Identify different packages used for data mining in R and their usage?
Answer: The different packages used for data mining in R are as follows:
- Tm-: It helps to perform data mining.
- Data.table: It enables fast reading of large files.
- GGplot: It offers various kinds of data visualization plots.
- Arules: It helps in associating with rule learning.
- Forecast: It offers functions for time-series analysis.
Intermediate R Interview Questions
20. Define clustering?
Answer: Clustering refers to the process of grouping objects that belong to the same class. It is a process of making a group of abstract objects in a different class with similar objects.
21. Why do we need clustering?
Answer: We need clustering because of the following reasons:
- To deal with the large databases that need highly scalable clustering algorithms.
- To deal with different kinds of attributes, including the algorithms that are capable of being applied to any kind of data, including binary data and categorical and interval-based numerical data.
- Clustering makes it possible to discover clusters with attribute shapes that are capable of detecting clusters that are not bound by distance measures.
- To deal with noisy, missing, erroneous data that lead to sparse quality clusters.
22. Define K-means clustering.
Answer: K-means clustering is a partitioning method in which the objects are classified as belonging to one of the K-groups. As a result, the partition is made between different sets of K clusters, with each object of the data set belonging to one cluster.
23. Define hierarchical clustering.
Answer: Hierarchical clustering supports the hierarchical decomposition of the given set of data objects, which can be classified based on hierarchical decomposition.
24. Identify the approaches to hierarchical clustering.
Answer: The two approaches to hierarchical clustering are:
- Agglomerative approach
- Divisive approach
25. Explain the Agglomerative approach.
Answer: The agglomerative approach or bottom-up approach starts with each object creating a discrete group. It helps in merging the different groups of objects which are closer to one another. This action continues until the groups are merged into one or until the final termination conditions hold.
26. Define the Divisive approach.
Answer: The divisive approach or top-down approach starts by defining each object in the same cluster. It is a continuous process under which a cluster is split into different smaller clusters. The action continues until each object in one cluster is down, or the termination condition holds.
27. Define the Rattle package in R.
Answer: Rattle package in the R programming language is for data mining. It helps in presenting the statistical and visual summaries of the data inputs and transforms them into a more readily modeled graphical representation of the outcomes. It supports user interactions using the graphical user interface or GUI by capturing as an R script, which can be readily executed in the R programming language, independently of the Rattle interface.
28. Define the White Noise Model.
Answer: White noise or WN model is a time series model. It supports the stationary process in the R language.
29. What are the key features of the WN model?
Answer: The key features of the WN or White Noise model are as follows:
- Fixed constant mean
- Fixed constant variance
- No correlation
30. Define Random Walk or RW model in the R programming language. What are its features?
Answer: The RW model or Random Walk model is a non-stationary process. Its key features are as follows:
- No specific mean or variance.
- Changes or increments are white noise.
- Strong dependence.
31. Define Principal Component Analysis.
Answer: Principal Component Analysis or PCA is a method that helps in dimensionality reduction. It has been observed that one observation can be related to different dimensions at one point in time, and this could create a chaotic situation. PCA helps in reducing the number of dimensions to control the situation.
32. What are the benefits of PCA?
Answer: The different benefits of PCA are as follows:
- The data is transformed into a new space having lesser or the same dimensions, which are called principal components.
- The first component carries the maximum amount of variance from the features in the original data.
- The second component carries the orthogonal to the first and maximum variability.
- Each component is made uncorrelated.
Advanced R Interview Questions
33. What is an “initialize()” function in R?
Answer: An “initialize()” function in R helps to initialize the private data members by declaring the object.
34. Can we fit a linear model over a scatter plot?
Answer: Yes, we can fit a linear model over a scatter plot by using the "ggplot2" package. The scatter plot can be made by using the geom_point() function, and on top of it, we can make a layer of the linear model by adding the geom_smooth() function.
35. What is a “predict()” function?
Answer: The “predict()” function helps to predict the values of the built model.
36. Is there any difference between the “this” and “predict” functions?
Answer: Yes, there is a difference in automatically selecting more sensible values in “this” than in the “predict” function.
36. What is the critical difference between a bar chart and a histogram?
Answer: The critical difference between a bar chart and a histogram is that the former allows us to plot the distribution of a categorical variable while the latter makes it possible to plot the distribution of a continuous variable.
37. Can we do left and right joins in R?
Answer: Yes, we can do left and right joins in the R programming language using the “dplyr” package.
38. Define a factor in R.
Answer: A factor in the R programming language represents the variables that take on a limited number of different values. These variables are also known as categorical variables.
39. What is the use of Factors in R?
Answer: Here are the main uses of Factors in R:
- To support statistical modeling.
- Factors help in treating data correctly.
- They also help in storing data as factors, thus allowing the use of modeling functions.
40. Can we convert a vector into values of scientific notation?
Answer: Yes, we can convert a vector into the values of the scientific notation by using the "format()” function.
41. Can we join multiple strings together?
Answer: Yes, we can join multiple strings together by either using the “paste()” function or the “string_c()” function from the StringR package.
42. What functions can you use for debugging in R?
Answer: Different functions that can be used for debugging in the R programming language are:
- recover()
- traceback()
- trace()
- debug()
- browser()
43. How can we rename the columns of a data frame?
Answer: We can rename the columns of a data frame by using the "colnames()” function so that the right information about the present value in the column can be conveyed.
44. Define correlation.
Answer: Correlation is a statistical tool that helps to find the strength of the association between two variables.
45. How can we find a correlation in R?
Answer: The "Cor()” function allows us to find correlations in the R programming language. It helps to find the correlation coefficient.
46. What is positive and negative correlation?
Answer: Positive correlation is when the correlation coefficient is more than zero. When it is near +1, then it is considered that there exists a higher positive relation between the variables. On the other hand, a negative correlation is considered when the correlation coefficient is lower than zero. Also, when it is near -1, then it is considered that there exists no positive or a negative relationship between the variables.
47. Can we extract one particular word from a string?
Answer: Yes, we can extract one particular word from a string using the "String_extract_all()” function from the “stringR” package.
48. Which function supports data manipulation in R?
Answer: Data manipulation in the R programming language can be done by using the "dplyr” package, which provides many functions for data manipulation, including filter().
49. Can we add a date to the data?
Answer: Yes, we can add a date in the data using the "cbind()” function.
50. Can we do a cross-product of two tables in R?
Answer: Yes, we can do the cross-product of two tables in the R programming language using the "merge()” function.
Conclusion
After going through the aforementioned R interview questions, you will be more confident and clear about several important R concepts. Also, these questions are among the most commonly asked R interview questions that you should know about in order to give your best in the upcoming interviews.
If you want to share more R interview questions that you have come across during your interviews, feel free to do that in the comments section below.
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