A new wave of ggplot2 extenders

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background-size: cover

# .Large[Supporting a new wave of ggplot2 extenders]
## .small[New points of entry for ggplot2 super-users]
## .small[https://bit.ly/ggextend-cowy]
#### .tiny[Dr. Evangeline Reynolds | 2023-10-13 | ASA, COWY, Image credit: Silas Baisch, Upsplash]

???

Title slide

---

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background-size: cover

# Unlocking ggplot2 as a computational engine 
## by extending ggplot2 statistical geometries
### Dr. Evangeline 'Gina' Reynolds

### Thursday May 19, 2022, 12:15AM
#### Photo Credit: Mike Lewis HeadSmart Media

---

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background-image: url(https://images.unsplash.com/photo-1542178432-52211bc52073?crop=entropy&cs=tinysrgb&fm=jpg&ixlib=rb-1.2.1&q=80&raw_url=true&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1374)
background-size: cover

# Creating new geom vocabulary for richer statistical visual stories 
## by extending ggplot2 statistical geometries
### Dr. Evangeline 'Gina' Reynolds

### Thursday May 19, 2022, 12:15AM
#### Photo Credit: Klim Sergeev

---

### 1. Why ggplot2
### 2. Limitations of base ggplot2
### 3. Promise of extension
### 4. ggplot2 super-users not leveraging extension
### 5. New super-user-tailored educational material
### 6. Challenges on the horizon

---

--
#               ... what's the fuss?

---
class: middle, inverse, center

# It lets you *'speak your plot into existence'*. (Thomas Lin Pederson)

---

# Hans Rosling & BBC in 2010

---

## ... I know having the data is not enough. I have to show it in ways people both enjoy and understand

---

# 'Here we go. Life expectancy on the y-axis'

---

# 'On the x-axis, wealth'

---

# 'Colors represent the different continents'

---

# 'Size represents population'

---
class: inverse, center, middle

#  Response to speaking plot into existence?

# 10 million views...

(also does animation at the end... which I don't show)

---
class: inverse, center, middle

# And ggplot2 also affords this build-up-your-plot-bit-by-bit experience.

--
# i.e. 'speaking your plot into existence.'

---

count: false
 
## Now, we all have Rosling capabilities with ggplot2
.panel1-scatter-auto[

```r
*ggplot(data = gapminder_2002)
```
]
 
.panel2-scatter-auto[
<img src="cowy-slides_files/figure-html/scatter_auto_01_output-1.png" width="432" />
]

---
count: false
 
## Now, we all have Rosling capabilities with ggplot2
.panel1-scatter-auto[

```r
ggplot(data = gapminder_2002) +
* aes(y = lifeExp)
```
]
 
.panel2-scatter-auto[
<img src="cowy-slides_files/figure-html/scatter_auto_02_output-1.png" width="432" />
]

---
count: false
 
## Now, we all have Rosling capabilities with ggplot2
.panel1-scatter-auto[

```r
ggplot(data = gapminder_2002) +
  aes(y = lifeExp) +
* aes(x = gdpPercap)
```
]
 
.panel2-scatter-auto[
<img src="cowy-slides_files/figure-html/scatter_auto_03_output-1.png" width="432" />
]

---
count: false
 
## Now, we all have Rosling capabilities with ggplot2
.panel1-scatter-auto[

```r
ggplot(data = gapminder_2002) +
  aes(y = lifeExp) +
  aes(x = gdpPercap) +
* geom_point()
```
]
 
.panel2-scatter-auto[
<img src="cowy-slides_files/figure-html/scatter_auto_04_output-1.png" width="432" />
]

---
count: false
 
## Now, we all have Rosling capabilities with ggplot2
.panel1-scatter-auto[

```r
ggplot(data = gapminder_2002) +
  aes(y = lifeExp) +
  aes(x = gdpPercap) +
  geom_point() +
* aes(size = pop/1000000000)
```
]
 
.panel2-scatter-auto[
<img src="cowy-slides_files/figure-html/scatter_auto_05_output-1.png" width="432" />
]

---
count: false
 
## Now, we all have Rosling capabilities with ggplot2
.panel1-scatter-auto[

```r
ggplot(data = gapminder_2002) +
  aes(y = lifeExp) +
  aes(x = gdpPercap) +
  geom_point() +
  aes(size = pop/1000000000) +
* aes(color = continent)
```
]
 
.panel2-scatter-auto[
<img src="cowy-slides_files/figure-html/scatter_auto_06_output-1.png" width="432" />
]

---

## We get the 'Hans Rosling' speak-it experience ...

--
## ... because ggplot2 is based on the grammar of graphics, (gg = grammar of graphics) ...

