formattedFlextable

 library(presenter)
 library(dplyr)
 library(ggplot2)
 library(stringr)

Custom formatting

create a header word using dplyr::rename_with
format percentages with presenter::format_percent

diamonds %>% 
 select(-clarity) %>% 
 mutate(relative_price = price / max(price), .before = "x") %>% 
 group_by(across(where(is.ordered))) %>% 
 summarize(across(where(is.double), mean), .groups = "drop") %>% 
 format_percent(relative_price) %>% 
 rename_with(~str_c("category_", .), cut:color) %>% 
 rename_with(~str_c("property_", .), carat:table) %>% 
 rename_with(~str_c("dimension_", .), x:z) -> diamonds_summary
 
diamonds_summary %>% head
 #> # A tibble: 6 ×ばつ 9
 #> category_cut categor...1 prope...2 prope...3 prope...4 relat...5 dimen...6 dimen...7 dimen...8
 #> <ord> <ord> <dbl> <dbl> <dbl> <formt> <dbl> <dbl> <dbl>
 #> 1 Fair D 0.920 64.0 59.0 23% 6.02 5.96 3.84
 #> 2 Fair E 0.857 63.3 59.4 20% 5.91 5.86 3.72
 #> 3 Fair F 0.905 63.5 59.5 20% 5.99 5.93 3.79
 #> 4 Fair G 1.02 64.3 58.8 23% 6.17 6.11 3.96
 #> 5 Fair H 1.22 64.6 58.7 27% 6.58 6.50 4.22
 #> 6 Fair I 1.20 64.2 59.2 25% 6.56 6.49 4.19
 #> # ... with abbreviated variable names 1category_color, 2property_carat,
 #> # 3property_depth, 4property_table, 5relative_price, 6dimension_x,
 #> # 7dimension_y, 8dimension_z

Header words are automatically identified by the following regular expression: "^.*(?=(_|\\.))". Leaving the header_word argument blank produces the same result as providing the commented out argument below. The last id column controls merging and greyscaling. Numeric columns are given some automatic formatting within the make_flextable function.

diamonds_summary %>% 
 make_flextable(last_id_col = 2,
 # header_words = c("category", "property", "dimension"), 
 theme = "zebra_gold")

category	property	relative_price	dimension
Fair	D	0.92	64.05	58.97	23%	6.02	5.96	3.84
E	0.86	63.32	59.36	20%	5.91	5.86	3.72
F	0.90	63.51	59.45	20%	5.99	5.93	3.79
G	1.02	64.34	58.77	23%	6.17	6.11	3.96
H	1.22	64.59	58.70	27%	6.58	6.50	4.22
I	1.20	64.22	59.24	25%	6.56	6.49	4.19
J	1.34	64.36	58.92	26%	6.75	6.68	4.32
Good	D	0.74	62.37	58.54	18%	5.62	5.63	3.50
E	0.75	62.20	58.78	18%	5.62	5.63	3.50
F	0.78	62.20	58.91	19%	5.69	5.71	3.54
G	0.85	62.53	58.47	22%	5.85	5.86	3.65
H	0.91	62.50	58.61	23%	5.97	5.97	3.73
I	1.06	62.48	58.77	27%	6.25	6.26	3.90
J	1.10	62.40	58.81	24%	6.38	6.39	3.98
Very Good	D	0.70	61.75	58.04	18%	5.50	5.53	3.41
E	0.68	61.73	58.04	17%	5.43	5.46	3.37
F	0.74	61.72	57.85	20%	5.61	5.64	3.47
G	0.77	61.84	57.78	21%	5.66	5.69	3.51
H	0.92	61.97	57.90	24%	5.98	6.01	3.71
I	1.05	61.93	58.11	28%	6.27	6.30	3.89
J	1.13	61.90	58.28	27%	6.46	6.50	4.01
Premium	D	0.72	61.17	58.72	19%	5.60	5.57	3.42
E	0.72	61.18	58.78	19%	5.59	5.56	3.41
F	0.83	61.26	58.68	23%	5.88	5.84	3.59
G	0.84	61.28	58.70	24%	5.86	5.83	3.58
H	1.02	61.32	58.79	28%	6.24	6.23	3.81
I	1.14	61.33	58.77	32%	6.49	6.45	3.96
J	1.29	61.39	58.87	33%	6.81	6.77	4.17
Ideal	D	0.57	61.68	55.97	14%	5.19	5.20	3.20
E	0.58	61.69	55.97	14%	5.22	5.23	3.22
F	0.66	61.68	55.92	18%	5.41	5.42	3.34
G	0.70	61.70	55.90	20%	5.51	5.52	3.40
H	0.80	61.73	55.97	21%	5.73	5.74	3.54
I	0.91	61.79	56.02	24%	5.98	5.99	3.70
J	1.06	61.82	56.01	26%	6.32	6.33	3.91

