IN THIS ARTICLE
- How Important Is Practicing R Interview Questions?
- Technical R Interview Questions
- General R Interview Questions
- FAQs About the R Interview Process
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Given that data analytics is becoming increasingly widespread, it’s unsurprising that R, a statistical programming language, is gaining popularity too. If you’re looking to land a job in data analytics, R should definitely be part of your repertoire—even if a job description doesn’t specify it is a requirement, knowing how to program in R can be key to advancing your career.
Programming in R is such an important skill set that many employers are now beginning to conduct interviews specifically dedicated to this language.
So if you’re looking to prepare for an R interview, then you’re in the right place. Below, we’ve assembled a list of the most commonly asked R interview questions, along with their answers, so that you can ace an R interview.
How Important Is Practicing R Interview Questions?
There’s no real substitute for knowing the ins and outs of R, but practicing R interview questions is still the best way to prepare. You should be able to answer both technical questions, and have an understanding of the theoretical concepts underlying R.
Technical R Interview Questions
Here are some of the most common R interview questions:
What is R?
R is a programming language and environment. The purpose of R is for statistical computing and is used mostly in data analysis and data visualization. It’s known for its highly sophisticated graphical visualization capabilities and its use as a statistical analytics environment. Most programmers that work with data as a major part of their career will learn R programming eventually, as it’s one of the most powerful programming languages for statistical analysis.
Can You Write and Explain Some of the Most Common Syntaxes in R?
There are several fundamental commands in R programming code that you will want to know for an interview. Here are some of those commands and what role they perform:
|==||Are equal to|
|a > b||Greater than|
|a < b||Less than|
|a >= b||Greater than or equal to.|
|a <= b||Less than or equal to.|
|sort(x)||Return x sorted|
|rev(x)||Return x reversed|
|table(x)||See counts of values|
|unique(x)||See unique values|
This is by no means a complete list of the many different operations that R programming can carry out. Take some time to review syntax and basic formatting before your interview.
How Do You List the Preloaded Datasets in R?
The packages that are preloaded in R are part of the MASS package, a built-in package that contains datasets and functions. In order to list these datasets, you’ll need to use the command data(). Then, once you’ve decided on which dataset you are going to work with, you’ll select your dataset of choice from the MASS package using the command data(*sample data set*) with the dataset’s name between the parentheses.
What Are Some Advantages of R?
R is not only a powerful graphics generator, but is also serviced by an active open-source community. It works across platforms and is easily downloaded and started up without any real accommodations needed. R has also been developed to include machine learning capabilities, making it an essential tool when putting together a machine learning portfolio.
What Are Some Drawbacks of R?
R as a language has some drawbacks that can hamper the workflow of programmers, including:
- Memory heavy due to inefficiency
- Lacks basic security measures
- Complicated with a steep learning curve
- Inconsistency in package quality
Explain Some of the Similarities and Differences Between R and Python
The main difference between R and Python is that R was developed strictly for statistical analysis. Python, on the other hand, is a general-purpose programming language. Both languages are used to carry out functions like data cleaning, data analytics, and data visualization. Several other similarities include the ability to construct deep learning algorithms. Still, Python is significantly faster than R when running a program. R makes up for the lack of speed by being much more powerful than Python’s data analysis capabilities.
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When Is It Appropriate To Use the “Next” Statement in R?
The “next” statement is used to skip any remaining statements in a loop. As a loop control statement, it will stop the loop from completing another iteration and will continue to execute the rest of the program outside of the loop.
How Do You Assign a Variable in R?
There are several ways that you can assign a variable in R.
Source: Tutorials Point
How Can You Identify the Data Type of an Object?
Using the functions class() or typeof(), you can identify the data type of an object in R. The class() function returns the actual data type, whereas typeof() returns a more detailed idea of the type of data.
Talk About the Different Data Types As Well as Data Structures in R
R programming has 6 basic data types:
Also known as a “boolean” data type, logical data has only two different values, usually in the form of “true” and “false.”
As the name would suggest, numeric data represents all real numbers with or without decimal values.
This data type includes all integers.
The complex data type is for imaginary values.
Character is a data type that includes character variables and string variables.
Raw data is in the form of raw bytes.
Write a Custom Function in R
For this kind of question, apply the problem that you are trying to solve to the basic structure of an R function.
Consider the most useful and logical name for your custom function. Then, design the arguments based on what you hope to accomplish and ensure that the output is returned in the format that you need.
How Do You Import Data in R?
Importing data in R is formatted differently depending on the source of the dataset. Here are several different potential data sources with the function that would be used to import them into your R program:
How Do You Install a Package in R?
In order to install a package in R, you simply need to use the “install.packages()” command. This will allow you to install a package from the Comprehensive R Archive Network (CRAN).
How Do You Go About Selecting the Right Package for Solving a Problem Using R?
