How to Simulate Rnorm for Many Observations Using Different Mean and Sd Values in R

Use the
Map
Function to Simulaternorm
for Many Observations in R 
Use the
apply
Function to Simulaternorm
for Many Observations in R
This article will demonstrate multiple methods of simulating rnorm
for many observations using different mean and sd
values in R.
Use the Map
Function to Simulate rnorm
for Many Observations in R
The rnorm
function is used to generate random deviates for the normal distribution given the default mean equals 0
and standard deviation(sd
) is 1
. Note that the latter parameters can be passed optionally as the vector of elements. In this case, we stored predefined mean and sd
values as part of the data frame. Map
function applies the given function object to the corresponding elements of multiple vectors. It takes the function object as the first argument and vector objects as the following arguments. Notice that, number of vector objects should be equal to the mandatory parameters of the given function object. In the following example, we generate 5
deviates for each data
element. Also, we use the set.seed
function to specify the seed value for reproducible results between multiple program executions. The Map
function returns a list
object.
set.seed(123)
df1 < data.frame(
data = sample(1:64, 4),
mean = sample(1:64, 4),
sd = c(1, 4, 8, 20)
)
n < 5
func1 < function(x, y) rnorm(n, mean = x, sd = y)
list1 < Map(func1, df1$mean, df1$sd)
list1
Output:
[[1]]
[1] 3.129288 4.715065 3.460916 1.734939 2.313147
[[2]]
[1] 40.21735 46.89633 43.43926 43.60309 42.44273
[[3]]
[1] 45.55327 64.29531 53.98280 34.26706 55.61085
[[4]]
[1] 44.54417 32.64353 49.64050 33.47991 39.42218
Use the apply
Function to Simulate rnorm
for Many Observations in R
Alternatively, we can use the apply
function to simulate rnorm
for different rows in the data frame. apply
function is generally used to return values from applying the given function object to the specified margins of an array or a matrix. Margins are specified using the second parameter named MARGIN
. MARGIN
argument can have the value of 1
, which indicates the function to be applied to the rows of the matrix. On the other hand, value 2
denotes the columns of the matrix, and c(1,2)
indicates both  rows and columns of the matrix. The first argument of the apply
function can be an array or a matrix. Note, though, if the passed object is not an array, it gets coerced to the array type using as.matrix
or as.array
functions.
set.seed(123)
df1 < data.frame(
data = sample(1:64, 4),
mean = sample(1:64, 4),
sd = c(1, 4, 8, 20)
)
n < 5
func1 < function(x) rnorm(n, mean = x[1], sd = x[2])
apply(df1[1], 1, FUN = func1)
Output:
[,1] [,2] [,3] [,4]
[1,] 3.129288 40.21735 45.55327 44.54417
[2,] 4.715065 46.89633 64.29531 32.64353
[3,] 3.460916 43.43926 53.98280 49.64050
[4,] 1.734939 43.60309 34.26706 33.47991
[5,] 2.313147 42.44273 55.61085 39.42218
Founder of DelftStack.com. Jinku has worked in the robotics and automotive industries for over 8 years. He sharpened his coding skills when he needed to do the automatic testing, data collection from remote servers and report creation from the endurance test. He is from an electrical/electronics engineering background but has expanded his interest to embedded electronics, embedded programming and front/backend programming.
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