Exploded code

# Exploded code
## Using flipbookr and xaringan
### Me

---

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---

```r
*knitr::opts_chunk$set(echo = TRUE, warning = F, message = F)
```
]
 
.panel2-setup-auto[

]

---

```r
*library(tidyverse)
```
]
 
.panel2-e1-auto[

]

---
count: false

```r
library(tidyverse)
*library(ggseg)
```
]
 
.panel2-e1-auto[

]

---
count: false

```r
library(tidyverse)
library(ggseg)

*ggseg(
* view = "medial",
* )
```
]
 
.panel2-e1-auto[
![](ggseg_flipbook_files/figure-html/e1_auto_03_output-1.png)
]

---
count: false

```r
library(tidyverse)
library(ggseg)

ggseg(
  view = "medial",
  ) +
* theme_darkbrain()
```
]
 
.panel2-e1-auto[
![](ggseg_flipbook_files/figure-html/e1_auto_04_output-1.png)
]

---
count: false

```r
library(tidyverse)
library(ggseg)

ggseg(
  view = "medial",
  ) +
  theme_darkbrain() +
* labs(title = "dk",
*      subtitle = "medial view")
```
]
 
.panel2-e1-auto[
![](ggseg_flipbook_files/figure-html/e1_auto_05_output-1.png)
]

---

```r
*tibble(region = rep(c("transverse temporal",
*              "insula",
*              "precentral",
*              "superior parietal"), 2),
*      p = sample(seq(0, .5, .001), 8),
*      g = c("A", "B", "C", "D", "B", "A", "C","A"),
*      AgeG = c(rep("Young", 4),
*               rep("Old", 4 ))
*) 
```
]
 
.panel2-ex2-auto[

```
# A tibble: 8 × 4
  region                  p g     AgeG 
  <chr>               <dbl> <chr> <chr>
1 transverse temporal 0.016 A     Young
2 insula              0.215 B     Young
3 precentral          0.302 C     Young
4 superior parietal   0.276 D     Young
5 transverse temporal 0.442 B     Old  
6 insula              0.323 A     Old  
7 precentral          0.206 C     Old  
8 superior parietal   0.322 A     Old  
```
]

---
count: false

```r
tibble(region = rep(c("transverse temporal",
               "insula",
               "precentral",
               "superior parietal"), 2),
       p = sample(seq(0, .5, .001), 8),
       g = c("A", "B", "C", "D", "B", "A", "C","A"),
       AgeG = c(rep("Young", 4),
                rep("Old", 4 ))
) %>%
* ggseg(.data = .,
*       atlas = dk,
*       position = "stacked",
*       show.legend = FALSE)
```
]
 
.panel2-ex2-auto[
![](ggseg_flipbook_files/figure-html/ex2_auto_02_output-1.png)
]

---
count: false

```r
tibble(region = rep(c("transverse temporal",
               "insula",
               "precentral",
               "superior parietal"), 2),
       p = sample(seq(0, .5, .001), 8),
       g = c("A", "B", "C", "D", "B", "A", "C","A"),
       AgeG = c(rep("Young", 4),
                rep("Old", 4 ))
) %>%
  ggseg(.data = .,
        atlas = dk,
        position = "stacked",
        show.legend = FALSE) +
* aes(fill = p)
```
]
 
.panel2-ex2-auto[
![](ggseg_flipbook_files/figure-html/ex2_auto_03_output-1.png)
]

---
count: false

```r
tibble(region = rep(c("transverse temporal",
               "insula",
               "precentral",
               "superior parietal"), 2),
       p = sample(seq(0, .5, .001), 8),
       g = c("A", "B", "C", "D", "B", "A", "C","A"),
       AgeG = c(rep("Young", 4),
                rep("Old", 4 ))
) %>%
  ggseg(.data = .,
        atlas = dk,
        position = "stacked",
        show.legend = FALSE) +
  aes(fill = p) +
* scale_fill_viridis_c()
```
]
 
.panel2-ex2-auto[
![](ggseg_flipbook_files/figure-html/ex2_auto_04_output-1.png)
]

---
count: false

```r
tibble(region = rep(c("transverse temporal",
               "insula",
               "precentral",
               "superior parietal"), 2),
       p = sample(seq(0, .5, .001), 8),
       g = c("A", "B", "C", "D", "B", "A", "C","A"),
       AgeG = c(rep("Young", 4),
                rep("Old", 4 ))
) %>%
  ggseg(.data = .,
        atlas = dk,
        position = "stacked",
        show.legend = FALSE) +
  aes(fill = p) +
  scale_fill_viridis_c() +
* aes(color = g)
```
]
 
.panel2-ex2-auto[
![](ggseg_flipbook_files/figure-html/ex2_auto_05_output-1.png)
]

---
count: false

```r
tibble(region = rep(c("transverse temporal",
               "insula",
               "precentral",
               "superior parietal"), 2),
       p = sample(seq(0, .5, .001), 8),
       g = c("A", "B", "C", "D", "B", "A", "C","A"),
       AgeG = c(rep("Young", 4),
                rep("Old", 4 ))
) %>%
  ggseg(.data = .,
        atlas = dk,
        position = "stacked",
        show.legend = FALSE) +
  aes(fill = p) +
  scale_fill_viridis_c() +
  aes(color = g) +
* scale_colour_viridis_d(option = "magma")
```
]
 
