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 0.0562 -3.28 0.000376 0.5 -0.0288
#> 2 1 2 1.16 -3.15 0.000981 0.508 0.687
#> 3 1 3 -0.404 -3.01 0.00233 0.516 -0.311
#> 4 1 4 -1.13 -2.87 0.00505 0.524 -0.826
#> 5 1 5 0.183 -2.73 0.00998 0.533 0.0476
#> 6 1 6 -1.15 -2.59 0.0181 0.541 -0.848
#> 7 1 7 -0.527 -2.45 0.0300 0.549 -0.389
#> 8 1 8 0.672 -2.32 0.0458 0.557 0.349
#> 9 1 9 -1.18 -2.18 0.0647 0.565 -0.872
#> 10 1 10 -1.61 -2.04 0.0851 0.573 -1.35
#> # ... 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")