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 0.227 -2.97 0.000238 0.590 0.227
#> 2 1 2 1.12 -2.84 0.000640 0.869 1.12
#> 3 1 3 1.26 -2.71 0.00153 0.897 1.26
#> 4 1 4 0.204 -2.58 0.00326 0.581 0.204
#> 5 1 5 1.04 -2.44 0.00620 0.852 1.04
#> 6 1 6 -0.180 -2.31 0.0106 0.429 -0.180
#> 7 1 7 0.299 -2.18 0.0167 0.618 0.299
#> 8 1 8 1.73 -2.04 0.0243 0.959 1.73
#> 9 1 9 -0.770 -1.91 0.0338 0.221 -0.770
#> 10 1 10 0.385 -1.78 0.0463 0.650 0.385
#> # ℹ 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")