Visualization

class: center, middle, inverse, title-slide

# Visualization
### Colin Rundel
### 2019-10-14

---

exclude: true

---
class: middle

<img src="imgs/hex-ggplot2.png" width="50%" style="display: block; margin: auto;" />
---

## The Grammar of Graphics

- Visualisation concept created by Leland Wilkinson (1999)
    - to define the basic elements of a statistical graphic

- Adapted for R by Hadley Wickham (2009)
    - consistent and compact syntax to describe statistical graphics
    - highly modular as it breaks up graphs into semantic components

- It is not meant as a guide to which graph to use and how to best convey your data (more on that later).

---

## Terminology

A statistical graphic is a...

- mapping of **data**
- which may be **statistically transformed** (summarised, log-transformed, etc.)
- to **aesthetic attributes** (color, size, xy-position, etc.)
- using **geometric objects** (points, lines, bars, etc.)
- and mapped onto a specific **facet** and **coordinate system**

---

## Anatomy of a ggplot call

```r
ggplot(
  data = [dataframe], 
  mapping = aes(
    x = [var_x], y = [var_y], 
    color = [var_for_color], 
    shape = [var_for_shape],
    ...
  )
) +
  geom_[some_geom](
    mapping = aes(
      color = [var_for_geom_color],
      ...
    )
  ) +
  ... # other geometries
  scale_[some_axis]_[some_scale]() +
  facet_[some_facet]([formula]) +
  ... # other options
```

---

## Diamonds

```r
set.seed(20191014)
diamonds = sample_n(ggplot2::diamonds, 1000)
diamonds
```

```
## # A tibble: 1,000 x 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.39 Ideal I SI1 62.4 54 534 4.67 4.69 2.92
## 2 0.41 Premium E SI1 61.1 62 904 4.83 4.76 2.93
## 3 0.51 Very Good D VS2 62.2 57 1678 5.11 5.14 3.19
## 4 0.34 Ideal G VS2 62.4 57 517 4.46 4.48 2.79
## 5 1.26 Very Good G SI1 63.3 58 7910 6.88 6.84 4.34
## 6 0.43 Good F SI1 63.6 53 948 4.83 4.79 3.06
## 7 0.93 Premium I VS2 62.7 61 4073 6.24 6.14 3.88
## 8 1.14 Premium I SI1 62.2 58 4788 6.7 6.67 4.16
## 9 0.36 Fair F VS1 55.3 67 810 4.79 4.72 2.63
## 10 0.33 Ideal F VVS2 61.4 57 824 4.43 4.46 2.73
## # … with 990 more rows
```

```r
head(diamonds$cut)
```

```
## [1] Ideal Premium Very Good Ideal Very Good Good 
## Levels: Fair < Good < Very Good < Premium < Ideal
```

```r
head(diamonds$color)
```

```
## [1] I E D G G F
## Levels: D < E < F < G < H < I < J
```

---

## Example 1

.midi[
- Which data are used as an input?
- Are the variables statistically transformed before plotting?
- What geometric objects are used to represent the data?
- What variables are mapped onto which aesthetic attributes?
- What type of scales are used to map data to aesthetics?
]

---

```r
ggplot(data = diamonds, aes(x = carat, y = price)) +
  geom_point()
```

---

## Altering aesthetics

.midi[
- How did the plot change?
- Are these changes based on data or are the changes based on stylistic choices for the geometric objects?
]

---

```r
ggplot(data = diamonds, aes(x = carat, y = price)) +
  geom_point(alpha = 0.25, color = "blue")
```

---

## Example 2

---

```r
ggplot(data = diamonds, aes(x = carat, y = sqrt(price), color = color)) +
  geom_point()
```

---

## Example 3

---

```r
ggplot(data = diamonds, aes(x = carat, y = sqrt(price), color = table)) +
  geom_point()
```

---

## Example 4

---

```r
ggplot(data = diamonds, aes(x = cut, y = price, fill = color)) +
  geom_boxplot() +
  scale_y_log10()
```

---

```r
ggplot(data = diamonds, aes(x = cut, y = log(price,10), fill = color)) +
  geom_boxplot() +
  scale_y_continuous()
```

---

## Example 5

---

```r
ggplot(data = diamonds, aes(x = cut, fill=color)) +
  geom_bar(position = "dodge", color = "black") +
  coord_flip() +
  scale_fill_brewer(palette = "Blues")
```

---

## Example 6

---

```r
ggplot(data = ggplot2::diamonds, aes(x = price/carat, fill=color)) +
  geom_density(alpha=0.5) +
  facet_grid(rows = vars(color), cols = vars(cut)) +
  scale_x_sqrt() + 
  labs(x = "Price per carat")
```

---

## More `ggplot2` resources

- Visit https://ggplot2.tidyverse.org/ for ggplot2 documentation and helpful articles. Reference section contains lots of examples for each geometry type.

