In kyung541/binomial: Calculate Probabilities of a Binomial Random Variable

knitr::opts_chunk$set(collaspe = T, coomment = "#>" )
library(binomial)

Calculate Binomial Random Variable

The package Binomial is a minimal implementation for calculateing the probailities of random variabl and to visualize the relatvie frequencies in of the random variable.

Creating a Binomial random variable

The first step is to create a binomial variable object with the function bin_choose(n,k) with n and k.

n = trials
k = success

variable = bin_choose(n = 5,k=2)
variable

By defeault, bin_choose() creates a valid binomial varialbe with valid trails, success, and probability.

Calculating the binomial probability

Once you have defined a "binomial variable" object, you can use the function bin_probability(success, trials, prob) to calculate the probability of success in the number of trialsaccording to the probability (probability of success) you entered. The output will be an object of class "numeric", which will contain the the binomial probability.

binpro = bin_probability(success = 2, trials=5, prob=0.5)
binpro

Calculating and plot the binomial distribution

This package can also calculate the "binomial distribution" as well. You can use function bin_distribution(trials,prob) to create the "binomial distribution" in the class of "bindis" and "data.frame". It will give you a nice table of distribution.

bindist = bin_distribution(trials = 5, prob = 0.5)
bindist

The plot() function will create a nice barplot to display table with class of "bindis" that generate from "bin_distribution"

bindistrP = plot(bindist)
bindistrP

Calculating and plot the binomial cumulative distributioon

You can also calculate the binomial cumulative distribution through this package as well. You can use function bin_cumulative(trials, prob) to create a table of "binomial cumlative distribution" which is an object of class "bincum" and "data.frame".

bincumu = bin_cumulative(trials = 5, prob = 0.5)
bincumu

You can also use the plot() function, which can give you a nice line graph that display the table with class "bincum" that generated from function "bin_cumulative".

bincumuP = plot(bincumu)
bincumuP

Listing the variables

You can use the function bin_variable(trials, prob) to generate the numbers of trials and probability of success in a userfriendly, nice formated list.

binvar = bin_variable(trials = 10, prob = 0.3)
binvar

You can also use the functiono summary() to generate the summary of the list with the class of summary.binvar which generated from the function "bin_variable". It will give you a nice, customice list that display mean, mode , variance, skweness and kurtosis.