R is often considered one of the top languages like Python to handle data, machine learning (ML), and deep learning tasks. It has many libraries that have code already written. And, you can refactor such code to make your development even faster. You are bound to find a lot of well-optimized code for statistical analysis, plotting, charting, manipulating data in various ways, etc.

It was developed in 1993 by a team at Auckland University. Since then, R has been widely adopted in both academia and enterprises. However, it lacks any formal education, so a Data Science with R certification course is your best bet to learn R and data science.

R is right up there with Python in any job posting on the Internet for data science. So, a comprehensive R knowledge is imperative to get your dream job. Keeping that in mind, we have carefully drafted this list of interview questions. For best results, we suggest you try to answer the questions without looking at the answers.

However, before discussing some fascinating R problems, let us first shift our discussion to R and its impact on data science.

Increasing Use of R in Data Science

R is a compelling language. Let us go over some of the salient features which explain the popularity of R in data science:

1. It is free to use. R is open-source; unlike many other programming languages, you can look at the original implementation of R. It falls under the GNU license, which means you are allowed to download and tinker around with R.
2. R supports all the platforms. In other words, R has cross-platform support. You can basically use any operating system, and the code you have written would work as you intend it to in the first place.
3. It does not use a compiler and instead uses an interpreter, which makes the entire development process much more straightforward.
4. You can easily use any database with R. It would not matter if you have data stored in Excel, Access, SQL, NoSQL, SQLite, etc., as long as you are using R as your programming language.
5. R is a very flexible language. It helps you to reduce the gap between software development and analysis of data.

Top Interview Question Related to R

Listed below are some of the popular interview questions for R:

Q1. How would you be able to load a Comma Separated Value file in R?

Ans. We would need to use the “read.csv()” method. This function’s arguments should be your CSV file’s file path you want to load in R.

Q2. What do you mean by Rmarkdown, and how can you use it for data science?

Ans. Rmarkdown is the iPython (or Jupyter notebook) alternative of R. Rmarkdown is used to create some high-quality reports which would contain code, graphs, and texts. You can also output the Rmarkdown file into HTML, Word, or even a PDF document.

Q3. Let us say that our company has made some custom packages which you are required to use in the project post your selection. How would you install the said package?

Ans. All you need is one line of code to be able to install any package in R. If you know the name of the package in question, then you can run:

Install.packages(“The Name of your Package”)

And, your package would be installed.

Q4. What do you mean by Confusion Matrix?

Ans. Confusion Matrix is the tool that we have in R that is used to determine the model’s accuracy. The way it works is it creates a cross-tabulation of the positive classes with the opposing classes. In a typical confusion matrix, you would find out things like precision, recall, accuracy, sensitivity, prevalence, detection rate, and balanced accuracy. You can even generate a heat map of your confusion matrix’s output to see how your model performs. You can call the confusionmatrix() function, which you can find in the “caTools” package.

Q5. Name some of the functions you would find in the “dplyr” package of R?

Ans. You would find many functions under the “dplyr” package of R. Some of them are Filter, select, mutate, count, arrange, etc.

Q6. What is a random forest? How would you be able to build and evaluate a random forest in R?

Ans. Random forest happens to be a classifier. It is an ensemble classifier because it is a mixture of many decision trees. It works by combining the output obtained from various decision trees, which usually improves the model’s overall performance. You can very easily create a random forest classifier. You would need a dataset to try and fit your model. We begin our modeling the traditional way, i.e., we first divide the data into training and test sets. Then we train a random forest classifier on the training set. We use the randomForest() class to train our model and the arguments we pass in the data. We can very quickly test how our model does. For that, we would be using the data we left aside at the beginning (test set). We would be using the predict() method and then pass in the feature list to gather the predictions. The predictions thus obtained would be pitted against the real target values in the data. This is how we calculate the error, which helps us determine how well the model works.

We hope that we were able to provide you with excellent R interview questions. As a data science student, you should realize the importance of R in this field. If you want to get better at R programming, take a Data Science with R certification course.