ggsample and the CLT

# ggsample and the CLT
## Using flipbookr and xaringan
### Me

---

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    display: block;
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code.r.hljs.remark-code{
  position: relative;
  overflow-x: hidden;
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code.r.hljs.remark-code:hover{
  overflow-x:visible;
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---

```r
# population mean
*ggplot(data = faithful)
```
]
 
.panel2-faithful1-auto[
![](ggsample_and_the_clt_flipbook_files/figure-html/faithful1_auto_01_output-1.png)
]

---
count: false

```r
# population mean
ggplot(data = faithful) +
* aes(x = waiting)
```
]
 
.panel2-faithful1-auto[
![](ggsample_and_the_clt_flipbook_files/figure-html/faithful1_auto_02_output-1.png)
]

---
count: false

```r
# population mean
ggplot(data = faithful) +
  aes(x = waiting) +
* geom_rug()
```
]
 
.panel2-faithful1-auto[
![](ggsample_and_the_clt_flipbook_files/figure-html/faithful1_auto_03_output-1.png)
]

---
count: false

```r
# population mean
ggplot(data = faithful) +
  aes(x = waiting) +
  geom_rug() +
* geom_dotplot(dotsize = .5)
```
]
 
.panel2-faithful1-auto[
![](ggsample_and_the_clt_flipbook_files/figure-html/faithful1_auto_04_output-1.png)
]

---
count: false

```r
# population mean
ggplot(data = faithful) +
  aes(x = waiting) +
  geom_rug() +
  geom_dotplot(dotsize = .5) +
* ggxmean::geom_x_mean()
```
]
 
.panel2-faithful1-auto[
![](ggsample_and_the_clt_flipbook_files/figure-html/faithful1_auto_05_output-1.png)
]

---
count: false

```r
# population mean
ggplot(data = faithful) +
  aes(x = waiting) +
  geom_rug() +
  geom_dotplot(dotsize = .5) +
  ggxmean::geom_x_mean() +
* ggxmean::geom_x_mean_label()
```
]
 
.panel2-faithful1-auto[
![](ggsample_and_the_clt_flipbook_files/figure-html/faithful1_auto_06_output-1.png)
]

---

```r
# A single sample mean
last_plot() +
  scale_x_continuous(limits = c(40,100)) +
  ggsample::facet_sample(n_facets = 1, n_sampled = 20)
```
]
 
.panel2-single_sample-10[
![](ggsample_and_the_clt_flipbook_files/figure-html/single_sample_10_01_output-1.png)
]

---
count: false

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count: false

---
count: false

---
count: false

---
count: false

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count: false

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count: false

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count: false

---
count: false

---

```r
# A bunch of sample means
*last_plot()
```
]
 
.panel2-bunch_of_samples-auto[
![](ggsample_and_the_clt_flipbook_files/figure-html/bunch_of_samples_auto_01_output-1.png)
]

---
count: false

```r
# A bunch of sample means
last_plot() +
* ggsample::facet_sample(n_facets = 20, n_sampled = 20)
```
]
 
.panel2-bunch_of_samples-auto[
![](ggsample_and_the_clt_flipbook_files/figure-html/bunch_of_samples_auto_02_output-1.png)
]

---

```r
# How can we take many samples
*sample_wait_times <- function(x){

* sample(x = faithful$waiting, size = 20)

*} 
```
]
 
.panel2-make_a_function-auto[

]

---
count: false

```r
# How can we take many samples
sample_wait_times <- function(x){

sample(x = faithful$waiting, size = 20)

}

# check that function is working
*sample_wait_times()
```
]
 
.panel2-make_a_function-auto[

```
 [1] 56 89 49 75 79 64 89 82 77 74 48 85 53 45 67 82 54 58 50 82
```
]

---

```r
*set.seed(12345)
```
]
 
.panel2-normal_dist-auto[

]

---
count: false

```r
set.seed(12345)
*tibble(x = 1:10000)   # for 1000 trials
```
]
 
.panel2-normal_dist-auto[

```
# A tibble: 10,000 x 1
       x
   <int>
 1     1
 2     2
 3     3
 4     4
 5     5
 6     6
 7     7
 8     8
 9     9
10    10
11    11
12    12
13    13
14    14
15    15
16    16
17    17
18    18
19    19
20    20
21    21
22    22
23    23
24    24
25    25
26    26
27    27
28    28
29    29
30    30
31    31
32    32
33    33
34    34
35    35
36    36
37    37
38    38
39    39
40    40
41    41
42    42
43    43
44    44
45    45
46    46
47    47
48    48
49    49
50    50
51    51
52    52
53    53
54    54
55    55
# ... with 9,945 more rows
```
]

