DANL 310 Lecture 13knitr::kable and kableExtraByeong-Hak ChoeMarch 31, 20221 / 100

Creating tables using knitr::kable2 / 100

Change column names

The names of columns in a data frame may not be the same as what we want to display to readers.
We can use the col.names argument to replace the column names with a vector of new names.

library(ggplot2)
knitr::kable(
  iris,
  col.names = c('We', 'Need', 'Five', 'Names', 'Here')
)

3 / 100

Change column names

We
Need
Five
Names
Here


1
5
4
2
setosa

9
0
4
2
setosa

7
2
3
2
setosa

6
1
5
2
setosa

0
6
4
2
setosa

4
9
7
4
setosa

6
4
4
3
setosa

0
4
5
2
setosa

4
9
4
2
setosa

9
1
5
1
setosa

4
7
5
2
setosa

8
4
6
2
setosa

8
0
4
1
setosa

3
0
1
1
setosa

8
0
2
2
setosa

7
4
5
4
setosa

4
9
3
4
setosa

1
5
4
3
setosa

7
8
7
3
setosa

1
8
5
3
setosa

4
4
7
2
setosa

1
7
5
4
setosa

6
6
0
2
setosa

1
3
7
5
setosa

8
4
9
2
setosa

0
0
6
2
setosa

0
4
6
4
setosa

2
5
5
2
setosa

2
4
4
2
setosa

7
2
6
2
setosa

8
1
6
2
setosa

4
4
5
4
setosa

2
1
5
1
setosa

5
2
4
2
setosa

9
1
5
2
setosa

0
2
2
2
setosa

5
5
3
2
setosa

9
6
4
1
setosa

4
0
3
2
setosa

1
4
5
2
setosa

0
5
3
3
setosa

5
3
3
3
setosa

4
2
3
2
setosa

0
5
6
6
setosa

1
8
9
4
setosa

8
0
4
3
setosa

1
8
6
2
setosa

6
2
4
2
setosa

3
7
5
2
setosa

0
3
4
2
setosa

0
2
7
4
versicolor

4
2
5
5
versicolor

9
1
9
5
versicolor

5
3
0
3
versicolor

5
8
6
5
versicolor

7
8
5
3
versicolor

3
3
7
6
versicolor

9
4
3
0
versicolor

6
9
6
3
versicolor

2
7
9
4
versicolor

0
0
5
0
versicolor

9
0
2
5
versicolor

0
2
0
0
versicolor

1
9
7
4
versicolor

6
9
6
3
versicolor

7
1
4
4
versicolor

6
0
5
5
versicolor

8
7
1
0
versicolor

2
2
5
5
versicolor

6
5
9
1
versicolor

9
2
8
8
versicolor

1
8
0
3
versicolor

3
5
9
5
versicolor

1
8
7
2
versicolor

4
9
3
3
versicolor

6
0
4
4
versicolor

8
8
8
4
versicolor

7
0
0
7
versicolor

0
9
5
5
versicolor

7
6
5
0
versicolor

5
4
8
1
versicolor

5
4
7
0
versicolor

8
7
9
2
versicolor

0
7
1
6
versicolor

4
0
5
5
versicolor

0
4
5
6
versicolor

7
1
7
5
versicolor

3
3
4
3
versicolor

6
0
1
3
versicolor

5
5
0
3
versicolor

5
6
4
2
versicolor

1
0
6
4
versicolor

8
6
0
2
versicolor

0
3
3
0
versicolor

6
7
2
3
versicolor

7
0
2
2
versicolor

7
9
2
3
versicolor

2
9
3
3
versicolor

1
5
0
1
versicolor

7
8
1
3
versicolor

3
3
0
5
virginica

8
7
1
9
virginica

1
0
9
1
virginica

3
9
6
8
virginica

5
0
8
2
virginica

6
0
6
1
virginica

9
5
5
7
virginica

3
9
3
8
virginica

7
5
8
8
virginica

2
6
1
5
virginica

5
2
1
0
virginica

4
7
3
9
virginica

8
0
5
1
virginica

7
5
0
0
virginica

8
8
1
4
virginica

4
2
3
3
virginica

5
0
5
8
virginica

7
8
7
2
virginica

7
6
9
3
virginica

0
2
0
5
virginica

9
2
7
3
virginica

6
8
9
0
virginica

7
8
7
0
virginica

3
7
9
8
virginica

7
3
7
1
virginica

2
2
0
8
virginica

2
8
8
8
virginica

1
0
9
8
virginica

4
8
6
1
virginica

2
0
8
6
virginica

4
8
1
9
virginica

9
8
4
0
virginica

4
8
6
2
virginica

3
8
1
5
virginica

1
6
6
4
virginica

7
0
1
3
virginica

3
4
6
4
virginica

4
1
5
8
virginica

0
0
8
8
virginica

9
1
4
1
virginica

7
1
6
4
virginica

9
1
1
3
virginica

8
7
1
9
virginica

8
2
9
3
virginica

7
3
7
5
virginica

7
0
2
3
virginica

3
5
0
9
virginica

5
0
2
0
virginica

2
4
4
3
virginica

9
0
1
8
virginica

/ 100

Specify column alignment

To change the alignment of the table columns, you can use either a vector of values consisting of characters l (left), c (center), and r (right).
- kable(..., align = c('c', 'l')) or
- kable(..., align = 'cl')

# left, center, center, right, right
knitr::kable(iris, align = "lccrr")

5 / 100

Specify column alignment

Sepal.Length
Sepal.Width
Petal.Length
Petal.Width
Species


1
5
4
2
setosa

9
0
4
2
setosa

7
2
3
2
setosa

6
1
5
2
setosa

0
6
4
2
setosa

4
9
7
4
setosa

6
4
4
3
setosa

0
4
5
2
setosa

4
9
4
2
setosa

9
1
5
1
setosa

4
7
5
2
setosa

8
4
6
2
setosa

8
0
4
1
setosa

3
0
1
1
setosa

8
0
2
2
setosa

7
4
5
4
setosa

4
9
3
4
setosa

1
5
4
3
setosa

7
8
7
3
setosa

1
8
5
3
setosa

4
4
7
2
setosa

1
7
5
4
setosa

6
6
0
2
setosa

1
3
7
5
setosa

8
4
9
2
setosa

0
0
6
2
setosa

0
4
6
4
setosa

2
5
5
2
setosa

2
4
4
2
setosa

7
2
6
2
setosa

8
1
6
2
setosa

4
4
5
4
setosa

2
1
5
1
setosa

5
2
4
2
setosa

9
1
5
2
setosa

0
2
2
2
setosa

5
5
3
2
setosa

9
6
4
1
setosa

4
0
3
2
setosa

1
4
5
2
setosa

0
5
3
3
setosa

5
3
3
3
setosa

4
2
3
2
setosa

0
5
6
6
setosa

1
8
9
4
setosa

8
0
4
3
setosa

1
8
6
2
setosa

6
2
4
2
setosa

3
7
5
2
setosa

0
3
4
2
setosa

0
2
7
4
versicolor

4
2
5
5
versicolor

9
1
9
5
versicolor

5
3
0
3
versicolor

5
8
6
5
versicolor

7
8
5
3
versicolor

3
3
7
6
versicolor

9
4
3
0
versicolor

6
9
6
3
versicolor

2
7
9
4
versicolor

0
0
5
0
versicolor

9
0
2
5
versicolor

0
2
0
0
versicolor

1
9
7
4
versicolor

6
9
6
3
versicolor

7
1
4
4
versicolor

6
0
5
5
versicolor

8
7
1
0
versicolor

2
2
5
5
versicolor

6
5
9
1
versicolor

9
2
8
8
versicolor

1
8
0
3
versicolor

3
5
9
5
versicolor

1
8
7
2
versicolor

4
9
3
3
versicolor

6
0
4
4
versicolor

8
8
8
4
versicolor

7
0
0
7
versicolor

0
9
5
5
versicolor

7
6
5
0
versicolor

5
4
8
1
versicolor

5
4
7
0
versicolor

8
7
9
2
versicolor

0
7
1
6
versicolor

4
0
5
5
versicolor

0
4
5
6
versicolor

7
1
7
5
versicolor

3
3
4
3
versicolor

6
0
1
3
versicolor

5
5
0
3
versicolor

5
6
4
2
versicolor

1
0
6
4
versicolor

8
6
0
2
versicolor

0
3
3
0
versicolor

6
7
2
3
versicolor

7
0
2
2
versicolor

7
9
2
3
versicolor

2
9
3
3
versicolor

1
5
0
1
versicolor

7
8
1
3
versicolor

3
3
0
5
virginica

8
7
1
9
virginica

1
0
9
1
virginica

3
9
6
8
virginica

5
0
8
2
virginica

6
0
6
1
virginica

9
5
5
7
virginica

3
9
3
8
virginica

7
5
8
8
virginica

2
6
1
5
virginica

5
2
1
0
virginica

4
7
3
9
virginica

8
0
5
1
virginica

7
5
0
0
virginica

8
8
1
4
virginica

4
2
3
3
virginica

5
0
5
8
virginica

7
8
7
2
virginica

7
6
9
3
virginica

0
2
0
5
virginica

9
2
7
3
virginica

6
8
9
0
virginica

7
8
7
0
virginica

3
7
9
8
virginica

7
3
7
1
virginica

2
2
0
8
virginica

2
8
8
8
virginica

1
0
9
8
virginica

4
8
6
1
virginica

2
0
8
6
virginica

4
8
1
9
virginica

9
8
4
0
virginica

4
8
6
2
virginica

3
8
1
5
virginica

1
6
6
4
virginica

7
0
1
3
virginica

3
4
6
4
virginica

4
1
5
8
virginica

0
0
8
8
virginica

9
1
4
1
virginica

7
1
6
4
virginica

9
1
1
3
virginica

8
7
1
9
virginica

8
2
9
3
virginica

7
3
7
5
virginica

7
0
2
3
virginica

3
5
0
9
virginica

5
0
2
0
virginica

2
4
4
3
virginica

9
0
1
8
virginica

/ 100

Add a table caption

You can add a caption to the table via the caption argument.

knitr::kable(iris, caption = "An example table caption.")

7 / 100

Add a table caption

Table: An example table caption.

Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
5.1	3.5	1.4	0.2	setosa
4.9	3.0	1.4	0.2	setosa
4.7	3.2	1.3	0.2	setosa
4.6	3.1	1.5	0.2	setosa
5.0	3.6	1.4	0.2	setosa
5.4	3.9	1.7	0.4	setosa
4.6	3.4	1.4	0.3	setosa
5.0	3.4	1.5	0.2	setosa
4.4	2.9	1.4	0.2	setosa
4.9	3.1	1.5	0.1	setosa
5.4	3.7	1.5	0.2	setosa
4.8	3.4	1.6	0.2	setosa
4.8	3.0	1.4	0.1	setosa
4.3	3.0	1.1	0.1	setosa
5.8	4.0	1.2	0.2	setosa
5.7	4.4	1.5	0.4	setosa
5.4	3.9	1.3	0.4	setosa
5.1	3.5	1.4	0.3	setosa
5.7	3.8	1.7	0.3	setosa
5.1	3.8	1.5	0.3	setosa
5.4	3.4	1.7	0.2	setosa
5.1	3.7	1.5	0.4	setosa
4.6	3.6	1.0	0.2	setosa
5.1	3.3	1.7	0.5	setosa
4.8	3.4	1.9	0.2	setosa
5.0	3.0	1.6	0.2	setosa
5.0	3.4	1.6	0.4	setosa
5.2	3.5	1.5	0.2	setosa
5.2	3.4	1.4	0.2	setosa
4.7	3.2	1.6	0.2	setosa
4.8	3.1	1.6	0.2	setosa
5.4	3.4	1.5	0.4	setosa
5.2	4.1	1.5	0.1	setosa
5.5	4.2	1.4	0.2	setosa
4.9	3.1	1.5	0.2	setosa
5.0	3.2	1.2	0.2	setosa
5.5	3.5	1.3	0.2	setosa
4.9	3.6	1.4	0.1	setosa
4.4	3.0	1.3	0.2	setosa
5.1	3.4	1.5	0.2	setosa
5.0	3.5	1.3	0.3	setosa
4.5	2.3	1.3	0.3	setosa
4.4	3.2	1.3	0.2	setosa
5.0	3.5	1.6	0.6	setosa
5.1	3.8	1.9	0.4	setosa
4.8	3.0	1.4	0.3	setosa
5.1	3.8	1.6	0.2	setosa
4.6	3.2	1.4	0.2	setosa
5.3	3.7	1.5	0.2	setosa
5.0	3.3	1.4	0.2	setosa
7.0	3.2	4.7	1.4	versicolor
6.4	3.2	4.5	1.5	versicolor
6.9	3.1	4.9	1.5	versicolor
5.5	2.3	4.0	1.3	versicolor
6.5	2.8	4.6	1.5	versicolor
5.7	2.8	4.5	1.3	versicolor
6.3	3.3	4.7	1.6	versicolor
4.9	2.4	3.3	1.0	versicolor
6.6	2.9	4.6	1.3	versicolor
5.2	2.7	3.9	1.4	versicolor
5.0	2.0	3.5	1.0	versicolor
5.9	3.0	4.2	1.5	versicolor
6.0	2.2	4.0	1.0	versicolor
6.1	2.9	4.7	1.4	versicolor
5.6	2.9	3.6	1.3	versicolor
6.7	3.1	4.4	1.4	versicolor
5.6	3.0	4.5	1.5	versicolor
5.8	2.7	4.1	1.0	versicolor
6.2	2.2	4.5	1.5	versicolor
5.6	2.5	3.9	1.1	versicolor
5.9	3.2	4.8	1.8	versicolor
6.1	2.8	4.0	1.3	versicolor
6.3	2.5	4.9	1.5	versicolor
6.1	2.8	4.7	1.2	versicolor
6.4	2.9	4.3	1.3	versicolor
6.6	3.0	4.4	1.4	versicolor
6.8	2.8	4.8	1.4	versicolor
6.7	3.0	5.0	1.7	versicolor
6.0	2.9	4.5	1.5	versicolor
5.7	2.6	3.5	1.0	versicolor
5.5	2.4	3.8	1.1	versicolor
5.5	2.4	3.7	1.0	versicolor
5.8	2.7	3.9	1.2	versicolor
6.0	2.7	5.1	1.6	versicolor
5.4	3.0	4.5	1.5	versicolor
6.0	3.4	4.5	1.6	versicolor
6.7	3.1	4.7	1.5	versicolor
6.3	2.3	4.4	1.3	versicolor
5.6	3.0	4.1	1.3	versicolor
5.5	2.5	4.0	1.3	versicolor
5.5	2.6	4.4	1.2	versicolor
6.1	3.0	4.6	1.4	versicolor
5.8	2.6	4.0	1.2	versicolor
5.0	2.3	3.3	1.0	versicolor
5.6	2.7	4.2	1.3	versicolor
5.7	3.0	4.2	1.2	versicolor
5.7	2.9	4.2	1.3	versicolor
6.2	2.9	4.3	1.3	versicolor
5.1	2.5	3.0	1.1	versicolor
5.7	2.8	4.1	1.3	versicolor
6.3	3.3	6.0	2.5	virginica
5.8	2.7	5.1	1.9	virginica
7.1	3.0	5.9	2.1	virginica
6.3	2.9	5.6	1.8	virginica
6.5	3.0	5.8	2.2	virginica
7.6	3.0	6.6	2.1	virginica
4.9	2.5	4.5	1.7	virginica
7.3	2.9	6.3	1.8	virginica
6.7	2.5	5.8	1.8	virginica
7.2	3.6	6.1	2.5	virginica
6.5	3.2	5.1	2.0	virginica
6.4	2.7	5.3	1.9	virginica
6.8	3.0	5.5	2.1	virginica
5.7	2.5	5.0	2.0	virginica
5.8	2.8	5.1	2.4	virginica
6.4	3.2	5.3	2.3	virginica
6.5	3.0	5.5	1.8	virginica
7.7	3.8	6.7	2.2	virginica
7.7	2.6	6.9	2.3	virginica
6.0	2.2	5.0	1.5	virginica
6.9	3.2	5.7	2.3	virginica
5.6	2.8	4.9	2.0	virginica
7.7	2.8	6.7	2.0	virginica
6.3	2.7	4.9	1.8	virginica
6.7	3.3	5.7	2.1	virginica
7.2	3.2	6.0	1.8	virginica
6.2	2.8	4.8	1.8	virginica
6.1	3.0	4.9	1.8	virginica
6.4	2.8	5.6	2.1	virginica
7.2	3.0	5.8	1.6	virginica
7.4	2.8	6.1	1.9	virginica
7.9	3.8	6.4	2.0	virginica
6.4	2.8	5.6	2.2	virginica
6.3	2.8	5.1	1.5	virginica
6.1	2.6	5.6	1.4	virginica
7.7	3.0	6.1	2.3	virginica
6.3	3.4	5.6	2.4	virginica
6.4	3.1	5.5	1.8	virginica
6.0	3.0	4.8	1.8	virginica
6.9	3.1	5.4	2.1	virginica
6.7	3.1	5.6	2.4	virginica
6.9	3.1	5.1	2.3	virginica
5.8	2.7	5.1	1.9	virginica
6.8	3.2	5.9	2.3	virginica
6.7	3.3	5.7	2.5	virginica
6.7	3.0	5.2	2.3	virginica
6.3	2.5	5.0	1.9	virginica
6.5	3.0	5.2	2.0	virginica
6.2	3.4	5.4	2.3	virginica
5.9	3.0	5.1	1.8	virginica

8 / 100

Format numeric columns

You can set the maximum number of decimal places via the round() function and other formatting arguments such as the format() function in base R.

round(1.234567, 0)
round(1.234567, digits = 1)
round(1.234567, digits = 3)
format(1000, scientific = TRUE)
format(10000.123, big.mark = ",")

9 / 100

Format numeric columns

## [1] 1

## [1] 1.2

## [1] 1.235

## [1] "1e+03"

## [1] "10,000.12"

10 / 100

Format numeric columns

In kable, you can set the maximum number of decimal places via the digits argument.

d <- cbind(X1 = runif(3), X2 = 10^c(3, 5, 7), X3 = rnorm(3,
  0, 1000))
# at most 4 decimal places
knitr::kable(d, digits = 4)

11 / 100

Format numeric columns

X1
X2
X3


0.6919
1e+03
-2002.5477

0.1675
1e+05
-736.6384

0.3744
1e+07
251.4717

12 / 100

X1	X2	X3
0.6919	1e+03	-2002.5477
0.1675	1e+05	-736.6384
0.3744	1e+07	251.4717

Format numeric columns

You can set the different maximum numbers of decimal places via the digits argument.

# round columns separately
knitr::kable(d, digits = c(5, 0, 2))

13 / 100

Format numeric columns

X1
X2
X3


0.69186
1e+03
-2002.55

0.16753
1e+05
-736.64

0.37441
1e+07
251.47

14 / 100

X1	X2	X3
0.69186	1e+03	-2002.55
0.16753	1e+05	-736.64
0.37441	1e+07	251.47

Format numeric columns

You can use the format.args argument to not use scientific number formatting.

# do not use the scientific notation
knitr::kable(d, digits = 3, format.args = list(scientific = FALSE))

15 / 100

Format numeric columns

X1
X2
X3


0.692
1000
-2002.548

0.168
100000
-736.638

0.374
10000000
251.472

16 / 100

X1	X2	X3
0.692	1000	-2002.548
0.168	100000	-736.638
0.374	10000000	251.472

Format numeric columns

We can add commas to big numbers.

knitr::kable(d, digits = 3, format.args = list(big.mark = ",",
  scientific = FALSE))

17 / 100

Format numeric columns

X1
X2
X3


0.692
1,000
-2,002.548

0.168
100,000
-736.638

0.374
10,000,000
251.472

18 / 100

X1	X2	X3
0.692	1,000	-2,002.548
0.168	100,000	-736.638
0.374	10,000,000	251.472

Display missing values

By default, missing values (i.e., NA) are displayed as the character string NA in the table.
Here is the example of the table with NA.

d[rbind(c(1, 1), c(2, 3), c(3, 2))] <- NA
knitr::kable(d)  # NA is displayed by default

19 / 100

Display missing values

X1
X2
X3


NA
1e+03
-2002.5477

0.1675256
1e+05
NA

0.3744146
NA
251.4717

20 / 100

X1	X2	X3
NA	1e+03	-2002.5477
0.1675256	1e+05	NA
0.3744146	NA	251.4717

Display missing values

We can replace NA with empty strings by changing a global option via the options() function.

