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