---
count: false

```r
set.seed(12345)
tibble(x = 1:10000) %>%  # for 1000 trials
* mutate(sample = map(x, sample_wait_times))   # sample
```
]
 
.panel2-normal_dist-auto[

```
# A tibble: 10,000 x 2
       x sample    
   <int> <list>    
 1     1 <dbl [20]>
 2     2 <dbl [20]>
 3     3 <dbl [20]>
 4     4 <dbl [20]>
 5     5 <dbl [20]>
 6     6 <dbl [20]>
 7     7 <dbl [20]>
 8     8 <dbl [20]>
 9     9 <dbl [20]>
10    10 <dbl [20]>
11    11 <dbl [20]>
12    12 <dbl [20]>
13    13 <dbl [20]>
14    14 <dbl [20]>
15    15 <dbl [20]>
16    16 <dbl [20]>
17    17 <dbl [20]>
18    18 <dbl [20]>
19    19 <dbl [20]>
20    20 <dbl [20]>
21    21 <dbl [20]>
22    22 <dbl [20]>
23    23 <dbl [20]>
24    24 <dbl [20]>
25    25 <dbl [20]>
26    26 <dbl [20]>
27    27 <dbl [20]>
28    28 <dbl [20]>
29    29 <dbl [20]>
30    30 <dbl [20]>
31    31 <dbl [20]>
32    32 <dbl [20]>
33    33 <dbl [20]>
34    34 <dbl [20]>
35    35 <dbl [20]>
36    36 <dbl [20]>
37    37 <dbl [20]>
38    38 <dbl [20]>
39    39 <dbl [20]>
40    40 <dbl [20]>
41    41 <dbl [20]>
42    42 <dbl [20]>
43    43 <dbl [20]>
44    44 <dbl [20]>
45    45 <dbl [20]>
46    46 <dbl [20]>
47    47 <dbl [20]>
48    48 <dbl [20]>
49    49 <dbl [20]>
50    50 <dbl [20]>
51    51 <dbl [20]>
52    52 <dbl [20]>
53    53 <dbl [20]>
54    54 <dbl [20]>
55    55 <dbl [20]>
# ... with 9,945 more rows
```
]

---
count: false

```r
set.seed(12345)
tibble(x = 1:10000) %>%  # for 1000 trials
  mutate(sample = map(x, sample_wait_times)) %>%  # sample
* mutate(sample_mean = map_dbl(sample, mean))   # and compute means
```
]
 
.panel2-normal_dist-auto[

```
# A tibble: 10,000 x 3
       x sample     sample_mean
   <int> <list>           <dbl>
 1     1 <dbl [20]>        71.0
 2     2 <dbl [20]>        65.4
 3     3 <dbl [20]>        69.6
 4     4 <dbl [20]>        76.8
 5     5 <dbl [20]>        71.3
 6     6 <dbl [20]>        68.7
 7     7 <dbl [20]>        73.1
 8     8 <dbl [20]>        77.5
 9     9 <dbl [20]>        73.3
10    10 <dbl [20]>        68.9
11    11 <dbl [20]>        76.0
12    12 <dbl [20]>        68.0
13    13 <dbl [20]>        71.6
14    14 <dbl [20]>        71.4
15    15 <dbl [20]>        67.6
16    16 <dbl [20]>        67.0
17    17 <dbl [20]>        75  
18    18 <dbl [20]>        71.2
19    19 <dbl [20]>        72.7
20    20 <dbl [20]>        70.4
21    21 <dbl [20]>        72.6
22    22 <dbl [20]>        69  
23    23 <dbl [20]>        71.8
24    24 <dbl [20]>        70.2
25    25 <dbl [20]>        65.9
26    26 <dbl [20]>        70.5
27    27 <dbl [20]>        77.6
28    28 <dbl [20]>        70.8
29    29 <dbl [20]>        72.8
30    30 <dbl [20]>        73.6
31    31 <dbl [20]>        69.4
32    32 <dbl [20]>        75.4
33    33 <dbl [20]>        70.6
34    34 <dbl [20]>        71.8
35    35 <dbl [20]>        67.3
36    36 <dbl [20]>        68.3
37    37 <dbl [20]>        63.6
38    38 <dbl [20]>        71.4
39    39 <dbl [20]>        65.9
40    40 <dbl [20]>        75.2
41    41 <dbl [20]>        65.4
42    42 <dbl [20]>        66.2
43    43 <dbl [20]>        75.2
44    44 <dbl [20]>        71.3
45    45 <dbl [20]>        71.4
46    46 <dbl [20]>        77.3
47    47 <dbl [20]>        68.9
48    48 <dbl [20]>        71.8
49    49 <dbl [20]>        69.4
50    50 <dbl [20]>        65.2
51    51 <dbl [20]>        65.8
52    52 <dbl [20]>        76.4
53    53 <dbl [20]>        75.0
54    54 <dbl [20]>        69  
55    55 <dbl [20]>        72.0
# ... with 9,945 more rows
```
]