.panel2-ex2-auto[
![](ggseg_flipbook_files/figure-html/ex2_auto_06_output-1.png)
]

---
count: false

```r
tibble(region = rep(c("transverse temporal",
               "insula",
               "precentral",
               "superior parietal"), 2),
       p = sample(seq(0, .5, .001), 8),
       g = c("A", "B", "C", "D", "B", "A", "C","A"),
       AgeG = c(rep("Young", 4),
                rep("Old", 4 ))
) %>%
  ggseg(.data = .,
        atlas = dk,
        position = "stacked",
        show.legend = FALSE) +
  aes(fill = p) +
  scale_fill_viridis_c() +
  aes(color = g) +
  scale_colour_viridis_d(option = "magma") +
* aes(fill = NULL)
```
]
 
.panel2-ex2-auto[
![](ggseg_flipbook_files/figure-html/ex2_auto_07_output-1.png)
]

---
count: false

```r
tibble(region = rep(c("transverse temporal",
               "insula",
               "precentral",
               "superior parietal"), 2),
       p = sample(seq(0, .5, .001), 8),
       g = c("A", "B", "C", "D", "B", "A", "C","A"),
       AgeG = c(rep("Young", 4),
                rep("Old", 4 ))
) %>%
  ggseg(.data = .,
        atlas = dk,
        position = "stacked",
        show.legend = FALSE) +
  aes(fill = p) +
  scale_fill_viridis_c() +
  aes(color = g) +
  scale_colour_viridis_d(option = "magma") +
  aes(fill = NULL) +
* facet_wrap(~AgeG, ncol = 3)
```
]
 
.panel2-ex2-auto[
![](ggseg_flipbook_files/figure-html/ex2_auto_08_output-1.png)
]

---
count: false

```r
tibble(region = rep(c("transverse temporal",
               "insula",
               "precentral",
               "superior parietal"), 2),
       p = sample(seq(0, .5, .001), 8),
       g = c("A", "B", "C", "D", "B", "A", "C","A"),
       AgeG = c(rep("Young", 4),
                rep("Old", 4 ))
) %>%
  ggseg(.data = .,
        atlas = dk,
        position = "stacked",
        show.legend = FALSE) +
  aes(fill = p) +
  scale_fill_viridis_c() +
  aes(color = g) +
  scale_colour_viridis_d(option = "magma") +
  aes(fill = NULL) +
  facet_wrap(~AgeG, ncol = 3) +
* labs(title = "Ungrouped data")
```
]
 
.panel2-ex2-auto[
![](ggseg_flipbook_files/figure-html/ex2_auto_09_output-1.png)
]

---
count: false

```r
tibble(region = rep(c("transverse temporal",
               "insula",
               "precentral",
               "superior parietal"), 2),
       p = sample(seq(0, .5, .001), 8),
       g = c("A", "B", "C", "D", "B", "A", "C","A"),
       AgeG = c(rep("Young", 4),
                rep("Old", 4 ))
) %>%
  ggseg(.data = .,
        atlas = dk,
        position = "stacked",
        show.legend = FALSE) +
  aes(fill = p) +
  scale_fill_viridis_c() +
  aes(color = g) +
  scale_colour_viridis_d(option = "magma") +
  aes(fill = NULL) +
  facet_wrap(~AgeG, ncol = 3) +
  labs(title = "Ungrouped data")

*layer_data(plot = last_plot())
```
]
 
