The goal of {TidyDensity}
is to make working with random numbers from
different distributions easy. All tidy_
distribution functions provide
the following components:
- [
r_
] - [
d_
] - [
q_
] - [
p_
]
You can install the released version of {TidyDensity}
from
CRAN with:
install.packages("TidyDensity")
And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("spsanderson/TidyDensity")
This is a basic example which shows you how to solve a common problem:
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 -1.01 -4.27 0.000206 0.5 -0.341
#> 2 1 2 -0.725 -4.11 0.000584 0.508 -0.200
#> 3 1 3 -0.809 -3.95 0.00144 0.516 -0.241
#> 4 1 4 1.24 -3.78 0.00310 0.524 0.812
#> 5 1 5 1.38 -3.62 0.00582 0.533 0.901
#> 6 1 6 0.855 -3.46 0.00953 0.541 0.578
#> 7 1 7 2.02 -3.30 0.0136 0.549 1.54
#> 8 1 8 1.66 -3.14 0.0170 0.557 1.12
#> 9 1 9 -0.704 -2.97 0.0187 0.565 -0.190
#> 10 1 10 -0.0670 -2.81 0.0185 0.573 0.112
#> # … 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")