**Why R language?**

In R, quality of some packages is less than perfect.

In R, no one to complain, if something doesn’t work.

R is a software Application that many people devote their own time to developing.

R commands give little thought to memory management, and So R can consume all available memory.

**What is Predictive Analysis in R?**

Predictive analysis is the branch of advanced analysis. It used to make predictions about unknown future events. The Predictive analysis contains data collection, statistics, and deployment. It uses many techniques from data mining, statistics, machine learning and analyzes current data to make predictions about future. It also allows the business users to create Predictive intelligence.

**What is Predictive analysis process in R?**

Define Project – It includes Project outcomes, business objectives, deliverables, scoping of the effects.

Data Collection – For predictive analysis, it collects data from different sources to analysis. Thus it provides a complete view of customer interactions.

Data Analysis – It is the process of cleaning, transforming, inspecting and modeling data. The goal of this process is to discover useful information.

Statistics – This process enables to confirm the assumptions. Hence it uses the assumption to test using a statistical model.

Modeling – An accurate predictive model about future is been created using predictive modeling. There are also options to choose the best model.

Deployment – To deploy the analytical results into everyday decision-making.

Model Monitoring – To ensure that it is providing an expected result, we have to manage model.

Capgemini R Programming Recently Asked Interview Questions Answers |

**What is the need for Predictive Analysis in R?**

Secure a competitive Stronghold It helps you to play your competitors’ weaknesses and company’s strengths. Hence, it allows you to check the actions of consumers and your competitors’ marketing and sales.

Do more than evaluating the past Employee analysis helps to check your company details. It will summarize past failure or past success. Therefore, the most important thing is that predictive analysis helps in learning from past experiences.

Maintain business integrity by managing fraud, First of all, Fraud investigators can look into only a set number of cases each week. Secondly, they use company’s past experience to score transactions according to their level of risk.

Advance your core business Capability The next step to growth is to improve company core offering. Thus At its core, it focuses on using it to optimize your approach to the market.

**What is Descriptive analysis in R?**

It does exactly what the name Implies “Describe”. it allows us to learn from our past and to understand how they might influence future outcomes. The main goal of is to find out the reasons behind previous success or failure in the past. Hence, Most of the social analysis is descriptive analysis. For Example – the company’s production, financials, operations, sales, finance, inventory, and customers.

**What is recycling of elements in an R vector? Give an example.**

When two vectors of different length are involved in an operation then the elements of the shorter vector are reused to complete the operation. This is called element recycling.

Example – v1 <- c(4,1,0,6) and v2 <- c(2,4) then v1*v2 gives (8,4,0,24). The elements 2 and 4 are repeated.

**What is R lists?**

Lists are the object which Contains elements of different types – like strings, numbers, vectors and another list inside it. A list can also contain a matrix or a function as its elements. The List is created using list() Function. In other words, a list is a generic vector containing other objects. For Example, The variable x is containing copies of three vectors n, s, b and a numeric value 3.

n = c(2, 3, 5)

s = c(“aa”, “bb”, “cc”, “dd”, “ee” )

b = c(TRUE, FALSE, TRUE, FALSE, FALSE )

x = list( n, s, b, 3) # x contains copies of n, s, b)

**Explain how to create a list in R?**

Create a list containing string, numbers, vectors and logical values. For Example:

List_data <- list("Green", "Yellow", c(5,6,7), TRUE, 51.2)

print(list_data) When we execute the above code, it produces the following result-

[[1]]

[1] “Green”

[[2]]

[1] “Yellow”

[[3]]

[1] 5, 6, 7

[[4]]

[1] TRUE

[[5]]

[1] 51.2

**How to use sapply in R?**

Multipy all values by 10:

> sapply(BOD,function(x) 10 * x)

Time demand

[1,] 10 80

[2,] 20 100

[3,] 30 190

Used for array, margin set to 1:

> x <- array(1:9) > sapply(x,function(x) x * 10)

