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_
]
Installation
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")
Example
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 1.54 -2.82 0.000447 0.938 1.54
#> 2 1 2 0.644 -2.69 0.00139 0.740 0.644
#> 3 1 3 -1.30 -2.55 0.00376 0.0968 -1.30
#> 4 1 4 -0.485 -2.42 0.00898 0.314 -0.485
#> 5 1 5 -1.30 -2.28 0.0189 0.0974 -1.30
#> 6 1 6 0.155 -2.15 0.0352 0.562 0.155
#> 7 1 7 -1.76 -2.01 0.0586 0.0390 -1.76
#> 8 1 8 0.562 -1.88 0.0879 0.713 0.562
#> 9 1 9 -1.69 -1.74 0.120 0.0455 -1.69
#> 10 1 10 -0.0214 -1.61 0.149 0.491 -0.0214
#> # ℹ 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")