# replace NA with empty strings
opts <- options(knitr.kable.NA = "")
knitr::kable(d)

21 / 100

Display missing values

X1
X2
X3

1e+03
-2002.5477

0.1675256
1e+05

0.3744146

251.4717

22 / 100

X1	X2	X3
	1e+03	-2002.5477
0.1675256	1e+05
0.3744146		251.4717

Display missing values

Or with "**"
We can use options(opts) to restore global R options.

options(knitr.kable.NA = "**")
knitr::kable(d)
options(opts)  # restore global R options

23 / 100

Display missing values

X1
X2
X3


**
1e+03
-2002.5477

0.1675256
1e+05
**

0.3744146
**
251.4717

24 / 100

X1	X2	X3
**	1e+03	-2002.5477
0.1675256	1e+05	**
0.3744146	**	251.4717

Creating tables using kableExtra25 / 100

Getting Started with `kableExtra`

# install.packages("kableExtra")
library(kableExtra)
dt <- mtcars[1:5, 1:6]

26 / 100

Getting Started with `kableExtra`

## 
## Attaching package: 'kableExtra'

## The following object is masked from 'package:dplyr':
## 
##     group_rows

27 / 100

Basic HTML table

Basic HTML output of kable may look crude. To the end, it's just a plain HTML table without any CSS features.

kbl(dt)

28 / 100

Basic HTML table
 
      
    mpg 
    cyl 
    disp 
    hp 
    drat 
    wt 
  


    Mazda RX4 
0 



90 
620 
  

    Mazda RX4 Wag 
0 



90 
875 
  

    Datsun 710 
8 



85 
320 
  

    Hornet 4 Drive 
4 



08 
215 
  

    Hornet Sportabout 
7 



15 
440 
  

/ 100

	mpg	cyl	disp	hp	drat	wt
Mazda RX4	21.0	6	160	110	3.90	2.620
Mazda RX4 Wag	21.0	6	160	110	3.90	2.875
Datsun 710	22.8	4	108	93	3.85	2.320
Hornet 4 Drive	21.4	6	258	110	3.08	3.215
Hornet Sportabout	18.7	8	360	175	3.15	3.440

Bootstrap theme

When used on a HTML table, kable_styling() will automatically apply twitter bootstrap theme to the table.
Now it should looks the same as the original pandoc output (the one when you don’t specify format in kable()) but this time, you are controlling it.

dt %>%
  kbl() %>%
  kable_styling()

30 / 100

Bootstrap theme
 
      
    mpg 
    cyl 
    disp 
    hp 
    drat 
    wt 
  


    Mazda RX4 
0 



90 
620 
  

    Mazda RX4 Wag 
0 



90 
875 
  

    Datsun 710 
8 



85 
320 
  

    Hornet 4 Drive 
4 



08 
215 
  

    Hornet Sportabout 
7 



15 
440 
  

/ 100

	mpg	cyl	disp	hp	drat	wt
Mazda RX4	21.0	6	160	110	3.90	2.620
Mazda RX4 Wag	21.0	6	160	110	3.90	2.875
Datsun 710	22.8	4	108	93	3.85	2.320
Hornet 4 Drive	21.4	6	258	110	3.08	3.215
Hornet Sportabout	18.7	8	360	175	3.15	3.440

Alternative themes

kableExtra offers a few in-house alternative HTML table themes other than the default bootstrap theme.
Right now there are 6 of them:
- kable_paper,
- kable_classic,
- kable_classic_2,
- kable_minimal,
- kable_material,
- kable_material_dark.
These functions are alternatives to kable_styling.
- You can specify any additional formatting options in kable_styling in these functions too.
- hover is available.

dt %>%
  kbl() %>%
  kable_paper("hover", full_width = F)

32 / 100

Alternative themes
 
      
    mpg 
    cyl 
    disp 
    hp 
    drat 
    wt 
  


    Mazda RX4 
0 



90 
620 
  

    Mazda RX4 Wag 
0 



90 
875 
  

    Datsun 710 
8 



85 
320 
  

    Hornet 4 Drive 
4 



08 
215 
  

    Hornet Sportabout 
7 



15 
440 
  

/ 100

	mpg	cyl	disp	hp	drat	wt
Mazda RX4	21.0	6	160	110	3.90	2.620
Mazda RX4 Wag	21.0	6	160	110	3.90	2.875
Datsun 710	22.8	4	108	93	3.85	2.320
Hornet 4 Drive	21.4	6	258	110	3.08	3.215
Hornet Sportabout	18.7	8	360	175	3.15	3.440

Alternative themes

You can choose an html_font for the table.

dt %>%
  kbl(caption = "Recreating booktabs style table") %>%
  kable_classic(full_width = F, html_font = "Cambria")

34 / 100

Alternative themes
Recreating booktabs style table
 
      
    mpg 
    cyl 
    disp 
    hp 
    drat 
    wt 
  


    Mazda RX4 
0 



90 
620 
  

    Mazda RX4 Wag 
0 



90 
875 
  

    Datsun 710 
8 



85 
320 
  

    Hornet 4 Drive 
4 



08 
215 
  

    Hornet Sportabout 
7 



15 
440 
  

/ 100

Recreating booktabs style table
	mpg	cyl	disp	hp	drat	wt
Mazda RX4	21.0	6	160	110	3.90	2.620
Mazda RX4 Wag	21.0	6	160	110	3.90	2.875
Datsun 710	22.8	4	108	93	3.85	2.320
Hornet 4 Drive	21.4	6	258	110	3.08	3.215
Hornet Sportabout	18.7	8	360	175	3.15	3.440

Alternative themes

This is the kable_classic_2 theme.

dt %>%
  kbl() %>%
  kable_classic_2(full_width = F)

36 / 100

Alternative themes
 
      
    mpg 
    cyl 
    disp 
    hp 
    drat 
    wt 
  


    Mazda RX4 
0 



90 
620 
  

    Mazda RX4 Wag 
0 



90 
875 
  

    Datsun 710 
8 



85 
320 
  

    Hornet 4 Drive 
4 



08 
215 
  

    Hornet Sportabout 
7 



15 
440 
  

/ 100

	mpg	cyl	disp	hp	drat	wt
Mazda RX4	21.0	6	160	110	3.90	2.620
Mazda RX4 Wag	21.0	6	160	110	3.90	2.875
Datsun 710	22.8	4	108	93	3.85	2.320
Hornet 4 Drive	21.4	6	258	110	3.08	3.215
Hornet Sportabout	18.7	8	360	175	3.15	3.440

Alternative themes

This is the kable_minimal theme.

dt %>%
  kbl() %>%
  kable_minimal()

38 / 100

Alternative themes
 
      
    mpg 
    cyl 
    disp 
    hp 
    drat 
    wt 
  


    Mazda RX4 
0 



90 
620 
  

    Mazda RX4 Wag 
0 



90 
875 
  

    Datsun 710 
8 



85 
320 
  

    Hornet 4 Drive 
4 



08 
215 
  

    Hornet Sportabout 
7 



15 
440 
  

/ 100

	mpg	cyl	disp	hp	drat	wt
Mazda RX4	21.0	6	160	110	3.90	2.620
Mazda RX4 Wag	21.0	6	160	110	3.90	2.875
Datsun 710	22.8	4	108	93	3.85	2.320
Hornet 4 Drive	21.4	6	258	110	3.08	3.215
Hornet Sportabout	18.7	8	360	175	3.15	3.440

Alternative themes

This is the kable_material theme.

dt %>%
  kbl() %>%
  kable_material(c("striped", "hover"))

40 / 100

Alternative themes
 
      
    mpg 
    cyl 
    disp 
    hp 
    drat 
    wt 
  


    Mazda RX4 
0 



90 
620 
  

    Mazda RX4 Wag 
0 



90 
875 
  

    Datsun 710 
8 



85 
320 
  

    Hornet 4 Drive 
4 



08 
215 
  

    Hornet Sportabout 
7 



15 
440 
  

/ 100

	mpg	cyl	disp	hp	drat	wt
Mazda RX4	21.0	6	160	110	3.90	2.620
Mazda RX4 Wag	21.0	6	160	110	3.90	2.875
Datsun 710	22.8	4	108	93	3.85	2.320
Hornet 4 Drive	21.4	6	258	110	3.08	3.215
Hornet Sportabout	18.7	8	360	175	3.15	3.440

Alternative themes

This is the kable_material_dark theme.

dt %>%
  kbl() %>%
  kable_material_dark()

42 / 100

Alternative themes
 
      
    mpg 
    cyl 
    disp 
    hp 
    drat 
    wt 
  


    Mazda RX4 
0 



90 
620 
  

    Mazda RX4 Wag 
0 



90 
875 
  

    Datsun 710 
8 



85 
320 
  

    Hornet 4 Drive 
4 



08 
215 
  

    Hornet Sportabout 
7 



15 
440 
  

/ 100

	mpg	cyl	disp	hp	drat	wt
Mazda RX4	21.0	6	160	110	3.90	2.620
Mazda RX4 Wag	21.0	6	160	110	3.90	2.875
Datsun 710	22.8	4	108	93	3.85	2.320
Hornet 4 Drive	21.4	6	258	110	3.08	3.215
Hornet Sportabout	18.7	8	360	175	3.15	3.440

Table Styles44 / 100

Bootstrap table classes

You can take a look at the twitter bootstrap documentation site to get a sense of how the options---striped, bordered, hover, condensed and responsive---look like.
For example, to add striped lines (alternative row colors) to your table and you want to highlight the hovered row, you can simply type:

kbl(dt) %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

45 / 100

Bootstrap table classes
 
      
    mpg 
    cyl 
    disp 
    hp 
    drat 
    wt 
  


    Mazda RX4 
0 



90 
620 
  

    Mazda RX4 Wag 
0 



90 
875 
  

    Datsun 710 
8 



85 
320 
  

    Hornet 4 Drive 
4 



08 
215 
  

    Hornet Sportabout 
7 



15 
440 
  

/ 100

	mpg	cyl	disp	hp	drat	wt
Mazda RX4	21.0	6	160	110	3.90	2.620
Mazda RX4 Wag	21.0	6	160	110	3.90	2.875
Datsun 710	22.8	4	108	93	3.85	2.320
Hornet 4 Drive	21.4	6	258	110	3.08	3.215
Hornet Sportabout	18.7	8	360	175	3.15	3.440

Bootstrap table classes

The option condensed can also be handy in many cases when you don’t want your table to be too large.
- It has slightly shorter row height.

kbl(dt) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"))

47 / 100

Bootstrap table classes
 
      
    mpg 
    cyl 
    disp 
    hp 
    drat 
    wt 
  


    Mazda RX4 
0 



90 
620 
  

    Mazda RX4 Wag 
0 



90 
875 
  

    Datsun 710 
8 



85 
320 
  

    Hornet 4 Drive 
4 



08 
215 
  

    Hornet Sportabout 
7 



15 
440 
  

/ 100

	mpg	cyl	disp	hp	drat	wt
Mazda RX4	21.0	6	160	110	3.90	2.620
Mazda RX4 Wag	21.0	6	160	110	3.90	2.875
Datsun 710	22.8	4	108	93	3.85	2.320
Hornet 4 Drive	21.4	6	258	110	3.08	3.215
Hornet Sportabout	18.7	8	360	175	3.15	3.440

Bootstrap table classes

Tables with option responsive looks the same with others on a large screen.
- On a small screen like phone, they are horizontally scrollable.
- You can resize your window to see the result.

kbl(dt) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))

49 / 100

Bootstrap table classes
 
      
    mpg 
    cyl 
    disp 
    hp 
    drat 
    wt 
  


    Mazda RX4 
0 



90 
620 
  

    Mazda RX4 Wag 
0 



90 
875 
  

    Datsun 710 
8 



85 
320 
  

    Hornet 4 Drive 
4 



08 
215 
  

    Hornet Sportabout 
7 



15 
440 
  

/ 100

	mpg	cyl	disp	hp	drat	wt
Mazda RX4	21.0	6	160	110	3.90	2.620
Mazda RX4 Wag	21.0	6	160	110	3.90	2.875
Datsun 710	22.8	4	108	93	3.85	2.320
Hornet 4 Drive	21.4	6	258	110	3.08	3.215
Hornet Sportabout	18.7	8	360	175	3.15	3.440

Full width?

