5 Density

In this chapter, we introduce the density plot in ggplot2 style.

5.1 basic

We use diamonds dataset for illustration.

library(ggplot2)
library(dplyr)
library(patchwork)

data("diamonds")
diamonds

## # A tibble: 53,940 × 10
##    carat cut       color clarity depth table price     x     y     z
##    <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
##  1  0.23 Ideal     E     SI2      61.5    55   326  3.95  3.98  2.43
##  2  0.21 Premium   E     SI1      59.8    61   326  3.89  3.84  2.31
##  3  0.23 Good      E     VS1      56.9    65   327  4.05  4.07  2.31
##  4  0.29 Premium   I     VS2      62.4    58   334  4.2   4.23  2.63
##  5  0.31 Good      J     SI2      63.3    58   335  4.34  4.35  2.75
##  6  0.24 Very Good J     VVS2     62.8    57   336  3.94  3.96  2.48
##  7  0.24 Very Good I     VVS1     62.3    57   336  3.95  3.98  2.47
##  8  0.26 Very Good H     SI1      61.9    55   337  4.07  4.11  2.53
##  9  0.22 Fair      E     VS2      65.1    61   337  3.87  3.78  2.49
## 10  0.23 Very Good H     VS1      59.4    61   338  4     4.05  2.39
## # … with 53,930 more rows

Density plot can be used to show the distribution of data

 ggplot(diamonds, aes(x=price)) +
  geom_density(fill="#507991") +
  theme_classic()

5.2 With color

use fill as grouping variable

 ggplot(diamonds, aes(x=price)) +
  geom_density(aes(fill=cut), alpha=0.6) +
  theme_classic()

5.3 Adding histogram

 ggplot(diamonds, aes(x=price)) +
  geom_histogram(aes(y = ..density..), color=1, fill="white") + 
  geom_density(fill="#507991", alpha=0.5, color=NA, lwd=1) +
  theme_classic()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.