30+ R Interview Questions And Answers
R is a powerful language and environment for statistical computing and graphics. It is widely used among statisticians and data miners for developing statistical software and data analysis. Interviews for R positions assess a candidate's programming skills, statistical knowledge, and the ability to apply these in solving realworld data problems. Here, we list some common R interview questions organized into beginner and advanced categories, helpful for those preparing to showcase their R expertise.
Most asked R interview questions
Beginners
1.
What is R and why is it used?
1.
What is R and why is it used?
R is a programming language and software environment for statistical analysis, graphics representation, and reporting. It's used for data analysis by statisticians, data analysts, and researchers because it has a wide array of statistical and graphical methods, and it's opensource.
2.
Can you explain the difference between a list and a vector in R?
2.
Can you explain the difference between a list and a vector in R?
In R, a vector is a basic data structure that holds elements of the same type. A list, on the other hand, can contain items of different types, like numbers, strings, and even other lists or vectors.
3.
How do you install a package in R?
3.
How do you install a package in R?
You install a package in R using the install.packages() function, passing the name of the package as a string.
4.
What does the function set.seed() do?
4.
What does the function set.seed() do?
The set.seed() function sets the starting point for generating a sequence of random numbers. It's used to ensure reproducibility of code that involves random processes.
5.
How would you read a CSV file in R?
5.
How would you read a CSV file in R?
You use the read.csv() function to read a CSV file. You pass the file path as a parameter.
6.
Guess the output of the following R code: x < c(1, 2, 3, 4, 5); y 3; sum(y)
x < c(1, 2, 3, 4, 5)
y 3
sum(y)
6.
Guess the output of the following R code: x < c(1, 2, 3, 4, 5); y 3; sum(y)
x < c(1, 2, 3, 4, 5)
y 3
sum(y)
The code checks which elements of vector x are greater than 3, creating a boolean vector y. Then, sum(y) adds up the TRUE values represented as 1s, resulting in 2.
7.
Explain how to create a simple scatter plot in R.
7.
Explain how to create a simple scatter plot in R.
To create a scatter plot, you can use the plot() function, passing in the x and y datasets. Optionally, you can specify additional parameters such as main for the title, xlab for the xaxis label, and ylab for the yaxis label.
8.
What is a factor and when might you use it?
8.
What is a factor and when might you use it?
A factor is a data structure used for fields with a limited number of distinct values, like gender, country names, or survey responses. It is especially useful in statistical modeling.
9.
How do you subset a data frame in R?
9.
How do you subset a data frame in R?
You can subset a data frame by using the square brackets [], specifying the rows and columns you want to extract. An empty space before or after the comma means selecting all rows or columns, respectively.
10.
What does the function t() do in R?
10.
What does the function t() do in R?
The function t() computes the transpose of a matrix or data frame, flipping the rows and columns.
11.
What is the purpose of the 'apply' family of functions?
11.
What is the purpose of the 'apply' family of functions?
The 'apply' family (apply, lapply, sapply, tapply, etc.) are used to apply a function to the rows, columns, or elements of a data structure, often replacing the need for explicit loops.
12.
Can you provide an example of data manipulation using dplyr?
12.
Can you provide an example of data manipulation using dplyr?
Yes, here's a simple example using the dplyr package:
library(dplyr)
data % filter(column_name > value) %>% select(column1, column2)
13.
What is the difference between < and = in R?
13.
What is the difference between < and = in R?
< is the assignment operator and = can be used for assignment as well, but is often used within functions to define arguments.
14.
How do you handle missing values in data analysis with R?
14.
How do you handle missing values in data analysis with R?
You can use various strategies such as omitting missing values with na.omit(), filling them with a specific value using replace() or ifelse(), or predicting missing values using statistical techniques.
15.
Guess what the next code does: set.seed(123); rnorm(5)
set.seed(123)
rnorm(5)
15.
Guess what the next code does: set.seed(123); rnorm(5)
set.seed(123)
rnorm(5)
This code sets a seed for random number generation and generates a vector with 5 normally distributed random numbers. Setting the seed ensures that the sequence of random numbers can be replicated.
Advanced
1.
What are the four main types of data structures in R?
1.
What are the four main types of data structures in R?
The four main data structures are vectors, matrices, lists, and data frames. Vectors are a sequence of elements of the same type, matrices are 2dimensional arrays of the same type, lists can contain elements of different types, and data frames are tables where each column has values of one type.
2.
Explain how you would reshape a data frame from long to wide format.
2.
