Lec 11 - ggplot2 ecosystem & designing visualizations

class: center, middle, inverse, title-slide

# Lec 11 - ggplot2 ecosystem<br/>& designing visualizations
## <br/> Statistical Programming
### Sta 323 | Spring 2022
### <br/> Dr. Colin Rundel

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exclude: true

---
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# The wider ggplot2 ecosystem

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# ggthemes

---

## ggplot2 themes

```r
g = ggplot(palmerpenguins::penguins, aes(x=species, y=body_mass_g, fill=species)) + geom_boxplot()
```

.pull-left[

```r
g
```

```r
g + theme_dark()
```

<img src="Lec11_files/figure-html/theme3-1.png" width="50%" style="display: block; margin: auto;" />
]

.pull-right[

```r
g + theme_minimal()
```

```r
g + theme_void()
```

<img src="Lec11_files/figure-html/theme5-1.png" width="50%" style="display: block; margin: auto;" />
]

---

## ggthemes

.pull-left[

```r
g + ggthemes::theme_economist() + 
  ggthemes::scale_fill_economist()
```

```r
g + ggthemes::theme_fivethirtyeight() + 
  ggthemes::scale_fill_fivethirtyeight()
```

<img src="Lec11_files/figure-html/theme7-1.png" width="50%" style="display: block; margin: auto;" />
]

.pull-right[

```r
g + ggthemes::theme_gdocs() +
  ggthemes::scale_fill_gdocs()
```

```r
g + ggthemes::theme_wsj() +
  ggthemes::scale_fill_wsj()
```

<img src="Lec11_files/figure-html/theme9-1.png" width="50%" style="display: block; margin: auto;" />
]

---

## And for those who miss Excel

.pull-left[

```r
g + ggthemes::theme_excel() +
  ggthemes::scale_fill_excel()
```

<img src="Lec11_files/figure-html/theme10-1.png" width="100%" style="display: block; margin: auto;" />
]

.pull-right[

```r
g + ggthemes::theme_excel_new() +
  ggthemes::scale_fill_excel_new()
```

<img src="Lec11_files/figure-html/theme11-1.png" width="100%" style="display: block; margin: auto;" />
]

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# GGally

---

```r
GGally::ggpairs(palmerpenguins::penguins)
```

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---

```r
d = tibble(
  car = rownames(mtcars),
  weight = mtcars$wt,
  mpg = mtcars$mpg
) %>%
  filter(weight > 2.75, weight < 3.45)
```

.pull-left[

```r
ggplot(d, aes(x=weight, y=mpg)) +
  geom_point(color="red") +
* geom_text(
*   aes(label = car)
* )
```

<img src="Lec11_files/figure-html/ggrepel2-1.png" width="65%" style="display: block; margin: auto;" />
]

.pull-right[

```r
ggplot(d, aes(x=weight, y=mpg)) +
  geom_point(color="red") +
* ggrepel::geom_text_repel(
*   aes(label = car)
* )
```

<img src="Lec11_files/figure-html/ggrepel3-1.png" width="65%" style="display: block; margin: auto;" />
]

---

```r
ggplot(d, aes(x=weight, y=mpg)) +
  geom_point(color="red") +
  ggrepel::geom_text_repel(
    aes(label = car),
*   nudge_x = .1, box.padding = 1, point.padding = 0.6,
*   arrow = arrow(length = unit(0.02, "npc")), segment.alpha = 0.25
  )
```

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---

## Plot objects

```r
library(patchwork)

p1 = ggplot(palmerpenguins::penguins) + 
  geom_boxplot(aes(x = island, y = body_mass_g))

p2 = ggplot(palmerpenguins::penguins) + 
  geom_boxplot(aes(x = species, y = body_mass_g))

p3 = ggplot(palmerpenguins::penguins) + 
  geom_point(aes(x = flipper_length_mm, y = body_mass_g, color = sex))

p4 = ggplot(palmerpenguins::penguins) + 
  geom_point(aes(x = bill_length_mm, y = body_mass_g, color = sex))
```