.panel2-ex2-auto[
![](ggseg_flipbook_files/figure-html/ex2_auto_10_output-1.png)

```
    colour         y         x group subgroup PANEL   fill size linetype alpha
1     <NA>  34.46407  84.32563     1        1     3 grey20  0.5        1    NA
2     <NA>  33.33467  84.16625     1        1     3 grey20  0.5        1    NA
3     <NA>  26.38733  92.88468     1        1     3 grey20  0.5        1    NA
4     <NA>  27.83508  92.55951     1        1     3 grey20  0.5        1    NA
5     <NA>  32.99130  88.42844     1        1     3 grey20  0.5        1    NA
6     <NA>  34.31282  86.28085     1        1     3 grey20  0.5        1    NA
7     <NA>  34.46407  84.32563     1        1     3 grey20  0.5        1    NA
8     <NA>  23.91232  94.20152     2        1     3 grey20  0.5        1    NA
9     <NA>  23.90330  94.20817     2        1     3 grey20  0.5        1    NA
10    <NA>  23.91323  94.21681     2        1     3 grey20  0.5        1    NA
11    <NA>  23.92040  94.21003     2        1     3 grey20  0.5        1    NA
12    <NA>  23.91232  94.20152     2        1     3 grey20  0.5        1    NA
13    <NA> 108.81394 214.82147     3        1     3 grey20  0.5        1    NA
14    <NA> 107.65361 210.46947     3        1     3 grey20  0.5        1    NA
15    <NA> 104.40110 207.95788     3        1     3 grey20  0.5        1    NA
16    <NA> 101.39895 204.07948     3        1     3 grey20  0.5        1    NA
17    <NA>  89.95142 195.91792     3        1     3 grey20  0.5        1    NA
18    <NA>  86.66585 189.97995     3        1     3 grey20  0.5        1    NA
19    <NA>  83.26921 186.62237     3        1     3 grey20  0.5        1    NA
20    <NA>  73.29106 180.72329     3        1     3 grey20  0.5        1    NA
21    <NA>  65.48774 180.03683     3        1     3 grey20  0.5        1    NA
22    <NA>  61.54807 177.14671     3        1     3 grey20  0.5        1    NA
23    <NA>  60.99465 181.17933     3        1     3 grey20  0.5        1    NA
24    <NA>  61.81593 191.39561     3        1     3 grey20  0.5        1    NA
25    <NA>  64.18767 206.04892     3        1     3 grey20  0.5        1    NA
26    <NA>  65.36395 209.24998     3        1     3 grey20  0.5        1    NA
27    <NA>  70.32102 217.20044     3        1     3 grey20  0.5        1    NA
28    <NA>  73.19604 220.21282     3        1     3 grey20  0.5        1    NA
29    <NA>  88.53093 230.62533     3        1     3 grey20  0.5        1    NA
30    <NA>  90.22672 230.99573     3        1     3 grey20  0.5        1    NA
31    <NA>  98.13730 227.17123     3        1     3 grey20  0.5        1    NA
32    <NA> 101.07891 222.87775     3        1     3 grey20  0.5        1    NA
33    <NA> 103.63107 220.61792     3        1     3 grey20  0.5        1    NA
34    <NA> 104.69531 217.45709     3        1     3 grey20  0.5        1    NA
35    <NA> 108.81394 214.82147     3        1     3 grey20  0.5        1    NA
36    <NA> 184.31442 106.16003     4        1     3 grey20  0.5        1    NA
37    <NA> 180.91132  93.61281     4        1     3 grey20  0.5        1    NA
38    <NA> 173.94538  76.94933     4        1     3 grey20  0.5        1    NA
39    <NA> 168.89276  67.47581     4        1     3 grey20  0.5        1    NA
40    <NA> 163.11316  58.86546     4        1     3 grey20  0.5        1    NA
41    <NA> 157.50335  52.76675     4        1     3 grey20  0.5        1    NA
42    <NA> 156.46844  52.69114     4        1     3 grey20  0.5        1    NA
43    <NA> 154.55683  54.85056     4        1     3 grey20  0.5        1    NA
44    <NA> 151.74032  55.37262     4        1     3 grey20  0.5        1    NA
45    <NA> 149.33529  57.62704     4        1     3 grey20  0.5        1    NA
46    <NA> 144.95095  57.35486     4        1     3 grey20  0.5        1    NA
47    <NA> 141.50689  60.59761     4        1     3 grey20  0.5        1    NA
48    <NA> 136.85295  61.11059     4        1     3 grey20  0.5        1    NA
49    <NA> 132.21793  63.30225     4        1     3 grey20  0.5        1    NA
50    <NA> 128.95512  66.98750     4        1     3 grey20  0.5        1    NA
51    <NA> 124.05270  69.23790     4        1     3 grey20  0.5        1    NA
52    <NA> 123.23892  73.82631     4        1     3 grey20  0.5        1    NA
53    <NA> 126.11653  80.95370     4        1     3 grey20  0.5        1    NA
54    <NA> 133.15302  85.83042     4        1     3 grey20  0.5        1    NA
55    <NA> 141.51071  88.70268     4        1     3 grey20  0.5        1    NA
56    <NA> 146.76656  93.26094     4        1     3 grey20  0.5        1    NA
57    <NA> 157.19585  97.90184     4        1     3 grey20  0.5        1    NA
58    <NA> 166.38766 103.49982     4        1     3 grey20  0.5        1    NA
59    <NA> 170.97680 104.68918     4        1     3 grey20  0.5        1    NA
60    <NA> 178.06644 111.59143     4        1     3 grey20  0.5        1    NA
61    <NA> 180.56341 113.08734     4        1     3 grey20  0.5        1    NA
62    <NA> 182.70929 113.52920     4        1     3 grey20  0.5        1    NA
63    <NA> 183.95871 112.36570     4        1     3 grey20  0.5        1    NA
64    <NA> 184.31442 106.16003     4        1     3 grey20  0.5        1    NA
65    <NA>  48.35713 256.54807     5        1     3 grey20  0.5        1    NA
66    <NA>  45.02750 244.04403     5        1     3 grey20  0.5        1    NA
67    <NA>  33.05511 221.35735     5        1     3 grey20  0.5        1    NA
68    <NA>  33.11799 224.62905     5        1     3 grey20  0.5        1    NA
69    <NA>  41.05674 249.18920     5        1     3 grey20  0.5        1    NA
70    <NA>  45.47859 262.31775     5        1     3 grey20  0.5        1    NA
71    <NA>  47.16155 264.32461     5        1     3 grey20  0.5        1    NA
72    <NA>  48.33476 263.21036     5        1     3 grey20  0.5        1    NA
73    <NA>  48.35713 256.54807     5        1     3 grey20  0.5        1    NA
74    <NA>  31.75113 219.14196     6        1     3 grey20  0.5        1    NA
75    <NA>  31.74434 219.15742     6        1     3 grey20  0.5        1    NA
76    <NA>  31.75275 219.17732     6        1     3 grey20  0.5        1    NA
77    <NA>  31.75961 219.16237     6        1     3 grey20  0.5        1    NA
78    <NA>  31.75113 219.14196     6        1     3 grey20  0.5        1    NA
79    <NA>  30.77113 217.18196     7        1     3 grey20  0.5        1    NA
80    <NA>  30.76434 217.19742     7        1     3 grey20  0.5        1    NA
81    <NA>  30.77275 217.21732     7        1     3 grey20  0.5        1    NA
82    <NA>  30.77961 217.20237     7        1     3 grey20  0.5        1    NA
83    <NA>  30.77113 217.18196     7        1     3 grey20  0.5        1    NA
84    <NA> 161.62334 218.43732     8        1     3 grey20  0.5        1    NA
85    <NA> 157.68995 214.91403     8        1     3 grey20  0.5        1    NA
86    <NA> 152.43575 223.29315     8        1     3 grey20  0.5        1    NA
87    <NA> 149.66529 225.30004     8        1     3 grey20  0.5        1    NA
88    <NA> 138.37893 224.17000     8        1     3 grey20  0.5        1    NA
89    <NA> 126.96534 225.61354     8        1     3 grey20  0.5        1    NA
90    <NA> 117.54399 223.65238     8        1     3 grey20  0.5        1    NA
91    <NA> 112.99908 218.37809     8        1     3 grey20  0.5        1    NA
92    <NA> 109.54029 216.32304     8        1     3 grey20  0.5        1    NA
93    <NA> 106.89191 216.03190     8        1     3 grey20  0.5        1    NA
94    <NA> 104.23573 217.24576     8        1     3 grey20  0.5        1    NA
95    <NA>  98.48910 226.26403     8        1     3 grey20  0.5        1    NA
96    <NA>  90.26738 231.86209     8        1     3 grey20  0.5        1    NA
97    <NA>  89.88409 238.72863     8        1     3 grey20  0.5        1    NA
98    <NA>  87.17573 241.77224     8        1     3 grey20  0.5        1    NA
99    <NA>  83.16538 249.06756     8        1     3 grey20  0.5        1    NA
100   <NA>  84.48085 252.91240     8        1     3 grey20  0.5        1    NA
 [ reached 'max' / getOption("max.print") -- omitted 3137 rows ]
```
]

