Binomial Random Numbers Generation in R

We will learn how to generate Bernoulli or Binomial Random Numbers (Binomial distribution) in R with the example of a flip of a coin. This tutorial is based on how to generate random numbers according to different statistical probability distributions in R. Our focus is on binomial random numbers generation in R.

Binomial Random Numbers in R

We know that in Bernoulli distribution, either something will happen or not such as a coin flip has two outcomes head or tail (either head will occur or head will not occur i.e. tail will occur). For an unbiased coin, there will be a 50% chance that the head or tail will occur in the long run. To generate a random number that is binomial in R, use the rbinom(n, size, prob) command.

rbinom(n, size, prob) #command has three parameters, namey

where
‘$n$’ is the number of observations
‘$size$’ is the number of trials (it may be zero or more)
‘$prob$’ is the probability of success on each trial for example 1/2

Examples of Generation Binomial Random Numbers

  • One coin is tossed 10 times with a probability of success=0.5
    the coin will be fair (unbiased coin as p=1/2)
    rbinom(n=10, size=1, prob=1/2)
    OUTPUT: 1 1 0 0 1 1 1 1 0 1
  • Two coins are tossed 10 times with a probability of success=0.5
  • rbinom(n=10, size=2, prob=1/2)
    OUTPUT: 2 1 2 1 2 0 1 0 0 1
  • One coin is tossed one hundred thousand times with a probability of success=0.5
    rbinom(n=100,000, size=1, prob=1/2)
  • store simulation results in $x$ vector
    x <- rbinom(n=100000, size=5, prob=1/2)
    count 1’s in x vector
    sum(x)
    find the frequency distribution
    table(x)
    creates a frequency distribution table with frequency
    t = (table(x)/n *100)
    plot frequency distribution table
    plot(table(x),ylab = "Probability",main = "size=5,prob=0.5")
Binomial Random Numbers

View the Video tutorial on rbinom command

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Sampling in R Language: Important MCQs 10

The post is about Sampling in R Language. There are 20 multiple-choice questions from the sampling, ggplot2 package, and R language basics too. Let us start with the Quiz sampling in R Language.

R Language Online Quiz with Answers

1. A data analyst creates a scatterplot with a lot of data points. It is difficult for the analyst to distinguish the individual points on the plot because they overlap. What function could the analyst use to make the points easier to find?

 
 
 
 

2. For the following population x<-c(1,2,3,4,5)what will be the R command for finding mean?

 
 
 
 

3. Which of the following functions lets you display smaller groups, or subsets, of your data?

 
 
 
 

4. Which is the R command for selecting a sample of size 6 from the population yp <- c(111, 159, 121, 198, 120, 136, 14, 129, 17, 115, 186, 119, 121, 153, 143)

 
 
 
 

5. Who introduced tidyverse to “share an underlying design philosophy, grammar, and data structures, of tidy data?

 
 
 
 

6. A data analyst is considering using tibbles instead of basic data frames. What are some of the limitations of tibbles?

 
 
 
 

7. Which of these dplyr verbs can be used to retrieve columns of a dataset?

 
 
 
 

8. What is the R command for generating bi-variate normal distribution

 
 
 
 

9. A data analyst is working with a data frame named cars. The analyst notices that all the column names in the data frame are capitalized. What R code lets the analyst change all the column names to lowercase?

 
 
 
 

10. A data analyst is working with a data frame called “salary_data”. They want to create a new column named “total_wages” that adds together data in the “standard_wages” and “overtime_wages” columns. What R code lets the analyst create the “total_wages” column?

 
 
 
 

11. What is the R command for selecting sample of size $n=10$ by probability proportional to size (PPS) with $N=40$.

 
 
 
 

12. Suppose, I have a vector x <- c(3, 6, 1, 19, 12, 8)and I want to set all elements of this vector that are less than 6 to be equal to zero. What R code achieves this?

 
 
 
 

13. We obtain the 10000 random sample of size 6 under SRSWOR using the following population (111, 158, 122, 193, 111, 148, 112, 128, 113, 151, 185, 200, 199, 121, 115, 114) which is the R command for repeating this procedure 150 times?

 
 
 
 

14. In ggplot2, what function do you use to map variables in your data to visual features of your plot?

 
 
 
 

15. Which is the R command for selecting a sample of size 3 from the population y<- c(11, 150, 121, 192, 233, 129, 117, 186, 129, 189, 159)

 
 
 
 

16. A data analyst creates a plot for visualization. The analyst wants to add a caption to the plot to help communicate important information. What function could the analyst use?

 
 
 
 

17. For the following population y<- c(1,2,3,4,5), what will be the R command for finding variance?

 
 
 
 

18. What is the class of the object defined by the expression x <- c(4, "a", TRUE)?

 
 
 
 

19. A data analyst is working with a data frame named “stores”. It has separate columns for city (“city”) and state (“state”). The analyst wants to combine the two columns into a single column named “location”, with the “city” and “state” separated by a comma. What R code lets the analyst create the location column?

 
 
 
 

20. In ggplot2, which of the following concepts refers to the shape, color, and size of data points in a plot?

 
 
 
 

The dplyr package is used for data manipulation and transformation. It gives a set of functions that make it easy to perform common data manipulation tasks, which include (1) filtering, (2) grouping, (3) summarizing, (4) arranging, and (5) joining data frames.