--
## ... which breaks up the plot elements into *orthogonal* parts ...

--
## ... and allows user to manipulate those parts bit by bit.

---
class: inverse, center, middle

> # "the Grammar of Graphics makes [building plots] easy because you've just got all these, like, little nice decomposable components" -- Hadley Wickham

---

## Leland Wilkinson identified orthogonal elements in 'The Grammar of Graphics'. Wilkinson on the Policy Viz Podcast [here](https://policyviz.com/podcast/episode-201-leland-wilkinson/).

---

# Skip at COWY: discussing origins of ggplot2

Hadley Wickham, ggplot2 author on it's motivation:

> ### And, you know, I'd get a dataset. And, *in my head I could very clearly kind of picture*, I want to put this on the x-axis. Let's put this on the y-axis, draw a line, put some points here, break it up by this variable.

--
> ### And then, like, getting that vision out of my head, and into reality, it's just really, really hard. Just, like, felt harder than it should be. Like, there's a lot of custom programming involved,

---

> ### where I just felt, like, to me, I just wanted to say, like, you know, *this is what I'm thinking, this is how I'm picturing this plot. Like you're the computer 'Go and do it'.*

--
> ### ... and I'd also been reading about the Grammar of Graphics by Leland Wilkinson, I got to meet him a couple of times and ... I was, like, this book has been, like, written for me.
https://www.trifacta.com/podcast/tidy-data-with-hadley-wickham/

---

count: false
 
# And ggplot2 gives us even more than Rosling...
.panel1-scatter2-1[

```r
ggplot(data = gapminder_2002) +
  aes(y = lifeExp) +
  aes(x = gdpPercap) +
  scale_x_log10() +
  geom_point() +
  aes(size = pop/1000000000) +
  aes(color = continent) +
  labs(x = "GDP per capita (US$)") +
* geom_smooth(method = lm)
```
]
 
.panel2-scatter2-1[
<img src="cowy-slides_files/figure-html/scatter2_1_01_output-1.png" width="432" />
]

---
class: inverse, middle, center

# ggplot2 let us "... create graphical 'poems'." - Hadley Wickham (2010) in 'A Layered Grammar of Graphics', *Journal of Computational and Graphical Statistics*

# But... sometimes there are limits to what we can express

---
class: inverse, center, middle

# Part 2. The problem

## Sometimes we don't have the vocabulary that's needed to 'speak' fluently.

---

## Example: What's the graphical poem here?

???
Consider for example, a the seemingly simple enterprise of adding a vertical line at the mean of x, perhaps atop a histogram or density plot.

---

```r
*airquality
```
]
 
