A minimal flipbook

# A minimal flipbook
## With flipbookr and xaringan
### You!

---

---

```r
*cbind(x1 = 3, x2 = c(4:1, 2:5))
```
]
 
.panel2-my_cars-auto[

```
     x1 x2
[1,]  3  4
[2,]  3  3
[3,]  3  2
[4,]  3  1
[5,]  3  2
[6,]  3  3
[7,]  3  4
[8,]  3  5
```
]

---
count: false

```r
cbind(x1 = 3, x2 = c(4:1, 2:5)) ->
* x
```
]
 
.panel2-my_cars-auto[

]

---
count: false

```r
cbind(x1 = 3, x2 = c(4:1, 2:5)) ->
  x

*letters[1:8]
```
]
 
.panel2-my_cars-auto[

```
[1] "a" "b" "c" "d" "e" "f" "g" "h"
```
]

---
count: false

```r
cbind(x1 = 3, x2 = c(4:1, 2:5)) ->
  x

letters[1:8] ->
* dimnames(x)[[1]]
```
]
 
.panel2-my_cars-auto[

]

---
count: false

```r
cbind(x1 = 3, x2 = c(4:1, 2:5)) ->
  x

letters[1:8] ->
  dimnames(x)[[1]]

*x 
```
]
 
.panel2-my_cars-auto[

```
  x1 x2
a  3  4
b  3  3
c  3  2
d  3  1
e  3  2
f  3  3
g  3  4
h  3  5
```
]

---
count: false

```r
cbind(x1 = 3, x2 = c(4:1, 2:5)) ->
  x

letters[1:8] ->
  dimnames(x)[[1]]

x %>%
* apply(X = .,
*       MARGIN = 2,
*       FUN = mean,
*       trim = .2)
```
]
 
.panel2-my_cars-auto[

```
x1 x2 
 3  3 
```
]

---
count: false

```r
cbind(x1 = 3, x2 = c(4:1, 2:5)) ->
  x

letters[1:8] ->
  dimnames(x)[[1]]

x %>%
  apply(X = .,
        MARGIN = 2,
        FUN = mean,
        trim = .2)

*apply(x, 2, sum)
```
]
 
.panel2-my_cars-auto[

```
x1 x2 
 3  3 
```

```
x1 x2 
24 24 
```
]

---
count: false

```r
cbind(x1 = 3, x2 = c(4:1, 2:5)) ->
  x

letters[1:8] ->
  dimnames(x)[[1]]

x %>%
  apply(X = .,
        MARGIN = 2,
        FUN = mean,
        trim = .2)

apply(x, 2, sum) ->
* col.sums
```
]
 
.panel2-my_cars-auto[

```
x1 x2 
 3  3 
```
]

---
count: false

```r
cbind(x1 = 3, x2 = c(4:1, 2:5)) ->
  x

letters[1:8] ->
  dimnames(x)[[1]]

x %>%
  apply(X = .,
        MARGIN = 2,
        FUN = mean,
        trim = .2)

apply(x, 2, sum) ->
  col.sums

*apply(x, 1, sum)
```
]
 
.panel2-my_cars-auto[

```
x1 x2 
 3  3 
```

```
a b c d e f g h 
7 6 5 4 5 6 7 8 
```
]

---
count: false

```r
cbind(x1 = 3, x2 = c(4:1, 2:5)) ->
  x

letters[1:8] ->
  dimnames(x)[[1]]

x %>%
  apply(X = .,
        MARGIN = 2,
        FUN = mean,
        trim = .2)

apply(x, 2, sum) ->
  col.sums

apply(x, 1, sum) ->
* row.sums
```
]
 
.panel2-my_cars-auto[

```
x1 x2 
 3  3 
```
]

---
count: false

```r
cbind(x1 = 3, x2 = c(4:1, 2:5)) ->
  x

letters[1:8] ->
  dimnames(x)[[1]]

x %>%
  apply(X = .,
        MARGIN = 2,
        FUN = mean,
        trim = .2)

apply(x, 2, sum) ->
  col.sums

apply(x, 1, sum) ->
  row.sums

*rbind(cbind(x,
*           Rtot = row.sums),
*     Ctot = c(col.sums, sum(col.sums)))
```
]
 
