From Data to Visualization 1

class: center, middle, title-slide

.title[
# From Data to Visualization 1
]
.author[
### Claus O. Wilke
]
.date[
### last updated: 2022-06-26
]

---

## Topics covered

1. Visualizing amounts

2. Visualizing distributions

3. Visualizing associations and trends

---
class: middle center

## Visualizing amounts

---

## We often encounter datasets containing simple amounts

---

## We often encounter datasets containing simple amounts

Example: Highest grossing movies Dec. 2017

<br>

.center[
<table>
 <thead>
  <tr>
   <th style="text-align:right;"> rank </th>
   <th style="text-align:left;"> title </th>
   <th style="text-align:right;"> amount </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:right;"> 1 </td>
   <td style="text-align:left;"> Star Wars </td>
   <td style="text-align:right;"> 71.57 </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 2 </td>
   <td style="text-align:left;"> Jumanji </td>
   <td style="text-align:right;"> 36.17 </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 3 </td>
   <td style="text-align:left;"> Pitch Perfect 3 </td>
   <td style="text-align:right;"> 19.93 </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 4 </td>
   <td style="text-align:left;"> Greatest Showman </td>
   <td style="text-align:right;"> 8.81 </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 5 </td>
   <td style="text-align:left;"> Ferdinand </td>
   <td style="text-align:right;"> 7.32 </td>
  </tr>
</tbody>
</table>
]

.tiny-font.absolute-bottom-right[
Data source: Box Office Mojo
]

---

## We can visualize amounts with bar plots

<br>

.center.move-up-1em[
![](data-to-visualization-1_files/figure-html/boxoffice-vertical-1.svg)
]

---

## Bars can also run horizontally

<br>

.center.move-up-1em[
![](data-to-visualization-1_files/figure-html/boxoffice-horizontal-1.svg)
]

---

## Pay attention to the order of the bars

<br>

.center.move-up-1em[
![](data-to-visualization-1_files/figure-html/boxoffice-horizontal-unordered-1.svg)
]

---

## Pay attention to the order of the bars

<br>

.center.move-up-1em[
![](data-to-visualization-1_files/figure-html/boxoffice-horizontal-1.svg)
]

---

## We can use dots instead of bars

<br>

.center.move-up-1em[
![](data-to-visualization-1_files/figure-html/boxoffice-dotplot-1.svg)
]

---

## Dots are preferable if we want to truncate the axes

.center.move-up-1em[
![](data-to-visualization-1_files/figure-html/Americas-life-expect-1.svg)
]

---

## Dots are preferable if we want to truncate the axes

.center.move-up-1em[
![](data-to-visualization-1_files/figure-html/Americas-life-expect-bad1-1.svg)
]

.absolute-bottom-right[
bar lengths do<br>not accurately<br>represent the<br>data values
]

---

## Dots are preferable if we want to truncate the axes

.center.move-up-1em[
![](data-to-visualization-1_files/figure-html/Americas-life-expect-bad2-1.svg)
]

.absolute-bottom-right[
key features<br>of the data<br>are obscured
]

---

## Dots are preferable if we want to truncate the axes

.center.move-up-1em[
![](data-to-visualization-1_files/figure-html/Americas-life-expect-1.svg)
]

---

## We use grouped bars for higher-dimensional datasets

<br>

.center.move-up-1em[
![](data-to-visualization-1_files/figure-html/income-by-age-race-dodged-1.svg)
]

.absolute-bottom-right[
Data source: United States Census Bureau, 2016
]

---

## We are free to choose by which variable to group

<br>

.center[
![](data-to-visualization-1_files/figure-html/income-by-race-age-dodged-1.svg)
]

.absolute-bottom-right[
Data source: United States Census Bureau, 2016
]

---

## We can also use multiple plot panels (facets)

.center[
![](data-to-visualization-1_files/figure-html/income-by-age-race-faceted-1.svg)
]

.absolute-bottom-right[
Data source: United States Census Bureau, 2016
]