By default, a bootstrap table takes 100% of the width.
For some small tables with only few columns, a page wide table looks awful.
To make it look better, you can specify whether you want the table to have full_width or not in kable_styling.
By default, full_width is set to be TRUE for HTML tables.

kbl(dt) %>%
  kable_paper(bootstrap_options = "striped", full_width = F)

51 / 100

Full width?
 
      
    mpg 
    cyl 
    disp 
    hp 
    drat 
    wt 
  


    Mazda RX4 
0 



90 
620 
  

    Mazda RX4 Wag 
0 



90 
875 
  

    Datsun 710 
8 



85 
320 
  

    Hornet 4 Drive 
4 



08 
215 
  

    Hornet Sportabout 
7 



15 
440 
  

/ 100

	mpg	cyl	disp	hp	drat	wt
Mazda RX4	21.0	6	160	110	3.90	2.620
Mazda RX4 Wag	21.0	6	160	110	3.90	2.875
Datsun 710	22.8	4	108	93	3.85	2.320
Hornet 4 Drive	21.4	6	258	110	3.08	3.215
Hornet Sportabout	18.7	8	360	175	3.15	3.440

Position

Table position only matters when the table doesn’t have full_width.
You can choose to align the table to center, left or right side of the page.

kbl(dt) %>%
  kable_styling(bootstrap_options = "striped", full_width = F, position = "left")

53 / 100

Position
 
      
    mpg 
    cyl 
    disp 
    hp 
    drat 
    wt 
  


    Mazda RX4 
0 



90 
620 
  

    Mazda RX4 Wag 
0 



90 
875 
  

    Datsun 710 
8 



85 
320 
  

    Hornet 4 Drive 
4 



08 
215 
  

    Hornet Sportabout 
7 



15 
440 
  

/ 100

	mpg	cyl	disp	hp	drat	wt
Mazda RX4	21.0	6	160	110	3.90	2.620
Mazda RX4 Wag	21.0	6	160	110	3.90	2.875
Datsun 710	22.8	4	108	93	3.85	2.320
Hornet 4 Drive	21.4	6	258	110	3.08	3.215
Hornet Sportabout	18.7	8	360	175	3.15	3.440

Position

Becides left, right, and center options, you can also wrap text around the table using the float-left or float-right options.

kbl(dt) %>%
  kable_styling(bootstrap_options = "striped", full_width = F, position = "float_right")

55 / 100

Position
 
      
    mpg 
    cyl 
    disp 
    hp 
    drat 
    wt 
  


    Mazda RX4 
0 



90 
620 
  

    Mazda RX4 Wag 
0 



90 
875 
  

    Datsun 710 
8 



85 
320 
  

    Hornet 4 Drive 
4 



08 
215 
  

    Hornet Sportabout 
7 



15 
440 
  

/ 100

	mpg	cyl	disp	hp	drat	wt
Mazda RX4	21.0	6	160	110	3.90	2.620
Mazda RX4 Wag	21.0	6	160	110	3.90	2.875
Datsun 710	22.8	4	108	93	3.85	2.320
Hornet 4 Drive	21.4	6	258	110	3.08	3.215
Hornet Sportabout	18.7	8	360	175	3.15	3.440

	mpg	cyl	disp	hp	drat	wt
Mazda RX4	21.0	6	160	110	3.90	2.620
Mazda RX4 Wag	21.0	6	160	110	3.90	2.875
Datsun 710	22.8	4	108	93	3.85	2.320
Hornet 4 Drive	21.4	6	258	110	3.08	3.215
Hornet Sportabout	18.7	8	360	175	3.15	3.440

Fixed Table Header Row

If you happened to have a very long table, you may consider to use this fixed_header option to fix the header row on top as your readers scroll.
By default, the background is set to white. If you need a different color, you can set fixed_header = list(enabled = T, background = "red").

kbl(mtcars[1:10, 1:5]) %>%
  kable_styling(fixed_thead = T)

59 / 100

Fixed Table Header Row
 
      
    mpg 
    cyl 
    disp 
    hp 
    drat 
  


    Mazda RX4 
0 

0 

90 
  

    Mazda RX4 Wag 
0 

0 

90 
  

    Datsun 710 
8 

0 

85 
  

    Hornet 4 Drive 
4 

0 

08 
  

    Hornet Sportabout 
7 

0 

15 
  

    Valiant 
1 

0 

76 
  

    Duster 360 
3 

0 

21 
  

    Merc 240D 
4 

7 

69 
  

    Merc 230 
8 

8 

92 
  

    Merc 280 
2 

6 

92 
  

/ 100

	mpg	cyl	disp	hp	drat
Mazda RX4	21.0	6	160.0	110	3.90
Mazda RX4 Wag	21.0	6	160.0	110	3.90
Datsun 710	22.8	4	108.0	93	3.85
Hornet 4 Drive	21.4	6	258.0	110	3.08
Hornet Sportabout	18.7	8	360.0	175	3.15
Valiant	18.1	6	225.0	105	2.76
Duster 360	14.3	8	360.0	245	3.21
Merc 240D	24.4	4	146.7	62	3.69
Merc 230	22.8	4	140.8	95	3.92
Merc 280	19.2	6	167.6	123	3.92

Column / Row Specification61 / 100

Column spec

When you have a table with lots of explanatory texts, you may want to specified the column width for different column.
Also, sometimes, you may want to highlight a column (e.g. a “Total” column) by making it bold.
In these scenario, you can use column_spec().

text_tbl <- data.frame(
  Items = c("Item 1", "Item 2", "Item 3"),
  Features = c(
    "Lorem ipsum dolor sit amet, consectetur adipiscing elit. Proin vehicula tempor ex. Morbi malesuada sagittis turpis, at venenatis nisl luctus a. ",
    "In eu urna at magna luctus rhoncus quis in nisl. Fusce in velit varius, posuere risus et, cursus augue. Duis eleifend aliquam ante, a aliquet ex tincidunt in. ", 
    "Vivamus venenatis egestas eros ut tempus. Vivamus id est nisi. Aliquam molestie erat et sollicitudin venenatis. In ac lacus at velit scelerisque mattis. "
  )
)
kbl(text_tbl) %>%
  kable_paper(full_width = F) %>%
  column_spec(1, bold = T, border_right = T) %>%
  column_spec(2, width = "30em", background = "yellow")

62 / 100

Column spec
 
    Items 
    Features 
  
    Item 1 
    Lorem ipsum dolor sit amet, consectetur adipiscing elit. Proin vehicula tempor ex. Morbi malesuada sagittis turpis, at venenatis nisl luctus a. 
  
    Item 2 
    In eu urna at magna luctus rhoncus quis in nisl. Fusce in velit varius, posuere risus et, cursus augue. Duis eleifend aliquam ante, a aliquet ex tincidunt in. 
  
    Item 3 
    Vivamus venenatis egestas eros ut tempus. Vivamus id est nisi. Aliquam molestie erat et sollicitudin venenatis. In ac lacus at velit scelerisque mattis. 
  
63 / 100

Items	Features
Item 1	Lorem ipsum dolor sit amet, consectetur adipiscing elit. Proin vehicula tempor ex. Morbi malesuada sagittis turpis, at venenatis nisl luctus a.
Item 2	In eu urna at magna luctus rhoncus quis in nisl. Fusce in velit varius, posuere risus et, cursus augue. Duis eleifend aliquam ante, a aliquet ex tincidunt in.
Item 3	Vivamus venenatis egestas eros ut tempus. Vivamus id est nisi. Aliquam molestie erat et sollicitudin venenatis. In ac lacus at velit scelerisque mattis.

Row spec

Similar with column_spec, you can define specifications for rows.
Currently, you can either bold or italicize an entire row.

kbl(dt) %>%
  kable_paper("striped", full_width = F) %>%
  column_spec(5:7, bold = T) %>%
  row_spec(3:5, bold = T, color = "white", background = "#D7261E")

64 / 100

Row spec
 
      
    mpg 
    cyl 
    disp 
    hp 
    drat 
    wt 
  


    Mazda RX4 
0 



90 
620 
  

    Mazda RX4 Wag 
0 



90 
875 
  

    Datsun 710 
8 



85 
320 
  

    Hornet 4 Drive 
4 



08 
215 
  

    Hornet Sportabout 
7 



15 
440 
  

/ 100

	mpg	cyl	disp	hp	drat	wt
Mazda RX4	21.0	6	160	110	3.90	2.620
Mazda RX4 Wag	21.0	6	160	110	3.90	2.875
Datsun 710	22.8	4	108	93	3.85	2.320
Hornet 4 Drive	21.4	6	258	110	3.08	3.215
Hornet Sportabout	18.7	8	360	175	3.15	3.440

Header Rows

One special case of row_spec is that you can specify the format of the header row via row_spec(row = 0, ...).

kbl(dt) %>%
  kable_paper("striped", full_width = F) %>%
  row_spec(0, angle = -45)

66 / 100

Header Rows
 
      
    mpg 
    cyl 
    disp 
    hp 
    drat 
    wt 
  


    Mazda RX4 
0 



90 
620 
  

    Mazda RX4 Wag 
0 



90 
875 
  

    Datsun 710 
8 



85 
320 
  

    Hornet 4 Drive 
4 



08 
215 
  

    Hornet Sportabout 
7 



15 
440 
  

/ 100

	mpg	cyl	disp	hp	drat	wt
Mazda RX4	21.0	6	160	110	3.90	2.620
Mazda RX4 Wag	21.0	6	160	110	3.90	2.875
Datsun 710	22.8	4	108	93	3.85	2.320
Hornet 4 Drive	21.4	6	258	110	3.08	3.215
Hornet Sportabout	18.7	8	360	175	3.15	3.440

Cell/Text Specification68 / 100

Conditional logic

It is very easy to use cell_spec with conditional logic. Here is an example.

library(dplyr)
mtcars[1:10, 1:2] %>%
  mutate(
    car = row.names(.),
    mpg = cell_spec(mpg, "html", color = ifelse(mpg > 20, "red", "blue")),
    cyl = cell_spec(cyl, "html", color = "white", align = "c", angle = 45,
                    background = factor(cyl, c(4, 6, 8),
                                        c("#666666", "#999999", "#BBBBBB")))
  ) %>%
  select(car, mpg, cyl) %>%
  kbl(format = "html", escape = F) %>%
  kable_paper("striped", full_width = F)