[1] 10 20 30 40 50 60 70 80 90

Two dimension array, margin can be 1 or 2:

> x <- array(1:9,c(3,3)) > x

[,1] [,2] [,3]

[1,] 1 4 7

[2,] 2 5 8

[3,] 3 6 9

> sapply(x,function(x) x * 10)

[1] 10 20 30 40 50 60 70 80 90

sapply: returns a vector, matrix or an array

# sapply : returns a vector, an array or matrix

sapply(c(1:3), function(x) x^2)

[1] 1 4 9

**What is R matrices?**

A matrix is a two-dimensional rectangular data set and thus it can be created using vector input to the matrix function. In addition, a matrix is a collection of numbers arranged into a fixed number of rows and columns. Usually, the numbers are the real numbers. By using a matrix function we can reproduce a memory representation of the matrix in R. Hence, the data elements must be of the same basic type.

**How many methods are available to use the matrices?**

There are so many methods to solve the matrices like adding, subtraction, negative etc.

**What is control structure in R?**

R has the standard control structures we would expect. expr can be multiple statements by enclosing them in braces { }. It is more efficient to use built-in functions rather than control structures whenever possible. These allow us to control the flow of execution of a script typically inside of a function. Control structures define the flow of the program. The decision is been based on the evaluation of a variable.

**How many control statements are present in R?**

There are eight control statements are present in R.

**Name all control statements present in R?**

If

If-else

For

Nested loops

While

Repeat and break

Next

return.

**Explain how to access list elements in R?**

Create a list containing a vector, a list and a matrix.

list_data <- list(c("Feb","Mar","Apr"))

list("white",13.4)), matrix(c(3,9,5,1,-2,8), nrow = 2)

For Example: Give names to the elements in the list:

Names(list_data) <- c(“1 st Quarter”, “A Matrix”, “A Inner list”)

Access the first element of the list:

print(list_data[1])

Access the third element. As it also a list, all its elements will print:

Print(list_data[3])

By using the name of the element access the list elements:

Print(list_data$A Matrix)

It will produced the following result after executing the above code:

$”1 st Quarter” [1] “Feb”, "Mar”, "Apr”

$A_Inner_list

$A_Inner_list[[1]]

[1] “White”

$A_Inner_list[[2]]

[1] 13.4

$ “A Matrix” [1]

[1] [2] [3]

[1] 3 5 -2

[2] 9 1 8

**Explain how to manipulate list elements in R?**

Create a list containing a vector, a matrix and a list.

list_data <- list(c("Feb","Mar","Apr"),

matrix(c(3,9,5,1,-2,8), nrow = 2), list("green",12.3))

For Example:

Give names to the elements in the list:

names(list_data) <- c("1st Quarter", "A_Matrix", "A Inner list")

Add element at the end of the list:

list_data[4] <- "New element"

print(list_data[4])

Remove the last element:

list_data[4] <- NULL # Print the 4th Element.print(list_data[4])

Update the 3rd Element:

list_data[3] <- "updated element"

print(list_data[3])

When we execute the above code, it produces the following result:

[[1]]

[1] "New element"

$NULL

$`A Inner list`

[1] "updated element"

**Explain how to generate lists in R?**

We can use a colon to generate a list of numbers. For example:

-3:3

[1] -3 -2 -1 0 1 2 3

**Explain how to operate on lists in R?**

R allows to Operate on all list values at once. For example:

c(1,3,5) + 4

This and the Apply function allow you to avoid most for loops.

[1] 5, 7, 9

**What are features of R functions?**

Function component describes the three main components of a function.

Lexical scoping teaches how R finds values from names.

In R, every operation is a function call.

Function arguments discuss the three ways of supplying arguments to a function. it shows to call

The function is given a list of arguments and to the impact of lazy evaluation.

Special calls describe two special types of function infix and replacement functions.

Return values discuss how and when functions return values. it also shows how you can ensure

that a function does something before it exists.