---
class: middle center

## Visualizing distributions

---

## Passengers on the Titanic

.center.small-font[
<table>
 <thead>
  <tr>
   <th style="text-align:right;"> age </th>
   <th style="text-align:left;"> sex </th>
   <th style="text-align:left;"> class </th>
   <th style="text-align:left;"> survived </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:right;"> 0.17 </td>
   <td style="text-align:left;"> female </td>
   <td style="text-align:left;"> 3rd </td>
   <td style="text-align:left;"> survived </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 0.33 </td>
   <td style="text-align:left;"> male </td>
   <td style="text-align:left;"> 3rd </td>
   <td style="text-align:left;"> died </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 0.80 </td>
   <td style="text-align:left;"> male </td>
   <td style="text-align:left;"> 2nd </td>
   <td style="text-align:left;"> survived </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 0.83 </td>
   <td style="text-align:left;"> male </td>
   <td style="text-align:left;"> 2nd </td>
   <td style="text-align:left;"> survived </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 0.83 </td>
   <td style="text-align:left;"> male </td>
   <td style="text-align:left;"> 3rd </td>
   <td style="text-align:left;"> survived </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 0.92 </td>
   <td style="text-align:left;"> male </td>
   <td style="text-align:left;"> 1st </td>
   <td style="text-align:left;"> survived </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 1.00 </td>
   <td style="text-align:left;"> female </td>
   <td style="text-align:left;"> 2nd </td>
   <td style="text-align:left;"> survived </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 1.00 </td>
   <td style="text-align:left;"> female </td>
   <td style="text-align:left;"> 3rd </td>
   <td style="text-align:left;"> survived </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 1.00 </td>
   <td style="text-align:left;"> male </td>
   <td style="text-align:left;"> 2nd </td>
   <td style="text-align:left;"> survived </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 1.00 </td>
   <td style="text-align:left;"> male </td>
   <td style="text-align:left;"> 2nd </td>
   <td style="text-align:left;"> survived </td>
  </tr>
</tbody>
</table>
&nbsp;&nbsp;&nbsp;&nbsp;
<table>
 <thead>
  <tr>
   <th style="text-align:right;"> age </th>
   <th style="text-align:left;"> sex </th>
   <th style="text-align:left;"> class </th>
   <th style="text-align:left;"> survived </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:right;"> 1.0 </td>
   <td style="text-align:left;"> male </td>
   <td style="text-align:left;"> 3rd </td>
   <td style="text-align:left;"> survived </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 1.5 </td>
   <td style="text-align:left;"> female </td>
   <td style="text-align:left;"> 3rd </td>
   <td style="text-align:left;"> died </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 1.5 </td>
   <td style="text-align:left;"> female </td>
   <td style="text-align:left;"> 3rd </td>
   <td style="text-align:left;"> died </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 2.0 </td>
   <td style="text-align:left;"> female </td>
   <td style="text-align:left;"> 1st </td>
   <td style="text-align:left;"> died </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 2.0 </td>
   <td style="text-align:left;"> female </td>
   <td style="text-align:left;"> 2nd </td>
   <td style="text-align:left;"> survived </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 2.0 </td>
   <td style="text-align:left;"> female </td>
   <td style="text-align:left;"> 3rd </td>
   <td style="text-align:left;"> died </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 2.0 </td>
   <td style="text-align:left;"> female </td>
   <td style="text-align:left;"> 3rd </td>
   <td style="text-align:left;"> died </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 2.0 </td>
   <td style="text-align:left;"> male </td>
   <td style="text-align:left;"> 2nd </td>
   <td style="text-align:left;"> survived </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 2.0 </td>
   <td style="text-align:left;"> male </td>
   <td style="text-align:left;"> 2nd </td>
   <td style="text-align:left;"> survived </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 2.0 </td>
   <td style="text-align:left;"> male </td>
   <td style="text-align:left;"> 2nd </td>
   <td style="text-align:left;"> survived </td>
  </tr>
</tbody>
</table>
&nbsp;&nbsp;&nbsp;&nbsp;
<table>
 <thead>
  <tr>
   <th style="text-align:right;"> age </th>
   <th style="text-align:left;"> sex </th>
   <th style="text-align:left;"> class </th>
   <th style="text-align:left;"> survived </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:right;"> 3 </td>
   <td style="text-align:left;"> female </td>
   <td style="text-align:left;"> 2nd </td>
   <td style="text-align:left;"> survived </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 3 </td>
   <td style="text-align:left;"> female </td>
   <td style="text-align:left;"> 3rd </td>
   <td style="text-align:left;"> survived </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 3 </td>
   <td style="text-align:left;"> male </td>
   <td style="text-align:left;"> 2nd </td>
   <td style="text-align:left;"> survived </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 3 </td>
   <td style="text-align:left;"> male </td>
   <td style="text-align:left;"> 2nd </td>
   <td style="text-align:left;"> survived </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 3 </td>
   <td style="text-align:left;"> male </td>
   <td style="text-align:left;"> 3rd </td>
   <td style="text-align:left;"> survived </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 3 </td>
   <td style="text-align:left;"> male </td>
   <td style="text-align:left;"> 3rd </td>
   <td style="text-align:left;"> survived </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 4 </td>
   <td style="text-align:left;"> female </td>
   <td style="text-align:left;"> 2nd </td>
   <td style="text-align:left;"> survived </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 4 </td>
   <td style="text-align:left;"> female </td>
   <td style="text-align:left;"> 2nd </td>
   <td style="text-align:left;"> survived </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 4 </td>
   <td style="text-align:left;"> female </td>
   <td style="text-align:left;"> 3rd </td>
   <td style="text-align:left;"> survived </td>
  </tr>
  <tr>
   <td style="text-align:right;"> 4 </td>
   <td style="text-align:left;"> female </td>
   <td style="text-align:left;"> 3rd </td>
   <td style="text-align:left;"> survived </td>
  </tr>
</tbody>
</table>
]