.panel2-my_cars-auto[

```
x1 x2 
 3  3 
```

```
     x1 x2 Rtot
a     3  4    7
b     3  3    6
c     3  2    5
d     3  1    4
e     3  2    5
f     3  3    6
g     3  4    7
h     3  5    8
Ctot 24 24   48
```
]

---
count: false

```r
cbind(x1 = 3, x2 = c(4:1, 2:5)) ->
  x

letters[1:8] ->
  dimnames(x)[[1]]

x %>%
  apply(X = .,
        MARGIN = 2,
        FUN = mean,
        trim = .2)

apply(x, 2, sum) ->
  col.sums

apply(x, 1, sum) ->
  row.sums

rbind(cbind(x,
            Rtot = row.sums),
      Ctot = c(col.sums, sum(col.sums)))
```
]
 
.panel2-my_cars-auto[

```
x1 x2 
 3  3 
```

```
     x1 x2 Rtot
a     3  4    7
b     3  3    6
c     3  2    5
d     3  1    4
e     3  2    5
f     3  3    6
g     3  4    7
h     3  5    8
Ctot 24 24   48
```
]

---

```r
*list(a = 1:10,
*         beta = exp(-3:3),
*         logic = c(TRUE,FALSE,FALSE,TRUE))
```
]
 
.panel2-lapply-auto[

```
$a
 [1]  1  2  3  4  5  6  7  8  9 10

$beta
[1]  0.04978707  0.13533528  0.36787944  1.00000000  2.71828183  7.38905610
[7] 20.08553692

$logic
[1]  TRUE FALSE FALSE  TRUE
```
]

---
count: false

```r
list(a = 1:10,
          beta = exp(-3:3),
          logic = c(TRUE,FALSE,FALSE,TRUE)) ->
*x 
```
]
 
.panel2-lapply-auto[

]

---
count: false

```r
list(a = 1:10,
          beta = exp(-3:3),
          logic = c(TRUE,FALSE,FALSE,TRUE)) ->
x

# compute the list mean for each list element
*x 
```
]
 
.panel2-lapply-auto[

```
$a
 [1]  1  2  3  4  5  6  7  8  9 10

$beta
[1]  0.04978707  0.13533528  0.36787944  1.00000000  2.71828183  7.38905610
[7] 20.08553692

$logic
[1]  TRUE FALSE FALSE  TRUE
```
]

---
count: false

```r
list(a = 1:10,
          beta = exp(-3:3),
          logic = c(TRUE,FALSE,FALSE,TRUE)) ->
x

# compute the list mean for each list element
x %>%
*lapply(., mean)
```
]
 
.panel2-lapply-auto[

```
$a
[1] 5.5

$beta
[1] 4.535125

$logic
[1] 0.5
```
]

---
count: false

```r
list(a = 1:10,
          beta = exp(-3:3),
          logic = c(TRUE,FALSE,FALSE,TRUE)) ->
x

# compute the list mean for each list element
x %>%
lapply(., mean)

# median and quartiles for each list element
*x 
```
]
 
.panel2-lapply-auto[

```
$a
[1] 5.5

$beta
[1] 4.535125

$logic
[1] 0.5
```

```
$a
 [1]  1  2  3  4  5  6  7  8  9 10

$beta
[1]  0.04978707  0.13533528  0.36787944  1.00000000  2.71828183  7.38905610
[7] 20.08553692

$logic
[1]  TRUE FALSE FALSE  TRUE
```
]

---
count: false

```r
list(a = 1:10,
          beta = exp(-3:3),
          logic = c(TRUE,FALSE,FALSE,TRUE)) ->
x

# compute the list mean for each list element
x %>%
lapply(., mean)

# median and quartiles for each list element
x %>%
*lapply(., quantile, probs = 1:3/4)
```
]
 
.panel2-lapply-auto[

```
$a
[1] 5.5

$beta
[1] 4.535125

$logic
[1] 0.5
```

```
$a
 25%  50%  75% 
3.25 5.50 7.75

$beta
      25%       50%       75% 
0.2516074 1.0000000 5.0536690

$logic
25% 50% 75% 
0.0 0.5 1.0 
```
]

---
count: false

```r
list(a = 1:10,
          beta = exp(-3:3),
          logic = c(TRUE,FALSE,FALSE,TRUE)) ->
x

# compute the list mean for each list element
x %>%
lapply(., mean)

# median and quartiles for each list element
x %>%
lapply(., quantile, probs = 1:3/4)

*x 
```
]
 
