This function takes in a single argument of .x a vector and will
return a tibble of information similar to the tidy_ distribution functions.
The y column is set equal to dy from the density function.
Examples
x <- mtcars$mpg
tidy_empirical(.x = x, .distribution_type = "continuous")
#> # A tibble: 32 × 7
#> sim_number x y dx dy p q
#> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1 21 2.97 0.000113 0.625 10.4
#> 2 1 2 21 4.21 0.000452 0.625 10.4
#> 3 1 3 22.8 5.44 0.00142 0.781 13.3
#> 4 1 4 21.4 6.68 0.00354 0.688 14.3
#> 5 1 5 18.7 7.92 0.00720 0.469 14.7
#> 6 1 6 18.1 9.16 0.0124 0.438 15
#> 7 1 7 14.3 10.4 0.0192 0.125 15.2
#> 8 1 8 24.4 11.6 0.0281 0.812 15.2
#> 9 1 9 22.8 12.9 0.0395 0.781 15.5
#> 10 1 10 19.2 14.1 0.0515 0.531 15.8
#> # ℹ 22 more rows
tidy_empirical(.x = x, .num_sims = 10, .distribution_type = "continuous")
#> # A tibble: 320 × 7
#> sim_number x y dx dy p q
#> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1 33.9 3.75 0.0000855 1 13.3
#> 2 1 2 33.9 5.03 0.000298 1 14.7
#> 3 1 3 33.9 6.31 0.000898 1 15
#> 4 1 4 15.2 7.59 0.00235 0.25 15.2
#> 5 1 5 18.7 8.87 0.00536 0.469 15.2
#> 6 1 6 18.1 10.2 0.0107 0.438 15.2
#> 7 1 7 15 11.4 0.0188 0.188 15.5
#> 8 1 8 22.8 12.7 0.0294 0.781 17.8
#> 9 1 9 17.8 14.0 0.0410 0.406 17.8
#> 10 1 10 21.5 15.3 0.0518 0.719 18.1
#> # ℹ 310 more rows