Table layout

Cell merging

vertical merging

merge_v() will merge adjacent duplicated cells for each column of the selection.

select_columns <- c("Species", "Petal.Length", "Petal.Width")
myft <- regulartable(iris[46:55,], col_keys = select_columns)
myft <- merge_v(myft, ~ Species + Petal.Width )
myft

Species	Petal.Length	Petal.Width
setosa	1.400	0.300
	1.600	0.200
	1.400
	1.500
	1.400
versicolor	4.700	1.400
	4.500	1.500
	4.900	1.500
	4.000	1.300
	4.600	1.500

horizontal merging

merge_h() will merge adjacent duplicated cells for each row of the selection.

select_columns <- c("Species", "Petal.Length", "Petal.Width")
myft <- regulartable(head(mtcars, n = 10 ) )
myft <- merge_h(myft)
# and add borders
myft <- border(myft, border = fp_border(), part = "all") 
myft

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
21.000	6.000	160.000	110.000	3.900	2.875	17.020	0.000	1.000	4.000
22.800	4.000	108.000	93.000	3.850	2.320	18.610	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		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
14.300	8.000	360.000	245.000	3.210	3.570	15.840	0.000		3.000	4.000
24.400	4.000	146.700	62.000	3.690	3.190	20.000	1.000	0.000	4.000	2.000
22.800	4.000	140.800	95.000	3.920	3.150	22.900	1.000	0.000	4.000	2.000
19.200	6.000	167.600	123.000	3.920	3.440	18.300	1.000	0.000	4.000

general merging function

merge_h() will merge cells for a given continuous selection of rows and cells.

select_columns <- c("Species", "Petal.Length", "Petal.Width")
myft <- regulartable(head(mtcars, n = 6 ) )
myft <- merge_at( myft, i = 1:3, j = 1:3)
myft <- border(myft, border = fp_border(), part = "all")
myft

mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb
21.000			110.000	3.900	2.620	16.460	0.000	1.000	4.000	4.000
			110.000	3.900	2.875	17.020	0.000	1.000	4.000	4.000
			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

delete merging informations

If you want to get rid of all merging (i.e. for development purposes), use merge_none():

merge_none(myft)

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

Manage headers and footers

For illustration, we will mainly work with headers but same can be apply with footers (use functions add_footer() or set_header_df() instead).

col_keys

Parameter col_keys define the variables to display and their order.

data <- iris[c(1:3, 51:53, 101:104),]

myft <- regulartable(data, col_keys = c("Species", "Sepal.Length", "Petal.Length") )
myft

Species	Sepal.Length	Petal.Length
setosa	5.100	1.400
setosa	4.900	1.400
setosa	4.700	1.300
versicolor	7.000	4.700
versicolor	6.400	4.500
versicolor	6.900	4.900
virginica	6.300	6.000
virginica	5.800	5.100
virginica	7.100	5.900
virginica	6.300	5.600

If parameter col_keys has variables that are not existing in the dataset, they will be considered as blank columns and can be used as separators (in fact they can be use as you want, there is only no mapping of data associated).

myft <- regulartable(
  data = data, 
  col_keys = c("Species", "col_1", "Sepal.Length", "Petal.Length") )
myft <- theme_vanilla(myft)
myft <- autofit(myft)
myft <- empty_blanks(myft)
myft

Species	Sepal.Length	Petal.Length
setosa	5.100	1.400
setosa	4.900	1.400
setosa	4.700	1.300
versicolor	7.000	4.700
versicolor	6.400	4.500
versicolor	6.900	4.900
virginica	6.300	6.000
virginica	5.800	5.100
virginica	7.100	5.900
virginica	6.300	5.600

col_keys default values are the names of the data.frame used to fill the flextable.

Change labels

Use set_header_labels() to replace labels of the bottom row of header.

ft <- regulartable( head( iris ) ) 
ft <- set_header_labels(ft, Sepal.Length = "Sepal", 
    Sepal.Width = "Sepal", Petal.Length = "Petal",
    Petal.Width = "Petal", Species = "Species" )
ft <- theme_vanilla(ft)
ft <- autofit(ft)
ft

Sepal	Sepal	Petal	Petal	Species
5.100	3.500	1.400	0.200	setosa
4.900	3.000	1.400	0.200	setosa
4.700	3.200	1.300	0.200	setosa
4.600	3.100	1.500	0.200	setosa
5.000	3.600	1.400	0.200	setosa
5.400	3.900	1.700	0.400	setosa

add a row of labels

Use add_header() to add an header row.

ft <- add_header(ft, Sepal.Length = "length",
    Sepal.Width = "width", Petal.Length = "length",
    Petal.Width = "width", Species = "Species", top = FALSE ) 
ft <- theme_vanilla(ft)
ft <- autofit(ft)
ft

Sepal	Sepal	Petal	Petal	Species
length	width	length	width	Species
5.100	3.500	1.400	0.200	setosa
4.900	3.000	1.400	0.200	setosa
4.700	3.200	1.300	0.200	setosa
4.600	3.100	1.500	0.200	setosa
5.000	3.600	1.400	0.200	setosa
5.400	3.900	1.700	0.400	setosa

ft <- add_header(ft, Sepal.Length = "Inches",
    Sepal.Width = "Inches", Petal.Length = "Inches",
    Petal.Width = "Inches", Species = "Species", top = TRUE )

ft <- add_footer(ft, Sepal.Length = "* This is a note" )
ft <- color(ft, color = "orange", part = "footer" )

