Extending ggplot2 statistical geometries

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# 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

---

# gg in ggplot2

# Grammar of Graphics

---

## Elements of the Grammar of Graphics (choices)

![](https://miro.medium.com/max/1400/1*MMZuYgeC_YjXNC1r4D4sog.png)

---

### A series of 'grammatical' choices:

- declarative mood: Declare data

- interrogative mood: ask for representation choose aesthetic mapping (what aesthetics (color, size, position) will represent what variables)

- nouns: geometric objects, 'geoms' or 'marks' (tableau's vocabulary) that take on the visual attributes

- modifiers (adjectives, adverbs): change defaults

- labels
  - aesthetic scales
  - coordinate system
  
--

- conditional mood:

- change data, aesthetic mapping, or defaults for specific geom layer
  
--

- punctuation: make decision to facet or not, breaking up your ideas
    
--

- interjections: annotation

- greetings: theme (plot look and feel)

---

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

# Build up your plot, bit by bit

---

# ggplot2 is called a 'declarative' graphing system.

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

# Describe the elements of the plot you have in your mind.

---

## This is fast. Because most of us, can **picture** the form of the plot we're going after rather clearly (i.e. what the horizontal position should represent, what color should represent, what 'marks' (points, lines) are going to appear on the plot).

## 'Getting' our plot made becomes a matter of describing what we're already picturing!

---
# So what's the promise of ggplot2?

## Getting the plot form you picture in your head ...

## ... into reality...

## ... by describing it.

---

## "Create graphical 'poems'." - Hadley Wickham (2010)

## 'Write statistical stories.' - (May 2022)

---
class: inverse, center, middle

# The problem?

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

---

## Example

???
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 experience
.panel1-basic-auto[

```r
airquality %>%
* ggplot(data = .)
```
]
 
.panel2-basic-auto[
<img src="statistical_geometries_files/figure-html/basic_auto_02_output-1.png" width="504" />
]

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

```r
airquality %>%
  ggplot(data = .) +
* aes(x = Ozone)
```
]
 
.panel2-basic-auto[
<img src="statistical_geometries_files/figure-html/basic_auto_03_output-1.png" width="504" />
]

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

```r
airquality %>%
  ggplot(data = .) +
  aes(x = Ozone) +
* geom_rug()
```
]
 
.panel2-basic-auto[
<img src="statistical_geometries_files/figure-html/basic_auto_04_output-1.png" width="504" />
]

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

```r
airquality %>%
  ggplot(data = .) +
  aes(x = Ozone) +
  geom_rug() +
* geom_histogram()
```
]
 
.panel2-basic-auto[
<img src="statistical_geometries_files/figure-html/basic_auto_05_output-1.png" width="504" />
]

---
count: false
 
### What's the 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="statistical_geometries_files/figure-html/basic_auto_06_output-1.png" width="504" />
]

---
count: false
 
### What's the 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[

]

