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formattedFlextable

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 × 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()

x	y	z
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%

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.

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