## R Programming Interview Questions

## A list of frequently asked __R Interview Questions and answers__ are given below.

__R Interview Questions and answers__

### 1) What is R?

R is a programming language and software environment for statistical computing and graphics. It is widely used among statisticians and data scientists for developing statistical software and data analysis. R is a free and open-source software, and is available for a variety of platforms including Windows, MacOS, and Linux.

R is known for its large collection of libraries and packages that extend its capabilities, allowing users to perform a wide range of statistical and graphical analyses. It is also a popular choice for data visualization, with many packages available for creating high-quality plots and charts. R has a large and active user community, which has contributed a vast number of packages for a wide range of applications.

In addition to its use in statistical analysis and data visualization, R is also used in a variety of other fields, including finance, biology, and medicine. It is a powerful tool for data manipulation, and is often used in conjunction with other software tools and languages to build data pipelines and perform data analysis at scale.

### 2) Differentiate between vector, List, Matrix, and Data frame.

Here is a comparison of the main differences between vectors, lists, matrices, and data frames in R:

Data Structure | Dimensions | Homogeneous Data | Indexed by | Examples |
---|---|---|---|---|

Vector | 1 | Yes | Numeric | c(1,2,3) |

List | 1 | No | Numeric | list(1, “a”, c(1,2)) |

Matrix | 2 | Yes | Numeric | matrix(1:6, nrow = 2, ncol = 3) |

Data Frame | 2 | No | Row/Column | data.frame(x = 1:3, y = c(“a”, “b”, “c”)) |

Here is a brief description of each data structure:

**Vector**: A vector is a one-dimensional array of data, with all elements having the same data type (e.g. numeric, character, logical). Vectors are created using the`c`

function, and can be indexed using numeric indices.**List**: A list is a one-dimensional collection of objects of different data types. Lists are created using the`list`

function, and can be indexed using numeric indices.**Matrix**: A matrix is a two-dimensional array of data, with all elements having the same data type. Matrices are created using the`matrix`

function, and can be indexed using a combination of row and column indices.**Data Frame**: A data frame is a two-dimensional collection of data, with rows and columns. Unlike a matrix, data frames can contain columns of different data types. Data frames are created using the`data.frame`

function, and can be indexed using row and column names.

### 3) Give names of those packages which are used for data imputation.

There are several packages in R that can be used for data imputation, which is the process of replacing missing values in a dataset with estimates. Some examples of packages that can be used for data imputation include:

`Amelia`

: This package provides a variety of methods for imputing missing values, including imputation based on mean, median, mode, and regression.`mi`

: This package provides functions for multiple imputation, which is a method of imputing missing values by creating multiple imputed datasets and then combining the results.`mice`

: This package is similar to`mi`

, and also provides functions for multiple imputation. It also includes tools for visualizing and diagnosing the imputed datasets.`VIM`

: This package provides visualization and diagnostic tools for identifying and imputing missing values in a dataset.`Hmisc`

: This package includes functions for imputing missing values using various methods, including regression and hot deck imputation.`imputeTS`

: This package is specifically designed for imputing time series data, and includes functions for imputing missing values using linear interpolation, splines, and Kalman filtering.

There are many other packages available for data imputation in R, and the appropriate package to use will depend on the specific needs of your dataset and the imputation method you wish to use.

### 4) Explain initialize() function in R?

The `initialize`

function is not a built-in function in R. It is possible that you are thinking of the `initialize`

function in the `shiny`

package, which is a framework for creating interactive web applications in R.

In the `shiny`

package, the `initialize`

function is used to specify code that should be run when a `shiny`

app is first started. This code can be used to set up the app’s environment, load data, or perform other tasks that need to be done before the app is ready to run.

Here is an example of how the `initialize`

function might be used in a `shiny`

app:

`Copy code````
library(shiny)
ui <- fluidPage()
server <- function(input, output) {
# Set up the app's environment when the app is first started
initialize <- function() {
# Load data
data <- read.csv("mydata.csv")
}
# Run the initialize function when the app is first started
callModule(initialize)
# Use the data in the app
output$plot <- renderPlot({
plot(data$x, data$y)
})
}
shinyApp(ui, server)
```

In this example, the `initializ`

https://datavalley.ai/blockchain-security/`e`

function is used to load a CSV file containing data that is used in the app. The `initialize`

function is called when the app is first started, and the data is then available to be used in other parts of the app.

### 5) How can we find the mean of one column with respect to another?

To find the mean of one column with respect to another in R, you can use the `tapply`

function. The `tapply`

function applies a function to subgroups of a data frame, and can be used to calculate summary statistics such as the mean.

Here is an example of how to use `tapply`

to find the mean of one column with respect to another:

`Copy code````
# Load the iris dataset
data(iris)
# Calculate the mean of the Sepal.Length column with respect to the Species column
mean_by_species <- tapply(iris$Sepal.Length, iris$Species, mean)
# Print the resulting vector
print(mean_by_species)
```

This will calculate the mean of the `Sepal.Length`

column for each unique value in the `Species`

column, and return a vector with the mean for each species.