69 / 100

Conditional logic
 
      
    car 
    mpg 
    cyl 
  


    Mazda RX4 
    Mazda RX4 
    21 
    6 
  

    Mazda RX4 Wag 
    Mazda RX4 Wag 
    21 
    6 
  

    Datsun 710 
    Datsun 710 
    22.8 
    4 
  

    Hornet 4 Drive 
    Hornet 4 Drive 
    21.4 
    6 
  

    Hornet Sportabout 
    Hornet Sportabout 
    18.7 
    8 
  

    Valiant 
    Valiant 
    18.1 
    6 
  

    Duster 360 
    Duster 360 
    14.3 
    8 
  

    Merc 240D 
    Merc 240D 
    24.4 
    4 
  

    Merc 230 
    Merc 230 
    22.8 
    4 
  

    Merc 280 
    Merc 280 
    19.2 
    6 
  

70 / 100

	car	mpg	cyl
Mazda RX4	Mazda RX4	21	6
Mazda RX4 Wag	Mazda RX4 Wag	21	6
Datsun 710	Datsun 710	22.8	4
Hornet 4 Drive	Hornet 4 Drive	21.4	6
Hornet Sportabout	Hornet Sportabout	18.7	8
Valiant	Valiant	18.1	6
Duster 360	Duster 360	14.3	8
Merc 240D	Merc 240D	24.4	4
Merc 230	Merc 230	22.8	4
Merc 280	Merc 280	19.2	6

Visualize data with Viridis Color

kableExtra package also comes with a few helper functions, including spec_color, spec_font_size, and spec_angle.
These functions can rescale continuous variables to certain scales.
For example, function spec_color would map a continuous variable to any viridis color palettes.

iris[1:10, ] %>%
  mutate_if(is.numeric, function(x) {
    cell_spec(x, bold = T,
              color = spec_color(x, end = 0.9),
              font_size = spec_font_size(x))
  }) %>%
  mutate(Species = cell_spec(
    Species, color = "white", bold = T,
    background = spec_color(1:10, end = 0.9, option = "A", direction = -1)
  )) %>%
  kable(escape = F, align = "c") %>%
  kable_paper(c("striped", "condensed"), full_width = F)

71 / 100

Visualize data with Viridis Color
 
    Sepal.Length 
    Sepal.Width 
    Petal.Length 
    Petal.Width 
    Species 
  


1 
5 
4 
2 
    setosa 
  

9 

4 
2 
    setosa 
  

7 
2 
3 
2 
    setosa 
  

6 
1 
5 
2 
    setosa 
  


6 
4 
2 
    setosa 
  

4 
9 
7 
4 
    setosa 
  

6 
4 
4 
3 
    setosa 
  


4 
5 
2 
    setosa 
  

4 
9 
4 
2 
    setosa 
  

9 
1 
5 
1 
    setosa 
  

/ 100

popover_dt <- data.frame(
  position = c("top", "bottom", "right", "left"),
  stringsAsFactors = FALSE
)
popover_dt$`Hover over these items` <- cell_spec(
  paste("Message on", popover_dt$position), # Cell texts
  popover = spec_popover(
    content = popover_dt$position,
    title = NULL,                           # title will add a Title Panel on top
    position = popover_dt$position
  ))
kbl(popover_dt, escape = FALSE) %>%
  kable_paper("striped", full_width = FALSE)

73 / 100

Grouped Columns / Rows74 / 100

Add header rows to group columns

Tables with multi-row headers can be very useful to demonstrate grouped data.
To do that, you can pipe your kable object into add_header_above().
The header variable is supposed to be a named character with the names as new column names and values as column span.
For your convenience, if column span equals to 1, you can ignore the =1 part so the function below can be written as add_header_above(c(" " = 1, "Group 1" = 2, "Group 2" = 2, "Group 3" = 2)).

kbl(dt) %>%
  kable_classic() %>%
  add_header_above(c(" " = 1, "Group 1" = 2, "Group 2" = 2, "Group 3" = 2))

75 / 100

Add header rows to group columns
 

Group 1

Group 2

Group 3


      
    mpg 
    cyl 
    disp 
    hp 
    drat 
    wt 
  


    Mazda RX4 
    21.0 
    6 
    160 
    110 
    3.90 
    2.620 
  

    Mazda RX4 Wag 
    21.0 
    6 
    160 
    110 
    3.90 
    2.875 
  

    Datsun 710 
    22.8 
    4 
    108 
    93 
    3.85 
    2.320 
  

    Hornet 4 Drive 
    21.4 
    6 
    258 
    110 
    3.08 
    3.215 
  

    Hornet Sportabout 
    18.7 
    8 
    360 
    175 
    3.15 
    3.440 
  

76 / 100

	Group 1	Group 2	Group 3
Mazda RX4	21.0	6	160	110	3.90	2.620
Mazda RX4 Wag	21.0	6	160	110	3.90	2.875
Datsun 710	22.8	4	108	93	3.85	2.320
Hornet 4 Drive	21.4	6	258	110	3.08	3.215
Hornet Sportabout	18.7	8	360	175	3.15	3.440

Add header rows to group columns

In fact, if you want to add another row of header on top, please feel free to do so.

kbl(dt) %>%
  kable_paper() %>%
  add_header_above(c(" ", "Group 1" = 2, "Group 2" = 2, "Group 3" = 2)) %>%
  add_header_above(c(" ", "Group 4" = 4, "Group 5" = 2)) %>%
  add_header_above(c(" ", "Group 6" = 6))

77 / 100

Add header rows to group columns
 

Group 6



Group 4

Group 5



Group 1

Group 2

Group 3


      
    mpg 
    cyl 
    disp 
    hp 
    drat 
    wt 
  


    Mazda RX4 
    21.0 
    6 
    160 
    110 
    3.90 
    2.620 
  

    Mazda RX4 Wag 
    21.0 
    6 
    160 
    110 
    3.90 
    2.875 
  

    Datsun 710 
    22.8 
    4 
    108 
    93 
    3.85 
    2.320 
  

    Hornet 4 Drive 
    21.4 
    6 
    258 
    110 
    3.08 
    3.215 
  

    Hornet Sportabout 
    18.7 
    8 
    360 
    175 
    3.15 
    3.440 
  

78 / 100

	Group 6
Mazda RX4	21.0	6	160	110	3.90	2.620
Mazda RX4 Wag	21.0	6	160	110	3.90	2.875
Datsun 710	22.8	4	108	93	3.85	2.320
Hornet 4 Drive	21.4	6	258	110	3.08	3.215
Hornet Sportabout	18.7	8	360	175	3.15	3.440

Group rows via labeling

Sometimes we want a few rows of the table being grouped together.
- E.g., different data groups for a categorical variable (e.g., age < 40, age > 40).

With the function group_rows()/pack_rows() in kableExtra, this kind of task can be completed in one line.
You only need to think about the row numbers in the “original R dataframe”.

kbl(mtcars[1:10, 1:6], caption = "Group Rows") %>%
  kable_paper("striped", full_width = F) %>%
  pack_rows("Group 1", 4, 7) %>%
  pack_rows("Group 2", 8, 10)

79 / 100

Group rows via labeling
Group Rows
 
      
    mpg 
    cyl 
    disp 
    hp 
    drat 
    wt 
  


    Mazda RX4 
0 

0 

90 
620 
  

    Mazda RX4 Wag 
0 

0 

90 
875 
  

    Datsun 710 
8 

0 

85 
320 
  
Group 1

    Hornet 4 Drive 
4 

0 

08 
215 
  

    Hornet Sportabout 
7 

0 

15 
440 
  

    Valiant 
1 

0 

76 
460 
  

    Duster 360 
3 

0 

21 
570 
  
Group 2

    Merc 240D 
4 

7 

69 
190 
  

    Merc 230 
8 

8 

92 
150 
  

    Merc 280 
2 

6 

92 
440 
  

/ 100

Group Rows
	mpg	cyl	disp	hp	drat	wt
Mazda RX4	21.0	6	160.0	110	3.90	2.620
Mazda RX4 Wag	21.0	6	160.0	110	3.90	2.875
Datsun 710	22.8	4	108.0	93	3.85	2.320
Group 1
Hornet 4 Drive	21.4	6	258.0	110	3.08	3.215
Hornet Sportabout	18.7	8	360.0	175	3.15	3.440
Valiant	18.1	6	225.0	105	2.76	3.460
Duster 360	14.3	8	360.0	245	3.21	3.570
Group 2
Merc 240D	24.4	4	146.7	62	3.69	3.190
Merc 230	22.8	4	140.8	95	3.92	3.150
Merc 280	19.2	6	167.6	123	3.92	3.440

Group rows via labeling

Another way to use pack_rows is to provide an grouping index, similar with add_header_above().

kbl(mtcars[1:10, 1:6], caption = "Group Rows") %>%
  kable_paper("striped", full_width = F) %>%
  pack_rows(index = c(" " = 3, "Group 1" = 4, "Group 2" = 3))

81 / 100

Group rows via labeling
Group Rows
 
      
    mpg 
    cyl 
    disp 
    hp 
    drat 
    wt 
  


    Mazda RX4 
0 

0 

90 
620 
  

    Mazda RX4 Wag 
0 

0 

90 
875 
  

    Datsun 710 
8 

0 

85 
320 
  
Group 1

    Hornet 4 Drive 
4 

0 

08 
215 
  

    Hornet Sportabout 
7 

0 

15 
440 
  

    Valiant 
1 

0 

76 
460 
  

    Duster 360 
3 

0 

21 
570 
  
Group 2

    Merc 240D 
4 

7 

69 
190 
  

    Merc 230 
8 

8 

92 
150 
  

    Merc 280 
2 

6 

92 
440 
  

/ 100

Group Rows
	mpg	cyl	disp	hp	drat	wt
Mazda RX4	21.0	6	160.0	110	3.90	2.620
Mazda RX4 Wag	21.0	6	160.0	110	3.90	2.875
Datsun 710	22.8	4	108.0	93	3.85	2.320
Group 1
Hornet 4 Drive	21.4	6	258.0	110	3.08	3.215
Hornet Sportabout	18.7	8	360.0	175	3.15	3.440
Valiant	18.1	6	225.0	105	2.76	3.460
Duster 360	14.3	8	360.0	245	3.21	3.570
Group 2
Merc 240D	24.4	4	146.7	62	3.69	3.190
Merc 230	22.8	4	140.8	95	3.92	3.150
Merc 280	19.2	6	167.6	123	3.92	3.440

Group rows via labeling

For advanced users, you can even define your own css for the group labeling using label_row_css argument.

kbl(dt) %>%
  kable_paper("striped", full_width = F) %>%
  pack_rows("Group 1", 3, 5, label_row_css = "background-color: #666; color: #fff;")