.panel2-basic-auto[

```
    Ozone Solar.R Wind Temp Month Day
1      41     190  7.4   67     5   1
2      36     118  8.0   72     5   2
3      12     149 12.6   74     5   3
4      18     313 11.5   62     5   4
5      NA      NA 14.3   56     5   5
6      28      NA 14.9   66     5   6
7      23     299  8.6   65     5   7
8      19      99 13.8   59     5   8
9       8      19 20.1   61     5   9
10     NA     194  8.6   69     5  10
11      7      NA  6.9   74     5  11
12     16     256  9.7   69     5  12
13     11     290  9.2   66     5  13
14     14     274 10.9   68     5  14
15     18      65 13.2   58     5  15
16     14     334 11.5   64     5  16
17     34     307 12.0   66     5  17
18      6      78 18.4   57     5  18
19     30     322 11.5   68     5  19
20     11      44  9.7   62     5  20
21      1       8  9.7   59     5  21
22     11     320 16.6   73     5  22
23      4      25  9.7   61     5  23
24     32      92 12.0   61     5  24
25     NA      66 16.6   57     5  25
26     NA     266 14.9   58     5  26
27     NA      NA  8.0   57     5  27
28     23      13 12.0   67     5  28
29     45     252 14.9   81     5  29
30    115     223  5.7   79     5  30
31     37     279  7.4   76     5  31
32     NA     286  8.6   78     6   1
33     NA     287  9.7   74     6   2
34     NA     242 16.1   67     6   3
35     NA     186  9.2   84     6   4
36     NA     220  8.6   85     6   5
37     NA     264 14.3   79     6   6
38     29     127  9.7   82     6   7
39     NA     273  6.9   87     6   8
40     71     291 13.8   90     6   9
41     39     323 11.5   87     6  10
42     NA     259 10.9   93     6  11
43     NA     250  9.2   92     6  12
44     23     148  8.0   82     6  13
45     NA     332 13.8   80     6  14
46     NA     322 11.5   79     6  15
47     21     191 14.9   77     6  16
48     37     284 20.7   72     6  17
49     20      37  9.2   65     6  18
50     12     120 11.5   73     6  19
51     13     137 10.3   76     6  20
52     NA     150  6.3   77     6  21
53     NA      59  1.7   76     6  22
54     NA      91  4.6   76     6  23
55     NA     250  6.3   76     6  24
56     NA     135  8.0   75     6  25
57     NA     127  8.0   78     6  26
58     NA      47 10.3   73     6  27
59     NA      98 11.5   80     6  28
60     NA      31 14.9   77     6  29
61     NA     138  8.0   83     6  30
62    135     269  4.1   84     7   1
63     49     248  9.2   85     7   2
64     32     236  9.2   81     7   3
65     NA     101 10.9   84     7   4
66     64     175  4.6   83     7   5
67     40     314 10.9   83     7   6
68     77     276  5.1   88     7   7
69     97     267  6.3   92     7   8
70     97     272  5.7   92     7   9
71     85     175  7.4   89     7  10
72     NA     139  8.6   82     7  11
73     10     264 14.3   73     7  12
74     27     175 14.9   81     7  13
75     NA     291 14.9   91     7  14
76      7      48 14.3   80     7  15
77     48     260  6.9   81     7  16
78     35     274 10.3   82     7  17
79     61     285  6.3   84     7  18
80     79     187  5.1   87     7  19
81     63     220 11.5   85     7  20
82     16       7  6.9   74     7  21
83     NA     258  9.7   81     7  22
84     NA     295 11.5   82     7  23
85     80     294  8.6   86     7  24
86    108     223  8.0   85     7  25
87     20      81  8.6   82     7  26
88     52      82 12.0   86     7  27
89     82     213  7.4   88     7  28
90     50     275  7.4   86     7  29
91     64     253  7.4   83     7  30
92     59     254  9.2   81     7  31
93     39      83  6.9   81     8   1
94      9      24 13.8   81     8   2
95     16      77  7.4   82     8   3
96     78      NA  6.9   86     8   4
97     35      NA  7.4   85     8   5
98     66      NA  4.6   87     8   6
99    122     255  4.0   89     8   7
100    89     229 10.3   90     8   8
101   110     207  8.0   90     8   9
102    NA     222  8.6   92     8  10
103    NA     137 11.5   86     8  11
104    44     192 11.5   86     8  12
105    28     273 11.5   82     8  13
106    65     157  9.7   80     8  14
107    NA      64 11.5   79     8  15
108    22      71 10.3   77     8  16
109    59      51  6.3   79     8  17
110    23     115  7.4   76     8  18
111    31     244 10.9   78     8  19
112    44     190 10.3   78     8  20
113    21     259 15.5   77     8  21
114     9      36 14.3   72     8  22
115    NA     255 12.6   75     8  23
116    45     212  9.7   79     8  24
117   168     238  3.4   81     8  25
118    73     215  8.0   86     8  26
119    NA     153  5.7   88     8  27
120    76     203  9.7   97     8  28
121   118     225  2.3   94     8  29
122    84     237  6.3   96     8  30
123    85     188  6.3   94     8  31
124    96     167  6.9   91     9   1
125    78     197  5.1   92     9   2
126    73     183  2.8   93     9   3
127    91     189  4.6   93     9   4
128    47      95  7.4   87     9   5
129    32      92 15.5   84     9   6
130    20     252 10.9   80     9   7
131    23     220 10.3   78     9   8
132    21     230 10.9   75     9   9
133    24     259  9.7   73     9  10
134    44     236 14.9   81     9  11
135    21     259 15.5   76     9  12
136    28     238  6.3   77     9  13
137     9      24 10.9   71     9  14
138    13     112 11.5   71     9  15
139    46     237  6.9   78     9  16
140    18     224 13.8   67     9  17
141    13      27 10.3   76     9  18
142    24     238 10.3   68     9  19
143    16     201  8.0   82     9  20
144    13     238 12.6   64     9  21
145    23      14  9.2   71     9  22
146    36     139 10.3   81     9  23
147     7      49 10.3   69     9  24
148    14      20 16.6   63     9  25
149    30     193  6.9   70     9  26
150    NA     145 13.2   77     9  27
151    14     191 14.3   75     9  28
152    18     131  8.0   76     9  29
153    20     223 11.5   68     9  30
```
]

