Density
definition - mistake - related - code
A density plot is a representation of the distribution of a numeric variable. It uses a kernel density estimate to show the probability density function of the variable (see more).
It is a smoothed version of the histogram and is used in the same concept. Here is an example showing the distribution of the night price of Rbnb appartements in the south of France.
# Libraries
library(tidyverse)
library(hrbrthemes)
library(viridis)
# Load dataset from github
data <- read.table("https://raw.githubusercontent.com/holtzy/data_to_viz/master/Example_dataset/1_OneNum.csv", header=TRUE)
# Make the histogram
data %>%
filter( price<300 ) %>%
ggplot( aes(x=price)) +
geom_density(fill="#69b3a2", color="#e9ecef", alpha=0.8) +
ggtitle("Night price distribution of Airbnb appartements") +
theme_ipsum()
Density plots are used to study the distribution of one or a few variables. Checking the distribution of your variables one by one is probably the first task you should do when you get a new dataset. It delivers a good quantity of information. Several distribution shapes exist, here is an illustration of the 6 most common ones:
# Build dataset with different distributions
data <- data.frame(
type = c( rep("edge peak", 1000), rep("comb", 1000), rep("normal", 1000), rep("uniform", 1000), rep("bimodal", 1000), rep("skewed", 1000) ),
value = c( rnorm(900), rep(3, 100), rnorm(360, sd=0.5), rep(c(-1,-0.75,-0.5,-0.25,0,0.25,0.5,0.75), 80), rnorm(1000), runif(1000), rnorm(500, mean=-2), rnorm(500, mean=2), abs(log(rnorm(1000))) )
)
# Represent it
data %>%
ggplot( aes(x=value)) +
geom_density(fill="#69b3a2", color="#e9ecef", alpha=0.9, adjust = 0.5) +
facet_wrap(~type, scale="free") +
theme_ipsum() +
theme(
panel.spacing = unit(0.1, "lines"),
axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank()
)
Checking this distribution also helps you discovering mistakes in the
data. For example, the comb distribution can often denote a
rounding that has been applied to the variable or another mistake.
As a second step, histogram allow to compare the distribution of a few variables. Don’t compare more than 3 or 4, it would make the figure cluttered and unreadable. This comparison can be done showing the 2 variables on the same graphic and using transparency.
# Build dataset with different distributions
data <- data.frame(
type = c( rep("variable 1", 1000), rep("variable 2", 1000) ),
value = c( rnorm(1000), rnorm(1000, mean=4) )
)
# Represent it
data %>%
ggplot( aes(x=value, fill=type)) +
geom_density( color="#e9ecef", alpha=0.6) +
scale_fill_manual(values=c("#69b3a2", "#404080")) +
theme_ipsum() +
labs(fill="")
A common variation of the histogram is the mirror histogram: it puts face to face 2 histograms to compare their distribution.
data <- data.frame(
x = rnorm(1000),
y = rnorm(1000, mean=2)
)
data %>%
ggplot( aes(x) ) +
geom_density( aes(x = x, y = ..density..), binwidth = diff(range(data$x))/30, fill="#69b3a2" ) +
geom_label( aes(x=4.5, y=0.25, label="variable1"), color="#69b3a2") +
geom_density( aes(x = y, y = -..density..), binwidth = diff(range(data$x))/30, fill= "#404080") +
geom_label( aes(x=4.5, y=-0.25, label="variable2"), color="#404080") +
theme_ipsum() +
xlab("value of x")
Data To Viz is a comprehensive classification of chart types organized by data input format. Get a high-resolution version of our decision tree delivered to your inbox now!
A work by Yan Holtz for data-to-viz.com