Alternatively, you can use the `aggregate`

function to achieve the same result. Here is an example of how to use `aggregate`

to calculate the mean of one column with respect to another:

`Copy code````
# Load the iris dataset
data(iris)
# Calculate the mean of the Sepal.Length column with respect to the Species column
mean_by_species <- aggregate(Sepal.Length ~ Species, data = iris, mean)
# Print the resulting data frame
print(mean_by_species)
```

This will calculate the mean of the `Sepal.Length`

column for each unique value in the `Species`

column, and return a data frame with the mean for each species.

### 6) What is a Random Walk model?

A random walk is the simplest example of a non-stationary process. A random walk has no specified mean or variance, strong dependence over time, and its changes or increments are white noise. Simulating random walk in R:

**arima.sim(model=list(order=c(0,1,0)),n=40)->rw ts.plot(rw)**

### 7) What is a White Noise model?

It is a basic time series model and a simple example of a stationary process. A white noise model has a fixed constant mean, a fixed constant variance, and no correlation over time. We can simulate a white noise model in the following way:

**arima.sim(model=list(order=c(0,0,0)),n=50)->wn**

### 8) Give any five features of R.

- Simple and effective programming language.
- It is a data analysis software.
- It gives effective storage facility and data handling.
- It gives high extensible graphical techniques.
- It is an interpreted language.

### 9) Differentiate between R and Python in terms of functionality?

Here is a comparison of the main differences between R and Python in terms of functionality:

Feature | R | Python |
---|---|---|

Statistical analysis | Strong | Moderate |

Data visualization | Strong | Moderate |

Machine learning | Moderate | Strong |

Web development | Moderate | Strong |

Data manipulation | Strong | Strong |

Syntax | Imperative | Imperative/OOP |

Community | Large | Large |

R and Python are both powerful programming languages that are widely used in data science and other fields. Both languages have strong capabilities for statistical analysis and data visualization, but R is generally considered to be more specialized in these areas. Python, on the other hand, is more versatile and is often used for a wider range of tasks, including machine learning, web development, and data manipulation.

In terms of syntax, R uses an imperative programming style, which means that the code specifies a series of steps to be executed in order. Python also supports imperative programming, but also includes support for object-oriented programming (OOP), which allows users to define classes and objects to represent real-world entities.

Both R and Python have large and active communities, with many libraries and packages available for a wide range of applications. Ultimately, the choice of which language to use will depend on the specific needs of the task at hand and the preferences of the user.

### 10) What are the applications of R?

R is a powerful programming language and software environment for statistical computing and graphics. It is widely used in a variety of fields for a range of applications, including:

- Statistical analysis: R is widely used for statistical analysis and data modeling, with a large number of functions and packages available for tasks such as regression, classification, and hypothesis testing.
- Data visualization: R is known for its strong capabilities for creating high-quality plots and charts, and is often used for data visualization and exploratory data analysis.
- Machine learning: R has a number of packages available for machine learning tasks such as classification, clustering, and feature selection.
- Data manipulation: R is a powerful tool for manipulating and cleaning data, and is often used in conjunction with other software tools and languages to build data pipelines.
- Finance: R is used in the finance industry for tasks such as risk management, portfolio optimization, and financial modeling.
- Biology: R is used in the field of biology for tasks such as gene expression analysis, sequence analysis, and population genetics.
- Medicine: R is used in the field of medicine for tasks such as analyzing clinical trial data, developing predictive models, and analyzing electronic health records.

These are just a few examples of the many ways in which R is used. It is a versatile language with a wide range of applications in fields such as science, engineering, and business.

### 11) Explain RStudio.

RStudio is an integrated development environment which allows us to interact with R more readily. RStudio is similar to the standard RGui, but it is considered more user-friendly. This IDE has various drop-down menus, windows with multiple tabs, and so many customization processes. The first time when we open RStudio, we will see three Windows. The fourth Window will be hidden by default.

### 12) What are the advantages and disadvantages of R?

**Advantages**

- Open Source
- Data Wrangling
- Array of Packages
- Platform Independent
- Machine Learning Operations

**Disadvantages**

- Weak origin
- Data Handling
- Basic Security
- Complicated Language
- Lesser Speed

### 13) What is the purpose behind R and Hadoop integration?

The purpose of integrating R with Hadoop is to enable the use of R for large-scale data processing and analysis. Hadoop is an open-source software framework for distributed storage and processing of large datasets. It allows users to store and process data on a cluster of commodity hardware, making it possible to handle very large datasets that would not be practical to process on a single machine.

Integrating R with Hadoop allows users to harness the power of Hadoop for distributed data processing, while still being able to use R for statistical analysis and data visualization. This can be particularly useful for tasks such as machine learning, where the data may be too large to fit in memory on a single machine.

There are several ways to integrate R with Hadoop, including using packages such as `rhipe`

and `RHadoop`

, which provide interfaces between R and Hadoop. These packages allow users to run R code on Hadoop clusters, and to read and write data to and from Hadoop using R.

Overall, the purpose of R and Hadoop integration is to enable users to perform large-scale data processing and analysis using the powerful tools and capabilities of both R and Hadoop.

### 14) Give the name of the Hadoop integration methods.

There are several methods for integrating R with Hadoop, including:

`rhipe`

: The`rhipe`

package provides an interface between R and Hadoop, allowing users to run R code on Hadoop clusters and to read and write data to and from Hadoop using R.`RHadoop`

: The`RHadoop`

package is a collection of R packages that provide an interface to the Hadoop ecosystem, including the MapReduce programming model and the HDFS file system.`rmr2`

: The`rmr2`

package is a set of bindings to the`Hadoop Streaming`

library, which allows users to write MapReduce programs in R.`sparklyr`

: The`sparklyr`

package provides an interface between R and Apache Spark, which is a fast, in-memory data processing engine for large-scale data processing.`RHipe`

: The`RHipe`

package provides an interface between R and Hadoop using the`Hipe`

library, which is a high-level interface for Hadoop.

These are just a few examples of the many methods available for integrating R with Hadoop. The appropriate method to use will depend on the specific needs and goals of the project, as well as the resources and infrastructure available.

### 15) What will be the output of the expression all(NA==NA)?

The output of the expression `all(NA==NA)`

in R will be `NA`

.

In R, the `==`

operator is used to test for equality between two values. However, when one or both of the values being compared is `NA`

, the result of the comparison is also `NA`

. This is because `NA`

represents a missing or undefined value, and it is not possible to determine whether two `NA`

values are equal or not.

The `all`

function returns a logical value indicating whether all elements of a logical vector are `TRUE`

. When applied to a vector containing `NA`

values, `all`

will return `NA`

if any of the elements are `NA`

.

Therefore, in the expression `all(NA==NA)`

, both operands of the `==`

operator are `NA`

, so the result of the comparison is also `NA`

. When this value is passed to the `all`

function, the result is `NA`

.

### 16) What is the difference b/w sample() and subset() in R?

Here is a comparison of the main differences between the `sample`

and `subset`

functions in R:

Function | Description |
---|---|

`sample` | Randomly selects a specified number of elements from a vector or data frame |

`subset` | Selects elements from a vector or data frame based on a logical condition |

Here is an example of how to use each function:

`Copy code````
# Generate a vector of random numbers
x <- rnorm(10)
# Select 3 random elements from the vector using sample
sample(x, 3)
# Select elements from the vector that are greater than 0 using subset
subset(x, x > 0)
```