---
count: false
 
### What's the 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

*g %>% layer_data(i = 2)  # histogram layer
```
]
 
.panel2-basic-auto[

```
    y count          x       xmin       xmax     density     ncount   ndensity
1   1     1   0.000000  -2.879310   2.879310 0.001497006 0.05882353 0.05882353
2   6     6   5.758621   2.879310   8.637931 0.008982036 0.35294118 0.35294118
3  17    17  11.517241   8.637931  14.396552 0.025449102 1.00000000 1.00000000
4  13    13  17.275862  14.396552  20.155172 0.019461078 0.76470588 0.76470588
5  13    13  23.034483  20.155172  25.913793 0.019461078 0.76470588 0.76470588
6   8     8  28.793103  25.913793  31.672414 0.011976048 0.47058824 0.47058824
7  10    10  34.551724  31.672414  37.431034 0.014970060 0.58823529 0.58823529
8   4     4  40.310345  37.431034  43.189655 0.005988024 0.23529412 0.23529412
9   8     8  46.068966  43.189655  48.948276 0.011976048 0.47058824 0.47058824
10  3     3  51.827586  48.948276  54.706897 0.004491018 0.17647059 0.17647059
11  2     2  57.586207  54.706897  60.465517 0.002994012 0.11764706 0.11764706
12  6     6  63.344828  60.465517  66.224138 0.008982036 0.35294118 0.35294118
13  1     1  69.103448  66.224138  71.982759 0.001497006 0.05882353 0.05882353
14  4     4  74.862069  71.982759  77.741379 0.005988024 0.23529412 0.23529412
15  5     5  80.620690  77.741379  83.500000 0.007485030 0.29411765 0.29411765
16  4     4  86.379310  83.500000  89.258621 0.005988024 0.23529412 0.23529412
17  1     1  92.137931  89.258621  95.017241 0.001497006 0.05882353 0.05882353
18  3     3  97.896552  95.017241 100.775862 0.004491018 0.17647059 0.17647059
19  0     0 103.655172 100.775862 106.534483 0.000000000 0.00000000 0.00000000
20  2     2 109.413793 106.534483 112.293103 0.002994012 0.11764706 0.11764706
21  2     2 115.172414 112.293103 118.051724 0.002994012 0.11764706 0.11764706
22  1     1 120.931034 118.051724 123.810345 0.001497006 0.05882353 0.05882353
23  0     0 126.689655 123.810345 129.568966 0.000000000 0.00000000 0.00000000
24  1     1 132.448276 129.568966 135.327586 0.001497006 0.05882353 0.05882353
25  0     0 138.206897 135.327586 141.086207 0.000000000 0.00000000 0.00000000
26  0     0 143.965517 141.086207 146.844828 0.000000000 0.00000000 0.00000000
27  0     0 149.724138 146.844828 152.603448 0.000000000 0.00000000 0.00000000
28  0     0 155.482759 152.603448 158.362069 0.000000000 0.00000000 0.00000000
29  0     0 161.241379 158.362069 164.120690 0.000000000 0.00000000 0.00000000
30  1     1 167.000000 164.120690 169.879310 0.001497006 0.05882353 0.05882353
   flipped_aes PANEL group ymin ymax colour   fill size linetype alpha
1        FALSE     1    -1    0    1     NA grey35  0.5        1    NA
2        FALSE     1    -1    0    6     NA grey35  0.5        1    NA
3        FALSE     1    -1    0   17     NA grey35  0.5        1    NA
4        FALSE     1    -1    0   13     NA grey35  0.5        1    NA
5        FALSE     1    -1    0   13     NA grey35  0.5        1    NA
6        FALSE     1    -1    0    8     NA grey35  0.5        1    NA
7        FALSE     1    -1    0   10     NA grey35  0.5        1    NA
8        FALSE     1    -1    0    4     NA grey35  0.5        1    NA
9        FALSE     1    -1    0    8     NA grey35  0.5        1    NA
10       FALSE     1    -1    0    3     NA grey35  0.5        1    NA
11       FALSE     1    -1    0    2     NA grey35  0.5        1    NA
12       FALSE     1    -1    0    6     NA grey35  0.5        1    NA
13       FALSE     1    -1    0    1     NA grey35  0.5        1    NA
14       FALSE     1    -1    0    4     NA grey35  0.5        1    NA
15       FALSE     1    -1    0    5     NA grey35  0.5        1    NA
16       FALSE     1    -1    0    4     NA grey35  0.5        1    NA
17       FALSE     1    -1    0    1     NA grey35  0.5        1    NA
18       FALSE     1    -1    0    3     NA grey35  0.5        1    NA
19       FALSE     1    -1    0    0     NA grey35  0.5        1    NA
20       FALSE     1    -1    0    2     NA grey35  0.5        1    NA
21       FALSE     1    -1    0    2     NA grey35  0.5        1    NA
22       FALSE     1    -1    0    1     NA grey35  0.5        1    NA
23       FALSE     1    -1    0    0     NA grey35  0.5        1    NA
24       FALSE     1    -1    0    1     NA grey35  0.5        1    NA
25       FALSE     1    -1    0    0     NA grey35  0.5        1    NA
26       FALSE     1    -1    0    0     NA grey35  0.5        1    NA
27       FALSE     1    -1    0    0     NA grey35  0.5        1    NA
28       FALSE     1    -1    0    0     NA grey35  0.5        1    NA
29       FALSE     1    -1    0    0     NA grey35  0.5        1    NA
30       FALSE     1    -1    0    1     NA grey35  0.5        1    NA
```
]

???
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.

---

## geom_histogram()

## counts the number of observations in a 'bin'

## draws rectangles accordingly

---

### 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="statistical_geometries_files/figure-html/cond_means_hard_auto_05_output-1.png" width="504" />
]

---
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="statistical_geometries_files/figure-html/cond_means_hard_auto_06_output-1.png" width="504" />
]

---
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="statistical_geometries_files/figure-html/cond_means_hard_auto_07_output-1.png" width="504" />
]

---
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="statistical_geometries_files/figure-html/cond_means_hard_auto_08_output-1.png" width="504" />
]

---
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="statistical_geometries_files/figure-html/cond_means_hard_auto_09_output-1.png" width="504" />
]

---

geom_boxplot(), example of compute happening under the hood

---

