Interactive visualizations for common distributions in RStudio

distrtoy.R

library(manipulate)

# Beta

x <- seq(0, 1, by=0.01)
manipulate(
{ ci <- qbeta(c(0.05, .5, 0.95), alpha, beta)
  plot(x, dbeta(x, alpha, beta),
       col="blue", lwd=2, type="l", las=1, bty="n",
       ylab="density", xlab="", ylim=c(0, 5.5),
       main="Beta distribution")
  box()
  mtext(paste(c("95% CI:", round(ci, 2)),
              collapse="  "), cex=0.8, side=3)
  if (printci) abline(v=ci, lty=c(3,2,3))
},
alpha=slider(0.001, 10, step=0.001, initial=1),
beta=slider(0.001, 10, step=0.001, initial=1),
printci=checkbox(TRUE, "Show 95% CI"))


# Gamma

x <- seq(0, 20, by=0.01)
manipulate(
{ ci <- qgamma(c(0.05, .5, 0.95), alpha, beta)
  plot(x, dgamma(x, alpha, beta),
       col="blue", lwd=2, type="l", las=1, bty="n",
       ylab="density", xlab="", ylim=c(0, 0.5),
       main="Gamma distribution")
  box()
  mtext(paste(c("95% CI:", round(ci, 2)),
              collapse="  "), cex=0.8, side=3)
  if (printci) abline(v=ci, lty=c(3,2,3))
},
alpha=slider(0.001, 10, step=0.001, initial=.001),
beta=slider(0.001, 2, step=0.001, initial=.001),
printci=checkbox(TRUE, "Show 95% CI"))


# Poisson

x <- seq(0, 20, by=1)
manipulate(
{ ci <- qpois(c(0.05, .5, 0.95), lambda)
  plot(x, dpois(x, lambda),
       col="blue", lwd=2, type="o", las=1, bty="n",
       ylab="density", xlab="", ylim=c(0, 0.27),
       main="Poisson distribution")
  box()
  mtext(paste(c("95% CI:", round(ci, 2)),
              collapse="  "), cex=0.8, side=3)
  if (printci) abline(v=ci, lty=c(3,2,3))
},
lambda=slider(1, 20, step=1, initial=10),
printci=checkbox(TRUE, "Show 95% CI"))


# Binomial

x <- seq(0, 20, by=1)
manipulate(
{ ci <- qbinom(c(0.05, .5, 0.95), n, p)
  plot(x, dbinom(x, n, p),
       col="blue", lwd=2, type="o", las=1, bty="n",
       ylab="density", xlab="", ylim=c(0, 0.95),
       main="Binomial distribution")
  box()
  mtext(paste(c("95% CI:", round(ci, 2)),
              collapse="  "), cex=0.8, side=3)
  if (printci) abline(v=ci, lty=c(3,2,3))
},
n=slider(1, 20, step=1, initial=10),
p=slider(0.001, 1, step=0.001, initial=0.5),
printci=checkbox(TRUE, "Show 95% CI"))


# Exponential

x <- seq(0, 5, by=0.01)
manipulate(
{ ci <- qexp(c(0.05, .5, 0.95), rate)
  plot(x, dexp(x, rate),
       col="blue", lwd=2, type="l", las=1, bty="n",
       ylab="density", xlab="", ylim=c(0, 2),
       main="Exponential distribution")
  box()
  mtext(paste(c("95% CI:", round(ci, 2)),
              collapse="  "), cex=0.8, side=3)
  if (printci) abline(v=ci, lty=c(3,2,3))
},
rate=slider(0.001, 5, step=0.001, initial=2.5),
printci=checkbox(TRUE, "Show 95% CI"))


# Normal

x <- seq(-6, 6, by=0.01)
manipulate(
{ sigma <- 1/sqrt(lambda)
  ci <- qnorm(c(0.05, .5, 0.95), mu, sigma)
  plot(x, dnorm(x, mu, sigma),
       col="blue", lwd=2, type="l", las=1, bty="n",
       ylab="density", xlab="", ylim=c(0, .65),
       main="Normal distribution")
  box()
  mtext(paste(c("95% CI:", round(ci, 2)),
              collapse="  "), cex=0.8, side=3)
  if (printci) abline(v=ci, lty=c(3,2,3))
  legend("top", sprintf("lambda=%s, sigma=%s", round(lambda, 2), round(sigma, 2)), cex=0.8, box.lwd=0)
},
mu=slider(-2, 2, step=0.01, initial=0),
lambda=slider(0.001, 2, step=0.001, initial=1),
printci=checkbox(TRUE, "Show 95% CI"))