---

## Histogram: Define bins and count cases

.pull-left.small-font[
<table>
 <thead>
  <tr>
   <th style="text-align:left;"> age range </th>
   <th style="text-align:right;"> count </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> 0–5 </td>
   <td style="text-align:right;"> 36 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> 6–10 </td>
   <td style="text-align:right;"> 19 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> 11–15 </td>
   <td style="text-align:right;"> 18 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> 16–20 </td>
   <td style="text-align:right;"> 99 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> 21–25 </td>
   <td style="text-align:right;"> 139 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> 26–30 </td>
   <td style="text-align:right;"> 121 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> 31–35 </td>
   <td style="text-align:right;"> 76 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> 36–40 </td>
   <td style="text-align:right;"> 74 </td>
  </tr>
</tbody>
</table>
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
<table>
 <thead>
  <tr>
   <th style="text-align:left;"> age range </th>
   <th style="text-align:right;"> count </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> 41–45 </td>
   <td style="text-align:right;"> 54 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> 46–50 </td>
   <td style="text-align:right;"> 50 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> 51–55 </td>
   <td style="text-align:right;"> 26 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> 56–60 </td>
   <td style="text-align:right;"> 22 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> 61–65 </td>
   <td style="text-align:right;"> 16 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> 66–70 </td>
   <td style="text-align:right;"> 3 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> 71–75 </td>
   <td style="text-align:right;"> 3 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> 76–80 </td>
   <td style="text-align:right;"> 0 </td>
  </tr>
</tbody>
</table>
]

.pull-right[
![](data-to-visualization-1_files/figure-html/titanic-age-hist-1.svg)
]

???

Figure redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)

---

# Histograms depend on the chosen bin width

.center[
![](data-to-visualization-1_files/figure-html/titanic-age-hist-binwidth-1.svg)
]

???

Figure redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)

---

## Alternative to histogram: Kernel density estimate (KDE)

.pull-left[
![](data-to-visualization-1_files/figure-html/titanic-age-hist2-1.svg)
]

.pull-right[
![](data-to-visualization-1_files/figure-html/titanic-age-kde-1.svg)
]

Histograms show raw counts, KDEs show proportions. (Total area = 1)

???

