# What is the use of set.seed in R?

The set.seed helps to create the replicate of the random generation. If the name of the object changes that does not mean the replication will be changed but if we change the position then it will. Here, in the below example x4 in the first random generation and the x_4 in the second random generation with the same set.seed are same but x4 and x4 in both are different.

## Example

Live Demo

set.seed(101)
x1<−rnorm(50)
x1

## Output

[1] −0.3260365 0.5524619 −0.6749438 0.2143595 0.3107692 1.1739663
[7] 0.6187899 −0.1127343 0.9170283 −0.2232594 0.5264481 −0.7948444
[13] 1.4277555 −1.4668197 −0.2366834 −0.1933380 −0.8497547 0.0584655
[19] −0.8176704 −2.0503078 −0.1637557 0.7085221 −0.2679805 −1.4639218
[25] 0.7444358 −1.4103902 0.4670676 −0.1193201 0.4672390 0.4981356
[31] 0.8949372 0.2791520 1.0078658 −2.0731065 1.1898534 −0.7243742
[37] 0.1679838 0.9203352 −1.6716048 0.4484691 0.4824588 0.7582138
[43] −2.3193274 −0.4595048 −1.1053837 0.4029283 0.5689349 −0.7060833
[49] −0.2900906 −1.4838781

## Example

Live Demo

x2<−rnorm(50)
x2

## Output

[1] −1.150255281 −0.274471162 0.577901003 −1.396902647 0.749057716
[6] −1.051186697 0.165380871 1.129809120 1.173722464 −0.427863232
[11] −0.259802108 −1.411173044 −0.641357554 0.112457509 0.422604331
[16] 0.386835291 −0.687798326 0.148902489 −0.057649748 −0.074823365
[21] 1.509897438 1.619937008 1.153158167 −0.077603595 −1.818934501
[26] −1.037444583 0.302492246 −1.277946167 0.138339048 −0.050984124
[31] 1.852147575 1.111675270 −0.511375322 −0.543881104 −1.728927284
[36] 0.470749539 0.005387122 1.348045786 0.724096713 1.552549165
[41] 1.325469832 −0.034265092 −0.361013398 −0.720165422 0.282014933
[46] −0.790525664 −0.444904551 1.364993169 0.497454338 −0.814396476

## Example

Live Demo

x3<−rnorm(50)
x3

## Output

[1] 0.26806584 −0.59220831 2.13348636 1.17274867 0.74676099 −0.23050869
[7] 0.08777170 −2.18373968 −0.46663159 1.68595984 −0.56792093 −0.04674302
[13] −0.15698059 1.60224244 0.76865367 −0.77162936 −0.63068198 −0.83028060
[19] −0.59111274 0.98108541 −0.66160527 −0.77241769 −2.01847347 −0.53358542
[25] 0.43472833 −0.77116734 −0.75394082 −0.29935782 1.66396643 −1.24432984
[31] −0.78313437 0.24483056 −0.14388717 −1.60863142 0.95157997 −1.81913169
[37] 1.78367171 1.88713936 1.49071878 −0.38059952 −0.90937501 −0.33809411
[43] −1.41188352 0.21754289 0.67012617 −0.28785938 0.46930350 −0.47007143
[49] −0.23926592 −0.44746249

## Example

Live Demo

x4<−rnorm(50)
x4

## Output

[1] −0.618829657 0.252963051 −0.753368175 0.732276853 −0.402586713
[6] −2.823000119 0.462973827 2.132869726 −0.270486687 0.248525349
[11] 0.038116475 0.394068950 −1.504085198 −1.586890794 −0.927118077
[16] 0.776197040 −0.780684440 −1.278567024 −0.001428128 −1.850978124
[21] 0.451505335 −0.432947055 0.713602899 0.960695470 0.381535210
[26] 1.218072798 −0.017137261 −0.038209493 1.243734395 −0.955858745
[31] 0.915425235 −0.939337976 0.112124820 0.553012619 0.531741963
[36] −0.873762389 −0.186849273 −0.213710488 −0.204011273 1.719709241
[41] 0.202033482 0.512655778 1.452400012 0.363865465 −0.875848946
[46] −0.014560733 −0.724493165 1.969370094 −0.536402427 −0.026232340