---
count: false
 
### What's the *base* ggplot2 experience
.panel1-basic-auto[

```r
airquality %>%
* ggplot(data = .)
```
]
 
.panel2-basic-auto[
<img src="cowy-slides_files/figure-html/basic_auto_02_output-1.png" width="432" />
]

---
count: false
 
### What's the *base* ggplot2 experience
.panel1-basic-auto[

```r
airquality %>%
  ggplot(data = .) +
* aes(x = Ozone)
```
]
 
.panel2-basic-auto[
<img src="cowy-slides_files/figure-html/basic_auto_03_output-1.png" width="432" />
]

---
count: false
 
### What's the *base* ggplot2 experience
.panel1-basic-auto[

```r
airquality %>%
  ggplot(data = .) +
  aes(x = Ozone) +
* geom_rug()
```
]
 
.panel2-basic-auto[
<img src="cowy-slides_files/figure-html/basic_auto_04_output-1.png" width="432" />
]

---
count: false
 
### What's the *base* ggplot2 experience
.panel1-basic-auto[

```r
airquality %>%
  ggplot(data = .) +
  aes(x = Ozone) +
  geom_rug() +
* geom_histogram()
```
]
 
.panel2-basic-auto[
<img src="cowy-slides_files/figure-html/basic_auto_05_output-1.png" width="432" />
]

---
count: false
 
### What's the *base* ggplot2 experience
.panel1-basic-auto[

```r
airquality %>%
  ggplot(data = .) +
  aes(x = Ozone) +
  geom_rug() +
  geom_histogram() +
* geom_vline(
*   xintercept =
*     mean(airquality$Ozone,
*          na.rm = T)
*   )
```
]
 
.panel2-basic-auto[
<img src="cowy-slides_files/figure-html/basic_auto_06_output-1.png" width="432" />
]

---
count: false
 
### What's the *base* ggplot2 experience
.panel1-basic-auto[

```r
airquality %>%
  ggplot(data = .) +
  aes(x = Ozone) +
  geom_rug() +
  geom_histogram() +
  geom_vline(
    xintercept =
      mean(airquality$Ozone,
           na.rm = T)
    ) ->
*g 
```
]
 
.panel2-basic-auto[

]

???
Creating this plot requires greater focus on ggplot2 *syntax*, likely detracting from discussion of *the mean* that statistical instructors desire.
It may require a discussion about dollar sign syntax and how geom_vline is actually a special geom -- an annotation -- rather than being mapped to the data. None of this is relevant to the point you as an instructor aim to make: maybe that the the mean is the balancing point of the data or maybe a comment about skewness.

---

### Adding the conditional means?

???

Further, for the case of adding a vertical line at the mean for different subsets of the data, a different approach is required. This enterprise may take instructor/analyst/student on an even larger detour -- possibly googling, and maybe landing on the following stack overflow page where 11,000 analytics souls (some repeats to be sure) have landed:

---

count: false
 
#### Conditional means (may require a trip to stackoverflow!)
.panel1-cond_means_hard-auto[

```r
*airquality
```
]
 
.panel2-cond_means_hard-auto[

---
count: false
 
#### Conditional means (may require a trip to stackoverflow!)
.panel1-cond_means_hard-auto[

```r
airquality %>%
* group_by(Month)
```
]
 
.panel2-cond_means_hard-auto[

```
# A tibble: 153 × 6
# Groups:   Month [5]
   Ozone Solar.R  Wind  Temp Month   Day
   <int>   <int> <dbl> <int> <int> <int>
 1    41     190   7.4    67     5     1
 2    36     118   8      72     5     2
 3    12     149  12.6    74     5     3
 4    18     313  11.5    62     5     4
 5    NA      NA  14.3    56     5     5
 6    28      NA  14.9    66     5     6
 7    23     299   8.6    65     5     7
 8    19      99  13.8    59     5     8
 9     8      19  20.1    61     5     9
10    NA     194   8.6    69     5    10
# … with 143 more rows
```
]

---
count: false
 
#### Conditional means (may require a trip to stackoverflow!)
.panel1-cond_means_hard-auto[

```r
airquality %>%
  group_by(Month) %>%
* summarise(
*   Ozone_mean =
*     mean(Ozone, na.rm = T)
*   )
```
]
 
