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<!DOCTYPE html>
<html lang="" xml:lang="">
<head>
<title>Dealing with issues of overplotting</title>
<meta charset="utf-8" />
<meta name="author" content="Claus O. Wilke" />
<script src="libs/header-attrs/header-attrs.js"></script>
<link href="libs/remark-css/default.css" rel="stylesheet" />
<link rel="stylesheet" href="Wilke-slides-theme.css" type="text/css" />
</head>
<body>
<textarea id="source">
class: center, middle, title-slide
# Dealing with issues of overplotting
### Claus O. Wilke
### last updated: 2022-04-25
---
## Be aware of points plotted exactly on top of one another
.center[
<!-- -->
]
???
Figure redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)
--
Technical term for this problem: overplotting
---
## Partial transparency helps highlight overlapping points
.center[
<!-- -->
]
???
Figure redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)
---
## A little jitter shows overlaps even more clearly
.center[
<!-- -->
]
???
Figure redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)
---
## But don't jitter too much
.center[
<!-- -->
]
???
Figure redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)
---
class: center middle
## 2D density plots: Contour lines and contour bands
---
## Contour lines are the 2D version of density plots
.center[
<!-- -->
]
???
Figure redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)
---
## We can vary shading for added effect
.center[
<!-- -->
]
???
Figure redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)
---
## What do we do when there are multiple groups?
.center[
<!-- -->
]
???
Figure redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)
---
## Colored contour lines can work for 2 to 3 groups
.center[
<!-- -->
]
???
Figure redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)
---
## What if there are multiple groups intermingled?
.center[
<!-- -->
]
???
Figure redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)
---
## Don't make plots that look like spaghetti
.center[
<!-- -->
]
???
Figure redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)
---
## Contour lines work well with small multiples (facets)
.center[
<!-- -->
]
???
Figure redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)
---
class: center middle
## 2D histograms: Rectangular and hex bins
---
## 2D histograms: rectangular bins
.center[
<!-- -->
]
--
.absolute-bottom-left[
We need to choose a bin size as in regular histograms
]
---
## 2D histograms: rectangular bins
.center[
<!-- -->
]
.absolute-bottom-left[
We need to choose a bin size as in regular histograms
]
---
## 2D histograms: hex bins
.center[
<!-- -->
]
---
## 2D histograms: hex bins
.center[
<!-- -->
]
---
## Choosing the right color scale
.center[
]
.absolute-bottom-left[
palette: SunsetDark
]
---
## Choosing the right color scale
.center[
<!-- -->
]
.absolute-bottom-left[
palette: Batlow
]
---
## Choosing the right color scale
.center[
<!-- -->
]
.absolute-bottom-left[
palette: YlOrRd
]
---
## Choosing the right color scale
.center[
<!-- -->
]
.absolute-bottom-left[
palette: BluYl
]
---
## Choosing the right color scale
.center[
<!-- -->
]
.absolute-bottom-left[
palette: Heat
]
---
## Choosing the right color scale
.center[
<!-- -->
]
.absolute-bottom-left[
palette: ag_GrnYl
]
---
## Choosing the right color scale
.center[
]
.absolute-bottom-left[
palette: SunsetDark
]
[//]: # "segment ends here"
---
class: center middle
## Creating 2D density plots and histograms in R
---
