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 × 9
#>   category_cut category_color property_carat property_depth property_table
#>   <ord>        <ord>                   <dbl>          <dbl>          <dbl>
#> 1 Fair         D                       0.920           64.0           59.0
#> 2 Fair         E                       0.857           63.3           59.4
#> 3 Fair         F                       0.905           63.5           59.5
#> 4 Fair         G                       1.02            64.3           58.8
#> 5 Fair         H                       1.22            64.6           58.7
#> 6 Fair         I                       1.20            64.2           59.2
#> # … with 4 more variables: relative_price <formttbl>, dimension_x <dbl>,
#> #   dimension_y <dbl>, dimension_z <dbl>

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()

formattedFlextable

Custom formatting

Automatic coloring