The `sample`

function is used to randomly select a specified number of elements from a vector or data frame. It takes two arguments: the data to be sampled, and the number of elements to select. The `sample`

function returns a random sample of the specified size from the data.

The `subset`

function, on the other hand, is used to select elements from a vector or data frame based on a logical condition. It takes two arguments: the data to be subsetted, and a logical condition specifying which elements should be selected. The `subset`

function returns a new vector or data frame containing only the elements that meet the specified condition.

### 17) Why do we use the command – install.packages(file.choose(), repos=NULL)?

This command is used to install an R package from the local directory by browsing and selecting the file.

### 18) Give the command to create a histogram and to remove a vector from the R workspace?

hist() and rm() function are used as a command to create a histogram and remove a vector from the R workspace.

### 19) Differentiate b/w “%%” and “%/%”.

The “%%” provides a reminder of the division of the first vector with the second, and the “%/%” gives the quotient of the division of the first vector with the second.

### 20) Why do we use apply() function in R?

This is used to apply the same function to each of the elements in an Array. For example, finding the mean of the rows in every row.

### 21) Differentiate between library() and require() functions.

If the desired package cannot be loaded, then the library() function gives an error message and display while the required () function is used inside the function and throws a warning message whenever a particular package is not found.

### 22) What is the t-test() in R?

The t-test() function is used to determine that the mean of the two groups are equal or not.

### 23) What is the use of with() and by() functions in R?

The with() function applies an expression to a dataset, and the by() function applies a function to each level of factors.