# Log-Normal

x <- seq(0, 3, by=0.01)
manipulate(
{ sigma <- 1/sqrt(lambda)
  ci <- qlnorm(c(0.05, .5, 0.95), mu, sigma)
  plot(x, dlnorm(x, mu, sigma),
       col="blue", lwd=2, type="l", las=1, bty="n",
       ylab="density", xlab="", ylim=c(0, 2),
       main="Log-Normal distribution")
  box()
  mtext(paste(c("95% CI:", round(ci, 2)),
              collapse="  "), cex=0.8, side=3)
   if (printci) abline(v=ci, lty=c(3,2,3))
   legend("top", sprintf("lambda=%s, sigma=%s", round(lambda, 2), round(sigma, 2)), cex=0.8, box.lwd=0)
},
mu=slider(0, 2, step=0.001, initial=0),
lambda=slider(0.001, 20, step=0.001, initial=0.001),
printci=checkbox(TRUE, "Show 95% CI"))


# Student t

x <- seq(-6, 6, by=0.01)
manipulate(
{ ci <- qt(c(0.05, .5, 0.95), df, delta)
  plot(x, dt(x, df, delta),
       col="blue", lwd=2, type="l", las=1, bty="n",
       ylab="density", xlab="", ylim=c(0, 0.4),
       main="Student t distribution")
  box()
  mtext(paste(c("95% CI:", round(ci, 2)),
              collapse="  "), cex=0.8, side=3)
  if (printci) abline(v=ci, lty=c(3,2,3))
},
df=slider(0.001, 10, step=0.001, initial=5),
delta=slider(-4, 4, step=0.001, initial=0),
printci=checkbox(TRUE, "Show 95% CI"))


# Cauchy

x <- seq(-4, 4, by=0.01)
manipulate(
{ ci <- qcauchy(c(0.05, .5, 0.95), location, scale)
  plot(x, dcauchy(x, location, scale),
       col="blue", lwd=2, type="l", las=1, bty="n",
       ylab="density", xlab="", ylim=c(0, 2),
       main="Cauchy distribution")
  box()
  mtext(paste(c("95% CI:", round(ci, 2)),
              collapse="  "), cex=0.8, side=3)
  if (printci) abline(v=ci, lty=c(3,2,3))
},
location=slider(-2, 2, step=0.001, initial=0),
scale=slider(0.001, 4, step=0.001, initial=0.2),
printci=checkbox(TRUE, "Show 95% CI"))


# Logistic

x <- seq(-6, 20, by=0.01)
manipulate(
{ ci <- qlogis(c(0.05, .5, 0.95), mu, s)
  plot(x, dlogis(x, mu, s),
       col="blue", lwd=2, type="l", las=1, bty="n",
       ylab="density", xlab="", ylim=c(0, 0.5),
       main="Logistic distribution")
  box()
  mtext(paste(c("95% CI:", round(ci, 2)),
              collapse="  "), cex=0.8, side=3)
  if (printci) abline(v=ci, lty=c(3,2,3))
},
mu=slider(-6, 6, step=0.001, initial=0),
s=slider(0.5, 10, step=0.001, initial=1),
printci=checkbox(TRUE, "Show 95% CI"))


# Geometric

x <- seq(0, 10, by=1)
manipulate(
{ ci <- qgeom(c(0.05, .5, 0.95), p)
  plot(x, dgeom(x, p),
       col="blue", lwd=2, type="o", las=1, bty="n",
       ylab="density", xlab="", ylim=c(0, 0.5),
       main="Geometric distribution")
  box()
  mtext(paste(c("95% CI:", round(ci, 2)),
              collapse="  "), cex=0.8, side=3)
  if (printci) abline(v=ci, lty=c(3,2,3))
},
p=slider(0.001, 1, step=0.001, initial=0.5),
printci=checkbox(TRUE, "Show 95% CI"))


# Hypergeometric

x <- seq(0, 10, by=1)
manipulate(
{ m <- min(N, m)
  n <- N-m
  k <- min(N, k)
  ci <- qhyper(c(0.05, .5, 0.95), n, m, k)
  plot(x, dhyper(x, n, m, k),
       col="blue", lwd=2, type="o", las=1, bty="n",
       ylab="density", xlab="", ylim=c(0, 1),
       main="Hypergeometric distribution")
  box()
  mtext(paste(c("95% CI:", round(ci, 2)),
              collapse="  "), cex=0.8, side=3)
  if (printci) abline(v=ci, lty=c(3,2,3))
},
m=slider(0, 10, step=1, initial=2),
N=slider(0, 10, step=1, initial=7),
k=slider(0, 10, step=1, initial=5),
printci=checkbox(TRUE, "Show 95% CI"))


# Weibull

x <- seq(0, 2.5, by=0.01)
manipulate(
{ ci <- qweibull(c(0.05, .5, 0.95), lambda, k)
  plot(x, dweibull(x, lambda, k),
       col="blue", lwd=2, type="l", las=1, bty="n",
       ylab="density", xlab="", ylim=c(0, 2.2),
       main="Weibull distribution")
  box()
  mtext(paste(c("95% CI:", round(ci, 2)),
              collapse="  "), cex=0.8, side=3)
  if (printci) abline(v=ci, lty=c(3,2,3))
},
lambda=slider(0, 5, step=0.001, initial=2.5),
k=slider(0, 2.5, step=0.001, initial=1),
printci=checkbox(TRUE, "Show 95% CI"))