# How to generate standard normal random numbers in R?

A standard normal distribution is the type of distribution that has mean equals to zero with standard deviation 1. If we want to generate standard normal random numbers then rnorm function of R can be used but need to pass the mean = 0 and standard deviation = 1 inside this function.

## Example

Live Demo

rnorm(10,0,1)

## Output

[1] 0.6936607 -0.7967657 -2.7544428 0.2688767 0.5278463 -1.5387568
[7] 1.1716632 -1.5033895 0.8112929 -1.0101065

## Example

Live Demo

rnorm(50,0,1)

## Output

[1] 2.58246666 -0.53083341 -0.57343343 1.08172756 1.30341849 -0.07440422
[7] -0.41869305 -0.96227706 -0.46899119 1.55428279 0.09162738 -0.96027221
[13] -0.84735327 -1.74949782 0.58541758 0.23117630 0.47402479 -0.72453853
[19] 0.07171564 1.13088794 0.18735157 0.25091758 -1.34728315 -0.39768159
[25] -0.38109955 -0.34019286 -1.51778561 -0.92222239 -1.22798041 -0.77350032
[31] -1.65852274 0.51227977 0.83822730 0.45359267 0.49714674 -1.47674552
[37] -0.01242228 1.60937112 0.38869615 1.73720338 0.56832087 -0.35619856
[43] -1.74371897 -0.77162373 -1.80142363 -0.92801065 0.92791947 0.14078622
[49] -1.55200961 -0.06995120

## Example

Live Demo

rnorm(60,0,1)

## Output

[1] -0.98030635 0.14934486 -1.55025640 0.80780101 -0.54240515 0.14488726
[7] 2.89290245 1.10729520 0.08050478 -0.44497057 1.10941494 1.74939247
[13] 0.84032675 0.47427879 0.11898992 1.85356655 0.19312780 -0.47810793
[19] 2.36569993 -0.45530246 -0.81494824 -1.99941347 -0.50359976 0.55592840
[25] 1.14048452 -1.02259883 -1.17629055 1.48930583 1.76136612 0.70749370
[31] 0.88976803 0.87302066 -0.90594396 -0.92584519 -0.57771767 -2.01680635
[37] 1.25990880 0.87272304 3.86728923 0.48660167 2.12238845 -1.23884756
[43] -0.29534035 -1.66654062 -0.92580904 0.46701435 -0.27171548 -0.79118171
[49] -1.87119180 -1.43572885 3.60672069 0.58631139 -0.38245860 0.62229426
[55] -0.54297322 -2.39866511 -1.91755583 -0.61459590 0.11865738 0.65653693

## Example

Live Demo

rnorm(80,0,1)

## Output

[1] -0.21167734 1.00334018 0.58986878 -1.15025242 0.83748340 0.04415646
[7] 0.21006101 -0.35285172 -0.53306794 -0.31683124 -0.15284674 1.72136890
[13] 0.67868984 -0.40103797 0.19409371 -0.31236848 1.08174032 0.82741254
[19] 1.52301592 0.92592501 -1.13193294 -0.52651889 -0.22310016 -0.93938644
[25] 0.27894221 -2.89894569 0.36546350 0.84345631 -0.81706708 0.18261437
[31] -0.69591250 1.09539577 -1.15864497 -0.22639388 -0.32866906 -1.12182835
[37] -0.08435003 1.81382691 0.04255180 -0.32941539 2.64070059 1.56935548
[43] -0.24635038 0.62292947 1.05232124 0.67012389 0.91400357 0.26348570
[49] -0.35494585 1.09602375 -1.39164787 -0.36638726 1.76550599 -0.22423221
[55] -0.33138915 -0.66652623 -0.50509947 -0.93338252 -2.70014038 -0.52016919
[61] 0.80396082 0.75912405 0.52966924 0.76088675 0.87390249 0.19404944
[67] -0.94092779 -1.20741440 -1.28536191 0.03052385 -2.23973254 -0.39531601
[73] -0.84322501 0.78849127 1.70032152 1.11591005 -1.15304534 -1.23219567
[79] 0.91807504 1.21157462

## Example

Live Demo

rnorm(100,0,1)

## Output

[1] -0.60163722 0.62726820 -0.78769462 0.72244706 -0.57654069 0.21386083
[7] -0.53096986 0.34563279 -0.97023650 -0.94702500 -0.37624883 0.44073439
[13] 0.51851495 -1.93362586 0.74274197 -0.81861024 -0.49963242 1.45553031
[19] -0.47880775 -0.23169624 0.46348261 -1.19764668 0.77737123 -0.50783209
[25] -1.58899290 0.50528381 1.89222336 -0.57809997 0.05806867 1.16785099
[31] -1.06614535 0.61556520 -0.83564718 -1.02615977 0.89271898 0.53811493
[37] -0.54849449 -0.62497474 0.25675859 0.70320768 0.05848728 0.78376690
[43] 0.44276061 -0.58697558 -0.59758547 1.22975543 1.46945195 -0.79496156
[49] -0.58237963 0.16137738 0.22260587 0.45833685 -0.17046269 0.44890726
[55] -0.15563031 0.73221957 -1.97896622 -1.47629166 -2.02214096 -0.96495535
[61] 0.63474420 1.34149420 -0.91755563 0.35488624 0.01262576 -0.34079663
[67] 0.07963539 0.88896173 1.75045613 -0.08678552 0.19245374 1.32575165
[73] 1.41738151 -1.35060833 0.63737697 0.33369705 1.27021960 1.00779108
[79] -1.19586882 0.72829141 -0.09938002 -0.79827963 -1.20575102 -1.09457152
[85] 0.66310803 -0.41086839 -0.50120916 0.02167787 0.60022806 2.94091060
[91] -0.39845012 0.82483674 -2.72699869 -0.48183377 0.57821380 -0.85565220
[97] 2.55905507 0.24447168 0.53042496 -0.31205488