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 -2.59 -3.60 0.000269 0.5 -Inf
#> 2 1 2 -2.10 -3.46 0.000849 0.508 -2.05
#> 3 1 3 0.831 -3.32 0.00227 0.516 -1.74
#> 4 1 4 -0.632 -3.18 0.00517 0.524 -1.54
#> 5 1 5 -0.114 -3.05 0.0101 0.533 -1.39
#> 6 1 6 1.31 -2.91 0.0169 0.541 -1.27
#> 7 1 7 -0.315 -2.77 0.0249 0.549 -1.16
#> 8 1 8 -0.688 -2.63 0.0331 0.557 -1.07
#> 9 1 9 0.0416 -2.50 0.0407 0.565 -0.981
#> 10 1 10 -0.116 -2.36 0.0474 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")