```r
*library(tidyverse)
*gapminder::gapminder %>%
* filter(year == 2002) %>%
* ggplot() +
* aes(x = continent) +
* aes(y = gdpPercap) +
* geom_boxplot() ->
*g

*g 
```
]
 
.panel2-boxplot-user[
<img src="statistical_geometries_files/figure-html/boxplot_user_01_output-1.png" width="504" />
]

---
count: false

```r
library(tidyverse)
gapminder::gapminder %>%
  filter(year == 2002) %>%
  ggplot() +
  aes(x = continent) +
  aes(y = gdpPercap) +
  geom_boxplot() ->
g

g %>%
* layer_data()
```
]
 
.panel2-boxplot-user[

```
        ymin      lower    middle     upper      ymax
1   241.1659   780.5778  1215.683  3314.887  6316.165
2  1270.3649  4858.3475  6994.775  8797.641 11460.600
3   611.0000  2092.7124  4090.925 19233.988 36023.105
4  4604.2117 11721.8515 23674.863 30373.363 44683.975
5 23189.8014 25064.2897 26938.778 28813.266 30687.755
                                                      outliers notchupper
1 11003.605, 7703.496, 12521.714, 9534.677, 9021.816, 7710.946   1770.967
2                                 33328.97, 18855.61, 39097.10   8239.592
3                                                                8805.508
4                                                               29055.213
5                                                               31127.242
  notchlower x flipped_aes PANEL group ymin_final ymax_final  xmin  xmax xid
1   660.3994 1       FALSE     1     1   241.1659   12521.71 0.625 1.375   1
2  5749.9582 2       FALSE     1     2  1270.3649   39097.10 1.625 2.375   2
3  -623.6574 3       FALSE     1     3   611.0000   36023.11 2.625 3.375   3
4 18294.5136 4       FALSE     1     4  4604.2117   44683.98 3.625 4.375   4
5 22750.3136 5       FALSE     1     5 23189.8014   30687.75 4.625 5.375   5
  newx new_width weight colour  fill size alpha shape linetype
1    1      0.75      1 grey20 white  0.5    NA    19    solid
2    2      0.75      1 grey20 white  0.5    NA    19    solid
3    3      0.75      1 grey20 white  0.5    NA    19    solid
4    4      0.75      1 grey20 white  0.5    NA    19    solid
5    5      0.75      1 grey20 white  0.5    NA    19    solid
```
]

---

# Like histogram, geom_boxplot does lots of compute in the background

### built from a number of primitives

- rect
- point
- segment

### conceptually distinct from those components

---

## ggplot2 intentionally lean for maintenance purposes.

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

## ggplot2 extensions system exists...

## thus, {ggxmean} r package https://github.com/EvaMaeRey/ggxmean, that gives us geom_x_mean(), and ~ 25 more geoms...

## Mostly creates geoms that inherit from existing geometries

## Cool mechanism, probably underutilized...

---

## Hand*schuh*

## hand shoe

## glove

---

## Nackt*schnecke*

## naked snail

## slug

---

## Daumen*kino*

## thumb movie

## flip book

---

# inheriting geoms: have their own conceptual identity, but inheriting from more primitive forms (segment, point, text, etc)

# geom_*point*_xy_means

# geom_*text*_coordinates

# geom_*segment*_residuals

---

# Inheriting geoms easy to use -- you know the grammar!

# Not horrible to make 
--
(especially now...)

---

# {ggxmean}:  A journey in geom extension...

# Usefulness of the inheritance geom extension mechanism

# for people that want to tell visual statistical stories in a fluid way! :-)

---

# Chapter 1: Wanting to tell a statistical story.

# What does Pearson correlation look like...?

--
.huge[

$$ cor(x,y) = \frac{\sum_{i=1}^n (x_i-\mu_x)(y_i-\mu_y)}{n *\sigma_x \sigma_y} $$
]

https://evamaerey.github.io/statistics/covariance_correlation.html#1

# 'by-hand' compute all values (mean of x, y, differences, sd etc)
--
, fragile
--
, not portable
--
, not delightful!
--
, no group-wise compute
:

---

# Chapter 2: fluidly telling the covariance, variance, sd, correlation story

Starting with the mean of x (so {ggxmean})

---

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

]

---
count: false

```r
library(ggxmean)
*palmerpenguins::penguins
```
]
 
.panel2-correlation-auto[

