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 1.39 -3.37 0.000231 0 -Inf
#> 2 1 2 0.573 -3.23 0.000610 0 -2.05
#> 3 1 3 2.04 -3.10 0.00144 0 -1.74
#> 4 1 4 0.540 -2.96 0.00304 0 -1.54
#> 5 1 5 0.934 -2.83 0.00574 0 -1.39
#> 6 1 6 1.22 -2.70 0.00977 0 -1.27
#> 7 1 7 0.950 -2.56 0.0152 0 -1.16
#> 8 1 8 -0.675 -2.43 0.0217 4.18e-284 -1.07
#> 9 1 9 0.560 -2.29 0.0293 1.11e-253 -0.981
#> 10 1 10 -0.996 -2.16 0.0382 5.45e-225 -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")