Complex header

The following dataset is typology, a dataset containing data for table headers.

#>           col_keys         colC          colB        colA
#> 1            sep_1                                       
#> 2            sep_2                                       
#> 3             year                       Year        Year
#> 4          premium                    Premium     Premium
#> 5      latest_eval                Latest Eval Latest Eval
#> 6     cape_cod_u_l     Cape Cod Ultimate Loss       (000)
#> 7      cape_cod_lr     Cape Cod   Ultimate LR        ( %)
#> 8 chain_ladder_u_l Chain Ladder Ultimate Loss            
#> 9  chain_ladder_lr Chain Ladder   Ultimate LR       (%\n)

The following dataset is x, it will be displayed in the table body.

#>   year   premium latest_eval cape_cod_u_l cape_cod_lr chain_ladder_u_l
#> 1 2001  8.920428    4.492365         6998          60         4.970296
#> 2 2002 12.660827    5.165556         7058          69         5.980417
#> 3 2003  8.757757    6.221537         6923          69         6.392572
#> 4 2004  9.852580    5.334078         6916          83         4.400530
#>   chain_ladder_lr
#> 1        69.33936
#> 2        69.06072
#> 3        71.40414
#> 4        70.23848
double_format <- function(x){
sprintf("%.3f", x)
}
percent_format <- function(x){
sprintf("%.2f %%", x)
}
x, col_keys = c("year", "premium", "latest_eval",
"sep_1", "cape_cod_u_l", "cape_cod_lr",
"sep_2", "chain_ladder_u_l", "chain_ladder_lr") )
ft <- set_formatter(ft, premium = double_format, latest_eval = double_format,
chain_ladder_lr = percent_format )
ft <- set_header_df(ft, mapping = typology, key = "col_keys" )
ft

Cape Cod

Cape Cod

Chain Ladder

Chain Ladder

Year

Premium

Latest Eval

Ultimate Loss

Ultimate LR

Ultimate Loss

Ultimate LR

Year

Premium

Latest Eval

(000)

( %)

(% )

2001

8.920

4.492

6998

60

4.970

69.34 %

2002

12.661

5.166

7058

69

5.980

69.06 %

2003

8.758

6.222

6923

69

6.393

71.40 %

2004

9.853

5.334

6916

83

4.401

70.24 %

ft <- merge_h(ft, part = "header")
ft <- merge_v(ft, part = "header", j = 1:3)
ft <- theme_zebra(ft, odd_header = "transparent", even_header = "transparent")
ft

Cape Cod

Chain Ladder

Year

Premium

Latest Eval

Ultimate Loss

Ultimate LR

Ultimate Loss

Ultimate LR

(000)

( %)

(% )

2001

8.920

4.492

6998

60

4.970

69.34 %

2002

12.661

5.166

7058

69

5.980

69.06 %

2003

8.758

6.222

6923

69

6.393

71.40 %

2004

9.853

5.334

6916

83

4.401

70.24 %

ft <- fontsize(ft, size = 11, part = "all")
ft <- fontsize(ft, i = 1:2, size = 12, part = "header")
ft <- color(ft, i = 1:2, color = "#007FA6", part = "header")
ft <- fontsize(ft, i = 3, size = 9, part = "header")
ft <- color(ft, i = 3, color = "gray", part = "header")
ft

Cape Cod

Chain Ladder

Year

Premium

Latest Eval

Ultimate Loss

Ultimate LR

Ultimate Loss

Ultimate LR

(000)

( %)

(% )

2001

8.920

4.492

6998

60

4.970

69.34 %

2002

12.661

5.166

7058

69

5.980

69.06 %

2003

8.758

6.222

6923

69

6.393

71.40 %

2004

9.853

5.334

6916

83

4.401

70.24 %

ft <- border(ft, border.bottom = fp_border(width = .75, color = "#007FA6"), part = "body" )
# color last border bottom of header and first border top of body
ft <- border(ft, border.bottom = fp_border(width = 2, color = "#007FA6"), part = "header" )
ft <- border(ft, i = 1, border.top = fp_border(width = 2, color = "#007FA6"), part = "body" )
ft