Automatic coloring

 tibble(x = letters[1:10],
 y = -5:4,
 z = -c(-.5, -.2, -.1, 0, .1, .2, .3, .4, .6, .9)) %>% 
 format_percent(z) %>% 
 rename_with(~str_c("sample_", .)) %>% 
 make_flextable()

sample
a	-5	50%
b	-4	20%
c	-3	10%
d	-2	-0%
e	-1	-10%
f	0	-20%
g	1	-30%
h	2	-40%
i	3	-60%
j	4	-90%

 tibble(x = letters[1:10],
 y = -5:4,
 z = -c(-.5, -.2, -.1, 0, .1, .2, .3, .4, .6, .9)) %>% 
 format_percent(z) %>% 
 make_flextable()

category		property			relative_price	dimension
cut	color	carat	depth	table	relative_price	x	y	z
Fair	D	0.92	64.05	58.97	23%	6.02	5.96	3.84
	E	0.86	63.32	59.36	20%	5.91	5.86	3.72
	F	0.90	63.51	59.45	20%	5.99	5.93	3.79
	G	1.02	64.34	58.77	23%	6.17	6.11	3.96
	H	1.22	64.59	58.70	27%	6.58	6.50	4.22
	I	1.20	64.22	59.24	25%	6.56	6.49	4.19
	J	1.34	64.36	58.92	26%	6.75	6.68	4.32
Good	D	0.74	62.37	58.54	18%	5.62	5.63	3.50
	E	0.75	62.20	58.78	18%	5.62	5.63	3.50
	F	0.78	62.20	58.91	19%	5.69	5.71	3.54
	G	0.85	62.53	58.47	22%	5.85	5.86	3.65
	H	0.91	62.50	58.61	23%	5.97	5.97	3.73
	I	1.06	62.48	58.77	27%	6.25	6.26	3.90
	J	1.10	62.40	58.81	24%	6.38	6.39	3.98
Very Good	D	0.70	61.75	58.04	18%	5.50	5.53	3.41
	E	0.68	61.73	58.04	17%	5.43	5.46	3.37
	F	0.74	61.72	57.85	20%	5.61	5.64	3.47
	G	0.77	61.84	57.78	21%	5.66	5.69	3.51
	H	0.92	61.97	57.90	24%	5.98	6.01	3.71
	I	1.05	61.93	58.11	28%	6.27	6.30	3.89
	J	1.13	61.90	58.28	27%	6.46	6.50	4.01
Premium	D	0.72	61.17	58.72	19%	5.60	5.57	3.42
	E	0.72	61.18	58.78	19%	5.59	5.56	3.41
	F	0.83	61.26	58.68	23%	5.88	5.84	3.59
	G	0.84	61.28	58.70	24%	5.86	5.83	3.58
	H	1.02	61.32	58.79	28%	6.24	6.23	3.81
	I	1.14	61.33	58.77	32%	6.49	6.45	3.96
	J	1.29	61.39	58.87	33%	6.81	6.77	4.17
Ideal	D	0.57	61.68	55.97	14%	5.19	5.20	3.20
	E	0.58	61.69	55.97	14%	5.22	5.23	3.22
	F	0.66	61.68	55.92	18%	5.41	5.42	3.34
	G	0.70	61.70	55.90	20%	5.51	5.52	3.40
	H	0.80	61.73	55.97	21%	5.73	5.74	3.54
	I	0.91	61.79	56.02	24%	5.98	5.99	3.70
	J	1.06	61.82	56.01	26%	6.32	6.33	3.91