There are many different methods for finding the right R package for your project. For some, this question may be a simple matter of preference. But for researchers and industry experts, selecting the right package has academic methodology behind it. Most literature suggests that you consult with peer-reviewed sources to ensure that the package you’re choosing is right for your project.
List Some of the Best Packages For:
- ggplot2 – the ggplot2 package is a data visualization package that can greatly improve the visual aesthetics of a graphical representation.
- Lattice package – this package produces elegant data visualizations for multivariate data
- Leaflet – this package helps create dynamic online maps
- iPlots – this package is a useful tool for creating box plots, mosaic plots, bar plots, parallel plots, and many others
- Rcrawler – a web crawling and extracting package with a range of uses
- dplyr – designed for dataframe manipulation
- MICE – helps with the imputation of missing data values
- Amelia – this program helps fit multiple imputation models
- missForest – a machine learning based imputation package
Tell Me About the Lattice Package
The lattice package is an add-on package that implements Trellis graphics. It helps with building data visualization for multivariate data and is the best method to depict complex data sets. Many different scatter plots can be generated by using the lattice package.
What Is the Use of With() in R?
The with() function is used to evaluate an R expression that is within an environment constructed based on a data frame. This can only be used on data frames.
What Is the Use of by() in R?
This function is used to take up data, specify its use, and then apply a function to it.
When Is It Appropriate To Use Mode()?
The mode() function extracts the most frequently occurring values within a data frame. It essentially finds the statistical mode of a vector or data frame.
What Is a Factor Variable, and Why Would You Use One?
Factor variables take on a limited number of different values. They are useful for displaying a character vector in a non-alphabetical order and a vector with a fixed set of values. Factor variables can contain variables of either string or numeric values.
When Is It Appropriate To Use the Which() Function?
This function returns the position of a value in a logical vector.
What Is the Basic Syntax for Creating Scatterplot Matrices in R?
The basic syntax to create a scatterplot in R is plot(x, y, main, xlab, ylab, xlim, ylim, axes).
Differentiate Between Lapply and Sapply
Both lapply and sapply perform the same operations in R programming code. However, lapply returns a list, whereas sapply simplifies the result into a vector or matrix.
How Do You Concatenate Strings in R?
Concatenating strings in R is done by using the paste() function. The syntax when using this function would have the data that you are concatenating within the parentheses. This would result in a new line of concatenated code that contains the items listed in the parentheses.
How Do You Read a CSV File in R?
In R, the read.csv() function is the default method to read a CSV file. This is a built-in function call that will read the data from the designated CSV file.
List the Different Sorting Algorithms Available in R
The primary algorithms for sorting within R programming are:
- Quick Sort
- Bubble Sort
- Merge Sort
- Insertion Sort
- Selection Sort
Describe Logistic Regression
A logistic regression model is a form of predictive analysis. It is similar to linear regression, in that it uses an input of data to estimate and make predictions. The difference is that logistic regression is primarily used for binary data, but can also be used for categorical variables. This means that it predicts the outcomes of two possibilities, typically worded as 0/1 or true/false. You can construct a linear regression model by using the lm() function with data that has been loaded into the program.
Can You Create an R Decision Tree?
Creating a decision tree in R is done by using the following syntax:
In this example, “formula” refers to the predictor and response variables. “Data” refers to the particular data set that is being used to construct the decision tree.
How Can You Extract the Last Name From a Full Name?
One possible way to do this is to use a strsplit() function within a sapply() function. The resulting function looks like this.
Using this method, you will separate the last word of the string that contains the full name.
What Is a Linear Regression Model? How Do You Go About Building It?
Linear regression is the process of using data in your possession to make predictions and is one of several types of linear models. A model that performs linear regression is used as a prediction model that makes educated estimations. R programming has a specific function for constructing this model. Analyzing a selected data set with the function lm() creates a linear regression model of that dataset. A linear regression model is usually used to analyze continuous variables, but can also be used for categorical variables as well.
Describe How R Can Be Used for Predictive Analysis
R programming excels at linear regression, among other tools used to perform predictive analysis. As a statistics-based program, R is able to construct prediction software based on the data that it receives through input. These predictive models work to show a linear relationship between data points that can be extended to future data values.
Write Code To Accomplish a Particular Task (Whiteboard Coding)
This task requires you to have an even stronger understanding of the subject matter as it takes your knowledge of the programming language and places it outside of the usual IDE format. Whiteboard questions will have you perform a written-out program to accomplish a certain task.
General R Interview Questions
After you’ve had a chance to prove your skills in R programming, you’ll likely be asked a number of questions about your skills as a programmer and your experience. It’s important to remember that this portion of the interview is a chance for you to tell your story and what brought you to where you are today. Make sure to highlight your experience, what motivates you to work your best, and why you would be a good candidate for the role that you are interviewing for.
Tell Me About Yourself
The answer to this question tells the interviewer who you are. Mentioning your skills, both technical and soft, is a key point of this professional summary. Mentioning why you are good for the role and what you bring to the table is also a good way to make a strong first impression. As your first chance to stand out as an applicant, this summary will shape much of the rest of the interview.