---

```r
p1 + p2 + p3 + p4
```

---

```r
p1 + p2 + p3 + p4 + plot_layout(nrow=1)
```

---

```r
p1 / (p2 + p3 + p4)
```

---

```r
p1 + p2 + p3 + p4 + 
  plot_annotation(title = "Palmer Penguins", tag_levels = c("A"))
```

---

```r
p1 + {
  p2 + {
    p3 + p4 + plot_layout(ncol = 1) + plot_layout(tag_level = 'new')
  }
} + 
  plot_layout(ncol = 1) +
  plot_annotation(tag_levels = c("1","a"), tag_prefix = "Fig ")
```

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---

.pull-left[

```r
airq = airquality
airq$Month = month.name[airq$Month]

ggplot(
  airq, 
  aes(Day, Temp, group = Month)
) + 
  geom_line() + 
  geom_segment(
    aes(xend = 31, yend = Temp), 
    linetype = 2, 
    colour = 'grey'
  ) + 
  geom_point(size = 2) + 
  geom_text(
    aes(x = 31.1, label = Month), 
    hjust = 0
  ) + 
* gganimate::transition_reveal(Day) +
  coord_cartesian(clip = 'off') + 
  labs(
    title = 'Temperature in New York', 
    y = 'Temperature (°F)'
  ) + 
  theme_minimal() + 
  theme(plot.margin = margin(5.5, 40, 5.5, 5.5))
```
]

.pull-right[
<img src="imgs/gganim_weather.gif" style="display: block; margin: auto;" />
]

.footnote[https://github.com/thomasp85/gganimate]

---

## More ggplot2 extensions

.center[
https://exts.ggplot2.tidyverse.org/gallery/
]

<br/>

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# Why do we visualize?

---

## Asncombe's Quartet

```r
datasets::anscombe %>% as_tibble()
```

```
## # A tibble: 11 × 8
##       x1    x2    x3    x4    y1    y2    y3    y4
##    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
##  1    10    10    10     8  8.04  9.14  7.46  6.58
##  2     8     8     8     8  6.95  8.14  6.77  5.76
##  3    13    13    13     8  7.58  8.74 12.7   7.71
##  4     9     9     9     8  8.81  8.77  7.11  8.84
##  5    11    11    11     8  8.33  9.26  7.81  8.47
##  6    14    14    14     8  9.96  8.1   8.84  7.04
##  7     6     6     6     8  7.24  6.13  6.08  5.25
##  8     4     4     4    19  4.26  3.1   5.39 12.5 
##  9    12    12    12     8 10.8   9.13  8.15  5.56
## 10     7     7     7     8  4.82  7.26  6.42  7.91
## 11     5     5     5     8  5.68  4.74  5.73  6.89
```

---

## Tidy anscombe

.midi[

```r
(tidy_anscombe = datasets::anscombe %>%
  pivot_longer(everything(), names_sep = 1, names_to = c("var", "group")) %>%
  pivot_wider(id_cols = group, names_from = var, 
              values_from = value, values_fn = list(value = list)) %>% 
  unnest(cols = c(x,y)))
```

```
## # A tibble: 44 × 3
##    group     x     y
##    <chr> <dbl> <dbl>
##  1 1        10  8.04
##  2 1         8  6.95
##  3 1        13  7.58
##  4 1         9  8.81
##  5 1        11  8.33
##  6 1        14  9.96
##  7 1         6  7.24
##  8 1         4  4.26
##  9 1        12 10.8 
## 10 1         7  4.82
## # … with 34 more rows
```
]

---

.midi[

```r
tidy_anscombe %>%
  group_by(group) %>%
  summarize(
    mean_x = mean(x), mean_y = mean(y), 
    sd_x = sd(x), sd_y = sd(y),
    cor = cor(x,y), .groups = "drop"
  )
```

```
## # A tibble: 4 × 6
##   group mean_x mean_y  sd_x  sd_y   cor
##   <chr>  <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 1          9   7.50  3.32  2.03 0.816
## 2 2          9   7.50  3.32  2.03 0.816
## 3 3          9   7.5   3.32  2.03 0.816
## 4 4          9   7.50  3.32  2.03 0.817
```
]