---
count: false

```r
tibble(region = rep(c("transverse temporal",
               "insula",
               "precentral",
               "superior parietal"), 2),
       p = sample(seq(0, .5, .001), 8),
       g = c("A", "B", "C", "D", "B", "A", "C","A"),
       AgeG = c(rep("Young", 4),
                rep("Old", 4 ))
) %>%
  ggseg(.data = .,
        atlas = dk,
        position = "stacked",
        show.legend = FALSE) +
  aes(fill = p) +
  scale_fill_viridis_c() +
  aes(color = g) +
  scale_colour_viridis_d(option = "magma") +
  aes(fill = NULL) +
  facet_wrap(~AgeG, ncol = 3) +
  labs(title = "Ungrouped data")

layer_data(plot = last_plot()) ->
* hide
```
]
 
.panel2-ex2-auto[
![](ggseg_flipbook_files/figure-html/ex2_auto_11_output-1.png)
]

---
count: false

```r
tibble(region = rep(c("transverse temporal",
               "insula",
               "precentral",
               "superior parietal"), 2),
       p = sample(seq(0, .5, .001), 8),
       g = c("A", "B", "C", "D", "B", "A", "C","A"),
       AgeG = c(rep("Young", 4),
                rep("Old", 4 ))
) %>%
  ggseg(.data = .,
        atlas = dk,
        position = "stacked",
        show.legend = FALSE) +
  aes(fill = p) +
  scale_fill_viridis_c() +
  aes(color = g) +
  scale_colour_viridis_d(option = "magma") +
  aes(fill = NULL) +
  facet_wrap(~AgeG, ncol = 3) +
  labs(title = "Ungrouped data")

layer_data(plot = last_plot()) ->
  hide

*ggseg
```
]
 
.panel2-ex2-auto[
![](ggseg_flipbook_files/figure-html/ex2_auto_12_output-1.png)

```
function (.data = NULL, atlas = "dk", position = "dispersed", 
    view = NULL, hemisphere = NULL, adapt_scales = TRUE, ...) 
{
    atlas <- if (!is.character(atlas)) {
        atlas
    }
    else {
        get(atlas)
    }
    if (!is_ggseg_atlas(atlas)) 
        atlas <- as_ggseg_atlas(atlas)
    atlas <- unnest(atlas, ggseg)
    stack <- match.arg(position, c("stacked", "dispersed"), several.ok = FALSE)
    if (stack == "stacked") {
        atlas <- stack_brain(atlas)
    }
    if (!is.null(hemisphere)) 
        atlas <- filter(atlas, hemi %in% hemisphere)
    if (!is.null(view)) {
        atlas <- filter(atlas, grepl(view, side))
        if (view == "lateral" & (all(c("left", "right") %in% 
            hemisphere) | is.null(hemisphere)) & stack == "dispersed") {
            atlas <- squish_position(atlas, hemisphere)
        }
    }
    if (!is.null(.data)) {
        if (is_brain_atlas(.data) | is_ggseg_atlas(.data)) 
            stop("Atlas given as '.data', did you mean to give it to 'atlas'?")
        atlas <- brain_join(.data, atlas)
        atlas <- filter(atlas, !is.na(.long))
    }
    gg <- ggplot(data = atlas, aes(x = .long, y = .lat, group = .id, 
        subgroup = .subid)) + geom_polygon(...) + coord_fixed()
    if (adapt_scales) {
        gg <- gg + scale_y_brain(atlas, stack) + scale_x_brain(atlas, 
            stack) + scale_labs_brain(atlas, stack)
    }
    gg + theme_brain()
}
<bytecode: 0x7fef8c773b90>
<environment: namespace:ggseg>
```
]