## Contour lines
Data preparation:
.tiny-font[
```r
blue_jays <- read_csv("https://wilkelab.org/SDS375/datasets/blue_jays.csv")
```
```
── Column specification ────────────────────────────────────────────────────────
cols(
bird_id = col_character(),
sex = col_character(),
bill_depth_mm = col_double(),
bill_width_mm = col_double(),
bill_length_mm = col_double(),
head_length_mm = col_double(),
body_mass_g = col_double(),
skull_size_mm = col_double()
)
```
]
---
## Contour lines
.tiny-font.pull-left[
```r
blue_jays %>%
ggplot(aes(body_mass_g, head_length_mm)) +
geom_point() +
theme_bw(14)
```
]
.pull-right[
<!-- -->
]
---
## Contour lines
.tiny-font.pull-left[
```r
blue_jays %>%
ggplot(aes(body_mass_g, head_length_mm)) +
* geom_density_2d() +
geom_point() +
theme_bw(14)
```
]
.pull-right[
<!-- -->
]
---
## Contour lines
.tiny-font.pull-left[
```r
blue_jays %>%
ggplot(aes(body_mass_g, head_length_mm)) +
* geom_density_2d(bins = 5) +
geom_point() +
theme_bw(14)
```
]
.pull-right[
<!-- -->
]
---
## Contour bands
.tiny-font.width-70.move-up-1em[
```r
ggplot(blue_jays, aes(body_mass_g, head_length_mm)) +
* geom_density_2d_filled(bins = 5) +
#
geom_point() +
theme_bw(14)
```
]
.center[
<!-- -->
]
---
## Contour bands
.tiny-font.width-70.move-up-1em[
```r
ggplot(blue_jays, aes(body_mass_g, head_length_mm)) +
* geom_density_2d_filled(bins = 5, alpha = 0.5) +
#
geom_point() +
theme_bw(14)
```
]
.center[
<!-- -->
]
---
## Contour bands
.tiny-font.width-70.move-up-1em[
```r
ggplot(blue_jays, aes(body_mass_g, head_length_mm)) +
geom_density_2d_filled(bins = 5, alpha = 0.5) +
* geom_density_2d(bins = 5, color = "black", size = 0.2) +
geom_point() +
theme_bw(14)
```
]
.center[
<!-- -->
]
---
## 2D histograms
.tiny-font.width-70.move-up-1em[
```r
ggplot(blue_jays, aes(body_mass_g, head_length_mm)) +
* geom_bin2d() +
theme_bw(14)
```
]
.center[
<!-- -->
]
---
## 2D histograms
.tiny-font.width-70.move-up-1em[
```r
ggplot(blue_jays, aes(body_mass_g, head_length_mm)) +
* geom_bin2d(binwidth = c(3, 3)) +
theme_bw(14)
```
]
.center[
<!-- -->
]
---
## 2D histograms
.tiny-font.width-70.move-up-1em[
```r
ggplot(blue_jays, aes(body_mass_g, head_length_mm)) +
* geom_bin2d(binwidth = c(1, 5)) +
theme_bw(14)
```
]
.center[
<!-- -->
]
---
## 2D histograms
.tiny-font.width-70.move-up-1em[
```r
ggplot(blue_jays, aes(body_mass_g, head_length_mm)) +
* geom_bin2d(binwidth = c(5, 1)) +
theme_bw(14)
```
]
.center[
<!-- -->
]
---
## Hex bins
.tiny-font.width-70.move-up-1em[
```r
ggplot(blue_jays, aes(body_mass_g, head_length_mm)) +
* geom_hex() +
theme_bw(14)
```
]
.center[
<!-- -->
]
---
## Hex bins
.tiny-font.width-70.move-up-1em[
```r
ggplot(blue_jays, aes(body_mass_g, head_length_mm)) +
* geom_hex(bins = 15) +
theme_bw(14)
```
]
.center[
<!-- -->
]
---
## Hex bins
.tiny-font.width-70.move-up-1em[
```r
ggplot(blue_jays, aes(body_mass_g, head_length_mm)) +
* geom_hex(bins = 10) +
theme_bw(14)
```
]
.center[
<!-- -->
]
[//]: # "segment ends here"
---
## Further reading
- Fundamentals of Data Visualization: [Chapter 18: Handling overlapping points](https://clauswilke.com/dataviz/overlapping-points.html)
- **ggplot2** reference documentation: [`geom_density_2d()`](https://ggplot2.tidyverse.org/reference/geom_density_2d.html)
- **ggplot2** reference documentation: [`geom_bin2d()`](https://ggplot2.tidyverse.org/reference/geom_bin2d.html)
- **ggplot2** reference documentation: [`geom_hex()`](https://ggplot2.tidyverse.org/reference/geom_hex.html)
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