This is a basic example which shows you how easy it is to generate
data with {TidyDensity}
:
library(TidyDensity)
library(dplyr)
library(ggplot2)
tidy_normal()
#> # A tibble: 50 × 7
#> sim_number x y dx dy p q
#> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1 -1.05 -3.13 0.000238 0.148 -1.05
#> 2 1 2 -0.0954 -3.01 0.000596 0.462 -0.0954
#> 3 1 3 0.446 -2.88 0.00135 0.672 0.446
#> 4 1 4 -0.356 -2.76 0.00277 0.361 -0.356
#> 5 1 5 -0.665 -2.64 0.00518 0.253 -0.665
#> 6 1 6 1.34 -2.52 0.00888 0.911 1.34
#> 7 1 7 1.04 -2.40 0.0140 0.851 1.04
#> 8 1 8 -1.12 -2.27 0.0208 0.132 -1.12
#> 9 1 9 -0.249 -2.15 0.0292 0.402 -0.249
#> 10 1 10 0.945 -2.03 0.0397 0.828 0.945
#> # ℹ 40 more rows
An example plot of the tidy_normal
data.
We can also take a look at the plots when the number of simulations is greater than nine. This will automatically turn off the legend as it will become too noisy.