.panel2-cond_means_hard-auto[

```
# A tibble: 5 × 2
  Month Ozone_mean
  <int>      <dbl>
1     5       23.6
2     6       29.4
3     7       59.1
4     8       60.0
5     9       31.4
```
]

---
count: false
 
#### Conditional means (may require a trip to stackoverflow!)
.panel1-cond_means_hard-auto[

```r
airquality %>%
  group_by(Month) %>%
  summarise(
    Ozone_mean =
      mean(Ozone, na.rm = T)
    ) ->
*airquality_by_month
```
]
 
.panel2-cond_means_hard-auto[

]

---
count: false
 
#### Conditional means (may require a trip to stackoverflow!)
.panel1-cond_means_hard-auto[

```r
airquality %>%
  group_by(Month) %>%
  summarise(
    Ozone_mean =
      mean(Ozone, na.rm = T)
    ) ->
airquality_by_month

*ggplot(airquality)
```
]
 
.panel2-cond_means_hard-auto[
<img src="cowy-slides_files/figure-html/cond_means_hard_auto_05_output-1.png" width="432" />
]

---
count: false
 
#### Conditional means (may require a trip to stackoverflow!)
.panel1-cond_means_hard-auto[

```r
airquality %>%
  group_by(Month) %>%
  summarise(
    Ozone_mean =
      mean(Ozone, na.rm = T)
    ) ->
airquality_by_month

ggplot(airquality) +
* aes(x = Ozone)
```
]
 
.panel2-cond_means_hard-auto[
<img src="cowy-slides_files/figure-html/cond_means_hard_auto_06_output-1.png" width="432" />
]

---
count: false
 
#### Conditional means (may require a trip to stackoverflow!)
.panel1-cond_means_hard-auto[

```r
airquality %>%
  group_by(Month) %>%
  summarise(
    Ozone_mean =
      mean(Ozone, na.rm = T)
    ) ->
airquality_by_month

ggplot(airquality) +
  aes(x = Ozone) +
* geom_histogram()
```
]
 
.panel2-cond_means_hard-auto[
<img src="cowy-slides_files/figure-html/cond_means_hard_auto_07_output-1.png" width="432" />
]

---
count: false
 
#### Conditional means (may require a trip to stackoverflow!)
.panel1-cond_means_hard-auto[

```r
airquality %>%
  group_by(Month) %>%
  summarise(
    Ozone_mean =
      mean(Ozone, na.rm = T)
    ) ->
airquality_by_month

ggplot(airquality) +
  aes(x = Ozone) +
  geom_histogram() +
* facet_grid(rows = vars(Month))
```
]
 
.panel2-cond_means_hard-auto[
<img src="cowy-slides_files/figure-html/cond_means_hard_auto_08_output-1.png" width="432" />
]

---
count: false
 
#### Conditional means (may require a trip to stackoverflow!)
.panel1-cond_means_hard-auto[

```r
airquality %>%
  group_by(Month) %>%
  summarise(
    Ozone_mean =
      mean(Ozone, na.rm = T)
    ) ->
airquality_by_month

ggplot(airquality) +
  aes(x = Ozone) +
  geom_histogram() +
  facet_grid(rows = vars(Month)) +
* geom_vline(data = airquality_by_month,
*            aes(xintercept =
*              Ozone_mean))
```
]
 
.panel2-cond_means_hard-auto[
<img src="cowy-slides_files/figure-html/cond_means_hard_auto_09_output-1.png" width="432" />
]

---

# Why is geom_smooth() is so easy, and my mean of x line is so hard?

### Like geom_smooth does a bunch of compute in the background

### then uses geometric primitives to show computed values

- line
- ribbon geometry

### geom_x_mean() doesn't exist; we have to do the compute

---

## ggplot2 intentionally lean for maintenance purposes.

## ... but geom_x_mean() would be so nice!

---
class: middle, center, inverse

## Part 3. ggplot2 extensions can be used to help restore that 'speak it into existance' feel, when base ggplot2 'words' fail

---

## Examples from ggxmean

https://github.com/EvaMaeRey/ggxmean, that gives us geom_x_mean(), and ~ 25 more geoms...

---

```r
*library(tidyverse)
```
]
 
.panel2-ols-auto[

]

---
count: false

```r
library(tidyverse)
*library(ggxmean)
```
]
 
.panel2-ols-auto[

]

---
count: false

```r
library(tidyverse)
library(ggxmean)
*#library(transformr) might help w/ animate  #library(transformr) might help w/ animate
```
]
 
.panel2-ols-auto[

]

---
count: false