.panel2-lapply-auto[

```
$a
[1] 5.5

$beta
[1] 4.535125

$logic
[1] 0.5
```

```
$a
 25%  50%  75% 
3.25 5.50 7.75

$beta
      25%       50%       75% 
0.2516074 1.0000000 5.0536690

$logic
25% 50% 75% 
0.0 0.5 1.0 
```

```
$a
 [1]  1  2  3  4  5  6  7  8  9 10

$beta
[1]  0.04978707  0.13533528  0.36787944  1.00000000  2.71828183  7.38905610
[7] 20.08553692

$logic
[1]  TRUE FALSE FALSE  TRUE
```
]

---
count: false

```r
list(a = 1:10,
          beta = exp(-3:3),
          logic = c(TRUE,FALSE,FALSE,TRUE)) ->
x

# compute the list mean for each list element
x %>%
lapply(., mean)

# median and quartiles for each list element
x %>%
lapply(., quantile, probs = 1:3/4)

x %>%
*sapply(., quantile)
```
]
 
.panel2-lapply-auto[

```
$a
[1] 5.5

$beta
[1] 4.535125

$logic
[1] 0.5
```

```
$a
 25%  50%  75% 
3.25 5.50 7.75

$beta
      25%       50%       75% 
0.2516074 1.0000000 5.0536690

$logic
25% 50% 75% 
0.0 0.5 1.0 
```

```
         a        beta logic
0%    1.00  0.04978707   0.0
25%   3.25  0.25160736   0.0
50%   5.50  1.00000000   0.5
75%   7.75  5.05366896   1.0
100% 10.00 20.08553692   1.0
```
]

---
count: false

```r
list(a = 1:10,
          beta = exp(-3:3),
          logic = c(TRUE,FALSE,FALSE,TRUE)) ->
x

# compute the list mean for each list element
x %>%
lapply(., mean)

# median and quartiles for each list element
x %>%
lapply(., quantile, probs = 1:3/4)

x %>%
sapply(., quantile)

# list of vectors
*sapply(3:9, seq)
```
]
 
.panel2-lapply-auto[

```
$a
[1] 5.5

$beta
[1] 4.535125

$logic
[1] 0.5
```

```
$a
 25%  50%  75% 
3.25 5.50 7.75

$beta
      25%       50%       75% 
0.2516074 1.0000000 5.0536690

$logic
25% 50% 75% 
0.0 0.5 1.0 
```

```
         a        beta logic
0%    1.00  0.04978707   0.0
25%   3.25  0.25160736   0.0
50%   5.50  1.00000000   0.5
75%   7.75  5.05366896   1.0
100% 10.00 20.08553692   1.0
```

```
[[1]]
[1] 1 2 3

[[2]]
[1] 1 2 3 4

[[3]]
[1] 1 2 3 4 5

[[4]]
[1] 1 2 3 4 5 6

[[5]]
[1] 1 2 3 4 5 6 7

[[6]]
[1] 1 2 3 4 5 6 7 8

[[7]]
[1] 1 2 3 4 5 6 7 8 9
```
]

---
count: false

```r
list(a = 1:10,
          beta = exp(-3:3),
          logic = c(TRUE,FALSE,FALSE,TRUE)) ->
x

# compute the list mean for each list element
x %>%
lapply(., mean)

# median and quartiles for each list element
x %>%
lapply(., quantile, probs = 1:3/4)

x %>%
sapply(., quantile)

# list of vectors
sapply(3:9, seq) ->
* i39
```
]
 
.panel2-lapply-auto[

```
$a
[1] 5.5

$beta
[1] 4.535125

$logic
[1] 0.5
```

```
$a
 25%  50%  75% 
3.25 5.50 7.75

$beta
      25%       50%       75% 
0.2516074 1.0000000 5.0536690

$logic
25% 50% 75% 
0.0 0.5 1.0 
```

```
         a        beta logic
0%    1.00  0.04978707   0.0
25%   3.25  0.25160736   0.0
50%   5.50  1.00000000   0.5
75%   7.75  5.05366896   1.0
100% 10.00 20.08553692   1.0
```
]