Figures redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)

---

## KDEs also depend on parameter settings

.center[
![](data-to-visualization-1_files/figure-html/titanic-age-kde-grid-1.svg)
]

???

Figure redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)

---

## Careful: Are bars stacked or overlapping?

.pull-left[
![](data-to-visualization-1_files/figure-html/titanic-age-hist-stacked-1.svg)
]

.pull-right[
![](data-to-visualization-1_files/figure-html/titanic-age-hist-overlap-1.svg)
]

Stacked or overlapping histograms are rarely a good choice.

???

Figures redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)

---

## Alternatively: Age pyramid

.center[
![](data-to-visualization-1_files/figure-html/titanic-age-pyramid-1.svg)
]

???

Figures redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)

---

## What if we want to show more than two distributions?

.pull-left.small-font[
Mean temperatures in Lincoln, NE, in January 2016:

.center[

|date       | mean temp|
|:----------|---------:|
|2016-01-01 |        24|
|2016-01-02 |        23|
|2016-01-03 |        23|
|2016-01-04 |        17|
|2016-01-05 |        29|
|2016-01-06 |        33|
|2016-01-07 |        30|
|2016-01-08 |        25|
|2016-01-09 |         9|
|2016-01-10 |        11|
|2016-01-11 |        28|
|2016-01-12 |        24|
|2016-01-13 |        33|
|2016-01-14 |        40|
|2016-01-15 |        29|
|2016-01-16 |        19|
|2016-01-17 |         5|
|2016-01-18 |        11|
|2016-01-19 |        22|
|2016-01-20 |        28|
|2016-01-21 |        25|
|2016-01-22 |        22|
|2016-01-23 |        28|
|2016-01-24 |        30|
|2016-01-25 |        26|
|2016-01-26 |        29|
|2016-01-27 |        33|
|2016-01-28 |        41|
|2016-01-29 |        41|
|2016-01-30 |        39|
|2016-01-31 |        35|
]]

.pull-right[
![](data-to-visualization-1_files/figure-html/temps_densities_january-1.svg)
]

---

## What if we want to show more than two distributions?

.pull-left.small-font[
Mean temperatures in Lincoln, NE, in January 2016:

.center[

.pull-right[
![](data-to-visualization-1_files/figure-html/temps_densities_january2-1.svg)

How can we compare distributions across months?
]

---

## A bad idea: Many overlapping density plots

.center[
![](data-to-visualization-1_files/figure-html/temps_densities_overlapping-1.svg)
]

---

## Another bad idea: Stacked density plots

.center[
![](data-to-visualization-1_files/figure-html/temps_densities_stacked-1.svg)
]

---

## Somewhat better: Small multiples

.center[
![](data-to-visualization-1_files/figure-html/temps_densities-1.svg)
]

---

## Instead: Show values along y, conditions along x
   
.center[
![](data-to-visualization-1_files/figure-html/temps_boxplots-1.svg)
]

???

Figure redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)

A boxplot is a crude way of visualizing a distribution.

---

## How to read a boxplot

.center[
![](data-to-visualization-1_files/figure-html/boxplot-schematic-1.svg)
]

???

Figure redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)

---

## If you like density plots, consider violins
   
.center[
![](data-to-visualization-1_files/figure-html/temps_violins-1.svg)
]

???

Figure redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)

A violin plot is a density plot rotated 90 degrees and then mirrored.

---

## How to read a violin plot

.center[
![](data-to-visualization-1_files/figure-html/violin-schematic-1.svg)
]

???

Figure redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)

---

## For small datasets, you can also use a strip chart

Advantage: Can see raw data points instead of abstract representation.

.center[
![](data-to-visualization-1_files/figure-html/temps_stripchart-1.svg)
]

???

Figure redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)

Horizontal jittering may be necessary to avoid overlapping points.

---

## For small datasets, you can also use a strip chart

Advantage: Can see raw data points instead of abstract representation.