---

```r
ggplot(tidy_anscombe, aes(x = x, y = y, color = as.factor(group))) +
  geom_point(size=2) +
  facet_wrap(~group) +
  geom_smooth(method="lm", se=FALSE, fullrange=TRUE, formula = y~x) +
  guides(color="none")
```

---

## DatasauRus

.pull-left-narrow[

```r
library(datasauRus)

ggplot(
  datasaurus_dozen, 
  aes(
    x = x, y = y, 
    color = dataset
  )
) +
  geom_point() +
  facet_wrap(~dataset) +
  guides(color="none")
```
]

.pull-right-wide[
<img src="Lec11_files/figure-html/unnamed-chunk-12-1.png" width="75%" style="display: block; margin: auto;" />
]

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---

.pull-left-narrow[

```r
datasauRus::datasaurus_dozen
```

```
## # A tibble: 1,846 × 3
##    dataset     x     y
##    <chr>   <dbl> <dbl>
##  1 dino     55.4  97.2
##  2 dino     51.5  96.0
##  3 dino     46.2  94.5
##  4 dino     42.8  91.4
##  5 dino     40.8  88.3
##  6 dino     38.7  84.9
##  7 dino     35.6  79.9
##  8 dino     33.1  77.6
##  9 dino     29.0  74.5
## 10 dino     26.2  71.4
## # … with 1,836 more rows
```
]

.pull-right-wide[

```r
datasaurus_dozen %>%
  group_by(dataset) %>%
  summarize(mean_x = mean(x), mean_y = mean(y), 
            sd_x = sd(x), sd_y = sd(y), 
            cor = cor(x,y), .groups = "drop")
```

```
## # A tibble: 12 × 6
##    dataset    mean_x mean_y  sd_x  sd_y     cor
##    <chr>       <dbl>  <dbl> <dbl> <dbl>   <dbl>
##  1 away         54.3   47.8  16.8  26.9 -0.0641
##  2 bullseye     54.3   47.8  16.8  26.9 -0.0686
##  3 circle       54.3   47.8  16.8  26.9 -0.0683
##  4 dino         54.3   47.8  16.8  26.9 -0.0645
##  5 dots         54.3   47.8  16.8  26.9 -0.0603
##  6 h_lines      54.3   47.8  16.8  26.9 -0.0617
##  7 high_lines   54.3   47.8  16.8  26.9 -0.0685
##  8 slant_down   54.3   47.8  16.8  26.9 -0.0690
##  9 slant_up     54.3   47.8  16.8  26.9 -0.0686
## 10 star         54.3   47.8  16.8  26.9 -0.0630
## 11 v_lines      54.3   47.8  16.8  26.9 -0.0694
## 12 wide_lines   54.3   47.8  16.8  26.9 -0.0666
```
]

---

## Simpson's Paradox

.pull-left[
<img src="Lec11_files/figure-html/unnamed-chunk-17-1.png" width="1050" style="display: block; margin: auto;" />
]

.pull-right[
<img src="Lec11_files/figure-html/unnamed-chunk-18-1.png" width="1050" style="display: block; margin: auto;" />
]

---
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# Designing effective visualizations

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## Gapminder

.center[
<iframe width="1000" height="560" src="https://www.youtube.com/embed/OwII-dwh-bk" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen>
</iframe>
]

---

## Keep it simple

.pull-left[

<img src="imgs/pie-3d.jpg" width="100%" style="display: block; margin: auto;" />
]

.pull-right[
<img src="Lec11_files/figure-html/pie-to-bar-1.png" width="100%" style="display: block; margin: auto;" />
]

---

## Judging relative area

.footnote[From Data to Viz caveat collection - [The issue with pie chart](https://www.data-to-viz.com/caveat/pie.html)]

