How to calculate population variance in R?

There is no function in R to calculate the population variance but we can use the population size and sample variance to find it. We know that the divisor in population variance is the population size and if we multiply the output of var(it calculates sample variance) function with (population size – 1)/population size then the output will be population variance.
 Live Demo
set.seed(141)
x1<-1:100
Sample_Variance<-var(x1)
Sample_Variance
[1] 841.6667
Population_Variance<-var(x1)*(99/100)
Population_Variance
[1] 833.25
 Live Demo
x2<-rnorm(500)
Sample_Variance<-var(x2)
Sample_Variance
[1] 1.013514
Population_Variance<-var(x2)*(499/500)
Population_Variance
[1] 1.011487
 Live Demo
x3<-round(rnorm(500),0) Sample_Variance<-var(x3)
Sample_Variance
[1] 1.088401
Population_Variance<-var(x3)*(499/500)
Population_Variance
[1] 1.086224
 Live Demo
x4<-rpois(150,10)
x4
[1] 15 13 11 4 10 9 13 12 8 12 7 13 10 18 8 11 15 8 9 14 7 14 8 11 7
[26] 6 10 12 7 15 13 12 13 11 9 7 15 11 17 10 17 11 9 10 17 11 4 11 11 9
[51] 11 10 11 10 16 11 6 4 9 5 5 6 6 6 10 10 10 13 10 6 10 9 7 11 13
[76] 12 7 5 10 7 7 10 7 10 10 14 11 11 9 6 13 9 5 11 13 11 10 10 6 15
[101] 7 12 7 9 13 6 9 13 13 11 11 16 5 12 14 10 10 10 13 7 4 16 6 13 6
[126] 4 9 7 9 7 8 12 12 10 10 9 8 4 10 8 9 7 13 7 11 9 8 8 10 12
Sample_Variance<-var(x4)
Sample_Variance
[1] 10.86694
Population_Variance<-var(x4)*(149/150)
 Population_Variance
[1] 10.79449
 Live Demo
x5<-sample(1:100,120,replace=TRUE)
x5
[1] 62 59 25 15 16 17 69 22 81 90 91 68 61 40 61 48 33 71 60 11 1 15 95 17 81
[26] 29 16 44 47 26 20 56 97 74 3 5 44 77 50 44 83 54 37 54 73 46 99 19 85 28
[51] 8 49 15 80 65 50 85 7 91 76 83 93 54 95 52 8 20 18 70 12 66 36 2 99 81
[76] 13 91 11 73 19 2 73 20 12 80 41 38 20 61 64 39 30 65 28 25 38 56 61 44 32
[101] 66 76 2 72 36 78 48 41 52 17 31 69 33 74 39 60 29 59 72 11
Sample_Variance<-var(x5)
Sample_Variance
[1] 892.7361
Population_Variance<-var(x5)*(119/120)
Population_Variance
[1] 885.2966
 Live Demo
x6<--sample(101:999,120)
x6
[1] -919 -502 -343 -523 -867 -405 -368 -447 -286 -578 -147 -665 -823 -598 -260
[16] -740 -569 -661 -386 -267 -185 -114 -608 -711 -638 -992 -552 -795 -291 -152
[31] -154 -211 -721 -388 -283 -234 -525 -942 -599 -176 -239 -788 -579 -875 -883
[46] -856 -143 -304 -407 -448 -717 -524 -273 -235 -167 -158 -659 -432 -803 -624
[61] -187 -312 -225 -802 -439 -453 -637 -571 -768 -664 -473 -331 -806 -265 -173
[76] -748 -623 -671 -989 -888 -950 -589 -487 -526 -668 -760 -414 -622 -248 -276
[91] -139 -951 -630 -885 -440 -191 -491 -685 -653 -132 -742 -477 -181 -505 -759
[106] -974 -741 -548 -593 -240 -527 -914 -402 -127 -860 -336 -333 -794 -891 -311
Sample_Variance<-var(x6)
Sample_Variance
[1] 62657.78
Population_Variance<-var(x6)*(119/120)
Population_Variance
[1] 62135.63
 Live Demo
x7<-rexp(50,3.5)
x7
[1] 0.205216964 0.133222130 0.488146733 0.244428905 0.833206350 0.069545948
[7] 0.195504191 0.539364253 1.099099582 1.835459402 0.170821138 0.342813864
[13] 0.108211014 0.392889843 0.069053900 0.083381383 0.282172880 1.299693448
[19] 0.033847926 0.248126373 0.537849065 0.508127648 0.148564885 0.047607303
[25] 0.247224701 0.171349073 0.089745700 0.157843010 0.870047906 0.790377494
[31] 0.285218089 0.107768506 0.806453962 0.565196530 0.283891426 0.129423319
[37] 0.116770751 0.238833628 0.379741206 0.009492331 0.343673059 0.072587659
[43] 0.076498866 0.504828741 0.313257385 0.427818704 0.372741859 0.210799536
[49] 0.155322546 0.504289020
Sample_Variance<-var(x7)
Sample_Variance
[1] 0.03401862
Population_Variance<-var(x7)*(49/50)
Population_Variance
[1] 0.03333825