.center[
![](data-to-visualization-1_files/figure-html/temps_stripchart2-1.svg)
]

Horizontal jittering may be necessary to avoid overlapping points.

???

Figure redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)

---

## For small datasets, you can also use a strip chart

Advantage: Can see raw data points instead of abstract representation.

.center[
![](data-to-visualization-1_files/figure-html/temps_stripchart3-1.svg)
]

Horizontal jittering may be necessary to avoid overlapping points.

???

Figure redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)

---

## And the final option, a ridgeline plot

.center[
![](data-to-visualization-1_files/figure-html/lincoln-ridgeline-polished-1.svg)
]

???

Figure redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)

Notice the single fill color. More colors would be distracting.

---
class: middle center

## Visualizing associations and trends

---

## We visualize associations with scatter plots

.center[
![](data-to-visualization-1_files/figure-html/blue-jays-scatter-1.svg)
]

???

Figure redrawn after [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)

---

## We visualize associations with scatter plots

.center[
![](data-to-visualization-1_files/figure-html/blue-jays-scatter-line-sex-1.svg)
]

???

Figure redrawn after [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)

---

## Regression lines emphasize the overall trend

.center[
![](data-to-visualization-1_files/figure-html/blue-jays-scatter-female-1.svg)
]

???

Figure redrawn after [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)

---

## Regression lines emphasize the overall trend

.center[
![](data-to-visualization-1_files/figure-html/blue-jays-scatter-line-female-1.svg)
]

???

Figure redrawn after [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)

---

## Regression lines emphasize the overall trend

.center[
![](data-to-visualization-1_files/figure-html/blue-jays-scatter-male-1.svg)
]

???

Figure redrawn after [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)

---

## Regression lines emphasize the overall trend

.center[
![](data-to-visualization-1_files/figure-html/blue-jays-scatter-line-male-1.svg)
]

???

Figure redrawn after [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)

---

## Regression lines emphasize the overall trend

.center[
![](data-to-visualization-1_files/figure-html/blue-jays-scatter-sex-line-1.svg)
]

???

Figure redrawn after [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)
---

## Regression lines emphasize the overall trend

.center[
![](data-to-visualization-1_files/figure-html/blue-jays-scatter-sex-error-1.svg)
]

???

Figure redrawn after [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)

---

## Detrending: Removing the underlying trend

.center[
![](data-to-visualization-1_files/figure-html/hpi-no-trendline-1.svg)
]

.small-font[
Did housing prices in California decline substantially from 1990 to 1998?
]

.small-font[
Did housing prices in West Virginia recover by 2017?
]

???

Figure redrawn after [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)

---

## Detrending: Removing the underlying trend

.center[
![](data-to-visualization-1_files/figure-html/hpi-trends-1.svg)
]

.small-font[
Did housing prices in California decline substantially from 1990 to 1998?
]

.small-font[
Did housing prices in West Virginia recover by 2017?
]

???

Figure redrawn after [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)

---

## Detrending: Removing the underlying trend

.center[
![](data-to-visualization-1_files/figure-html/hpi-detrended-1.svg)
]

.small-font[
Did housing prices in California decline substantially from 1990 to 1998? — yes
]

.small-font[
Did housing prices in West Virginia recover by 2017?  — no
]

???

Figure redrawn after [Claus O. Wilke. Fundamentals of Data Visualization. O'Reilly, 2019.](https://clauswilke.com/dataviz)

---

## Further reading

Relevant chapters from Fundamentals of Data Visualization:
- [Chapter 6: Visualizing amounts](https://clauswilke.com/dataviz/visualizing-amounts.html)
- [Chapter 7: Visualizing distributions: Histograms and density plots](https://clauswilke.com/dataviz/histograms-density-plots.html)
- [Chapter 9: Visualizing many distributions at once](https://clauswilke.com/dataviz/boxplots-violins.html)
- [Chapter 12: Visualizing associations among two or more quantitative variables](https://clauswilke.com/dataviz/visualizing-associations.html)
- [Chapter 14: Visualizing trends](https://clauswilke.com/dataviz/visualizing-trends.html)