DataExplorer (version 0.6.0)

plot_density: Visualize density estimates for continuous features

Description

This function visualizes density estimates for each continuous feature.

Usage

plot_density(data, title = NULL, ggtheme = theme_gray(),
  theme_config = list(), ...)

Arguments

data

input data

title

plot title

ggtheme

complete ggplot2 themes. The default is theme_gray.

theme_config

a list of configurations to be passed to theme.

…

other arguments to be passed to geom_density.

Details

To change default font family and size, you may pass base_size and base_family to ggtheme options, e.g., ggtheme = theme_gray(base_size = 15, base_family = "serif")

theme_config argument expects all inputs to be wrapped in a list object, e.g., to change the text color: theme_config = list("text" = element_text(color = "blue"))

See Also

geom_density plot_histogram

Examples

# NOT RUN {
# Plot using iris data
plot_density(iris)

# Plot using random data
set.seed(1)
data <- cbind(sapply(seq.int(4L), function(x) {
          runif(500, min = sample(100, 1), max = sample(1000, 1))
        }))
plot_density(data)

# Add color to density area
plot_density(data, fill = "black", alpha = 0.8)

# Plot with preset ggplot2 themes
library(ggplot2)
plot_density(data, ggtheme = theme_light())
plot_density(data, ggtheme = theme_minimal(base_size = 15))

# Plot with customized theme components
plot_density(data,
theme_config = list(
  "plot.background" = element_rect(fill = "yellow"),
  "aspect.ratio" = 1
))
# }