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 2.69 -2.83 0.000791 0.996 2.69
#> 2 1 2 1.75 -2.70 0.00214 0.960 1.75
#> 3 1 3 -0.821 -2.56 0.00522 0.206 -0.821
#> 4 1 4 -1.54 -2.42 0.0115 0.0620 -1.54
#> 5 1 5 -0.00182 -2.28 0.0228 0.499 -0.00182
#> 6 1 6 -0.658 -2.14 0.0411 0.255 -0.658
#> 7 1 7 -0.581 -2.01 0.0675 0.281 -0.581
#> 8 1 8 -0.0223 -1.87 0.101 0.491 -0.0223
#> 9 1 9 -1.59 -1.73 0.140 0.0557 -1.59
#> 10 1 10 -1.25 -1.59 0.179 0.106 -1.25
#> # 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.