rbnorm() is a function to randomly generate values from a bounded normal (really: a scaled beta) distribution with specified mean, standard deviation, and upper/lower bounds. I use this function to randomly generate data that we treat as interval for sake of getting means and standard deviations, but have discernible bounds (and even skew) to teach students about things like random sampling and central limit theorem.

rbnorm(n, mean, sd, lowerbound, upperbound, round = FALSE, seed)

Arguments

n

the number of observations to simulate

mean

a mean to approximate

sd

a standard deviation to approximate

lowerbound

a lower bound for the data to be generated

upperbound

an upper bound for the data to be generated

round

whether to round the values to whole integers. Defaults to FALSE

seed

set an optional seed

Value

The function returns a vector of simulated data approximating the user-specified conditions.

Details

I call it "bounded normal" when it's really a beta distribution. I'm aware of this. I took much of this code from somewhere. I forget where.

Examples

library(tibble) tibble(x = rbnorm(10000, 57, 14, 0, 100))
#> # A tibble: 10,000 x 1 #> x #> <dbl> #> 1 51.3 #> 2 75.9 #> 3 57.1 #> 4 66.4 #> 5 34.2 #> 6 71.4 #> 7 29.8 #> 8 42.5 #> 9 66.2 #> 10 56.2 #> # … with 9,990 more rows
tibble(x = rbnorm(10000, 57, 14, 0, 100, round = TRUE))
#> # A tibble: 10,000 x 1 #> x #> <dbl> #> 1 58 #> 2 27 #> 3 56 #> 4 53 #> 5 49 #> 6 80 #> 7 47 #> 8 74 #> 9 61 #> 10 76 #> # … with 9,990 more rows
tibble(x = rbnorm(10000, 57, 14, 0, 100, seed = 8675309))
#> # A tibble: 10,000 x 1 #> x #> <dbl> #> 1 72.8 #> 2 44.6 #> 3 66.9 #> 4 38.4 #> 5 39.8 #> 6 56.5 #> 7 45.5 #> 8 47.3 #> 9 41.4 #> 10 39.2 #> # … with 9,990 more rows