---
count: false

```r
tibble(region = rep(c("transverse temporal",
               "insula",
               "precentral",
               "superior parietal"), 2),
       p = sample(seq(0, .5, .001), 8),
       g = c("A", "B", "C", "D", "B", "A", "C","A"),
       AgeG = c(rep("Young", 4),
                rep("Old", 4 ))
) %>%
  ggseg(.data = .,
        atlas = dk,
        position = "stacked",
        show.legend = FALSE) +
  aes(fill = p) +
  scale_fill_viridis_c() +
  aes(color = g) +
  scale_colour_viridis_d(option = "magma") +
  aes(fill = NULL) +
  facet_wrap(~AgeG, ncol = 3) +
  labs(title = "Ungrouped data")

layer_data(plot = last_plot()) ->
  hide

ggseg ->
* hide
```
]
 
.panel2-ex2-auto[
![](ggseg_flipbook_files/figure-html/ex2_auto_13_output-1.png)
]

---
count: false

```r
tibble(region = rep(c("transverse temporal",
               "insula",
               "precentral",
               "superior parietal"), 2),
       p = sample(seq(0, .5, .001), 8),
       g = c("A", "B", "C", "D", "B", "A", "C","A"),
       AgeG = c(rep("Young", 4),
                rep("Old", 4 ))
) %>%
  ggseg(.data = .,
        atlas = dk,
        position = "stacked",
        show.legend = FALSE) +
  aes(fill = p) +
  scale_fill_viridis_c() +
  aes(color = g) +
  scale_colour_viridis_d(option = "magma") +
  aes(fill = NULL) +
  facet_wrap(~AgeG, ncol = 3) +
  labs(title = "Ungrouped data")

layer_data(plot = last_plot()) ->
  hide

ggseg ->
  hide

*dk
```
]
 
.panel2-ex2-auto[
![](ggseg_flipbook_files/figure-html/ex2_auto_14_output-1.png)

```
# dk cortical brain atlas
  regions: 35 
  hemispheres: left, right 
  side views: lateral, medial 
  palette: yes 
  use: ggplot() + geom_brain() 
----
   hemi  side    region                     label                       roi  
   <chr> <chr>   <chr>                      <chr>                       <chr>
 1 left  lateral bankssts                   lh_bankssts                 0002 
 2 left  lateral caudal middle frontal      lh_caudalmiddlefrontal      0004 
 3 left  lateral fusiform                   lh_fusiform                 0008 
 4 left  lateral inferior parietal          lh_inferiorparietal         0009 
 5 left  lateral inferior temporal          lh_inferiortemporal         0010 
 6 left  lateral lateral occipital          lh_lateraloccipital         0012 
 7 left  lateral lateral orbitofrontal      lh_lateralorbitofrontal     0013 
 8 left  lateral middle temporal            lh_middletemporal           0016 
 9 left  lateral pars opercularis           lh_parsopercularis          0019 
10 left  lateral pars orbitalis             lh_parsorbitalis            0020 
11 left  lateral pars triangularis          lh_parstriangularis         0021 
12 left  lateral postcentral                lh_postcentral              0023 
13 left  lateral precentral                 lh_precentral               0025 
14 left  lateral rostral middle frontal     lh_rostralmiddlefrontal     0028 
15 left  lateral superior frontal           lh_superiorfrontal          0029 
16 left  lateral superior parietal          lh_superiorparietal         0030 
17 left  lateral superior temporal          lh_superiortemporal         0031 
18 left  lateral supramarginal              lh_supramarginal            0032 
19 left  lateral temporal pole              lh_temporalpole             0034 
20 left  lateral transverse temporal        lh_transversetemporal       0035 
21 left  lateral insula                     lh_insula                   0036 
22 left  medial  caudal anterior cingulate  lh_caudalanteriorcingulate  0003 
23 left  medial  corpus callosum            lh_corpuscallosum           0005 
24 left  medial  cuneus                     lh_cuneus                   0006 
25 left  medial  entorhinal                 lh_entorhinal               0007 
26 left  medial  fusiform                   lh_fusiform                 0008 
27 left  medial  isthmus cingulate          lh_isthmuscingulate         0011 
28 left  medial  lateral occipital          lh_lateraloccipital         0012 
29 left  medial  lateral orbitofrontal      lh_lateralorbitofrontal     0013 
30 left  medial  lingual                    lh_lingual                  0014 
31 left  medial  medial orbitofrontal       lh_medialorbitofrontal      0015 
32 left  medial  parahippocampal            lh_parahippocampal          0017 
33 left  medial  paracentral                lh_paracentral              0018 
34 left  medial  pericalcarine              lh_pericalcarine            0022 
35 left  medial  postcentral                lh_postcentral              0023 
36 left  medial  posterior cingulate        lh_posteriorcingulate       0024 
37 left  medial  precentral                 lh_precentral               0025 
38 left  medial  precuneus                  lh_precuneus                0026 
39 left  medial  rostral anterior cingulate lh_rostralanteriorcingulate 0027 
40 left  medial  superior frontal           lh_superiorfrontal          0029 
41 left  medial  superior parietal          lh_superiorparietal         0030 
42 left  medial  frontal pole               lh_frontalpole              0033 
43 left  medial  temporal pole              lh_temporalpole             0034 
44 right medial  caudal anterior cingulate  rh_caudalanteriorcingulate  0003 
45 right medial  corpus callosum            rh_corpuscallosum           0005 
46 right medial  cuneus                     rh_cuneus                   0006 
47 right medial  entorhinal                 rh_entorhinal               0007 
48 right medial  fusiform                   rh_fusiform                 0008 
49 right medial  isthmus cingulate          rh_isthmuscingulate         0011 
50 right medial  lateral occipital          rh_lateraloccipital         0012 
51 right medial  lateral orbitofrontal      rh_lateralorbitofrontal     0013 
52 right medial  lingual                    rh_lingual                  0014 
53 right medial  medial orbitofrontal       rh_medialorbitofrontal      0015 
54 right medial  parahippocampal            rh_parahippocampal          0017 
55 right medial  paracentral                rh_paracentral              0018 
# … with 31 more rows
```
]