Explain how you would reshape a data frame from long to wide format.
You can use the spread() function from the 'tidyr' package by specifying the keyvalue pairs to spread the data frame to a wider format.
3.
How can you check for the presence of outliers in R?
3.
How can you check for the presence of outliers in R?
You can use graphical methods like boxplots or scatter plots, or calculate statistical metrics like the Interquartile Range (IQR) and then check for values that fall outside of 1.5*IQR below the first quartile or above the third quartile.
4.
What is S3 and S4 classes in R?
4.
What is S3 and S4 classes in R?
S3 and S4 are two objectoriented system classes in R. S3 is a simpler, more flexible system used for implementing basic objectoriented features, while S4 is a more formal method that allows for more rigorous class definitions.
5.
Can you name various plotting systems in R and their distinguishing features?
5.
Can you name various plotting systems in R and their distinguishing features?
The base R plotting system is simple and easy to use, but limited in customization. The lattice system is good for creating multipanel plots. 'ggplot2' is part of the tidyverse and allows for complex, multilayered graphics built using a consistent grammar.
6.
Show how to create a basic 'ggplot2' scatter plot.
6.
Show how to create a basic 'ggplot2' scatter plot.
First, load 'ggplot2' with library(ggplot2), then you can create a scatter plot using the ggplot() function followed by the geom_point() function.
library(ggplot2)
ggplot(data = df, aes(x = var1, y = var2)) + geom_point()
7.
Provide an example of a function you've written in R and its use case.
7.
Provide an example of a function you've written in R and its use case.
Sure, here's a custom function to calculate the mean of a numeric vector, but exclude any NA values:
calc_mean < function(x) {
mean(x, na.rm = TRUE)
}
8.
What is a closure in R and give an example?
8.
What is a closure in R and give an example?
A closure in R is a function along with the environment in which it was created. It allows for keeping state between function calls.
make_adder < function(n) {
function(x) n + x
}
adder_10 < make_adder(10)
adder_10(5) # This will return 15
9.
Explain lazy evaluation in R with an example.
9.
Explain lazy evaluation in R with an example.
Lazy evaluation is a feature in R where arguments are not evaluated until they are actually used. It can save time and memory when dealing with large datasets.
f < function(a) {
print('This will be printed!')
return(a * 2)
}
f(sys.sleep(5)) # The argument is not evaluated until needed, so no delay occurs
10.
How can you create a reproducible random number generation in R?
10.
How can you create a reproducible random number generation in R?
To create reproducible random number generation, use the set.seed() function with a specific integer before generating random numbers. This ensures the random numbers can be replicated.
11.
Can you explain vectorization in R?
11.
Can you explain vectorization in R?
Vectorization is the ability to perform operations on entire vectors without the need for explicit loops. This is not only concise but also more efficient computationally.
12.
What are Rmarkdown and knitr, and how do you use them?
12.
What are Rmarkdown and knitr, and how do you use them?
Rmarkdown is a framework for writing reproducible reports with R code embedded. 'knitr' is an engine used to convert Rmarkdown files into HTML, PDF, or other formats. You write the document in Rmarkdown syntax, embed R code chunks, and use 'knitr' to weave the code results into the report.
13.
Describe the concept of tidy data and its principles.
13.
Describe the concept of tidy data and its principles.
Tidy data is a structured way of organizing data where each variable forms a column, each observation forms a row, and each data table stores data about one kind of observation.
14.
Show a code sample where you optimize R code for better performance.
## Slow loopbased code
slow_function < function(data) {
res < numeric(length(data))
for (i in seq_along(data)) {
# Some complex calculations here
res[i] < slow_calculation(data[i])
}
return(res)
}
## Optimized vectorized code
fast_function < function(data) {
# Assume slow_calculation is now vectorized
res < slow_calculation(data)
return(res)
}
14.
Show a code sample where you optimize R code for better performance.
## Slow loopbased code
slow_function < function(data) {
res < numeric(length(data))
for (i in seq_along(data)) {
# Some complex calculations here
res[i] < slow_calculation(data[i])
}
return(res)
}
## Optimized vectorized code
fast_function < function(data) {
# Assume slow_calculation is now vectorized
res < slow_calculation(data)
return(res)
}
This example shows how to replace a slow loop with a faster vectorized operation. The optimized function will run much quicker given that slow_calculation() is vectorized.
15.
Guess what does this R code do: replicate(3, rnorm(2))
replicate(3, rnorm(2))
15.
Guess what does this R code do: replicate(3, rnorm(2))
replicate(3, rnorm(2))
This code uses the replicate() function to generate three sets of two normally distributed random numbers. It's similar to running rnorm(2) three separate times and collating the results.
R Interview Tips
Understand the Basics Thoroughly