What Made You Choose a Career in Data Science?
Tell your story and show a clear path in your decision-making process. There are many ways to stand out using just this question alone. From your interest in statistics to your background in a particular subject area, the possibilities for this answer are almost endless. The main focus of this section of your interview is telling your story and your unique journey.
Why Do You Use R?
This question can be a great way for an interviewer to differentiate someone who is using R programming simply to get a job and someone who has genuinely used it in projects and other areas of their work. As an R programmer, it is in your best interest to make sure that you have made your skills in R a key part of your portfolio and your work experience.
What Are the Objects You Use Most Frequently?
The objects that you use the most are likely related to the kind of data you work with most frequently. Whatever your experience is, this is again a question that you should tie into the greater story of your projects and your coding experience in R.
What Are Some of Your Favorite Functions in R?
If you happen to have a preferred method of visualizing your data or sorting your data, you might want to explain your process to the interviewer. Doing so can show them that you don’t simply copy and paste ideas from sources but that you also develop your own methods and work to find solutions.
What Have You Done To Improve Your Skills in R?
In just a few years, the nature of many programming jobs can change a lot, so you want to show that you’re continuing to learn and that you’re adaptable. This progress can be tracked on platforms like GitHub, where you can show a clear progression in skill over the years of your R programming use.
What Are Your Favorite Resources To Keep on Top of R Programming?
There are a lot of great resources that you can use to keep up-to-date with your R programming skills. These are just a few resources that you can consider for staying up to date.
Springboard’s Data Science Bootcamp is a training program that teaches data science from start to finish. R programming is taught alongside SQL and Python, in a process that blends programming practice, projects, and career coaching.
Datacamp is a great platform for practicing skills within data analytics and data science. Through the practice programs and skill checks, you are able to gradually scale up your skills within R programming.
The R Programming Tutorial is a resource that allows you to learn R at your own pace. This tutorial contains information about most aspects of R and presents them in a thorough, well-explained fashion.
Recall a Time When You Got Stuck While Working on a Project. What Did You Do?
What an interviewer wants to hear is that you dealt with your problem and moved on from it, taking away some education in the process. What an interviewer doesn’t want in an applicant is a lack of critical thinking skills. Showing that you took the problem seriously and applied yourself to fix it shows that you take initiative and that you are able to manage interruptions in your workflow without passing problems off to others or simply giving up.
Why Do You Want To Work for This Organization?
While the role may not have been your first choice, the answer to this question can still be very simple. Do as much research as you can on the organization itself and the specific position that you are applying for in order to give the most accurate response possible.
Why Do You Want To Work for This Organization?
In most technical interviews, it’s safe to assume that many others are going to have similar skills to your own. While you can stand out through your skills in R programming, you should also try to stand out in your soft skills as well. Think about how your worth ethic, your experience, or your people skills make you good at the job as well. Every person who interviews for a position has a story to tell, and yours is uniquely your own.
Have You Worked on Any R-Related Personal Projects? Tell Me About Them
When discussing your personal projects, make sure to highlight the ones that you’ve made available on your GitHub or portfolio site. This will give them a chance to look at your work and see if it matches what they are looking for. Ensuring the interviewer has access to your projects is a good way to let them see your work and the proof of your experience.
Do You Prefer an In-Person, Hybrid, or Fully Remote Working Environment?
Be honest about your preferred work environment and remember that settling for a position that isn’t the environment you prefer could backfire later, as the initial excitement of a new job wears off.
Do You Have Any Questions for Us?
Using this time to find out more about the job requirements and the culture of the place where you’ll be working. Remember, an interview is just as much an interview of the company you’ll be working for as it is an interview of you and your skills.
FAQs About the R Interview Process
We’ve got the answers to your most frequently asked questions:
Is an R Interview Difficult?
An R interview shouldn’t be more or less difficult than an interview for most other programming languages. You’ll be expected to show your proficiency in the language and your competency with some of the major problems that you’ll be asked to solve on the job.
The process of learning R is considered difficult by many experts. However, for those with experience in the language, finding the solutions to the questions that the interviewer gives you is very possible with the right preparation process.
How Long Does an R Interview Generally Last?
Most R interviews last anywhere from 45 minutes to an hour, depending on the extent of the information that is covered. While not all of that time will be dedicated to R programming code itself, you can expect the majority of the time to be your solving a problem that the interviewer presents to you.
How Can You Stand Out in an R Interview?
The best way to stand out in an R interview is to show proficiency. Taking some time to brush up on your R skills through coding challenges or through an online course can help you do this.
As R interviews will be primarily for data-related roles, make sure to have a strong data analytics or data science portfolio ready for the interviewer to be able to view. This is usually done through GitHub. Utilizing this portfolio is a great way to make your application stand out and present yourself as an experienced and driven candidate.
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