---
count: false

```r
list(a = 1:10,
          beta = exp(-3:3),
          logic = c(TRUE,FALSE,FALSE,TRUE)) ->
x

# compute the list mean for each list element
x %>%
lapply(., mean)

# median and quartiles for each list element
x %>%
lapply(., quantile, probs = 1:3/4)

x %>%
sapply(., quantile)

# list of vectors
sapply(3:9, seq) ->
  i39

*i39
```
]
 
.panel2-lapply-auto[

```
$a
[1] 5.5

$beta
[1] 4.535125

$logic
[1] 0.5
```

```
$a
 25%  50%  75% 
3.25 5.50 7.75

$beta
      25%       50%       75% 
0.2516074 1.0000000 5.0536690

$logic
25% 50% 75% 
0.0 0.5 1.0 
```

```
         a        beta logic
0%    1.00  0.04978707   0.0
25%   3.25  0.25160736   0.0
50%   5.50  1.00000000   0.5
75%   7.75  5.05366896   1.0
100% 10.00 20.08553692   1.0
```

```
[[1]]
[1] 1 2 3

[[2]]
[1] 1 2 3 4

[[3]]
[1] 1 2 3 4 5

[[4]]
[1] 1 2 3 4 5 6

[[5]]
[1] 1 2 3 4 5 6 7

[[6]]
[1] 1 2 3 4 5 6 7 8

[[7]]
[1] 1 2 3 4 5 6 7 8 9
```
]

---
count: false

```r
list(a = 1:10,
          beta = exp(-3:3),
          logic = c(TRUE,FALSE,FALSE,TRUE)) ->
x

# compute the list mean for each list element
x %>%
lapply(., mean)

# median and quartiles for each list element
x %>%
lapply(., quantile, probs = 1:3/4)

x %>%
sapply(., quantile)

# list of vectors
sapply(3:9, seq) ->
  i39

i39 %>%
*sapply(FUN = fivenum)
```
]
 
.panel2-lapply-auto[

```
$a
[1] 5.5

$beta
[1] 4.535125

$logic
[1] 0.5
```

```
$a
 25%  50%  75% 
3.25 5.50 7.75

$beta
      25%       50%       75% 
0.2516074 1.0000000 5.0536690

$logic
25% 50% 75% 
0.0 0.5 1.0 
```

```
         a        beta logic
0%    1.00  0.04978707   0.0
25%   3.25  0.25160736   0.0
50%   5.50  1.00000000   0.5
75%   7.75  5.05366896   1.0
100% 10.00 20.08553692   1.0
```

```
     [,1] [,2] [,3] [,4] [,5] [,6] [,7]
[1,]  1.0  1.0    1  1.0  1.0  1.0    1
[2,]  1.5  1.5    2  2.0  2.5  2.5    3
[3,]  2.0  2.5    3  3.5  4.0  4.5    5
[4,]  2.5  3.5    4  5.0  5.5  6.5    7
[5,]  3.0  4.0    5  6.0  7.0  8.0    9
```
]

---
count: false

```r
list(a = 1:10,
          beta = exp(-3:3),
          logic = c(TRUE,FALSE,FALSE,TRUE)) ->
x

# compute the list mean for each list element
x %>%
lapply(., mean)

# median and quartiles for each list element
x %>%
lapply(., quantile, probs = 1:3/4)

x %>%
sapply(., quantile)

# list of vectors
sapply(3:9, seq) ->
  i39

i39 %>%
sapply(FUN = fivenum) ->
* hide
```
]
 
.panel2-lapply-auto[

```
$a
[1] 5.5

$beta
[1] 4.535125

$logic
[1] 0.5
```

```
$a
 25%  50%  75% 
3.25 5.50 7.75

$beta
      25%       50%       75% 
0.2516074 1.0000000 5.0536690

$logic
25% 50% 75% 
0.0 0.5 1.0 
```

```
         a        beta logic
0%    1.00  0.04978707   0.0
25%   3.25  0.25160736   0.0
50%   5.50  1.00000000   0.5
75%   7.75  5.05366896   1.0
100% 10.00 20.08553692   1.0
```
]