- Refer to the [`ggplot2` cheatsheet](https://github.com/rstudio/cheatsheets/blob/master/data-visualization-2.1.pdf)

- Book - [ggplot2: Elegant Graphics for Data Analysis](https://ggplot2-book.org/)

---

## ggplot objects

```r
g = ggplot(diamonds, aes(x = carat, y = price)) + geom_point()
class(g)
```

```
## [1] "gg"     "ggplot"
```

.pull-left[

```r
g
```

<img src="Lec05_files/figure-html/unnamed-chunk-20-1.png" style="display: block; margin: auto;" />
]

.pull-right[

```r
g + geom_smooth(se=FALSE)
```

<img src="Lec05_files/figure-html/unnamed-chunk-21-1.png" style="display: block; margin: auto;" />
]

---

## Exercise 1

Note the presence, or lack thereof, of any outliers.

---

## Exercise 2

---
class: middle

.footnote[
`remotes::install_github("thomasp85/patchwork")`
]
---

## Plots

```r
library(patchwork)

p1 = ggplot(diamonds) + geom_point(aes(x = carat, y = price))

p2 = ggplot(diamonds) + geom_boxplot(aes(x = cut, y = price))

p3 = ggplot(diamonds) + geom_boxplot(aes(x = color, y = price))

p4 = ggplot(diamonds) + geom_boxplot(aes(x = clarity, y = price))
```

---

```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_layout(ncol = 1)
  }
} + plot_layout(ncol = 1)
```

---

```r
p1 + p2 + p3 + p4 + plot_annotation(title = "Diamonds data", tag_levels = c("A","1"))
```

---
class: center, middle

# Why do we visualize?

---

## Asncombe's Quartet

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

```
## # A tibble: 11 x 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 x 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))
```

```
## # A tibble: 4 x 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(vars(group)) +
  geom_smooth(method="lm", se=FALSE, fullrange=TRUE) +
  guides(color=FALSE)
```

---

## DatasauRus

.pull-left33[

```r
datasauRus::datasaurus_dozen
```

```
## # A tibble: 1,846 x 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-right66[

```r
datasauRus::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))
```

```
## # A tibble: 13 x 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
## 13 x_shape 54.3 47.8 16.8 26.9 -0.0656
```
]

---

```r
ggplot(datasauRus::datasaurus_dozen, aes(x = x, y = y, color = dataset)) +
  geom_point() +
  facet_wrap(vars(dataset)) +
  guides(color=FALSE)
```

---

## Simpson's Paradox

.pull-left[
<img src="Lec05_files/figure-html/unnamed-chunk-39-1.png" style="display: block; margin: auto;" />
]

.pull-right[
<img src="Lec05_files/figure-html/unnamed-chunk-40-1.png" style="display: block; margin: auto;" />
]

---
class: center, middle

# Designing effective visualizations

---

## Keep it simple

.pull-left[
<img src="imgs/pie-3d.jpg" width="300" style="display: block; margin: auto;" />
]

.pull-right[
<img src="Lec05_files/figure-html/pie-to-bar-1.png" width="500" style="display: block; margin: auto;" />
]

---

## Use color	to	draw	attention

.pull-left[
<img src="Lec05_files/figure-html/unnamed-chunk-41-1.png" width="500" style="display: block; margin: auto;" />
]

.pull-right[
<img src="Lec05_files/figure-html/unnamed-chunk-42-1.png" width="600" style="display: block; margin: auto;" />
]

---

## Tell a story

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

---

## Leave out non-story details

.pull-left[
<img src="imgs/vis_inj1.png" width="500" style="display: block; margin: auto;" />
]

.pull-right[
<img src="imgs/vis_inj2.png" width="500" style="display: block; margin: auto;" />
]

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

---

## Order / usage matters

.pull-left[
<img src="imgs/vis_order1.png" width="350" style="display: block; margin: auto;" />
]

.pull-right[
<img src="imgs/vis_order2.png" width="350" style="display: block; margin: auto;" />
]

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

---

## Be clear about missing data

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

---

## Reduce cognitive burden

.footnote[
http://www.storytellingwithdata.com/2012/09/some-finer-points-of-data-visualization.html 
Angela Zoss and Eric Monson, Duke DVS
]

---

## Use descriptive titles

.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
]

---
class: middle, center

# Chart Remakes / Makeovers

---

## The Why Axis - BLS

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

---

## The Why Axis - Gender Gap

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

---
class: middle, center

# Acknowledgments

---

## Acknowledgments

Above materials are derived in part from the following sources:

* Hadley Wickham - [R for Data Science](http://r4ds.had.co.nz/) & [Elegant Graphics for Data Analysis](https://ggplot2-book.org)

* [ggplot2 website](https://ggplot2.tidyverse.org/)

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