**What is function definition?**

An R function is been created using the keyword function. The basic syntax of an R function definition is as follows −

function_name <- function(arg_1, arg_2, …) {

Function body

}

**What are the components of R functions?**

The different parts of a function are −

Function Name − It is the actual name of the function because it stored in R environment as an object with this name.

Arguments − An argument is a placeholder. When a function invokes, we pass a value to the

argument. Arguments are optional; that is, a function may contain no arguments. Also, arguments can have default values.

Functions Body – In a function body, statements can be collected. and hence, it defines what the function does.

Return Value − the return value of a function is the last expression in the function body to check.

**What are Generic Functions in R?**

R has three object-oriented (OO) systems: [[S3]], [[S4]] and [[R5]]. … A method is a function associated with a particular type of object. S3 implements a style of object-oriented programming called generic-function OO.

**What are R packages?**

Packages are collections of R functions, data, and compiled code in a well-defined format. The directory where packages are stored is called the library. R comes with a standard set of packages. Others are available for download and installation. Once installed, they have to be loaded into the session to be used.

**Name the functions which helps in importing data from other applications in R?**

read.table()

readlines()

read.fwf

read.delim()

scan()

read.csv()

read.csv2()

**What is more functions in R and name them?**

We have to load the built-in foreign command to use these functions:

>library("foreign")

R more functions:

read.xpss

read.xport

read.dta

**List out some of the function that R provides?**

Mean

Median

Distribution

Covariance

Regression

**What is the distribution in R?**

R Functions for Probability Distributions. Every distribution that R handles has four functions. There is a root name, for example, the root name for the normal distribution is the norm. This root is prefixed by one of the letters. p for “probability”, the cumulative distribution function (c. d. f.)

**What are vector functions?**

In R, a function is a piece of code written to carry out a specified task. R Functions are called as objects because we can work with them exactly the same way we work with any other type of object. Vector functions are those functions which we used in vectors.

For Example: rep(), seq(), using all() and any(), more on c() etc.

Most common functions which we use in vector operations are –

rep()

seq()

**Explain how to repeat vectors in R?**

We can use the rep() function in several ways if we want to repeat the complete vector. For examples: specify the argument times 1. To repeat the vector c(0, 0, 7) three times, use this code:

> rep(c(0, 0, 7), times = 4)

[1] 0 0 7 0 0 7 0 0 7 0 0 7 2

We can also repeat every value by specifying the argument each, like this:

> rep(c(2, 4, 2), each = 2)

[1] 2 2 4 4 2 2 3

We can tell R for each value how often it has to repeat:

> rep(c(0, 7), times = c(4,3))

[1] 0 0 0 0 7 7 7 4

In seq, we use the argument length.out to define R. it will repeat the vector until it reaches that length, even if the last repetition is incomplete.

> rep(1:3,length.out=9)

[1] 1 2 3 1 2 3 1 2 3

**How to create vectors in R?**

a) To create a vector using integers:

For Example, We use the colon operator (:) in R.

The code 2:6 gives you a vector with the numbers 2 to 6, and 3:-4 create a vector with the numbers 3 to –4, both in steps of 1.

b) We use the seq() to make steps in a sequence.

Seq() function used to describe by which the numbers should decrease or increase.

For Example In R, the vector with a numbers 4.5 to 3.0 in steps of 0.5.

> seq(from = 4.5, to = 3.0, by = -0.5)

[1] 4.5 4.0 3.5 3.0 c

You can specify the length of the sequence by using the argument out. R calculates the step size itself. For Example We can make a vector of nine values going from –2.7 to 1.3 like this:

> seq(from = -2.7, to = 1.3, length.out = 9)

[1] -2.7 -2.2 -1.7 -1.2 -0.7 -0.2 0.3 0.8 1.3

**Can we update and delete any of the elements in a list?**

We can update any of the element but we can delete only the element at the end of the list.

**How many types of object are present In R?**

There are 6 types of objects present in R:

Vectors

Matrices

Arrays

Lists

Data Frames

Factors.