---
count: false

```r
tibble(region = rep(c("transverse temporal",
               "insula",
               "precentral",
               "superior parietal"), 2),
       p = sample(seq(0, .5, .001), 8),
       g = c("A", "B", "C", "D", "B", "A", "C","A"),
       AgeG = c(rep("Young", 4),
                rep("Old", 4 ))
) %>%
  ggseg(.data = .,
        atlas = dk,
        position = "stacked",
        show.legend = FALSE) +
  aes(fill = p) +
  scale_fill_viridis_c() +
  aes(color = g) +
  scale_colour_viridis_d(option = "magma") +
  aes(fill = NULL) +
  facet_wrap(~AgeG, ncol = 3) +
  labs(title = "Ungrouped data")

layer_data(plot = last_plot()) ->
  hide

ggseg ->
  hide

dk ->
* hide
```
]
 
.panel2-ex2-auto[
![](ggseg_flipbook_files/figure-html/ex2_auto_15_output-1.png)
]

---
count: false

```r
tibble(region = rep(c("transverse temporal",
               "insula",
               "precentral",
               "superior parietal"), 2),
       p = sample(seq(0, .5, .001), 8),
       g = c("A", "B", "C", "D", "B", "A", "C","A"),
       AgeG = c(rep("Young", 4),
                rep("Old", 4 ))
) %>%
  ggseg(.data = .,
        atlas = dk,
        position = "stacked",
        show.legend = FALSE) +
  aes(fill = p) +
  scale_fill_viridis_c() +
  aes(color = g) +
  scale_colour_viridis_d(option = "magma") +
  aes(fill = NULL) +
  facet_wrap(~AgeG, ncol = 3) +
  labs(title = "Ungrouped data")

layer_data(plot = last_plot()) ->
  hide

ggseg ->
  hide

dk ->
  hide
```
]
 
.panel2-ex2-auto[
![](ggseg_flipbook_files/figure-html/ex2_auto_16_output-1.png)
]

---

```r
*ggseg (atlas = aseg)
```
]
 
.panel2-ex3-auto[
![](ggseg_flipbook_files/figure-html/ex3_auto_01_output-1.png)
]

---
count: false

```r
ggseg (atlas = aseg) +
* aes(fill = region)
```
]
 
.panel2-ex3-auto[
![](ggseg_flipbook_files/figure-html/ex3_auto_02_output-1.png)
]

---
count: false

```r
ggseg (atlas = aseg) +
  aes(fill = region) +
* theme(legend.justification = c(1, 0))
```
]
 
.panel2-ex3-auto[
![](ggseg_flipbook_files/figure-html/ex3_auto_03_output-1.png)
]

---
count: false

```r
ggseg (atlas = aseg) +
  aes(fill = region) +
  theme(legend.justification = c(1, 0)) +
* theme(legend.position = "bottom")
```
]
 
.panel2-ex3-auto[
![](ggseg_flipbook_files/figure-html/ex3_auto_04_output-1.png)
]

---
count: false

```r
ggseg (atlas = aseg) +
  aes(fill = region) +
  theme(legend.justification = c(1, 0)) +
  theme(legend.position = "bottom") +
* theme(legend.text = element_text(size = 5))
```
]
 
.panel2-ex3-auto[
![](ggseg_flipbook_files/figure-html/ex3_auto_05_output-1.png)
]

---
count: false

```r
ggseg (atlas = aseg) +
  aes(fill = region) +
  theme(legend.justification = c(1, 0)) +
  theme(legend.position = "bottom") +
  theme(legend.text = element_text(size = 5)) +
* guides(fill = guide_legend(ncol = 3))
```
]
 
.panel2-ex3-auto[
![](ggseg_flipbook_files/figure-html/ex3_auto_06_output-1.png)
]

---
count: false

```r
ggseg (atlas = aseg) +
  aes(fill = region) +
  theme(legend.justification = c(1, 0)) +
  theme(legend.position = "bottom") +
  theme(legend.text = element_text(size = 5)) +
  guides(fill = guide_legend(ncol = 3))

*layer_data(plot = last_plot())
```
]
 