Cape Cod

Chain Ladder

Year

Premium

Latest Eval

Ultimate Loss

Ultimate LR

Ultimate Loss

Ultimate LR

(000)

( %)

(% )

2001

8.920

4.492

6998

60

4.970

69.34 %

2002

12.661

5.166

7058

69

5.980

69.06 %

2003

8.758

6.222

6923

69

6.393

71.40 %

2004

9.853

5.334

6916

83

4.401

70.24 %

ft <- empty_blanks(ft)
ft <- autofit(ft)
ft

Cape Cod

Chain Ladder

Year

Premium

Latest Eval

Ultimate Loss

Ultimate LR

Ultimate Loss

Ultimate LR

(000)

( %)

(% )

2001

8.920

4.492

6998

60

4.970

69.34 %

2002

12.661

5.166

7058

69

5.980

69.06 %

2003

8.758

6.222

6923

69

6.393

71.40 %

2004

9.853

5.334

6916

83

4.401

70.24 %

Conditional formatting

Formatting functions accept arguments i and j to select rows and columns to format. These arguments support formulas, index, logical (and character for columns’ names).

ft <- regulartable(head(mtcars))
ft <- color(ft, i = ~ drat > 3, j = ~ vs + am, color = "red")
ft <- bg(ft, i = ~ wt < 3, j = ~ mpg, bg = "#EFEF99")
ft <- bold(ft, i = 2:4, j = "cyl", bold = TRUE)
ft

mpg

cyl

disp

hp

drat

wt

qsec

vs

am

gear

carb

21.000

6.000

160.000

110.000

3.900

2.620

16.460

0.000

1.000

4.000

4.000

21.000

6.000

160.000

110.000

3.900

2.875

17.020

0.000

1.000

4.000

4.000

22.800

4.000

108.000

93.000

3.850

2.320

18.610

1.000

1.000

4.000

1.000

21.400

6.000

258.000

110.000

3.080

3.215

19.440

1.000

0.000

3.000

1.000

18.700

8.000

360.000

175.000

3.150

3.440

17.020

0.000

0.000

3.000

2.000

18.100

6.000

225.000

105.000

2.760

3.460

20.220

1.000

0.000

3.000

1.000

xtable objects

anova

if( require("xtable") ){
data(tli)
fm3 <- glm(disadvg ~ ethnicty*grade, data = tli, family = binomial)
ft <- xtable_to_flextable(xtable(anova(fm3)), hline.after = c(1))
ft
}

Df

Deviance

Resid. Df

Resid. Dev

NULL

99

129.49

ethnicty

3

47.24

96

82.25

grade

1

1.73

95

80.52

ethnicty:grade

3

7.20

92

73.32

adding horizontal lines

if( require("xtable") ){
bktbs <- xtable(matrix(1:10, ncol = 2))
hlines <- c(-1, 0, 1, nrow(bktbs))
ft <- xtable_to_flextable(bktbs, hline.after = hlines)
ft
}

1

2

1

1

6

2

2

7

3

3

8

4

4

9

5

5

10

rotate columns

if( require("xtable") ){
data(tli)
tli.table <- xtable(tli[1:10, ])
xtable::align(tli.table) <- "|r|r|clr|r|"
tli.table,
rotate.colnames = TRUE,
include.rownames = FALSE)
ft <- height(ft, i = 1, part = "header", height = 1)
ft
}