83 / 100

Group rows via labeling
 
      
    mpg 
    cyl 
    disp 
    hp 
    drat 
    wt 
  


    Mazda RX4 
0 



90 
620 
  

    Mazda RX4 Wag 
0 



90 
875 
  
Group 1

    Datsun 710 
8 



85 
320 
  

    Hornet 4 Drive 
4 



08 
215 
  

    Hornet Sportabout 
7 



15 
440 
  

/ 100

	mpg	cyl	disp	hp	drat	wt
Mazda RX4	21.0	6	160	110	3.90	2.620
Mazda RX4 Wag	21.0	6	160	110	3.90	2.875
Group 1
Datsun 710	22.8	4	108	93	3.85	2.320
Hornet 4 Drive	21.4	6	258	110	3.08	3.215
Hornet Sportabout	18.7	8	360	175	3.15	3.440

Row indentation

add_indent() add indention, which could be useful to make subgroups.

kbl(dt) %>%
  kable_paper("striped", full_width = F) %>%
  add_indent(c(1, 3, 5))

85 / 100

Row indentation
 
      
    mpg 
    cyl 
    disp 
    hp 
    drat 
    wt 
  


    Mazda RX4 
0 



90 
620 
  

    Mazda RX4 Wag 
0 



90 
875 
  

    Datsun 710 
8 



85 
320 
  

    Hornet 4 Drive 
4 



08 
215 
  

    Hornet Sportabout 
7 



15 
440 
  

/ 100

	mpg	cyl	disp	hp	drat	wt
Mazda RX4	21.0	6	160	110	3.90	2.620
Mazda RX4 Wag	21.0	6	160	110	3.90	2.875
Datsun 710	22.8	4	108	93	3.85	2.320
Hornet 4 Drive	21.4	6	258	110	3.08	3.215
Hornet Sportabout	18.7	8	360	175	3.15	3.440

Group rows via multi-row cell

Function pack_rows is great for showing simple structural information on rows but sometimes people may need to show structural information with multiple layers.
When it happens, you may consider to use collapse_rows instead,
- This will put repeating cells in columns into multi-row cells.
- The vertical allignment of the cell is controlled by valign with default as "top".

collapse_rows_dt <- data.frame(C1 = c(rep("a", 10), rep("b", 5)),
                 C2 = c(rep("c", 7), rep("d", 3), rep("c", 2), rep("d", 3)),
                 C3 = 1:15,
                 C4 = sample(c(0,1), 15, replace = TRUE))
kbl(collapse_rows_dt, align = "c") %>%
  kable_paper(full_width = F) %>%
  column_spec(1, bold = T) %>%
  collapse_rows(columns = 1:2, valign = "top")

87 / 100

Group rows via multi-row cell
 
    C1 
    C2 
    C3 
    C4 
  


    a 
    c 
    1 
    0 
  

    a 
    c 
    2 
    1 
  

    a 
    c 
    3 
    1 
  

    a 
    c 
    4 
    0 
  

    a 
    c 
    5 
    1 
  

    a 
    c 
    6 
    0 
  

    a 
    c 
    7 
    0 
  

    a 
    d 
    8 
    0 
  

    a 
    d 
    9 
    0 
  

    a 
    d 
    10 
    1 
  

    b 
    c 
    11 
    1 
  

    b 
    c 
    12 
    0 
  

    b 
    d 
    13 
    1 
  

    b 
    d 
    14 
    1 
  

    b 
    d 
    15 
    0 
  

88 / 100

C1	C2	C3	C4
a	c	1	0
a	c	2	1
a	c	3	1
a	c	4	0
a	c	5	1
a	c	6	0
a	c	7	0
a	d	8	0
a	d	9	0
a	d	10	1
b	c	11	1
b	c	12	0
b	d	13	1
b	d	14	1
b	d	15	0

Table Footnote89 / 100

Table Footnote

There are four notation systems in footnote, namely general, number, alphabet and symbol.

kbl(dt, align = "c") %>%
  kable_classic(full_width = F) %>%
  footnote(general = "Here is a general comments of the table. ",
           number = c("Footnote 1; ", "Footnote 2; "),
           alphabet = c("Footnote A; ", "Footnote B; "),
           symbol = c("Footnote Symbol 1; ", "Footnote Symbol 2")
           )

90 / 100

Table Footnote
 
      
    mpg 
    cyl 
    disp 
    hp 
    drat 
    wt 
  


    Mazda RX4 
    21.0 
    6 
    160 
    110 
    3.90 
    2.620 
  

    Mazda RX4 Wag 
    21.0 
    6 
    160 
    110 
    3.90 
    2.875 
  

    Datsun 710 
    22.8 
    4 
    108 
    93 
    3.85 
    2.320 
  

    Hornet 4 Drive 
    21.4 
    6 
    258 
    110 
    3.08 
    3.215 
  

    Hornet Sportabout 
    18.7 
    8 
    360 
    175 
    3.15 
    3.440 
  

Note: 

 Here is a general comments of the table. 

1 Footnote 1; 

2 Footnote 2; 

a Footnote A; 

b Footnote B; 

* Footnote Symbol 1; 

† Footnote Symbol 2

91 / 100

	mpg	cyl	disp	hp	drat	wt
Mazda RX4	21.0	6	160	110	3.90	2.620
Mazda RX4 Wag	21.0	6	160	110	3.90	2.875
Datsun 710	22.8	4	108	93	3.85	2.320
Hornet 4 Drive	21.4	6	258	110	3.08	3.215
Hornet Sportabout	18.7	8	360	175	3.15	3.440
Note:
Here is a general comments of the table.
¹ Footnote 1;
² Footnote 2;
^a Footnote A;
^b Footnote B;
^* Footnote Symbol 1;
^† Footnote Symbol 2

Table Footnote

You can also specify title for each category by using the ***_title arguments.
Default value for general_title is “Note:” and "" for the rest three.
You can also change the order using footnote_order. You can even display footnote as chunk texts (default is as a list) using footnote_as_chunk.
The font format of the titles are controlled by title_format with options including “italic” (default), “bold” and “underline”.

kbl(dt, align = "c") %>%
  kable_paper(full_width = F) %>%
  footnote(general = "Here is a general comments of the table. ",
           number = c("Footnote 1; ", "Footnote 2; "),
           alphabet = c("Footnote A; ", "Footnote B; "),
           symbol = c("Footnote Symbol 1; ", "Footnote Symbol 2"),
           general_title = "General: ", number_title = "Type I: ",
           alphabet_title = "Type II: ", symbol_title = "Type III: ",
           footnote_as_chunk = T, title_format = c("italic", "underline")
           )

92 / 100

Table Footnote
 
      
    mpg 
    cyl 
    disp 
    hp 
    drat 
    wt 
  


    Mazda RX4 
    21.0 
    6 
    160 
    110 
    3.90 
    2.620 
  

    Mazda RX4 Wag 
    21.0 
    6 
    160 
    110 
    3.90 
    2.875 
  

    Datsun 710 
    22.8 
    4 
    108 
    93 
    3.85 
    2.320 
  

    Hornet 4 Drive 
    21.4 
    6 
    258 
    110 
    3.08 
    3.215 
  

    Hornet Sportabout 
    18.7 
    8 
    360 
    175 
    3.15 
    3.440 
  


General:   Here is a general comments of the table. 

Type I:  1 Footnote 1;  2 Footnote 2; 

Type II:  a Footnote A;  b Footnote B; 

Type III:  * Footnote Symbol 1;  † Footnote Symbol 2

93 / 100

	mpg	cyl	disp	hp	drat	wt
Mazda RX4	21.0	6	160	110	3.90	2.620
Mazda RX4 Wag	21.0	6	160	110	3.90	2.875
Datsun 710	22.8	4	108	93	3.85	2.320
Hornet 4 Drive	21.4	6	258	110	3.08	3.215
Hornet Sportabout	18.7	8	360	175	3.15	3.440
General: Here is a general comments of the table.
Type I: ¹ Footnote 1; ² Footnote 2;
Type II: ^a Footnote A; ^b Footnote B;
Type III: ^* Footnote Symbol 1; ^† Footnote Symbol 2

Table Footnote

If you need to add footnote marks in table, you need to do it manually (no fancy) using footnote_mark_***().

dt_footnote <- dt
names(dt_footnote)[2] <- paste0(names(dt_footnote)[2], 
                                footnote_marker_symbol(1))
row.names(dt_footnote)[4] <- paste0(row.names(dt_footnote)[4], 
                                footnote_marker_alphabet(1))
kbl(dt_footnote, align = "c", 
      # Remember this escape = F
      escape = F) %>%
  kable_paper(full_width = F) %>%
  footnote(alphabet = "Footnote A; ",
           symbol = "Footnote Symbol 1; ",
           alphabet_title = "Type II: ", symbol_title = "Type III: ",
           footnote_as_chunk = T)

94 / 100

Table Footnote
 
      
    mpg 
    cyl* 
    disp 
    hp 
    drat 
    wt 
  


    Mazda RX4 
    21.0 
    6 
    160 
    110 
    3.90 
    2.620 
  

    Mazda RX4 Wag 
    21.0 
    6 
    160 
    110 
    3.90 
    2.875 
  

    Datsun 710 
    22.8 
    4 
    108 
    93 
    3.85 
    2.320 
  

    Hornet 4 Drivea 
    21.4 
    6 
    258 
    110 
    3.08 
    3.215 
  

    Hornet Sportabout 
    18.7 
    8 
    360 
    175 
    3.15 
    3.440 
  


Type II:  a Footnote A; 

Type III:  * Footnote Symbol 1; 

95 / 100

	mpg	cyl^*	disp	hp	drat	wt
Mazda RX4	21.0	6	160	110	3.90	2.620
Mazda RX4 Wag	21.0	6	160	110	3.90	2.875
Datsun 710	22.8	4	108	93	3.85	2.320
Hornet 4 Drive^a	21.4	6	258	110	3.08	3.215
Hornet Sportabout	18.7	8	360	175	3.15	3.440
Type II: ^a Footnote A;
Type III: ^* Footnote Symbol 1;

HTML Only Features96 / 100

Scroll box

If you have a huge table and you don’t want to reduce the font size to unreadable, you may want to put your HTML table in a scroll box.

