gaussian_class.r

# 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,
    sigma = NA,
    n_sample = NA,
    sample = NA,
                       
    initialize = function(mu, sigma, n_sample) {
      # Object Initiator
      # Args
      #   mu: mean of the distribution
      #   sigma: std. dev. of the distribution
      #   n_sample: size of the sample
                         
      self$mu <- mu
      self$sigma <- sigma
      self$n_sample <- n_sample
    },
                       
    get_sample_size = function() {
      # getter method
                         
      return (self$n_sample)
    },
                       
    set_sample_size = function(size) {
      # setter method
                         
      self$n_sample = size
    },
                       
    generate_sample = function() {
      # Generates the random sample
                         
      self$sample <- rnorm(self$n_sample, self$mu, self$sigma)
      return (self$sample)
    },
                       
    compute_stats = function() {
      # Computes basic statistics of the sample
                         
      r <- list(sample.mean = mean(self$sample), 
      sample.sd = sd(self$sample))
      return (r)
    },
                       
    plot_histogram = function(binwidth) {
      p <- qplot(self$sample, geom = "histogram",
                 breaks = seq(130, 200, binwidth),
                 colour = I("black"), fill = I("white"),
                 xlab = "X", ylab = "Count") +
            stat_function(
              fun = function(x, mean, sd, n, bw){
                      dnorm(x = x, mean = mean, sd = sd) * n * bw
                    },
                    args = c(mean = self$mu,
                             sd = self$sigma, 
                             n = self$n_sample, 
                             bw = binwidth)
            )
      return (p)
    }
  )
)

#### Implementation ####
sm <- GaussianSimulator$new(mu=165, sigma=6.6, n_sample=1000) #creates a new object of the class
r <- sm$generate_sample() #generates a random sample 
basic.stats <- sm$compute_stats() #computes basic statistics of the sample
p <- sm$plot_histogram(binwidth=2) #creates the histogram of the sample