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 -1.11 -3.03 0.000342 0.133 -1.11
#> 2 1 2 0.368 -2.91 0.000946 0.644 0.368
#> 3 1 3 -0.356 -2.79 0.00232 0.361 -0.356
#> 4 1 4 -0.463 -2.67 0.00507 0.322 -0.463
#> 5 1 5 1.71 -2.55 0.00983 0.957 1.71
#> 6 1 6 -0.221 -2.43 0.0170 0.413 -0.221
#> 7 1 7 -0.0446 -2.31 0.0261 0.482 -0.0446
#> 8 1 8 0.135 -2.19 0.0358 0.554 0.135
#> 9 1 9 0.213 -2.07 0.0441 0.584 0.213
#> 10 1 10 -0.808 -1.95 0.0496 0.210 -0.808
#> # … with 40 more rows
An 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")