## Example

Live Demo

set.seed(101)
x1<−rnorm(50)
x1

## Output

[1] −0.3260365 0.5524619 −0.6749438 0.2143595 0.3107692 1.1739663
[7] 0.6187899 −0.1127343 0.9170283 −0.2232594 0.5264481 −0.7948444
[13] 1.4277555 −1.4668197 −0.2366834 −0.1933380 −0.8497547 0.0584655
[19] −0.8176704 −2.0503078 −0.1637557 0.7085221 −0.2679805 −1.4639218
[25] 0.7444358 −1.4103902 0.4670676 −0.1193201 0.4672390 0.4981356
[31] 0.8949372 0.2791520 1.0078658 −2.0731065 1.1898534 −0.7243742
[37] 0.1679838 0.9203352 −1.6716048 0.4484691 0.4824588 0.7582138
[43] −2.3193274 −0.4595048 −1.1053837 0.4029283 0.5689349 −0.7060833
[49] −0.2900906 −1.4838781

## Example

Live Demo

x2<−rnorm(50)
x2

## Output

[1] −1.150255281 −0.274471162 0.577901003 −1.396902647 0.749057716
[6] −1.051186697 0.165380871 1.129809120 1.173722464 −0.427863232
[11] −0.259802108 −1.411173044 −0.641357554 0.112457509 0.422604331
[16] 0.386835291 −0.687798326 0.148902489 −0.057649748 −0.074823365
[21] 1.509897438 1.619937008 1.153158167 −0.077603595 −1.818934501
[26] −1.037444583 0.302492246 −1.277946167 0.138339048 −0.050984124
[31] 1.852147575 1.111675270 −0.511375322 −0.543881104 −1.728927284
[36] 0.470749539 0.005387122 1.348045786 0.724096713 1.552549165
[41] 1.325469832 −0.034265092 −0.361013398 −0.720165422 0.282014933
[46] −0.790525664 −0.444904551 1.364993169 0.497454338 −0.814396476

## Example

Live Demo

x3<−rnorm(50)
x3

## Output

[1] 0.26806584 −0.59220831 2.13348636 1.17274867 0.74676099 −0.23050869
[7] 0.08777170 −2.18373968 −0.46663159 1.68595984 −0.56792093 −0.04674302
[13] −0.15698059 1.60224244 0.76865367 −0.77162936 −0.63068198 −0.83028060
[19] −0.59111274 0.98108541 −0.66160527 −0.77241769 −2.01847347 −0.53358542
[25] 0.43472833 −0.77116734 −0.75394082 −0.29935782 1.66396643 −1.24432984
[31] −0.78313437 0.24483056 −0.14388717 −1.60863142 0.95157997 −1.81913169
[37] 1.78367171 1.88713936 1.49071878 −0.38059952 −0.90937501 −0.33809411
[43] −1.41188352 0.21754289 0.67012617 −0.28785938 0.46930350 −0.47007143
[49] −0.23926592 −0.44746249

## Example

Live Demo

x_4<−rnorm(50)
x_4

## Output

[1] −0.618829657 0.252963051 −0.753368175 0.732276853 −0.402586713
[6] −2.823000119 0.462973827 2.132869726 −0.270486687 0.248525349
[11] 0.038116475 0.394068950 −1.504085198 −1.586890794 −0.927118077
[16] 0.776197040 −0.780684440 −1.278567024 −0.001428128 −1.850978124
[21] 0.451505335 −0.432947055 0.713602899 0.960695470 0.381535210
[26] 1.218072798 −0.017137261 −0.038209493 1.243734395 −0.955858745
[31] 0.915425235 −0.939337976 0.112124820 0.553012619 0.531741963
[36] −0.873762389 −0.186849273 −0.213710488 −0.204011273 1.719709241
[41] 0.202033482 0.512655778 1.452400012 0.363865465 −0.875848946
[46] −0.014560733 −0.724493165 1.969370094 −0.536402427 −0.026232340

## Example

Live Demo

x4<−rnorm(50)
x4

## Output

[1] −0.16403235 −1.38327506 0.42351126 −0.79048891 1.20992485 0.89451677
[7] −0.10119854 0.29712257 0.19729772 −0.15698374 1.53657101 −2.16766968
[13] 0.59844815 0.04311236 1.29502719 0.70630294 0.34554508 −0.07989665
[19] 0.45480755 1.27625237 1.26483765 0.26925353 −0.12054409 0.79527135
[25] −0.51402764 −0.40659347 1.21971898 0.08371137 0.58990215 −0.51741928
[31] 0.76946349 0.80196974 −0.69686014 1.17785318 0.58584526 −0.46689388
[37] 0.38564964 −0.53460558 1.05666840 −0.20609327 0.60701224 −0.54806386
[43] −2.09997633 0.25081276 −0.05494528 −0.65972781 −1.45585738 0.02372943
[49] 0.54790809 −0.80890140