```
# A tibble: 344 × 8
   species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
   <fct>   <fct>              <dbl>         <dbl>             <int>       <int>
 1 Adelie  Torgersen           39.1          18.7               181        3750
 2 Adelie  Torgersen           39.5          17.4               186        3800
 3 Adelie  Torgersen           40.3          18                 195        3250
 4 Adelie  Torgersen           NA            NA                  NA          NA
 5 Adelie  Torgersen           36.7          19.3               193        3450
 6 Adelie  Torgersen           39.3          20.6               190        3650
 7 Adelie  Torgersen           38.9          17.8               181        3625
 8 Adelie  Torgersen           39.2          19.6               195        4675
 9 Adelie  Torgersen           34.1          18.1               193        3475
10 Adelie  Torgersen           42            20.2               190        4250
# … with 334 more rows, and 2 more variables: sex <fct>, year <int>
```
]

---
count: false

```r
library(ggxmean)
palmerpenguins::penguins %>%
* ggplot()
```
]
 
.panel2-correlation-auto[
<img src="statistical_geometries_files/figure-html/correlation_auto_03_output-1.png" width="504" />
]

---
count: false

```r
library(ggxmean)
palmerpenguins::penguins %>%
  ggplot() +
* aes(x = bill_length_mm)
```
]
 
.panel2-correlation-auto[
<img src="statistical_geometries_files/figure-html/correlation_auto_04_output-1.png" width="504" />
]

---
count: false

```r
library(ggxmean)
palmerpenguins::penguins %>%
  ggplot() +
  aes(x = bill_length_mm) +
* aes(y = flipper_length_mm)
```
]
 
.panel2-correlation-auto[
<img src="statistical_geometries_files/figure-html/correlation_auto_05_output-1.png" width="504" />
]

---
count: false

```r
library(ggxmean)
palmerpenguins::penguins %>%
  ggplot() +
  aes(x = bill_length_mm) +
  aes(y = flipper_length_mm) +
* geom_point()
```
]
 
.panel2-correlation-auto[
<img src="statistical_geometries_files/figure-html/correlation_auto_06_output-1.png" width="504" />
]

---
count: false

```r
library(ggxmean)
palmerpenguins::penguins %>%
  ggplot() +
  aes(x = bill_length_mm) +
  aes(y = flipper_length_mm) +
  geom_point() +
* ggxmean:::geom_x_mean()
```
]
 
.panel2-correlation-auto[
<img src="statistical_geometries_files/figure-html/correlation_auto_07_output-1.png" width="504" />
]

---
count: false

```r
library(ggxmean)
palmerpenguins::penguins %>%
  ggplot() +
  aes(x = bill_length_mm) +
  aes(y = flipper_length_mm) +
  geom_point() +
  ggxmean:::geom_x_mean() +
* ggxmean:::geom_y_mean()
```
]
 
.panel2-correlation-auto[
<img src="statistical_geometries_files/figure-html/correlation_auto_08_output-1.png" width="504" />
]

---
count: false

```r
library(ggxmean)
palmerpenguins::penguins %>%
  ggplot() +
  aes(x = bill_length_mm) +
  aes(y = flipper_length_mm) +
  geom_point() +
  ggxmean:::geom_x_mean() +
  ggxmean:::geom_y_mean() +
* ggxmean:::geom_xdiff()
```
]
 
.panel2-correlation-auto[
<img src="statistical_geometries_files/figure-html/correlation_auto_09_output-1.png" width="504" />
]

---
count: false

```r
library(ggxmean)
palmerpenguins::penguins %>%
  ggplot() +
  aes(x = bill_length_mm) +
  aes(y = flipper_length_mm) +
  geom_point() +
  ggxmean:::geom_x_mean() +
  ggxmean:::geom_y_mean() +
  ggxmean:::geom_xdiff() +
* ggxmean:::geom_ydiff()
```
]
 
.panel2-correlation-auto[
<img src="statistical_geometries_files/figure-html/correlation_auto_10_output-1.png" width="504" />
]

---
count: false

---
count: false

```r
library(ggxmean)
palmerpenguins::penguins %>%
  ggplot() +
  aes(x = bill_length_mm) +
  aes(y = flipper_length_mm) +
  geom_point() +
  ggxmean:::geom_x_mean() +
  ggxmean:::geom_y_mean() +
  ggxmean:::geom_xdiff() +
  ggxmean:::geom_ydiff() +
  ggxmean:::geom_x1sd(linetype = "dashed") +
* ggxmean:::geom_y1sd(linetype = "dashed")
```
]
 
.panel2-correlation-auto[
<img src="statistical_geometries_files/figure-html/correlation_auto_12_output-1.png" width="504" />
]

---
count: false

