Example
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 x 7
#> sim_number x y dx dy p q
#> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1 -0.730 -3.48 0.000205 0.5 -Inf
#> 2 1 2 0.210 -3.32 0.000595 0.508 -2.05
#> 3 1 3 -0.816 -3.15 0.00150 0.516 -1.74
#> 4 1 4 0.366 -2.99 0.00327 0.524 -1.54
#> 5 1 5 0.879 -2.82 0.00627 0.533 -1.39
#> 6 1 6 -0.542 -2.66 0.0106 0.541 -1.27
#> 7 1 7 0.643 -2.49 0.0160 0.549 -1.16
#> 8 1 8 -0.00589 -2.32 0.0223 0.557 -1.07
#> 9 1 9 -0.745 -2.16 0.0297 0.565 -0.981
#> 10 1 10 -0.601 -1.99 0.0397 0.573 -0.901
#> # ... with 40 more rowsAn example plot of the tidy_normal data.
tn <- tidy_normal(.n = 100, .num_sims = 6)
tidy_autoplot(tn, .plot_type = "density")
tidy_autoplot(tn, .plot_type = "quantile")
tidy_autoplot(tn, .plot_type = "probability")
tidy_autoplot(tn, .plot_type = "qq")
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.
tn <- tidy_normal(.n = 100, .num_sims = 20)
tidy_autoplot(tn, .plot_type = "density")
tidy_autoplot(tn, .plot_type = "quantile")
tidy_autoplot(tn, .plot_type = "probability")
tidy_autoplot(tn, .plot_type = "qq")