koushikkhan

5.1,3.5,1.4,0.2,Iris-setosa
4.9,3.0,1.4,0.2,Iris-setosa
4.7,3.2,1.3,0.2,Iris-setosa
4.6,3.1,1.5,0.2,Iris-setosa
5.0,3.6,1.4,0.2,Iris-setosa
5.4,3.9,1.7,0.4,Iris-setosa
4.6,3.4,1.4,0.3,Iris-setosa
5.0,3.4,1.5,0.2,Iris-setosa
4.4,2.9,1.4,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa

koushikkhan

# A class to generate sample from Gaussian Distribution
# mean, standard-deviation and sample size are being 
# expected to come from the user

library(R6)
library(ggplot2)

GaussianSimulator <- R6Class("GaussianSimulatorClass",
  public = list(
    mu = NA,

koushikkhan

# Usage of the class

# 1. create a new object of the class named `sm`
sm <- GaussianSimulator$new(mu=165, sigma=6.6, n_sample=1000) 

# 2. generate a random sample
r <- sm$generate_sample()

# 3. compute basic ststistics of the sample
basic.stats <- sm$compute_stats()

koushikkhan

LogNormalSimulator <- R6Class("LogNormalSimulatorClass",
  inherit = GaussianSimulator,
  public = list(
    log_normal_sample = NA,
    initialize = function(mu, sigma, n_sample) {
      super$initialize(mu = mu, sigma = sigma, n_sample = n_sample)
    },
    
    generate_lognormal_sample = function() {
      normal_sample <- self$generate_sample()

koushikkhan

ln.object <- LogNormalSimulator$new(mu = 5, sigma = 0.5, n_sample = 100000)
ln.sample <- test$generate_lognormal_sample()
ln.object$compute_stats()