grade

sex

disadvg

ethnicty

tlimth

6

M

YES

HISPANIC

43

7

M

NO

BLACK

88

5

F

YES

HISPANIC

34

3

M

YES

HISPANIC

65

8

M

YES

WHITE

75

5

M

NO

BLACK

74

8

F

YES

HISPANIC

72

4

M

YES

BLACK

79

6

M

NO

WHITE

88

7

M

YES

HISPANIC

87

tables

if( require("xtable") ){
Grade3 <- c("A","B","B","A","B","C","C","D","A","B",
"C","C","C","D","B","B","D","C","C","D")
Grade6 <- c("A","A","A","B","B","B","B","B","C","C",
"A","C","C","C","D","D","D","D","D","D")
Cohort <- table(Grade3, Grade6)
ft <- xtable_to_flextable(xtable(Cohort))
ft <- set_header_labels(ft, rowname = "Grade 3")
ft <- autofit(ft)
ft <- add_header(ft, A = "Grade 6")
ft <- merge_at(ft, i = 1, j = seq_len( ncol(Cohort) ) + 1,
part = "header" )
ft <- bold(ft, j = 1, bold = TRUE, part = "body")
ft <- height_all(ft, part = "header", height = .4)
ft
}

Grade 6

Grade 3

A

B

C

D

A

1

1

1

0

B

2

1

1

2

C

1

2

2

2

D

0

1

1

2

time series

if( require("xtable") ){
temp.ts <- ts(cumsum(1 + round(rnorm(100), 0)),
start = c(1954, 7), frequency = 12)
ft <- xtable_to_flextable(x = xtable(temp.ts, digits = 0),
NA.string = "-")
ft
}

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

1954

-

-

-

-

-

-

2

1

3

4

4

4

1955

6

8

11

12

15

15

16

17

19

20

19

21

1956

22

22

23

24

25

26

28

28

29

31

31

31

1957

32

34

34

33

36

36

36

36

35

35

36

37

1958

38

39

41

44

45

46

48

50

51

53

52

52

1959

52

53

55

56

56

58

58

60

61

62

60

60

1960

61

62

64

64

65

66

67

67

69

69

70

72

1961

75

76

77

79

78

79

82

84

84

85

87

87

1962

87

88

91

92

93

95

94

96

98

100

-

-

from scratch

if( require("xtable") ){
mat <- round(matrix(c(0.9, 0.89, 200, 0.045, 2.0), c(1, 5)), 4)
mat <- xtable(mat)
ft <- xtable_to_flextable(x = mat, NA.string = "-")
print(ft$col_keys)
ft <- flextable::display(ft, i = 1, col_key = "X1",
pattern = "{{val}}{{pow}}", part = "header",
formatters = list(val ~ as.character("R"), pow ~ as.character("2") ),
fprops = list(pow = fp_text(vertical.align = "superscript", font.size = 8))
)
ft <- flextable::display(ft, i = 1, col_key = "X2",
pattern = "{{val}}{{pow}}", part = "header",
formatters = list(val ~ as.character("\u03BC"), pow ~ as.character("x") ),
fprops = list(pow = fp_text(vertical.align = "superscript", font.size = 8))
)
ft <- flextable::display(ft, i = 1, col_key = "rowname",
pattern = "{{val}}{{pow}}", part = "body",
formatters = list(val ~ as.character("y"), pow ~ as.character("t-1") ),
fprops = list(pow = fp_text(vertical.align = "subscript", font.size = 8))
)
ft <- set_header_labels(ft, X3 = "F-stat", X4 = "S.E.E", X5 = "DW", rowname = "")
ft <- autofit(ft)
ft
}
#> [1] "rowname" "X1" "X2" "X3" "X4" "X5"

R2

μx

F-stat

S.E.E

DW

yt-1

0.90

0.89

200.00

0.04

2.00

Using within shiny applications

Use function htmltools_value() to get the html value of the flextable (suitable for an uiOutput).

library(shiny)
library(flextable)
ui <- fluidPage(
titlePanel("mtcars"),
sliderInput("mpg", "mpg Limit", min = 11, max = 33, value = 20)
),
uiOutput("mtcars_ft")
)
)
)
server <- function(input, output) {
library(dplyr)
output$mtcars_ft <- renderUI({
req(input$mpg)
mtcars %>%
mutate(car = rownames(.)) %>%
select(car, everything()) %>%
filter(mpg <= input$mpg) %>%
regulartable() %>%
})
}
# Run the application
shinyApp(ui = ui, server = server)