# merge identical cells
ft <- merge_h(ft, part = "header")
ft <- merge_v(ft, part = "header")
ft <- merge_at(ft, i = 1, j = 1:5, part = "footer")

ft <- theme_booktabs(ft)
ft

Inches				Species
Sepal		Petal
length	width	length	width
5.100	3.500	1.400	0.200	setosa
4.900	3.000	1.400	0.200	setosa
4.700	3.200	1.300	0.200	setosa
4.600	3.100	1.500	0.200	setosa
5.000	3.600	1.400	0.200	setosa
5.400	3.900	1.700	0.400	setosa
* This is a note

Define headers with a reference table

Use set_header_df() with a data.frame as parameter. Columns of the dataset will be transposed and joined using a key column.

Input dataset

Variable col_keys define key values to match with flextable column keys (defined by argument col_keys of flextable() function).

This key column will not be displayed. Other variables will added as rows. Note that variables names are not displayed.

typology <- data.frame(
  col_keys = c( "Sepal.Length", "Sepal.Width", "Petal.Length",
                "Petal.Width", "Species" ),
  type = c("double", "double", "double", "double", "factor"),
  what = c("Sepal", "Sepal", "Petal", "Petal", "Species"),
  measure = c("Length", "Width", "Length", "Width", "Species"),
  stringsAsFactors = FALSE )
autofit( theme_vanilla(flextable(typology)) )

col_keys	type	what	measure
Sepal.Length	double	Sepal	Length
Sepal.Width	double	Sepal	Width
Petal.Length	double	Petal	Length
Petal.Width	double	Petal	Width
Species	factor	Species	Species

Add the dataset as header rows

Then use set_header_df() with parameter key that specifies name of the column used to permform the join. Order of columns matters, first column will be first row, second one will be the second row, etc.

ft <- regulartable( head( iris ) )
ft <- set_header_df( ft, mapping = typology, key = "col_keys" )

ft <- merge_h(ft, part = "header")
ft <- merge_v(ft, part = "header")

ft <- theme_vanilla(ft)
ft <- autofit(ft)
ft

double				factor
Sepal		Petal		Species
Length	Width	Length	Width	Species
5.100	3.500	1.400	0.200	setosa
4.900	3.000	1.400	0.200	setosa
4.700	3.200	1.300	0.200	setosa
4.600	3.100	1.500	0.200	setosa
5.000	3.600	1.400	0.200	setosa
5.400	3.900	1.700	0.400	setosa

Cell widths and heights

The default sizes of flextable columns and rows are calculated with a simple algorithm. This will drive to inadequate rows heights and columns widths in some cases (when data values are wider than headers). You can use function dim to get flextable dimensions.

ft_base <- regulartable(head(iris))
ft_base <- theme_tron_legacy(ft_base)
ft_base

Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
5.100	3.500	1.400	0.200	setosa
4.900	3.000	1.400	0.200	setosa
4.700	3.200	1.300	0.200	setosa
4.600	3.100	1.500	0.200	setosa
5.000	3.600	1.400	0.200	setosa
5.400	3.900	1.700	0.400	setosa

dim(ft_base)
#> $widths
#> Sepal.Length  Sepal.Width Petal.Length  Petal.Width      Species 
#>         0.75         0.75         0.75         0.75         0.75 
#> 
#> $heights
#> [1] 0.25 0.25 0.25 0.25 0.25 0.25 0.25

Pretty dimensions

Function dim_pretty() is computing optimized widths and heights.

dim_pretty(ft_base)
#> $widths
#> [1] 0.9460630 0.8688196 0.9074752 0.8302318 0.5991121
#> 
#> $heights
#> [1] 0.2044994 0.1756366 0.1756366 0.1756366 0.1756366 0.1756366 0.1756366

Adjusts automatically cell widths and heights

Function autofit() optimises widths and heights of the flextable. This function is almost always to be called once when using flextable objects, it makes compact tables.

ft <- autofit(ft_base, add_w = 0, add_h = 0)

dim(ft)
#> $widths
#> Sepal.Length  Sepal.Width Petal.Length  Petal.Width      Species 
#>    0.9460630    0.8688196    0.9074752    0.8302318    0.5991121 
#> 
#> $heights
#> [1] 0.2044994 0.1756366 0.1756366 0.1756366 0.1756366 0.1756366 0.1756366
ft

Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
5.100	3.500	1.400	0.200	setosa
4.900	3.000	1.400	0.200	setosa
4.700	3.200	1.300	0.200	setosa
4.600	3.100	1.500	0.200	setosa
5.000	3.600	1.400	0.200	setosa
5.400	3.900	1.700	0.400	setosa

Adjusts manually cell widths and heights

Function width() and height() let you control dimensions of a flextable. height_all() is an helper function to set the same height to each part of the table.

ft <- autofit(ft_base)
ft <- width(ft, j = ~ Species, width = 2)
ft <- height_all( ft, height = .4 )
ft <- height( ft, i = 3, height = 1 )
ft

Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
5.100	3.500	1.400	0.200	setosa
4.900	3.000	1.400	0.200	setosa
4.700	3.200	1.300	0.200	setosa
4.600	3.100	1.500	0.200	setosa
5.000	3.600	1.400	0.200	setosa
5.400	3.900	1.700	0.400	setosa