When you use scroll_box, you can specify either height or width.

kbl(cbind(mtcars, mtcars)) %>%
  kable_paper() %>%
  scroll_box(width = "500px", height = "200px")

97 / 100

Scroll box
 
      
    mpg 
    cyl 
    disp 
    hp 
    drat 
    wt 
    qsec 
    vs 
    am 
    gear 
    carb 
    mpg 
    cyl 
    disp 
    hp 
    drat 
    wt 
    qsec 
    vs 
    am 
    gear 
    carb 
  


    Mazda RX4 
0 

0 

90 
620 
46 




0 

0 

90 
620 
46 




  

    Mazda RX4 Wag 
0 

0 

90 
875 
02 




0 

0 

90 
875 
02 




  

    Datsun 710 
8 

0 

85 
320 
61 




8 

0 

85 
320 
61 




  

    Hornet 4 Drive 
4 

0 

08 
215 
44 




4 

0 

08 
215 
44 




  

    Hornet Sportabout 
7 

0 

15 
440 
02 




7 

0 

15 
440 
02 




  

    Valiant 
1 

0 

76 
460 
22 




1 

0 

76 
460 
22 




  

    Duster 360 
3 

0 

21 
570 
84 




3 

0 

21 
570 
84 




  

    Merc 240D 
4 

7 

69 
190 
00 




4 

7 

69 
190 
00 




  

    Merc 230 
8 

8 

92 
150 
90 




8 

8 

92 
150 
90 




  

    Merc 280 
2 

6 

92 
440 
30 




2 

6 

92 
440 
30 




  

    Merc 280C 
8 

6 

92 
440 
90 




8 

6 

92 
440 
90 




  

    Merc 450SE 
4 

8 

07 
070 
40 




4 

8 

07 
070 
40 




  

    Merc 450SL 
3 

8 

07 
730 
60 




3 

8 

07 
730 
60 




  

    Merc 450SLC 
2 

8 

07 
780 
00 




2 

8 

07 
780 
00 




  

    Cadillac Fleetwood 
4 

0 

93 
250 
98 




4 

0 

93 
250 
98 




  

    Lincoln Continental 
4 

0 

00 
424 
82 




4 

0 

00 
424 
82 




  

    Chrysler Imperial 
7 

0 

23 
345 
42 




7 

0 

23 
345 
42 




  

    Fiat 128 
4 

7 

08 
200 
47 




4 

7 

08 
200 
47 




  

    Honda Civic 
4 

7 

93 
615 
52 




4 

7 

93 
615 
52 




  

    Toyota Corolla 
9 

1 

22 
835 
90 




9 

1 

22 
835 
90 




  

    Toyota Corona 
5 

1 

70 
465 
01 




5 

1 

70 
465 
01 




  

    Dodge Challenger 
5 

0 

76 
520 
87 




5 

0 

76 
520 
87 




  

    AMC Javelin 
2 

0 

15 
435 
30 




2 

0 

15 
435 
30 




  

    Camaro Z28 
3 

0 

73 
840 
41 




3 

0 

73 
840 
41 




  

    Pontiac Firebird 
2 

0 

08 
845 
05 




2 

0 

08 
845 
05 




  

    Fiat X1-9 
3 

0 

08 
935 
90 




3 

0 

08 
935 
90 




  

    Porsche 914-2 
0 

3 

43 
140 
70 




0 

3 

43 
140 
70 




  

    Lotus Europa 
4 

1 

77 
513 
90 




4 

1 

77 
513 
90 




  

    Ford Pantera L 
8 

0 

22 
170 
50 




8 

0 

22 
170 
50 




  

    Ferrari Dino 
7 

0 

62 
770 
50 




7 

0 

62 
770 
50 




  

    Maserati Bora 
0 

0 

54 
570 
60 




0 

0 

54 
570 
60 




  

    Volvo 142E 
4 

0 

11 
780 
60 




4 

0 

11 
780 
60 




  

/ 100

Scroll box

You can also specify width using a percentage.

kbl(cbind(mtcars, mtcars)) %>%
  add_header_above(c("a" = 5, "b" = 18)) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "200px")

99 / 100

Scroll box
 
a

b


      
    mpg 
    cyl 
    disp 
    hp 
    drat 
    wt 
    qsec 
    vs 
    am 
    gear 
    carb 
    mpg 
    cyl 
    disp 
    hp 
    drat 
    wt 
    qsec 
    vs 
    am 
    gear 
    carb 
  


    Mazda RX4 
0 

0 

90 
620 
46 




0 

0 

90 
620 
46 




  

    Mazda RX4 Wag 
0 

0 

90 
875 
02 




0 

0 

90 
875 
02 




  

    Datsun 710 
8 

0 

85 
320 
61 




8 

0 

85 
320 
61 




  

    Hornet 4 Drive 
4 

0 

08 
215 
44 




4 

0 

08 
215 
44 




  

    Hornet Sportabout 
7 

0 

15 
440 
02 




7 

0 

15 
440 
02 




  

    Valiant 
1 

0 

76 
460 
22 




1 

0 

76 
460 
22 




  

    Duster 360 
3 

0 

21 
570 
84 




3 

0 

21 
570 
84 




  

    Merc 240D 
4 

7 

69 
190 
00 




4 

7 

69 
190 
00 




  

    Merc 230 
8 

8 

92 
150 
90 




8 

8 

92 
150 
90 




  

    Merc 280 
2 

6 

92 
440 
30 




2 

6 

92 
440 
30 




  

    Merc 280C 
8 

6 

92 
440 
90 




8 

6 

92 
440 
90 




  

    Merc 450SE 
4 

8 

07 
070 
40 




4 

8 

07 
070 
40 




  

    Merc 450SL 
3 

8 

07 
730 
60 




3 

8 

07 
730 
60 




  

    Merc 450SLC 
2 

8 

07 
780 
00 




2 

8 

07 
780 
00 




  

    Cadillac Fleetwood 
4 

0 

93 
250 
98 




4 

0 

93 
250 
98 




  

    Lincoln Continental 
4 

0 

00 
424 
82 




4 

0 

00 
424 
82 




  

    Chrysler Imperial 
7 

0 

23 
345 
42 




7 

0 

23 
345 
42 




  

    Fiat 128 
4 

7 

08 
200 
47 




4 

7 

08 
200 
47 




  

    Honda Civic 
4 

7 

93 
615 
52 




4 

7 

93 
615 
52 




  

    Toyota Corolla 
9 

1 

22 
835 
90 




9 

1 

22 
835 
90 




  

    Toyota Corona 
5 

1 

70 
465 
01 




5 

1 

70 
465 
01 




  

    Dodge Challenger 
5 

0 

76 
520 
87 




5 

0 

76 
520 
87 




  

    AMC Javelin 
2 

0 

15 
435 
30 




2 

0 

15 
435 
30 




  

    Camaro Z28 
3 

0 

73 
840 
41 




3 

0 

73 
840 
41 




  

    Pontiac Firebird 
2 

0 

08 
845 
05 




2 

0 

08 
845 
05 




  

    Fiat X1-9 
3 

0 

08 
935 
90 




3 

0 

08 
935 
90 




  

    Porsche 914-2 
0 

3 

43 
140 
70 




0 

3 

43 
140 
70 




  

    Lotus Europa 
4 

1 

77 
513 
90 




4 

1 

77 
513 
90 




  

    Ford Pantera L 
8 

0 

22 
170 
50 




8 

0 

22 
170 
50 




  

    Ferrari Dino 
7 

0 

62 
770 
50 




7 

0 

62 
770 
50 




  

    Maserati Bora 
0 

0 

54 
570 
60 




0 

0 

54 
570 
60 




  

    Volvo 142E 
4 

0 

11 
780 
60 




4 

0 

11 
780 
60 




  

/ 100

	mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb	mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb
Mazda RX4	21.0	6	160.0	110	3.90	2.620	16.46	0	1	4	4	21.0	6	160.0	110	3.90	2.620	16.46	0	1	4	4
Mazda RX4 Wag	21.0	6	160.0	110	3.90	2.875	17.02	0	1	4	4	21.0	6	160.0	110	3.90	2.875	17.02	0	1	4	4
Datsun 710	22.8	4	108.0	93	3.85	2.320	18.61	1	1	4	1	22.8	4	108.0	93	3.85	2.320	18.61	1	1	4	1
Hornet 4 Drive	21.4	6	258.0	110	3.08	3.215	19.44	1	0	3	1	21.4	6	258.0	110	3.08	3.215	19.44	1	0	3	1
Hornet Sportabout	18.7	8	360.0	175	3.15	3.440	17.02	0	0	3	2	18.7	8	360.0	175	3.15	3.440	17.02	0	0	3	2
Valiant	18.1	6	225.0	105	2.76	3.460	20.22	1	0	3	1	18.1	6	225.0	105	2.76	3.460	20.22	1	0	3	1
Duster 360	14.3	8	360.0	245	3.21	3.570	15.84	0	0	3	4	14.3	8	360.0	245	3.21	3.570	15.84	0	0	3	4
Merc 240D	24.4	4	146.7	62	3.69	3.190	20.00	1	0	4	2	24.4	4	146.7	62	3.69	3.190	20.00	1	0	4	2
Merc 230	22.8	4	140.8	95	3.92	3.150	22.90	1	0	4	2	22.8	4	140.8	95	3.92	3.150	22.90	1	0	4	2
Merc 280	19.2	6	167.6	123	3.92	3.440	18.30	1	0	4	4	19.2	6	167.6	123	3.92	3.440	18.30	1	0	4	4
Merc 280C	17.8	6	167.6	123	3.92	3.440	18.90	1	0	4	4	17.8	6	167.6	123	3.92	3.440	18.90	1	0	4	4
Merc 450SE	16.4	8	275.8	180	3.07	4.070	17.40	0	0	3	3	16.4	8	275.8	180	3.07	4.070	17.40	0	0	3	3
Merc 450SL	17.3	8	275.8	180	3.07	3.730	17.60	0	0	3	3	17.3	8	275.8	180	3.07	3.730	17.60	0	0	3	3
Merc 450SLC	15.2	8	275.8	180	3.07	3.780	18.00	0	0	3	3	15.2	8	275.8	180	3.07	3.780	18.00	0	0	3	3
Cadillac Fleetwood	10.4	8	472.0	205	2.93	5.250	17.98	0	0	3	4	10.4	8	472.0	205	2.93	5.250	17.98	0	0	3	4
Lincoln Continental	10.4	8	460.0	215	3.00	5.424	17.82	0	0	3	4	10.4	8	460.0	215	3.00	5.424	17.82	0	0	3	4
Chrysler Imperial	14.7	8	440.0	230	3.23	5.345	17.42	0	0	3	4	14.7	8	440.0	230	3.23	5.345	17.42	0	0	3	4
Fiat 128	32.4	4	78.7	66	4.08	2.200	19.47	1	1	4	1	32.4	4	78.7	66	4.08	2.200	19.47	1	1	4	1
Honda Civic	30.4	4	75.7	52	4.93	1.615	18.52	1	1	4	2	30.4	4	75.7	52	4.93	1.615	18.52	1	1	4	2
Toyota Corolla	33.9	4	71.1	65	4.22	1.835	19.90	1	1	4	1	33.9	4	71.1	65	4.22	1.835	19.90	1	1	4	1
Toyota Corona	21.5	4	120.1	97	3.70	2.465	20.01	1	0	3	1	21.5	4	120.1	97	3.70	2.465	20.01	1	0	3	1
Dodge Challenger	15.5	8	318.0	150	2.76	3.520	16.87	0	0	3	2	15.5	8	318.0	150	2.76	3.520	16.87	0	0	3	2
AMC Javelin	15.2	8	304.0	150	3.15	3.435	17.30	0	0	3	2	15.2	8	304.0	150	3.15	3.435	17.30	0	0	3	2
Camaro Z28	13.3	8	350.0	245	3.73	3.840	15.41	0	0	3	4	13.3	8	350.0	245	3.73	3.840	15.41	0	0	3	4
Pontiac Firebird	19.2	8	400.0	175	3.08	3.845	17.05	0	0	3	2	19.2	8	400.0	175	3.08	3.845	17.05	0	0	3	2
Fiat X1-9	27.3	4	79.0	66	4.08	1.935	18.90	1	1	4	1	27.3	4	79.0	66	4.08	1.935	18.90	1	1	4	1
Porsche 914-2	26.0	4	120.3	91	4.43	2.140	16.70	0	1	5	2	26.0	4	120.3	91	4.43	2.140	16.70	0	1	5	2
Lotus Europa	30.4	4	95.1	113	3.77	1.513	16.90	1	1	5	2	30.4	4	95.1	113	3.77	1.513	16.90	1	1	5	2
Ford Pantera L	15.8	8	351.0	264	4.22	3.170	14.50	0	1	5	4	15.8	8	351.0	264	4.22	3.170	14.50	0	1	5	4
Ferrari Dino	19.7	6	145.0	175	3.62	2.770	15.50	0	1	5	6	19.7	6	145.0	175	3.62	2.770	15.50	0	1	5	6
Maserati Bora	15.0	8	301.0	335	3.54	3.570	14.60	0	1	5	8	15.0	8	301.0	335	3.54	3.570	14.60	0	1	5	8
Volvo 142E	21.4	4	121.0	109	4.11	2.780	18.60	1	1	4	2	21.4	4	121.0	109	4.11	2.780	18.60	1	1	4	2