---
count: false

```r
set.seed(12345)
tibble(x = 1:10000) %>%  # for 1000 trials
  mutate(sample = map(x, sample_wait_times)) %>%  # sample
  mutate(sample_mean = map_dbl(sample, mean)) %>%  # and compute means
* ggplot()
```
]
 
.panel2-normal_dist-auto[
![](ggsample_and_the_clt_flipbook_files/figure-html/normal_dist_auto_05_output-1.png)
]

---
count: false

```r
set.seed(12345)
tibble(x = 1:10000) %>%  # for 1000 trials
  mutate(sample = map(x, sample_wait_times)) %>%  # sample
  mutate(sample_mean = map_dbl(sample, mean)) %>%  # and compute means
  ggplot() +
* aes(x = sample_mean)
```
]
 
.panel2-normal_dist-auto[
![](ggsample_and_the_clt_flipbook_files/figure-html/normal_dist_auto_06_output-1.png)
]

---
count: false

```r
set.seed(12345)
tibble(x = 1:10000) %>%  # for 1000 trials
  mutate(sample = map(x, sample_wait_times)) %>%  # sample
  mutate(sample_mean = map_dbl(sample, mean)) %>%  # and compute means
  ggplot() +
  aes(x = sample_mean) +
* geom_rug()
```
]
 
.panel2-normal_dist-auto[
![](ggsample_and_the_clt_flipbook_files/figure-html/normal_dist_auto_07_output-1.png)
]

---
count: false

---
count: false

```r
set.seed(12345)
tibble(x = 1:10000) %>%  # for 1000 trials
  mutate(sample = map(x, sample_wait_times)) %>%  # sample
  mutate(sample_mean = map_dbl(sample, mean)) %>%  # and compute means
  ggplot() +
  aes(x = sample_mean) +
  geom_rug() +
  geom_histogram() +
* ggxmean::geom_normal_dist(fill = "magenta",
*                           height = 7000)
```
]
 
.panel2-normal_dist-auto[
![](ggsample_and_the_clt_flipbook_files/figure-html/normal_dist_auto_09_output-1.png)
]

---
count: false

```r
set.seed(12345)
tibble(x = 1:10000) %>%  # for 1000 trials
  mutate(sample = map(x, sample_wait_times)) %>%  # sample
  mutate(sample_mean = map_dbl(sample, mean)) %>%  # and compute means
  ggplot() +
  aes(x = sample_mean) +
  geom_rug() +
  geom_histogram() +
  ggxmean::geom_normal_dist(fill = "magenta",
                            height = 7000) +
* ggxmean::geom_normal_dist_zlines(color = "magenta",
*                           height = 7000)
```
]
 
.panel2-normal_dist-auto[
![](ggsample_and_the_clt_flipbook_files/figure-html/normal_dist_auto_10_output-1.png)
]

---
count: false

```r
set.seed(12345)
tibble(x = 1:10000) %>%  # for 1000 trials
  mutate(sample = map(x, sample_wait_times)) %>%  # sample
  mutate(sample_mean = map_dbl(sample, mean)) %>%  # and compute means
  ggplot() +
  aes(x = sample_mean) +
  geom_rug() +
  geom_histogram() +
  ggxmean::geom_normal_dist(fill = "magenta",
                            height = 7000) +
  ggxmean::geom_normal_dist_zlines(color = "magenta",
                            height = 7000) +
* ggxmean::geom_x_mean()
```
]
 
.panel2-normal_dist-auto[
![](ggsample_and_the_clt_flipbook_files/figure-html/normal_dist_auto_11_output-1.png)
]

---
count: false

```r
set.seed(12345)
tibble(x = 1:10000) %>%  # for 1000 trials
  mutate(sample = map(x, sample_wait_times)) %>%  # sample
  mutate(sample_mean = map_dbl(sample, mean)) %>%  # and compute means
  ggplot() +
  aes(x = sample_mean) +
  geom_rug() +
  geom_histogram() +
  ggxmean::geom_normal_dist(fill = "magenta",
                            height = 7000) +
  ggxmean::geom_normal_dist_zlines(color = "magenta",
                            height = 7000) +
  ggxmean::geom_x_mean() +
* ggxmean::geom_x_mean_label()
```
]
 