To answer tough interview questions confidently, begin with a strong foundation in the basics. Dedicate time to revising core principles, and ensure you have a solid understanding of the R ecosystem, basic syntax, and fundamental data structures. Practice coding by hand, and work on realworld examples to embed the concepts in your memory. Being wellprepared in the essentials will make it easier to tackle complex questions along the way.
Improve ProblemSolving Skills

Challenging interview questions often test your problemsolving abilities. To enhance this skill, work on a variety of R coding problems. Don't rush to find answers online; instead, take your time to think through problems. Focus on understanding the problem, breaking it down into smaller parts, and then addressing each piece methodically. Remember, the interviewer is interested in your thought process just as much as the correct answer.
Stay Calm and Take Your Time

A common mistake candidates make when faced with tough questions is to respond in haste. Interviewers appreciate it when you take a moment to gather your thoughts. If you're unsure about a question, it's acceptable to ask for clarification. Taking a deep breath and approaching the problem calmly can prevent you from overlooking important details and help you demonstrate a levelheaded approach to problemsolving.
Demonstrate Your Reasoning

For more difficult questions, outline your reasoning and talk the interviewer through your thought process. Even if you're not completely certain about your solution, your ability to approach complex issues analytically is valuable. Discuss your assumptions, the steps you're taking, and why you think the approach might work. This not only shows your analytical skills but also your communication abilities which are crucial in a collaborative work environment.
Prepare for Conceptual and Practical Questions

Finally, be ready for both conceptual and practical questions. You might be asked to explain a concept in R or to write actual R code. For conceptual questions, use examples to illustrate your points. For practical coding questions, practice coding by hand or in an IDE with no syntax highlighting or autocomplete. This could sharpen your memory and help you focus on solving the problem rather than relying on external aids.
FAQs
How much do R programmers make?
Salaries for R programmers can vary depending on factors like location, experience, and the complexity of the role. Generally, in the United States, you can expect R programmers to earn anywhere between $90,000 to $160,000 annually. However, this range may differ globally, and freelance or contract R developers may charge an hourly rate depending on their skill level. Read more
Is there a demand for R programmers?
Absolutely, the demand for R programmers continues to be strong due to the essential role of data analysis in modern business. Companies in finance, biotech, research, and ecommerce all seek skilled R programmers to help them analyze data and make informed decisions. As data continues to drive strategy, the need for adept R programmers is expected to sustain. Read more
What does an R developer do?
An R developer specializes in writing software for the R programming language, focusing extensively on data analysis, statistical modeling, and visualization. Their tasks may include data cleaning, analysis, creating custom R packages, developing predictive models, and producing dynamic reports and graphs. They work across various sectors, solving complex data problems and turning data into actionable insights. Read more
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