```r
library(ggxmean)
palmerpenguins::penguins %>%
  ggplot() +
  aes(x = bill_length_mm) +
  aes(y = flipper_length_mm) +
  geom_point() +
  ggxmean:::geom_x_mean() +
  ggxmean:::geom_y_mean() +
  ggxmean:::geom_xdiff() +
  ggxmean:::geom_ydiff() +
  ggxmean:::geom_x1sd(linetype = "dashed") +
  ggxmean:::geom_y1sd(linetype = "dashed") +
* ggxmean:::geom_diffsmultiplied()
```
]
 
.panel2-correlation-auto[
<img src="statistical_geometries_files/figure-html/correlation_auto_13_output-1.png" width="504" />
]

---
count: false

```r
library(ggxmean)
palmerpenguins::penguins %>%
  ggplot() +
  aes(x = bill_length_mm) +
  aes(y = flipper_length_mm) +
  geom_point() +
  ggxmean:::geom_x_mean() +
  ggxmean:::geom_y_mean() +
  ggxmean:::geom_xdiff() +
  ggxmean:::geom_ydiff() +
  ggxmean:::geom_x1sd(linetype = "dashed") +
  ggxmean:::geom_y1sd(linetype = "dashed") +
  ggxmean:::geom_diffsmultiplied() +
* ggxmean:::geom_xydiffsmean(alpha = 1)
```
]
 
.panel2-correlation-auto[
<img src="statistical_geometries_files/figure-html/correlation_auto_14_output-1.png" width="504" />
]

---
count: false

```r
library(ggxmean)
palmerpenguins::penguins %>%
  ggplot() +
  aes(x = bill_length_mm) +
  aes(y = flipper_length_mm) +
  geom_point() +
  ggxmean:::geom_x_mean() +
  ggxmean:::geom_y_mean() +
  ggxmean:::geom_xdiff() +
  ggxmean:::geom_ydiff() +
  ggxmean:::geom_x1sd(linetype = "dashed") +
  ggxmean:::geom_y1sd(linetype = "dashed") +
  ggxmean:::geom_diffsmultiplied() +
  ggxmean:::geom_xydiffsmean(alpha = 1) +
* ggxmean:::geom_rsq1()
```
]
 
.panel2-correlation-auto[
<img src="statistical_geometries_files/figure-html/correlation_auto_15_output-1.png" width="504" />
]

---
count: false

```r
library(ggxmean)
palmerpenguins::penguins %>%
  ggplot() +
  aes(x = bill_length_mm) +
  aes(y = flipper_length_mm) +
  geom_point() +
  ggxmean:::geom_x_mean() +
  ggxmean:::geom_y_mean() +
  ggxmean:::geom_xdiff() +
  ggxmean:::geom_ydiff() +
  ggxmean:::geom_x1sd(linetype = "dashed") +
  ggxmean:::geom_y1sd(linetype = "dashed") +
  ggxmean:::geom_diffsmultiplied() +
  ggxmean:::geom_xydiffsmean(alpha = 1) +
  ggxmean:::geom_rsq1() +
* ggxmean:::geom_corrlabel()
```
]
 
.panel2-correlation-auto[
<img src="statistical_geometries_files/figure-html/correlation_auto_16_output-1.png" width="504" />
]

---
count: false

```r
library(ggxmean)
palmerpenguins::penguins %>%
  ggplot() +
  aes(x = bill_length_mm) +
  aes(y = flipper_length_mm) +
  geom_point() +
  ggxmean:::geom_x_mean() +
  ggxmean:::geom_y_mean() +
  ggxmean:::geom_xdiff() +
  ggxmean:::geom_ydiff() +
  ggxmean:::geom_x1sd(linetype = "dashed") +
  ggxmean:::geom_y1sd(linetype = "dashed") +
  ggxmean:::geom_diffsmultiplied() +
  ggxmean:::geom_xydiffsmean(alpha = 1) +
  ggxmean:::geom_rsq1() +
  ggxmean:::geom_corrlabel() +
* facet_wrap(~species)
```
]
 
.panel2-correlation-auto[
<img src="statistical_geometries_files/figure-html/correlation_auto_17_output-1.png" width="504" />
]

---

# Chapter 3, Visually telling an MA206 story

## Linear model (OLS)

---

```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