---

## Use color	to	draw	attention

.pull-left[
<img src="Lec11_files/figure-html/unnamed-chunk-21-1.png" width="100%" style="display: block; margin: auto;" />
]

.pull-right[
<img src="Lec11_files/figure-html/unnamed-chunk-22-1.png" width="100%" style="display: block; margin: auto;" />
]

---

## Tell a story

<br/>

.pull-left[
<img src="imgs/time-series-story-1.png" width="98%" style="display: block; margin: auto;" />
]

.pull-right[
<img src="imgs/time-series-story-2.png" width="100%" style="display: block; margin: auto;" />
]

.footnote[
*Credit*: Angela Zoss and Eric Monson, Duke DVS
]

---

## Leave out non-story details

.pull-left[
<img src="imgs/vis_inj1.png" width="100%" style="display: block; margin: auto;" />
]

.pull-right[
<img src="imgs/vis_inj2.png" width="96%" style="display: block; margin: auto;" />
]

.footnote[
*Credit*: Angela Zoss and Eric Monson, Duke DVS
]

---

## Ordering matter

.pull-left[
<img src="imgs/vis_order1.png" width="80%" style="display: block; margin: auto;" />
]

.pull-right[
<img src="imgs/vis_order2.png" width="80%" style="display: block; margin: auto;" />
]

.footnote[
*Credit*: Angela Zoss and Eric Monson, Duke DVS
]

---

## Clearly indicate missing data

<br/>

.footnote[
http://ivi.sagepub.com/content/10/4/271, Angela Zoss and Eric Monson, Duke DVS
]

---

## Reduce cognitive load

.footnote[
http://www.storytellingwithdata.com/2012/09/some-finer-points-of-data-visualization.html
]

---

## Use descriptive titles

.pull-left[
<img src="imgs/vis-title-1.png" width="80%" style="display: block; margin: auto;" />
]

.pull-right[
<img src="imgs/vis-title-2.png" width="80%" style="display: block; margin: auto;" />
]

.footnote[
*Credit*: Angela Zoss and Eric Monson, Duke DVS
]

---

## Annotate figures directly

.footnote[
https://bl.ocks.org/susielu/23dc3082669ee026c552b85081d90976
]

---

## All of the data doesn't tell a story

.footnote[
http://www.nytimes.com/interactive/2014/06/05/upshot/how-the-recession-reshaped-the-economy-in-255-charts.html
]

---

## All of the data doesn't tell a story

.footnote[
http://www.nytimes.com/interactive/2014/06/05/upshot/how-the-recession-reshaped-the-economy-in-255-charts.html
]

---

## All of the data doesn't tell a story

.footnote[
http://www.nytimes.com/interactive/2014/06/05/upshot/how-the-recession-reshaped-the-economy-in-255-charts.html
]

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# Chart Remakes / Makeovers

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## The Why Axis - Gender Gap

.footnote[
http://thewhyaxis.info/gap-remake/
]

---

## The Why Axis - BLS

.footnote[
http://thewhyaxis.info/defaults/
]

---

## Other Resources

- Duke Library - Center for Data and Visualization Sciences - https://library.duke.edu/data/

- Tidy tuesday - https://github.com/rfordatascience/tidytuesday

- Flowing data - https://flowingdata.com/

- Twitter - #dataviz, #tidytuesday

- Books:
  - Wickham, Navarro, Pedersen. ggplot2: Elegant Graphics for Data Analysis. (in progress) 3rd edition. Springer, 2021.
  - Wilke. Fundamentals of Data Visualization. O’Reilly Media, 2019.
  - Healy. Data Visualization: A Practical Introduction. Princeton University Press, 2018.
  - Tufte. The visual display of quantitative information. 2nd edition. Connecticut Graphics Press, 2015.

---

## Acknowledgments

Above materials are derived in part from the following sources:

* Visualization training materials developed by Angela Zoss and Eric Monson, [Duke DVS](http://libcms.oit.duke.edu/data/)