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I have developed this exercise with Excel in another post for the same calculations , I am going to develop  it this time with  “R”.

1    28    31   130.0    68.12
2    24    28   143.0   127.89
3    28    20   136.0    89.03
4    32    34   130.5    78.28
5    22    15   125.0   134.08
6    26    37   147.5   135.31
7    24    19   135.0   130.48
8    28    22   125.0    86.48
9    24    26   127.0   129.47
10   30    21   139.0    82.43
11   22    20   121.5   127.41
12   30    38   150.5    71.21
13   24    17   120.0   132.06
14   26    20   125.0    90.85

We import the data into R.
We are going to apply the Mahalanobis Distance formula:
D^2 = (x – μ)’ Σ^-1 (x – μ)
We calculate ﻿μ (mean) with:
mean<-colMeans(x)
26.28571  24.85714 132.50000 105.93571
We calculate Σ (covariance matrix (Sx)) with:
Sx<-cov(x)
> Sx
long.  12.813187  56.90110  49.11538 -70.62066
peso   12.076923  49.11538  92.80769 -46.06962
mg.kg -72.154066 -70.62066 -46.06962 714.00118
The default value for the Mahalanobis function is inverted=FALSE, so the function will calculate the inverse of Sx. If we calculated appart remember to change to TRUE.
See R help:

O.K. Let´s go:
>D2<-mahalanobis(x,mean,Sx)
> D2
 5.571677 2.863499 2.686127 7.766153 2.379621 6.366793 2.135347 1.538248
 2.018812 5.143830 3.082734 5.470313 3.158651 1.818195

These are the values in the Diagonal Matrix we saw with the calculations in Excel.