.panel2-normal_dist-auto[
![](ggsample_and_the_clt_flipbook_files/figure-html/normal_dist_auto_12_output-1.png)
]

---

```r
# And so
*set.seed(123456)
```
]
 
.panel2-faithfulsamples-auto[

]

---
count: false

```r
# And so
set.seed(123456)
*ggplot(data = faithful)
```
]
 
.panel2-faithfulsamples-auto[
![](ggsample_and_the_clt_flipbook_files/figure-html/faithfulsamples_auto_02_output-1.png)
]

---
count: false

```r
# And so
set.seed(123456)
ggplot(data = faithful) +
* aes(x = waiting)
```
]
 
.panel2-faithfulsamples-auto[
![](ggsample_and_the_clt_flipbook_files/figure-html/faithfulsamples_auto_03_output-1.png)
]

---
count: false

```r
# And so
set.seed(123456)
ggplot(data = faithful) +
  aes(x = waiting) +
* geom_rug()
```
]
 
.panel2-faithfulsamples-auto[
![](ggsample_and_the_clt_flipbook_files/figure-html/faithfulsamples_auto_04_output-1.png)
]

---
count: false

```r
# And so
set.seed(123456)
ggplot(data = faithful) +
  aes(x = waiting) +
  geom_rug() +
* geom_vline(xintercept = rep(71, 20), color = "red")
```
]
 
.panel2-faithfulsamples-auto[
![](ggsample_and_the_clt_flipbook_files/figure-html/faithfulsamples_auto_05_output-1.png)
]

---
count: false

```r
# And so
set.seed(123456)
ggplot(data = faithful) +
  aes(x = waiting) +
  geom_rug() +
  geom_vline(xintercept = rep(71, 20), color = "red") +
* geom_dotplot(dotsize = .5)
```
]
 
.panel2-faithfulsamples-auto[
![](ggsample_and_the_clt_flipbook_files/figure-html/faithfulsamples_auto_06_output-1.png)
]

---
count: false

```r
# And so
set.seed(123456)
ggplot(data = faithful) +
  aes(x = waiting) +
  geom_rug() +
  geom_vline(xintercept = rep(71, 20), color = "red") +
  geom_dotplot(dotsize = .5) +
* ggxmean::geom_x_mean(linetype = "dashed")
```
]
 
.panel2-faithfulsamples-auto[
![](ggsample_and_the_clt_flipbook_files/figure-html/faithfulsamples_auto_07_output-1.png)
]

---
count: false

---
count: false

```r
# And so
set.seed(123456)
ggplot(data = faithful) +
  aes(x = waiting) +
  geom_rug() +
  geom_vline(xintercept = rep(71, 20), color = "red") +
  geom_dotplot(dotsize = .5) +
  ggxmean::geom_x_mean(linetype = "dashed") +
  ggxmean::geom_x_mean_label() +
* ggsample::facet_sample(n_facets = 16,
*                        n_sampled = 20)
```
]
 
.panel2-faithfulsamples-auto[
![](ggsample_and_the_clt_flipbook_files/figure-html/faithfulsamples_auto_09_output-1.png)
]

---
count: false

```r
# And so
set.seed(123456)
ggplot(data = faithful) +
  aes(x = waiting) +
  geom_rug() +
  geom_vline(xintercept = rep(71, 20), color = "red") +
  geom_dotplot(dotsize = .5) +
  ggxmean::geom_x_mean(linetype = "dashed") +
  ggxmean::geom_x_mean_label() +
  ggsample::facet_sample(n_facets = 16,
                         n_sampled = 20) +
* ggxmean:::geom_tdist(height = 7, fill = "goldenrod")
```
]
 
.panel2-faithfulsamples-auto[
![](ggsample_and_the_clt_flipbook_files/figure-html/faithfulsamples_auto_10_output-1.png)
]

---
count: false

```r
# And so
set.seed(123456)
ggplot(data = faithful) +
  aes(x = waiting) +
  geom_rug() +
  geom_vline(xintercept = rep(71, 20), color = "red") +
  geom_dotplot(dotsize = .5) +
  ggxmean::geom_x_mean(linetype = "dashed") +
  ggxmean::geom_x_mean_label() +
  ggsample::facet_sample(n_facets = 16,
                         n_sampled = 20) +
  ggxmean:::geom_tdist(height = 7, fill = "goldenrod") +
* ggxmean:::geom_ttestconf(color = "darkgreen",
*                          alpha = 1)
```
]
 
.panel2-faithfulsamples-auto[
![](ggsample_and_the_clt_flipbook_files/figure-html/faithfulsamples_auto_11_output-1.png)
]

---