The package is part of the tidyverse, a collection of R packages designed to work together seamlessly for data analysis and visualization.

Some key functions available in dplyr R Package include:

  • filter(): Used to subset rows based on specified conditions.
  • select(): Used to choose specific columns from a data frame.
  • arrange(): Used to reorder rows based on one or more columns.
  • mutate(): Used to create new columns or modify existing ones.
  • group_by(): Used to group data by one or more variables.
  • summarize(): Used to compute summary statistics for groups of data.
  • join(): Used to merge data frames based on common keys.

The dplyr package provides a powerful and efficient toolkit for data manipulation in R.

R FAQS Logo: Quiz Sampling in R Language

Quiz Sampling in R Language

  • What is the class of the object defined by the expression x <- c(4, “a”, TRUE)?
  • Suppose, I have a vector x <- c(3, 6, 1, 19, 12, 8)and I want to set all elements of this vector that are less than 6 to be equal to zero. What R code achieves this?
  • We obtain the 10000 random sample of size 6 under SRSWOR using the following population (111, 158, 122, 193, 111, 148, 112, 128, 113, 151, 185, 200, 199, 121, 115, 114) which is the R command for repeating this procedure 150 times?
  • Which is the R command for selecting a sample of size 6 from the population yp <- c(111, 159, 121, 198, 120, 136, 14, 129, 17, 115, 186, 119, 121, 153, 143)
  • For the following population y<- c(1,2,3,4,5), what will be the R command for finding variance?
  • For the following population x<-c(1,2,3,4,5)what will be the R command for finding the mean?
  • Which is the R command for selecting a sample of size 3 from the population y<- c(11, 150, 121, 192, 233, 129, 117, 186, 129, 189, 159)
  • What is the R command for generating bi-variate normal distribution
  • What is the R command for selecting a sample of size $n=10$ by probability proportional to size (PPS) with $N=40$?
  • Which of these dplyr verbs can be used to retrieve columns of a dataset?
  • Who introduced tidyverse to “share an underlying design philosophy, grammar, and data structures, of tidy data?
  • A data analyst is working with a data frame called “salary_data”. They want to create a new column named “total_wages” that adds together data in the “standard_wages” and “overtime_wages” columns. What R code lets the analyst create the “total_wages” column?
  • A data analyst is working with a data frame named “stores”. It has separate columns for city (“city”) and state (“state”). The analyst wants to combine the two columns into a single column named “location”, with the “city” and “state” separated by a comma. What R code lets the analyst create the location column?
  • A data analyst is considering using tibbles instead of basic data frames. What are some of the limitations of tibbles?
  • A data analyst is working with a data frame named cars. The analyst notices that all the column names in the data frame are capitalized. What R code lets the analyst change all the column names to lowercase?
  • In ggplot2, what function do you use to map variables in your data to visual features of your plot?
  • In ggplot2, which of the following concepts refers to the shape, color, and size of data points in a plot?
  • Which of the following functions lets you display smaller groups, or subsets, of your data?
  • A data analyst creates a scatterplot with a lot of data points. It is difficult for the analyst to distinguish the individual points on the plot because they overlap. What function could the analyst use to make the points easier to find?
  • A data analyst creates a plot for visualization. The analyst wants to add a caption to the plot to help communicate important information. What function could the analyst use?

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Questions about R: Important Frequently Asked

This post is about some frequently asked Questions about R Language. The frequently asked questions are about compilers in R, R packages, just in just-in-time compilers, procedural programming in R, and the Recycling rule of vectors. These questions will help you prepare for examinations and interviews.

Frequently Asked Questions About R

Questions about R Language

Question: What is a Compiler in R Language?
Answer: A compiler is software that transforms computer code (source code) to another computer language (target language, i.e., object code).

Question: What is a package in R Language?
Answer: The R package is a collection of R functions, compiled code, sample data, and help documentation. The R packages are stored in a directory called “library” in the R environment. The R language also installed a set of packages during installation.

Question: What is JIT?
Answer:
JIT standards for “Just in Time” compiler. It is a method to improve the run-time performance of a computer program.

Question: What is procedural Programming in R Language?
Answer:
Procedural programming is derived from structured programming and it is based on the concept of procedure call. Procedures are also known as routines, subroutines, or functions. It contains a series of computational steps to be carried out. Any procedure may be called (at any point) during a program’s execution.

Mathematical Operation in R

Question: What is the recycling of elements in a vector?
Answer: When a mathematical operation (such as addition, subtraction, multiplication, division, etc) is performed on two vectors of different lengths (the number of elements in both vectors is different), the element having a shorter length is reused to complete the mathematical operations.

vect1 <- c(4, 1, 4, 5, 6, 9)
vect2 <- c(2, 5)
vect1 * vect2 

###
8, 5, 8, 25, 12, 45

The elements of vect2 are recycled to complete the operation of all elements of vect1.

Question: What is the difference between a data frame and a matrix in R Language?
Answer: In R, the data frame contains heterogeneous data (different columns of the data frame may have different types of variable) while a matrix contains homogeneous data (all the columns of the matrix have the same type of variable). In a matrix, similar data types can be stored while in a data frame, different types of data can be stored.

See Questions about R language Missing Values

MCQs General Knowledge, MCQs in Statistics