Data Visualization: Histogram

vis_hist.R

# distributions
# normal distribution
hist(rbeta(10000, 5, 5), ann = FALSE, col = 'blue')

# skewed distributions
hist(rbeta(10000, 2, 5), ann = FALSE, col = 'blue')
hist(rbeta(10000, 5, 2), ann = FALSE, col = 'blue')

# histogram
# store the results of hist function
h <- hist(mtcars$mpg)

# display number of breaks
h$breaks

# frequency of the intervals
h$counts

# frequency density
h$density

# mid points of the intervals
h$mids

# varible name
h$xname

# whether intervals are of equal size
h$equidist

# bins
hist(mtcars$mpg, breaks = 10)

# intervals
hist(mtcars$mpg, breaks = c(10, 18, 24, 30, 35))
hist(mtcars$mpg, breaks = 'Sturges')
hist(mtcars$mpg, breaks = 'Scott')
hist(mtcars$mpg, breaks = 'FD')

# frequency density
hist(mtcars$mpg, probability = TRUE)

# color
hist(mtcars$mpg, col = 'blue')

# border color
hist(mtcars$mpg, border = 'red')

# labels
hist(mtcars$mpg, labels = TRUE)
hist(mtcars$mpg, labels = c("6", "12", "8", "2", "4"))

# putting it all together
hist(mtcars$mpg, labels = TRUE, prob = TRUE,
     ylim = c(0, 0.1), xlab = 'Miles Per Gallon',
     main = 'Distribution of Miles Per Gallon',
     col = rainbow(5))