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 × 7
#> sim_number x y dx dy p q
#> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1 -0.0690 -2.05 0.000557 0.5 -0.405
#> 2 1 2 0.173 -1.95 0.00153 0.508 -0.206
#> 3 1 3 -0.160 -1.85 0.00375 0.516 -0.483
#> 4 1 4 -1.13 -1.75 0.00829 0.524 -2.29
#> 5 1 5 -0.169 -1.65 0.0165 0.533 -0.491
#> 6 1 6 0.661 -1.55 0.0300 0.541 0.179
#> 7 1 7 1.06 -1.45 0.0496 0.549 0.515
#> 8 1 8 -0.535 -1.34 0.0758 0.557 -0.851
#> 9 1 9 0.577 -1.24 0.108 0.565 0.112
#> 10 1 10 0.158 -1.14 0.144 0.573 -0.217
#> # … 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")