---
count: false

```r
list(a = 1:10,
          beta = exp(-3:3),
          logic = c(TRUE,FALSE,FALSE,TRUE)) ->
x

# compute the list mean for each list element
x %>%
lapply(., mean)

# median and quartiles for each list element
x %>%
lapply(., quantile, probs = 1:3/4)

x %>%
sapply(., quantile)

# list of vectors
sapply(3:9, seq) ->
  i39

i39 %>%
sapply(FUN = fivenum) ->
  hide

*i39
```
]
 
.panel2-lapply-auto[

```
$a
[1] 5.5

$beta
[1] 4.535125

$logic
[1] 0.5
```

```
$a
 25%  50%  75% 
3.25 5.50 7.75

$beta
      25%       50%       75% 
0.2516074 1.0000000 5.0536690

$logic
25% 50% 75% 
0.0 0.5 1.0 
```

```
         a        beta logic
0%    1.00  0.04978707   0.0
25%   3.25  0.25160736   0.0
50%   5.50  1.00000000   0.5
75%   7.75  5.05366896   1.0
100% 10.00 20.08553692   1.0
```

```
[[1]]
[1] 1 2 3

[[2]]
[1] 1 2 3 4

[[3]]
[1] 1 2 3 4 5

[[4]]
[1] 1 2 3 4 5 6

[[5]]
[1] 1 2 3 4 5 6 7

[[6]]
[1] 1 2 3 4 5 6 7 8

[[7]]
[1] 1 2 3 4 5 6 7 8 9
```
]

---
count: false

```r
list(a = 1:10,
          beta = exp(-3:3),
          logic = c(TRUE,FALSE,FALSE,TRUE)) ->
x

# compute the list mean for each list element
x %>%
lapply(., mean)

# median and quartiles for each list element
x %>%
lapply(., quantile, probs = 1:3/4)

x %>%
sapply(., quantile)

# list of vectors
sapply(3:9, seq) ->
  i39

i39 %>%
sapply(FUN = fivenum) ->
  hide

i39 %>%
*vapply(., FUN = fivenum,
*      c(Min. = 0, "1st Qu." = 0,
*        Median = 0, "3rd Qu." = 0, Max. = 0))
```
]
 
.panel2-lapply-auto[

```
$a
[1] 5.5

$beta
[1] 4.535125

$logic
[1] 0.5
```

```
$a
 25%  50%  75% 
3.25 5.50 7.75

$beta
      25%       50%       75% 
0.2516074 1.0000000 5.0536690

$logic
25% 50% 75% 
0.0 0.5 1.0 
```

```
         a        beta logic
0%    1.00  0.04978707   0.0
25%   3.25  0.25160736   0.0
50%   5.50  1.00000000   0.5
75%   7.75  5.05366896   1.0
100% 10.00 20.08553692   1.0
```

```
        [,1] [,2] [,3] [,4] [,5] [,6] [,7]
Min.     1.0  1.0    1  1.0  1.0  1.0    1
1st Qu.  1.5  1.5    2  2.0  2.5  2.5    3
Median   2.0  2.5    3  3.5  4.0  4.5    5
3rd Qu.  2.5  3.5    4  5.0  5.5  6.5    7
Max.     3.0  4.0    5  6.0  7.0  8.0    9
```
]

---
count: false

```r
list(a = 1:10,
          beta = exp(-3:3),
          logic = c(TRUE,FALSE,FALSE,TRUE)) ->
x

# compute the list mean for each list element
x %>%
lapply(., mean)

# median and quartiles for each list element
x %>%
lapply(., quantile, probs = 1:3/4)

x %>%
sapply(., quantile)

# list of vectors
sapply(3:9, seq) ->
  i39

i39 %>%
sapply(FUN = fivenum) ->
  hide

i39 %>%
vapply(., FUN = fivenum,
       c(Min. = 0, "1st Qu." = 0,
         Median = 0, "3rd Qu." = 0, Max. = 0))
```
]
 
.panel2-lapply-auto[

```
$a
[1] 5.5

$beta
[1] 4.535125

$logic
[1] 0.5
```

```
$a
 25%  50%  75% 
3.25 5.50 7.75

$beta
      25%       50%       75% 
0.2516074 1.0000000 5.0536690

$logic
25% 50% 75% 
0.0 0.5 1.0 
```

```
         a        beta logic
0%    1.00  0.04978707   0.0
25%   3.25  0.25160736   0.0
50%   5.50  1.00000000   0.5
75%   7.75  5.05366896   1.0
100% 10.00 20.08553692   1.0
```