### 24) Differentiate b/w lapply and sapply.

The lapply is used to show the output in the form of the list, whereas sapply is used to show the output in the form of a vector or data frame.

### 25) Explain aggregate() function.

The aggregate() function is used to aggregate data in R. There are two methods which are collapsing data by using one or more BY variable and other is an aggregate() function in which By variable should be in the list.

### 26) Explain the doBy package?

This package is used to define the desired table using function and model formula.

### 27) Explain the use of the table() function.

This function is used to create the frequency table in R.

### 28) Explain fitdistr() function?

This function is used to give the maximum likelihood fitting of univariate distribution and defined under the MASS package.

### 29) What are GGobi and iPlots?

The GGobi is an open-source program for visualization to exploring high dimensional typed data, and the iPlots is a package which provides bar plots, mosaic plots, box plots, parallel plots, histograms, and scatter plots.

### 30) Explain the lattice package.

The lattice package is meant to improve upon the base R graphics by giving better defaults and has the ability to display multivariate relationships easily.

### 31) Explain anova() function.

The anova() function is used for comparing the nested models.

### 32) Explain cv.lm() and stepAIC() function.

The cv.lm() function is defined under the DAAG package used for k-fold validation while the stepAIC() function is defined under the MASS package that performs stepwise model selection under exactAIC.

### 33) Explain leaps() function.

The leaps() function is used to perform the all-subsets regression and defined under the leaps package.

### 34) Explain relaimpo and robust package.

This package is used to measure the relative importance of every predictor in the model, and the robust package gives a library of robust methods, including regression.

### 35) Give full form of MANOVA and what is the use of it.

MANOVA stands for Multivariate Analysis of Variance, and it is used to test more than one dependent variable simultaneously.

### 36) Explain mashapiro.test() and barlett.test().

This function defines in the mvnormtest package and produces the Shapiro-wilk test to multivariate normality. The barlett.test() is used to provide a parametric k-sample test of the equality of variances.

### 37) Explain the use of the forecast package.

The forecast package gives the functions which are used to automatic selection of exponential and ARIMA models.

### 38) Differentiate between qda() and lda() function.

The qda() function prints a quadratic discriminant function while lda() function print the discriminant functions based on the centered variable.

### 39) Explain the auto.arima() and principal() function.

The auto.arima() function handle both the seasonal and non-seasonal ARIMA model and the principal() function used for rotating and extracting the principal components.

### 40) Explain FactoMineR.

The FactoMineR is a package that includes qualitative and quantitative variables. The observations and supplementary variables are also included in these packages.

### 41) What is the full form of SEM and CFA?

CFA stands for Confirmatory Factor Analysis, and SEM stands for Structural Equation Modeling.

### 42) Define cluster.stats() and pvclust() function().

The cluster.stats() function define in the fpc package that provides a method for comparing the similarity of two cluster solutions using different validation criteria, and the pvclust() function is defined in the pvclust package that provides p-values for hierarchical clustering.

### 43) Define MATLAB and party packages.

This package includes wrapper functions and variable which are used for replicating Matlab function calls.

### 44) Explain S3 and S4 systems.

In oops, the S3 is used to overload any function. So that we can call the functions with different names, and it depends on the type of input parameter or the number of parameters, and the S4 is the most important characteristic of oops. However, this is a limitation, as it is quite difficult to debug. There is an optional reference class for S4.

### 45) Give names of visualization packages.

There are the following packages of visualization in R:

- Plotly
- ggplot2
- tidyquant
- geofacet
- googleVis
- Shiny

### 46) Explain Chi-Square Test

The Chi-Square Test is used to analyze the frequency table (i.e., contingency table), which is formed by two categorical variables. The chi-square test evaluates whether there is a significant relationship between the categories of the two variables.

### 47) Explain Random Forest.

The Random Forest is also known as Decision Tree Forest. It is one of the popular decision tree-based ensemble models. The accuracy of these models is higher than other decision trees. This algorithm is used for both classification and regression applications.

### 48) Explain Time Series Analysis.

Any metric which is measured over regular time intervals creates a time series. Analysis of time series is commercially important due to industrial necessity and relevance, especially with respect to the forecasting (demand, supply, and sale, etc.). A series of data points in which each data point is associated with a timestamp is known as time series.

### 49) Explain Pie chart in R.

R programming language has several libraries for creating charts and graphs. A pie-chart is a representation of values in the form of slices of a circle with different colors.

### 50) Explain Histogram.

A histogram is a type of bar chart which shows the frequency of the number of values which are compared with a set of values ranges. The histogram is used for the distribution, whereas a bar chart is used for comparing different entities. In the histogram, each bar represents the height of the number of values present in the given range.