Getting Started with TidyDensity

library(TidyDensity)

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 -0.946  -3.09 0.000253 0.5   -0.765
#>  2 1              2 -1.36   -2.95 0.000762 0.508 -1.10 
#>  3 1              3  0.611  -2.81 0.00199  0.516  0.125
#>  4 1              4 -0.980  -2.67 0.00453  0.524 -0.789
#>  5 1              5 -0.890  -2.52 0.00903  0.533 -0.726
#>  6 1              6 -1.35   -2.38 0.0160   0.541 -1.10 
#>  7 1              7  1.05   -2.24 0.0258   0.549  0.364
#>  8 1              8 -0.497  -2.10 0.0386   0.557 -0.477
#>  9 1              9  0.892  -1.96 0.0548   0.565  0.276
#> 10 1             10  0.0587 -1.82 0.0753   0.573 -0.168
#> # … with 40 more rows
#> # ℹ Use `print(n = ...)` to see 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")