pdplot

This package contains a single function, pd_plot which visualizes probability density/mass functions provided in the base R. The available probability density/mass functions include the following: normal, logistic, binomial, chi-square, poisson, exponential, cauchy, beta, gamma, geometric, Student's t and F.

Installation

install.packages(devtools)
devtools::install_github("tegedik/pdplot")

Usage

pd_plot follows the convention of density distributions in R. First you need to create a vector of quantiles and pass it as the first argument in the function. Then, you need to select the correct parameter(s) for each distribution. Finally, you need to enter the type of the distribution. Here is an example with normal distribution,

pd_plot(seq(-4, 4, 0.01), mean=0, sd=1, type="normal")

This will create a normal probability distribution with mean 0 and sd 1. After calling this function, you can also overlay other density curves using lines argument. For example,

pd_plot(seq(-4, 4, 0.01), mean=0, sd=1, type="normal")
pd_plot(seq(-4, 4, 0.01), mean=0, sd=2, type="normal", lines=TRUE)

Here is an example with binomial distribution,

pd_plot(0:25, size=20, prob=0.4, type="binomial")
pd_plot(0:25, size=30, prob=0.5, type="binomial", lines=TRUE)

Again, you need to pick right arguments for the parameter values: in this case, size and prob. Although the code is not very efficient and scalable at this point, you can use it with apply family functions or purrr:map.

pd_plot(0:20, lambda=6, type="poisson")
rep_func <- function(rep){pd_plot(0:20, lambda=rep, type="poisson", lines=TRUE)}
rep <- 7:10
lapply(rep, rep_func)