Types of Sorting Algorithm in R Programming - GeeksforGeeks

17 Jun, 2021
There are multiple ways by which data can be sorted in the R language. It’s up to the data Analyst to consider the most suitable method based upon the structure of the data. There are multiple algorithms for performing sorting on the data in the R programming language. Below different types of sorting function have been discussed. A sample of 10 random numbers between 1 to 100 from an array is used. We are going to discuss the following sorting algorithm:
Bubble Sort
Insertion Sort
Selection Sort
Merge Sort
Quick Sort
In this algorithm, two adjacent elements are compared and swapped if the criteria are met. In bubble sort, in each iteration, the largest element is brought to the end of the array(in case of increasing) by swapping elements, hence the name of the algorithm is bubble sort. To understand the bubble sort algorithm in detail please refer to Bubble Sort.
R
# function to sort the array using bubble sortbubble_sort <- function(x){    # calculate the length of array    n <- length(x)             # run loop n-1 times        for (i in 1 : (n - 1)) {              # run loop (n-i) times            for (j in 1 : (n - i)) {                  # compare elements                if (x[j] > x[j + 1]) {                    temp <- x[j]                      x[j] <- x[j + 1]                      x[j + 1] <- temp                }            }        }      x} # take 10 random numbers between 1 - 100arr <- sample(1 : 100, 10) # sort the array and store the result# in sorted_arraysorted_array <- bubble_sort(arr) # print sorted_arraysorted_array
  
Output:
 
[1]  2 19 26 68 74 76 80 81 82 91
 
In this sorting algorithm, sorted and unsorted elements are compared, and the unsorted element is placed in its correct position after each iteration. In this algorithm, the first element is assumed to be sorted and the second element is stored separately as a key element that needs to be sorted. The key is then compared with the sorted element. If the sorted element is greater than the key element, their places are swapped, and the key element becomes the first element. To understand the Insertion sort algorithm in detail please refer to Insertion Sort.
 
R
# insertion sort function to sort arrayinsertion_sort <- function(x){      # calculate the length of array    n <- length(x)      # outer loop    for (i in 2 : (n))    {          # store first element as key        key = x[i]        j   = i - 1          # compare key with elements for          # its correct position        while (j > 0 && x[j] > key)        {          x[j + 1] = x[j]          j = j - 1        }      # Place key at its correct position      x[j + 1] = key    }      # return sorted array    x} # take sample arrayarr <- sample(1 : 100, 10) # call insertion sort functionsorted_arr <- insertion_sort(arr) # print sorted arraysorted_arr
  
Output:
 
[1] 10 27 30 41 58 77 80 89 90 85
 
This sorting algorithm is widely used in the R language. Here, the smallest element from the unsorted list is pushed to the start of the list at every iteration. To understand the Selection sort algorithm in detail please refer to Selection Sort.
 
R
# function to sort array using selection sortselection_sort <- function(x){      # length of array    n <- length(x)      for (i in 1 : (n - 1))    {          # assume element at i is minimum        min_index <- i          for (j in (i + 1) : (n))        {              # check if element at j is smaller              # than element at min_index            if (x[j] < x[min_index]) {                  # if yes, update min_index                min_index = j            }        }          # swap element at i with element at min_index        temp <- x[i]          x[i] <- x[min_index]          x[min_index] <- temp    }    x} # take sample inputarr <- sample(1 : 100, 10) # sort arraysorted_arr <- selection_sort(arr) # print arraysorted_arr
  
Output
 
[1] 6 16 21 28 31 48 57 73 85 99
 
This is a divide and conquers algorithm. We divide the array into two parts from mid, sort those two array,s and merge them. The entire process is done recursively. To understand the Merge sort algorithm in detail please refer to Merge Sort.
 
R
# function to merge two sorted arraysmerge <- function(a, b) {      # create temporary array    temp <- numeric(length(a) + length(b))         # take two variables which initially points to      # starting of the sorted sub arrays      # and j which points to starting of starting      # of temporary array    astart <- 1      bstart <- 1      j <- 1    for(j in 1 : length(temp)) {         # if a[astart] < b[bstart]        if((astart <= length(a) &&            a[astart] < b[bstart]) ||            bstart > length(b)) {              # insert a[astart] in temp and increment              # astart pointer to next            temp[j] <- a[astart]            astart <- astart + 1        }      else {            temp[j] <- b[bstart]            bstart <- bstart + 1                 }    }    temp} # function to sort the arraymergeSort <- function(arr) {         # if length of array is greater than 1,      # then perform sorting    if(length(arr) > 1) {                 # find mid point through which          # array need to be divided        mid <- ceiling(length(arr)/2)                 # first part of array will be from 1 to mid        a <- mergeSort(arr[1:mid])                # second part of array will be          # from (mid+1) to length(arr)        b <- mergeSort(arr[(mid+1):length(arr)])                 # merge above sorted arrays        merge(a, b)    }  # else just return arr with single element  else {        arr    }} # take sample inputarr <- sample(1:100, 10) # call mergeSort functionresult <- mergeSort(arr) # print resultresult
  
Output
 
[1] 6 8 16 19 21 24 35 38 74 90
 
This is a divide and conquers algorithm. It picks an element as a pivot and partitions the given array around the picked pivot. Pivot can be random. To understand the Merge sort algorithm in detail please refer to Quick Sort.
 
R
# function to sort the valuesquickSort <- function(arr) {     # Pick a number at random  random_index <- sample(seq_along(arr), 1);  pivot <- arr[random_index]  arr <- arr[-random_index]     # Create array for left and right values.  left <- c()  right <- c()     # Move all smaller and equal values to the  # left and bigger values to the right.  # compare element with pivot  left<-arr[which(arr <= pivot)]  right<-arr[which(arr > pivot)]     if (length(left) > 1)  {    left <- quickSort(left)  }  if (length(right) > 1)  {    right <- quickSort(right)  }     # Return the sorted values.  return(c(left, pivot, right))} # take sample arrayarr <- sample(1:100, 10) # call quickSort functionresult <- quickSort(arr) # print resultresult
  
Output:
 
[1] 13 18 21 38 70 74 80 83 95 99
 
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