density_plots.qmd

# Density plots {#sec-density-plots}


```{r}
#| label: setup
#| message: false
#| warning: false
#| include: false
library(tidyverse)
library(lubridate)
library(scales)
library(knitr)
library(kableExtra)
library(colorblindr)
library(downlit)
# fonts ----
library(extrafont)
library(sysfonts)
source("_common.R")
# use font
showtext::showtext_auto()
# set theme
ggplot2::theme_set(theme_ggp2g(
    base_size = 15))
```

```{r}
#| label: status
#| results: "asis"
#| echo: false
# status ----
# polishing, restructuring, drafting, complete
status("complete")
```


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<br>

```{r}
#| label: full_code_display
#| eval: true
#| echo: false
#| warning: false
#| message: false
#| out-height: '60%'
#| out-width: '60%'
#| fig-align: right
library(palmerpenguins) 
library(ggplot2)
penguins <- palmerpenguins::penguins
labs_density <- labs(
  title = "Adult foraging penguins",
  subtitle = "Distribution of flipper length",
  x = "Flipper length (millimeters)")
ggp2_density <- ggplot(data = penguins, 
        aes(x = flipper_length_mm)) + 
      geom_density() 

ggp2_density +
  labs_density
```

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**This graph requires:**
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**`r emo::ji("check")` a numeric (continuous) variable**
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## Description

A density plot displays data distribution using a smooth curve instead of bars. It helps compare multiple sets of data and the area under the curve represents the total probability. Instead of dividing the `x` axis into discrete ‘bins’ to create groupings for the variable's values, density plots transform the distribution according to a kernel density estimate. Legends are used to explain each curve, and different colors are used to differentiate them.



## Set up

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**PACKAGES:**
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Install packages.
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```{r}
#| label: pkg_code_density
#| code-fold: show
#| eval: false
#| echo: true
#| warning: false
#| message: false
#| results: hide
install.packages("palmerpenguins")
library(palmerpenguins) 
library(ggplot2)
```
:::



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**DATA:**
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![Artwork by Allison Horst](www/lter_penguins.png){fig-align="right" width="100%" height="100%"}
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The `penguins` data.
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```{r}
#| label: data_code_density
#| code-fold: show
#| eval: true
#| echo: true
penguins <- palmerpenguins::penguins
glimpse(penguins)
```
:::

## Grammar

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**CODE:**
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Create labels with `labs()`

Initialize the graph with `ggplot()` and provide `data`

Map `flipper_length_mm` to the `x` axis

Add the `geom_density()` layer

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::: {style="font-size: 0.85em;"}
```{r}
#| label: code_graph_density
#| code-fold: show
#| eval: false
#| echo: true 
#| warning: false
#| message: false
labs_density <- labs(
  title = "Adult foraging penguins",
  subtitle = "Distribution of flipper length",
  x = "Flipper length (millimeters)")
ggp2_density <- ggplot(data = penguins, 
        aes(x = flipper_length_mm)) + 
      geom_density() 

ggp2_density +
  labs_density
```
:::



::: {style="font-size: 1.10em; color: #1e83c8;"}
**GRAPH:**
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```{r}
#| label: create_graph_density
#| eval: true
#| echo: false
#| warning: false
#| message: false
#| layout-nrow: 1
#| column: page-inset-right
labs_density <- labs(
  title = "Adult foraging penguins",
  subtitle = "Distribution of flipper length",
  x = "Flipper length (millimeters)")
ggp2_density <- ggplot(data = penguins, 
        aes(x = flipper_length_mm)) + 
      geom_density() 

ggp2_density +
  labs_density
```

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A downside of using density plots is the lack of interpretability of the `y` axis. 
:::