Top 100 R Programming Interview Questions & Answers

Answer: R is an open-source programming language that is commonly utilized for statistical analysis and data processing. It is known for its command-line interface and can be run on various platforms such as Windows, Linux, and macOS. R is considered a cutting-edge tool in the field of data analysis and statistics.

Answer: Following are the key features of R:

• R is a powerful and widely-used programming language for statistical computing and data analysis.
• It has a large and active user community, with a vast array of available packages and libraries for a wide range of applications.
• R is open-source, meaning it is freely available for possible modification and redistribution.
• It has a highly expressive syntax and a rich set of built-in functions and data types.
• R is designed for handling large amounts of data and can handle both structured and unstructured data.
• It has strong support for data visualization, with a variety of tools and libraries for creating high-quality graphics and charts.
• R integrates seamlessly with other popular tools and technologies, such as SQL, Hadoop, and Python.
• R is actively developed and maintained by a team of dedicated contributors and volunteers, ensuring that it remains a cutting-edge tool for data science.

Answer: In R, there are different data types that can be used to store and manipulate data. These data types include:

• Numeric: Numeric data types store numbers, either integers or floating-point values. These data types are used for numerical calculations and are typically stored in memory as binary digits.
• Character: Character data types store text values, such as strings of letters, numbers, and symbols. These data types are often used to store text data and are stored in memory as characters.
• Factor: Factor data types are used to store categorical data, such as gender, country, or product type. Factors are typically stored as integer values in memory, with each unique category assigned a corresponding integer value.
• Logical: Logical data types store boolean values, either TRUE or FALSE. These data types are typically used in conditional statements to evaluate whether a certain condition is met.

Answer: There are several data visualization techniques in R, including:

• Bar plots: for comparing categorical data
• Histograms: for visualizing the distribution of numeric data
• Scatter plots: for showing the relationship between two numeric variables
• Line plots: for visualizing the trend of a numeric variable over time
• Box plots: for displaying the range and quartiles of numeric data
• Bubble plots: for visualizing three-dimensional data
• Heatmaps: for visualizing the intensity of data across two dimensions
• Pie charts: for displaying proportions of a whole
• Network diagrams: for showing relationships between elements in a network
• Sankey diagrams: for showing flows between elements in a system.

Answer: In R, a function is a block of code that takes one or more inputs (called arguments), performs a set of operations on those inputs, and returns one or more outputs (called return values). Functions are useful because they allow us to reuse code and avoid repeating the same operations multiple times. To write a function in R, we use the “function” keyword followed by a pair of parentheses, inside which we specify the input arguments. The body of the function is contained within a pair of curly braces, and within this body, we can perform any operations we want on the input arguments and return the desired output.

Answer: In R, OOP is implemented through the use of the S3 and S4 classes. S3 classes are the simplest and most commonly used in R, and consist of a set of attributes and methods that define the object’s characteristics and behaviour. S4 classes, on the other hand, are more complex and provide more control over object behaviour and inheritance. OOP in R allows for the creation of more organized and modular code, as well as the ability to reuse and extend existing classes and objects. It also enables the use of polymorphism, which allows objects of different classes to be treated similarly, allowing for more flexible and dynamic code.

Answer: To handle exceptions in R, we use the tryCatch() function which takes a code block as its first argument and one or more error-handling functions as subsequent arguments. These error-handling functions are executed when an error or exception occurs within the code block, allowing us to handle the error in a specific way.

Answer: Time series analysis is a statistical method used to analyze and model the patterns and trends of data over time. This is typically used to forecast future values based on past data. In R, time series analysis involves using functions and packages such as ts(), decompose(), and forecast() to manipulate and analyze time series data. This can include operations such as smoothing, filtering, and decomposition to better understand the underlying patterns and trends in the data. Time series analysis in R also often involves visualizing the data using plots and graphs to better understand the trends and patterns.

Answer: Clustering and classification are two closely related techniques used in data mining and machine learning to analyze and understand large datasets. Clustering is a technique that involves grouping data points into clusters or groups based on their similarity or distance to one another. This allows us to identify patterns and relationships within the data and to understand the underlying structure of the dataset. For example, we may cluster a dataset of customers based on their purchasing behavior, and use this information to create targeted marketing campaigns. Classification, on the other hand, involves assigning data points to predefined categories or classes based on their characteristics or features. This allows us to predict the class or category that a new data point belongs to based on its features. For example, we may use a classification algorithm to predict whether a customer will make a purchase based on their demographic information and past purchasing behavior.

Answer: Parallel computing in R refers to the ability of the programming language to distribute computational tasks across multiple processors or cores in a computer. This allows for faster and more efficient processing of large datasets and complex algorithms. To use parallel computing in R, the user first needs to have a computer with multiple processors or cores. They can then use the parallel package in R to create a cluster of workers, which are the individual processors or cores that will be used for parallel computation. Once the cluster is created, the user can use the parallel version of common R functions, such as lapply and apply, to distribute the computation across the cluster. This can be done by specifying the cluster object as an argument in the function call.

Answer: rbind function can be used to join two data frames (datasets). The two data frames must have the same variables, but they do not have to be in the same order.

Answer: In R, subset() functions help you to select variables and observations while through sample() function you can choose a random sample of size n from a dataset.

Answer:

• Though R programming can easily connects to DBMS is not a database
• R does not consist of any graphical user interface
• Though it connects to Excel/Microsoft Office easily, R language does not provide any spreadsheet view of data

Answer: In R the data objects can be converted from one form to another. For example, we can create a data frame by merging many lists. This involves a series of R commands to bring the data into the new format. This is called data reshaping.

Answer: All you need to do is use the “read.csv()” function and specify the path of the file.

Answer: There are 5 types of sorting algorithms are used which are:

•  Bubble Sort
• Selection Sort
• Merge Sort
• Quick Sort
• Bucket Sort

Answer: Rattle is a popular GUI for data mining using R. It presents statistical and visual summaries of data, transforms data so that it can be readily modelled, builds both unsupervised and supervised machine learning models from the data, presents the performance of models graphically, and scores new datasets for deployment into production. A key features is that all of your interactions through the graphical user interface are captured as an R script that can be readily executed in R independently of the Rattle interface.

Answer: The conversion of the rows of the matrix in column and column of the matrix in a row is known as transpose. In R we can do it in two ways first by using the t() function and by iterating over each value using Loops.

Answer: ShinyR is easy to build interaction between web applications through R Language, where you can host any standalone application on a webpage or embed or program them in R Markdown documents or build a dashboard. You can also extend the Shiny application with various themes (CSS), widgets (HTML), and actions (JavaScript). It unites the computational power of R with the interactivity of the modern web.  It is also very easy to write a program in shinyR. It comes with a variety of built-in input widgets with minimum syntax. We can plot diagrams, tables, and all those things which we can do in an R language.

Answer:

• Step 1: Experiment with gathering a sample of observed values.
• Step 2: Create a relationship model using the Im() function in R.
• Step 3: Find the coefficients from the model created.
• Step 4: Create the mathematical equation.
• Step 5: Find a summary of the relationship model to know the average error in prediction which is also called residuals.
• Step 6: Predict the new data from using the predict() function in R.