.panel2-ex3-auto[
![](ggseg_flipbook_files/figure-html/ex3_auto_07_output-1.png)

```
      fill       y       x group subgroup PANEL colour size linetype alpha
1   grey50 2.70275 2.07673     1        1     1     NA  0.5        1    NA
2   grey50 2.70389 2.07683     1        1     1     NA  0.5        1    NA
3   grey50 2.70801 2.07043     1        1     1     NA  0.5        1    NA
4   grey50 2.71468 2.05593     1        1     1     NA  0.5        1    NA
5   grey50 2.72005 2.04113     1        1     1     NA  0.5        1    NA
6   grey50 2.72201 2.03423     1        1     1     NA  0.5        1    NA
7   grey50 2.72152 2.03393     1        1     1     NA  0.5        1    NA
8   grey50 2.71507 2.03013     1        1     1     NA  0.5        1    NA
9   grey50 2.64708 1.99823     1        1     1     NA  0.5        1    NA
10  grey50 2.64008 1.99553     1        1     1     NA  0.5        1    NA
11  grey50 2.63748 1.99453     1        1     1     NA  0.5        1    NA
12  grey50 2.62050 1.99383     1        1     1     NA  0.5        1    NA
13  grey50 2.60743 1.99253     1        1     1     NA  0.5        1    NA
14  grey50 2.60672 1.99123     1        1     1     NA  0.5        1    NA
15  grey50 2.60461 1.98683     1        1     1     NA  0.5        1    NA
16  grey50 2.59809 1.97363     1        1     1     NA  0.5        1    NA
17  grey50 2.58979 1.95483     1        1     1     NA  0.5        1    NA
18  grey50 2.58497 1.93763     1        1     1     NA  0.5        1    NA
19  grey50 2.58518 1.92713     1        1     1     NA  0.5        1    NA
20  grey50 2.58675 1.92473     1        1     1     NA  0.5        1    NA
21  grey50 2.59240 1.91583     1        1     1     NA  0.5        1    NA
22  grey50 2.62696 1.87533     1        1     1     NA  0.5        1    NA
23  grey50 2.66597 1.83793     1        1     1     NA  0.5        1    NA
24  grey50 2.67372 1.83053     1        1     1     NA  0.5        1    NA
25  grey50 2.68077 1.82393     1        1     1     NA  0.5        1    NA
26  grey50 2.71632 1.79013     1        1     1     NA  0.5        1    NA
27  grey50 2.75477 1.76153     1        1     1     NA  0.5        1    NA
28  grey50 2.76325 1.75753     1        1     1     NA  0.5        1    NA
29  grey50 2.77225 1.75323     1        1     1     NA  0.5        1    NA
30  grey50 2.82205 1.74173     1        1     1     NA  0.5        1    NA
31  grey50 2.87257 1.73143     1        1     1     NA  0.5        1    NA
32  grey50 2.88189 1.72753     1        1     1     NA  0.5        1    NA
33  grey50 2.89226 1.72333     1        1     1     NA  0.5        1    NA
34  grey50 2.94390 1.69833     1        1     1     NA  0.5        1    NA
35  grey50 2.98909 1.66553     1        1     1     NA  0.5        1    NA
36  grey50 2.99626 1.65763     1        1     1     NA  0.5        1    NA
37  grey50 2.99837 1.65523     1        1     1     NA  0.5        1    NA
38  grey50 2.99811 1.64243     1        1     1     NA  0.5        1    NA
39  grey50 2.99045 1.61893     1        1     1     NA  0.5        1    NA
40  grey50 2.97873 1.59183     1        1     1     NA  0.5        1    NA
41  grey50 2.97043 1.57253     1        1     1     NA  0.5        1    NA
42  grey50 2.96815 1.56593     1        1     1     NA  0.5        1    NA
43  grey50 2.96761 1.56423     1        1     1     NA  0.5        1    NA
44  grey50 2.96180 1.55543     1        1     1     NA  0.5        1    NA
45  grey50 2.95807 1.54813     1        1     1     NA  0.5        1    NA
46  grey50 2.95850 1.54723     1        1     1     NA  0.5        1    NA
47  grey50 2.96111 1.54213     1        1     1     NA  0.5        1    NA
48  grey50 2.96848 1.52653     1        1     1     NA  0.5        1    NA
49  grey50 2.96925 1.51603     1        1     1     NA  0.5        1    NA
50  grey50 2.95833 1.51153     1        1     1     NA  0.5        1    NA
51  grey50 2.93957 1.50963     1        1     1     NA  0.5        1    NA
52  grey50 2.93327 1.50913     1        1     1     NA  0.5        1    NA
53  grey50 2.93007 1.50893     1        1     1     NA  0.5        1    NA
54  grey50 2.91157 1.50213     1        1     1     NA  0.5        1    NA
55  grey50 2.89827 1.49113     1        1     1     NA  0.5        1    NA
56  grey50 2.89768 1.48883     1        1     1     NA  0.5        1    NA
57  grey50 2.89594 1.48193     1        1     1     NA  0.5        1    NA
58  grey50 2.88911 1.46163     1        1     1     NA  0.5        1    NA
59  grey50 2.87727 1.44653     1        1     1     NA  0.5        1    NA
60  grey50 2.85868 1.44563     1        1     1     NA  0.5        1    NA
61  grey50 2.83632 1.45643     1        1     1     NA  0.5        1    NA
62  grey50 2.82954 1.46113     1        1     1     NA  0.5        1    NA
63  grey50 2.82487 1.46433     1        1     1     NA  0.5        1    NA
64  grey50 2.80757 1.46913     1        1     1     NA  0.5        1    NA
65  grey50 2.77936 1.47103     1        1     1     NA  0.5        1    NA
66  grey50 2.75261 1.46683     1        1     1     NA  0.5        1    NA
67  grey50 2.73779 1.46043     1        1     1     NA  0.5        1    NA
68  grey50 2.73470 1.45673     1        1     1     NA  0.5        1    NA
69  grey50 2.72884 1.44973     1        1     1     NA  0.5        1    NA
70  grey50 2.70514 1.43373     1        1     1     NA  0.5        1    NA
71  grey50 2.67622 1.43013     1        1     1     NA  0.5        1    NA
72  grey50 2.64887 1.43803     1        1     1     NA  0.5        1    NA
73  grey50 2.63021 1.44683     1        1     1     NA  0.5        1    NA
74  grey50 2.62397 1.44973     1        1     1     NA  0.5        1    NA
75  grey50 2.60991 1.45623     1        1     1     NA  0.5        1    NA
76  grey50 2.54390 1.49833     1        1     1     NA  0.5        1    NA
77  grey50 2.47797 1.54063     1        1     1     NA  0.5        1    NA
78  grey50 2.46393 1.54743     1        1     1     NA  0.5        1    NA
79  grey50 2.45269 1.55273     1        1     1     NA  0.5        1    NA
80  grey50 2.39226 1.57113     1        1     1     NA  0.5        1    NA
81  grey50 2.32948 1.58373     1        1     1     NA  0.5        1    NA
82  grey50 2.31701 1.58633     1        1     1     NA  0.5        1    NA
83  grey50 2.31576 1.58653     1        1     1     NA  0.5        1    NA
84  grey50 2.30566 1.57403     1        1     1     NA  0.5        1    NA
85  grey50 2.29573 1.55443     1        1     1     NA  0.5        1    NA
86  grey50 2.29459 1.55113     1        1     1     NA  0.5        1    NA
87  grey50 2.28987 1.53713     1        1     1     NA  0.5        1    NA
88  grey50 2.26775 1.46643     1        1     1     NA  0.5        1    NA
89  grey50 2.24415 1.39653     1        1     1     NA  0.5        1    NA
90  grey50 2.23861 1.38283     1        1     1     NA  0.5        1    NA
91  grey50 2.23747 1.37983     1        1     1     NA  0.5        1    NA
92  grey50 2.21875 1.36933     1        1     1     NA  0.5        1    NA
93  grey50 2.19352 1.36303     1        1     1     NA  0.5        1    NA
94  grey50 2.18908 1.36223     1        1     1     NA  0.5        1    NA
95  grey50 2.17557 1.35983     1        1     1     NA  0.5        1    NA
96  grey50 2.03983 1.33963     1        1     1     NA  0.5        1    NA
97  grey50 2.02626 1.33743     1        1     1     NA  0.5        1    NA
98  grey50 2.02420 1.33703     1        1     1     NA  0.5        1    NA
99  grey50 2.01465 1.33053     1        1     1     NA  0.5        1    NA
100 grey50 2.00548 1.32183     1        1     1     NA  0.5        1    NA
 [ reached 'max' / getOption("max.print") -- omitted 5085 rows ]
```
]

