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R is a programming language and free software environment primarily used for statistical computing and graphics. It is widely used among statisticians and data miners for developing statistical software and data analysis. R provides a wide variety of statistical techniques such as linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering etc. It also supports procedural programming with functions and object-oriented programming with generic functions. R allows integration with the procedures written in the C, C++, .Net, Python or FORTRAN languages for efficiency. In addition to its command-line interface, several graphical user interfaces are available for use with R.

Ask the right questions secure the right R talent among an increasingly shrinking pool of talent.

First 20 minutes

General R app knowledge and experience

The first 20 minutes of the interview should seek to understand the candidate's general background in R application development, including their experience with various programming languages, databases, and their approach to designing scalable and maintainable systems.

How would you import data in R?

There are several ways to import data in R, such as using the read.table or read.csv functions for text and CSV files respectively. For Excel files, readxl library can be used, and for databases, DBI and RMySQL libraries are commonly used.

What are the different data types in R?

R has several data types, including numeric, character, logical, complex, and raw. Additionally, it has several data structures such as vector, list, matrix, data frame, and factors.

Describe the difference between a matrix and a data frame in R.

A matrix in R is a two-dimensional data structure where each element must have the same mode, i.e., numeric, character, or logical. On the other hand, a data frame can contain elements of different modes in different columns, much like a table in a relational database.

How would you handle missing values in a dataset in R?

In R, missing values can be handled using functions like is.na() to identify them, na.omit() to remove them, or functions like mean(), median(), mode() to replace them with statistical measures. There are also packages like 'mice' that provide more advanced methods for imputing missing values.

What are the different types of loops in R?

R supports several types of loops, including: for, while, and repeat. Additionally, looping can also be done using the apply family of functions, which can be more efficient in many cases.

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What you’re looking for early on

Does the candidate have a strong understanding of R programming language?

Has the candidate demonstrated problem-solving skills?

Is the candidate able to communicate effectively?

Does the candidate have experience with data analysis?

Next 20 minutes

Specific R development questions

The next 20 minutes of the interview should focus on the candidate's expertise with specific backend frameworks, their understanding of RESTful APIs, and their experience in handling data storage and retrieval efficiently.

Describe the difference between lapply and sapply in R.

Both lapply and sapply are part of the apply family of functions in R, used for looping over arrays. lapply returns a list regardless of the input, while sapply attempts to simplify the output to a vector or matrix if possible.

How would you merge two data frames in R?

In R, two data frames can be merged using the merge() function. It takes two data frames as arguments and merges them on the basis of common columns. If no common column is found, it merges on the basis of row names.

What are the different ways to subset a data frame in R?

In R, a data frame can be subset using the $ operator, the square bracket [ ] operator, the double square bracket [[ ]] operator, and the subset() function. Each method has its own use cases and advantages.

Describe the difference between ggplot2 and base graphics in R.

Base graphics in R are the traditional plotting functions, which offer a lot of flexibility but can be complex for intricate plots. ggplot2 is a part of the tidyverse, and uses a layered grammar of graphics approach, which can be more intuitive and produces more visually appealing plots.

How would you debug a function in R?

R provides several functions for debugging, such as debug() to step through the function execution, traceback() to print stack traces of the last error message, and browser() to pause execution at a given point and examine the environment.

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At this point, a skilled R engineer should demonstrate strong problem-solving abilities, proficiency in R programming language, and knowledge of software development methodologies. Red flags include lack of hands-on experience, inability to articulate complex concepts, or unfamiliarity with standard coding practices.

Digging deeper

Code questions

These will help you see the candidate's real-world development capabilities with R.

What does the following R code do?
x <- c(1,2,3,4,5)
mean(x)
This code calculates the mean (average) of the numbers in the vector x.
What will be the output of the following R code?
x <- c(1,2,3,4,5)
ifelse(x>3, 'Yes', 'No')
The code will return a vector with 'No' for each element of x that is less than or equal to 3, and 'Yes' for each element that is greater than 3.
What does the following R code do?
x <- c(1,2,3,4,5)
y <- c(6,7,8,9,10)
z <- cbind(x,y)
This code binds the vectors x and y into a two-column matrix z.
What does the following R code do?
library(parallel)
cl <- makeCluster(2)
clusterExport(cl, 'x')
This code uses the parallel library to create a cluster with 2 cores and then exports the variable 'x' to each of the cores in the cluster.

Wrap-up questions

Final candidate for R Developer role questions

The final few questions should evaluate the candidate's teamwork, communication, and problem-solving skills. Additionally, assess their knowledge of microservices architecture, serverless computing, and how they handle R application deployments. Inquire about their experience in handling system failures and their approach to debugging and troubleshooting.

What are the different ways to apply a function to a data frame in R?

In R, a function can be applied to a data frame using the apply() function for row or column wise operation, lapply() and sapply() for list or vector input, and tapply() for applying a function over subsets of a vector.

Describe the difference between a list and a vector in R.

A vector is a sequence of data elements of the same basic type in R. A list, on the other hand, is a data structure which can hold elements of different types, like numbers, strings, vectors, and other lists as well.

How would you handle large datasets in R?

For large datasets in R, data.table package can be used for fast aggregation of large data, including faster indexing, fast ordered joins, fast assignment, and fast grouping. Additionally, using efficient data structures like matrices and data frames, and efficient functions from the apply family can help.

R application related

Product Perfect's R development capabilities

Beyond hiring for your R engineering team, you may be in the market for additional help. Product Perfect provides seasoned expertise in R projects, and can engage in multiple capacities.

R Consultive assessments

R Application Conversion Assessment R Application Integration Assessment R Data & Database Integration Assessment

Custom R development projects

Custom R App Conversion R Custom App Development Process App UI Design for R Facelifts

Hiring guide for R Engineers

R Developer Hiring Guide

What you’re looking for early on

The ideal back-end app developer

What you’re looking to see on the R engineer at this point.

R Consultive assessments

Custom R development projects