Draw Normal, Left & Right Skewed Distributions (2 Examples) | ggplot2 Density Plot | tidyr Package

R code of this video:

x2 = rbeta(1000, 5, 2),
x3 = rnorm(1000),
x4 = rbeta(1000, 2, 5),
x5 = rbeta(1000, 2, 10))

plot(density(data$x1), col = 2, # Overlay all columns as densities
xlim = c(- 3, 3),
ylim = c(0, 5))
lines(density(data$x2), col = 3)
lines(density(data$x3), col = 4)
lines(density(data$x4), col = 5)
lines(density(data$x5), col = 6)
legend("topleft", # Add legend to plot
legend = c("x1 = rbeta(1000, 10, 2)",
"x2 = rbeta(1000, 5, 2)",
"x3 = rnorm(1000)",
"x4 = rbeta(1000, 2, 5)",
"x5 = rbeta(1000, 2, 10)"),
col = 2:6,
lty = 1,
cex = 0.8)

library("tidyr")

data_long ＜- data %＞% # Convert wide to long data
pivot_longer(colnames(data)) %＞%

library("ggplot2") # Load ggplot2 package

ggplot(data_long, # Draw all densities in ggplot2 plot
aes(value,
fill = name)) +
geom_density(alpha = 0.25)

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[haraldurkarlsson]

Beta distributions are used in Bayes work and it would have been nice to get more detail on those and how they work. Nice pivoting to ggplot2 (no pun intended).