---
count: false

layer_data(plot = last_plot()) ->
* hide
```
]
 
.panel2-ex3-auto[
![](ggseg_flipbook_files/figure-html/ex3_auto_08_output-1.png)
]

---
count: false

layer_data(plot = last_plot()) ->
  hide

*aseg
```
]
 
.panel2-ex3-auto[
![](ggseg_flipbook_files/figure-html/ex3_auto_09_output-1.png)

```
# aseg subcortical brain atlas
  regions: 18 
  hemispheres: right, left, midline 
  side views: coronal, sagittal 
  palette: yes 
  use: ggplot() + geom_brain() 
----
   hemi    region                  side     label                        
   <chr>   <chr>                   <chr>    <chr>                        
 1 left    thalamus proper         coronal  Left-Thalamus-Proper         
 2 right   thalamus proper         coronal  Right-Thalamus-Proper        
 3 right   lateral ventricle       coronal  Right-Lateral-Ventricle      
 4 left    hippocampus             coronal  Left-Hippocampus             
 5 left    lateral ventricle       coronal  Left-Lateral-Ventricle       
 6 right   putamen                 coronal  Right-Putamen                
 7 right   amygdala                coronal  Right-Amygdala               
 8 left    putamen                 coronal  Left-Putamen                 
 9 right   ventral DC              coronal  Right-VentralDC              
10 left    ventral DC              coronal  Left-VentralDC               
11 right   hippocampus             coronal  Right-Hippocampus            
12 left    amygdala                coronal  Left-Amygdala                
13 left    pallidum                coronal  Left-Pallidum                
14 right   pallidum                coronal  Right-Pallidum               
15 left    caudate                 coronal  Left-Caudate                 
16 right   caudate                 coronal  Right-Caudate                
17 midline 3rd ventricle           sagittal x3rd-ventricle               
18 midline 4th ventricle           sagittal x4th-ventricle               
19 midline brain stem              sagittal Brain-Stem                   
20 midline CC anterior             sagittal CC_Anterior                  
21 midline CC central              sagittal CC_Central                   
22 midline CC mid anterior         sagittal CC_Mid_Anterior              
23 midline CC mid posterior        sagittal CC_Mid_Posterior             
24 midline CC posterior            sagittal CC_Posterior                 
25 midline cerebellum cortex       sagittal Right-Cerebellum-Cortex      
26 midline cerebellum white matter sagittal Right-Cerebellum-White-Matter
```
]