a					b
	mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb	mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb
Mazda RX4	21.0	6	160.0	110	3.90	2.620	16.46	0	1	4	4	21.0	6	160.0	110	3.90	2.620	16.46	0	1	4	4
Mazda RX4 Wag	21.0	6	160.0	110	3.90	2.875	17.02	0	1	4	4	21.0	6	160.0	110	3.90	2.875	17.02	0	1	4	4
Datsun 710	22.8	4	108.0	93	3.85	2.320	18.61	1	1	4	1	22.8	4	108.0	93	3.85	2.320	18.61	1	1	4	1
Hornet 4 Drive	21.4	6	258.0	110	3.08	3.215	19.44	1	0	3	1	21.4	6	258.0	110	3.08	3.215	19.44	1	0	3	1
Hornet Sportabout	18.7	8	360.0	175	3.15	3.440	17.02	0	0	3	2	18.7	8	360.0	175	3.15	3.440	17.02	0	0	3	2
Valiant	18.1	6	225.0	105	2.76	3.460	20.22	1	0	3	1	18.1	6	225.0	105	2.76	3.460	20.22	1	0	3	1
Duster 360	14.3	8	360.0	245	3.21	3.570	15.84	0	0	3	4	14.3	8	360.0	245	3.21	3.570	15.84	0	0	3	4
Merc 240D	24.4	4	146.7	62	3.69	3.190	20.00	1	0	4	2	24.4	4	146.7	62	3.69	3.190	20.00	1	0	4	2
Merc 230	22.8	4	140.8	95	3.92	3.150	22.90	1	0	4	2	22.8	4	140.8	95	3.92	3.150	22.90	1	0	4	2
Merc 280	19.2	6	167.6	123	3.92	3.440	18.30	1	0	4	4	19.2	6	167.6	123	3.92	3.440	18.30	1	0	4	4
Merc 280C	17.8	6	167.6	123	3.92	3.440	18.90	1	0	4	4	17.8	6	167.6	123	3.92	3.440	18.90	1	0	4	4
Merc 450SE	16.4	8	275.8	180	3.07	4.070	17.40	0	0	3	3	16.4	8	275.8	180	3.07	4.070	17.40	0	0	3	3
Merc 450SL	17.3	8	275.8	180	3.07	3.730	17.60	0	0	3	3	17.3	8	275.8	180	3.07	3.730	17.60	0	0	3	3
Merc 450SLC	15.2	8	275.8	180	3.07	3.780	18.00	0	0	3	3	15.2	8	275.8	180	3.07	3.780	18.00	0	0	3	3
Cadillac Fleetwood	10.4	8	472.0	205	2.93	5.250	17.98	0	0	3	4	10.4	8	472.0	205	2.93	5.250	17.98	0	0	3	4
Lincoln Continental	10.4	8	460.0	215	3.00	5.424	17.82	0	0	3	4	10.4	8	460.0	215	3.00	5.424	17.82	0	0	3	4
Chrysler Imperial	14.7	8	440.0	230	3.23	5.345	17.42	0	0	3	4	14.7	8	440.0	230	3.23	5.345	17.42	0	0	3	4
Fiat 128	32.4	4	78.7	66	4.08	2.200	19.47	1	1	4	1	32.4	4	78.7	66	4.08	2.200	19.47	1	1	4	1
Honda Civic	30.4	4	75.7	52	4.93	1.615	18.52	1	1	4	2	30.4	4	75.7	52	4.93	1.615	18.52	1	1	4	2
Toyota Corolla	33.9	4	71.1	65	4.22	1.835	19.90	1	1	4	1	33.9	4	71.1	65	4.22	1.835	19.90	1	1	4	1
Toyota Corona	21.5	4	120.1	97	3.70	2.465	20.01	1	0	3	1	21.5	4	120.1	97	3.70	2.465	20.01	1	0	3	1
Dodge Challenger	15.5	8	318.0	150	2.76	3.520	16.87	0	0	3	2	15.5	8	318.0	150	2.76	3.520	16.87	0	0	3	2
AMC Javelin	15.2	8	304.0	150	3.15	3.435	17.30	0	0	3	2	15.2	8	304.0	150	3.15	3.435	17.30	0	0	3	2
Camaro Z28	13.3	8	350.0	245	3.73	3.840	15.41	0	0	3	4	13.3	8	350.0	245	3.73	3.840	15.41	0	0	3	4
Pontiac Firebird	19.2	8	400.0	175	3.08	3.845	17.05	0	0	3	2	19.2	8	400.0	175	3.08	3.845	17.05	0	0	3	2
Fiat X1-9	27.3	4	79.0	66	4.08	1.935	18.90	1	1	4	1	27.3	4	79.0	66	4.08	1.935	18.90	1	1	4	1
Porsche 914-2	26.0	4	120.3	91	4.43	2.140	16.70	0	1	5	2	26.0	4	120.3	91	4.43	2.140	16.70	0	1	5	2
Lotus Europa	30.4	4	95.1	113	3.77	1.513	16.90	1	1	5	2	30.4	4	95.1	113	3.77	1.513	16.90	1	1	5	2
Ford Pantera L	15.8	8	351.0	264	4.22	3.170	14.50	0	1	5	4	15.8	8	351.0	264	4.22	3.170	14.50	0	1	5	4
Ferrari Dino	19.7	6	145.0	175	3.62	2.770	15.50	0	1	5	6	19.7	6	145.0	175	3.62	2.770	15.50	0	1	5	6
Maserati Bora	15.0	8	301.0	335	3.54	3.570	14.60	0	1	5	8	15.0	8	301.0	335	3.54	3.570	14.60	0	1	5	8
Volvo 142E	21.4	4	121.0	109	4.11	2.780	18.60	1	1	4	2	21.4	4	121.0	109	4.11	2.780	18.60	1	1	4	2

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	Group 6
	Group 4				Group 5
	Group 1		Group 2		Group 3
	mpg	cyl	disp	hp	drat	wt
Mazda RX4	21.0	6	160	110	3.90	2.620
Mazda RX4 Wag	21.0	6	160	110	3.90	2.875
Datsun 710	22.8	4	108	93	3.85	2.320
Hornet 4 Drive	21.4	6	258	110	3.08	3.215
Hornet Sportabout	18.7	8	360	175	3.15	3.440

C1	C2	C3	C4
a	c	1	0
a	c	2	1
a	c	3	1
a	c	4	0
a	c	5	1
a	c	6	0
a	c	7	0
a	d	8	0
a	d	9	0
a	d	10	1
b	c	11	1
b	c	12	0
b	d	13	1
b	d	14	1
b	d	15	0

C1	C2	C3	C4
a	c	1	0
a	c	2	1
a	c	3	1
a	c	4	0
a	c	5	1
a	c	6	0
a	c	7	0
a	d	8	0
a	d	9	0
a	d	10	1
b	c	11	1
b	c	12	0
b	d	13	1
b	d	14	1
b	d	15	0

DANL 310 Lecture 13

knitr::kable and kableExtra

Byeong-Hak Choe

March 31, 2022

Creating tables using knitr::kable

Change column names

Change column names

Specify column alignment

Specify column alignment

Add a table caption

Add a table caption

Format numeric columns

Format numeric columns

Format numeric columns

Format numeric columns

Format numeric columns

Format numeric columns

Format numeric columns

Format numeric columns

Format numeric columns

Format numeric columns

Display missing values

Display missing values

Display missing values

Display missing values

Display missing values

Display missing values

Creating tables using kableExtra

Getting Started with kableExtra

Getting Started with kableExtra

Basic HTML table

Basic HTML table

Bootstrap theme

Bootstrap theme

Alternative themes

Alternative themes

Alternative themes

Alternative themes

Alternative themes

Alternative themes

Alternative themes

Alternative themes

Alternative themes

Alternative themes

Alternative themes

Alternative themes

Table Styles

Bootstrap table classes

Bootstrap table classes

Bootstrap table classes

Bootstrap table classes

Bootstrap table classes

Bootstrap table classes

Full width?

Full width?

Position

Position

Position

Position

Font size

Font size

Fixed Table Header Row

Fixed Table Header Row

Column / Row Specification

Column spec

Column spec

Row spec

Row spec

Header Rows

Header Rows

Cell/Text Specification

Conditional logic

Conditional logic

Visualize data with Viridis Color

Visualize data with Viridis Color

Popover Message

Grouped Columns / Rows

Add header rows to group columns

Add header rows to group columns

Add header rows to group columns

Creating tables using `knitr::kable`

Creating tables using `kableExtra`

Getting Started with `kableExtra`

Getting Started with `kableExtra`

Creating tables using `knitr::kable`

C1	C2	C3	C4
a	c	1	0
a	c	2	1
a	c	3	1
a	c	4	0
a	c	5	1
a	c	6	0
a	c	7	0
a	d	8	0
a	d	9	0
a	d	10	1
b	c	11	1
b	c	12	0
b	d	13	1
b	d	14	1
b	d	15	0