The Great Cherry Ripe Scandal, Part II

First we set up the chocolate randomisation process:

chocolate_types<-c("Cherry Ripe","Dairy Milk","Crunchie", "Caramello","Flake")

make_pack<-function(){
  sample(chocolate_types, 8, replace=TRUE)
}

make_pack()

## [1] "Dairy Milk" "Flake"      "Dairy Milk" "Crunchie"   "Flake"     
## [6] "Crunchie"   "Caramello"  "Flake"

make_pack()

## [1] "Caramello"  "Crunchie"   "Crunchie"   "Flake"      "Crunchie"  
## [6] "Caramello"  "Dairy Milk" "Dairy Milk"

replicate(5, make_pack())

##      [,1]         [,2]          [,3]          [,4]         [,5]         
## [1,] "Dairy Milk" "Dairy Milk"  "Crunchie"    "Crunchie"   "Cherry Ripe"
## [2,] "Dairy Milk" "Flake"       "Cherry Ripe" "Crunchie"   "Flake"      
## [3,] "Flake"      "Caramello"   "Cherry Ripe" "Caramello"  "Dairy Milk" 
## [4,] "Dairy Milk" "Cherry Ripe" "Cherry Ripe" "Crunchie"   "Dairy Milk" 
## [5,] "Flake"      "Flake"       "Flake"       "Flake"      "Crunchie"   
## [6,] "Dairy Milk" "Caramello"   "Cherry Ripe" "Caramello"  "Dairy Milk" 
## [7,] "Flake"      "Caramello"   "Flake"       "Dairy Milk" "Cherry Ripe"
## [8,] "Dairy Milk" "Dairy Milk"  "Crunchie"    "Crunchie"   "Dairy Milk"

Now, we need to work out which bar we have the most of, and count them

count_most<-function(a_pack){
    count <- table(a_pack)
    most <- which.max(count)
    count[most] 
}

a_test_pack<-make_pack()
a_test_pack

## [1] "Caramello"  "Crunchie"   "Flake"      "Flake"      "Crunchie"  
## [6] "Crunchie"   "Flake"      "Dairy Milk"

count_most(a_test_pack)

## Crunchie 
##        3

We’re going to do this lots of times, so we need a function that asks how many times and then does it:

make_and_count_most<-function(how_many){
    replicate(how_many,{
        pack<-make_pack()
        count_most(pack)
    })
}

make_and_count_most(3)

##  Dairy Milk   Caramello Cherry Ripe 
##           4           3           4

And, finally, we take advantage of the power of modern computing by running this a whole heap of times. For curiosity – because the code was written to be clear, not to be fast – I’ll also see how long it takes on my three-year old laptop.

a_whole_heap <- 1e6
system.time(results <- make_and_count_most(a_whole_heap))

##    user  system elapsed 
## 150.227   0.485 150.902

table(results)

## results
##      2      3      4      5      6      7      8 
## 161771 558492 227978  45578   5739    433      9

table(results[names(results)=="Cherry Ripe"])

## 
##      2      3      4      5      6      7      8 
##  45346 125405  45582   9146   1168     81      2