library(flextable)
use_df_printer()
set_flextable_defaults(
theme_fun = theme_booktabs,
big.mark = " ",
font.color = "#666666",
border.color = "#666666",
padding = 3,
)
Dans l’exemple suivant, nous agrégeons la célèbre table “pingouins”.
Tout d’abord, créons un résumé des données originalles.
library(tidyverse)
library(palmerpenguins)
dat <- penguins |>
select(species, island, ends_with("mm")) |>
group_by(species, island) |>
summarise(
across(
where(is.numeric),
.fns = list(
avg = ~ mean(.x, na.rm = TRUE),
sd = ~ sd(.x, na.rm = TRUE)
)
),
.groups = "drop") |>
rename_with(~ tolower(gsub("_mm_", "_", .x, fixed = TRUE)))
print(dat)
## # A tibble: 5 × 8
## species island bill_length_avg bill_length_sd bill_depth_avg bill_depth_sd
## <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 Adelie Biscoe 39.0 2.48 18.4 1.19
## 2 Adelie Dream 38.5 2.47 18.3 1.13
## 3 Adelie Torgers… 39.0 3.03 18.4 1.34
## 4 Chinstrap Dream 48.8 3.34 18.4 1.14
## 5 Gentoo Biscoe 47.5 3.08 15.0 0.981
## # … with 2 more variables: flipper_length_avg <dbl>, flipper_length_sd <dbl>
A partir de ce jeu de données, nous créons un ‘flextable’ et
restructurons noms des colonnes en étiquettes de l’en-tête réparties
sur plusieurs lignes en utilisant separate_header()
.
ft <- dat |>
flextable() |>
separate_header() |>
autofit()
ft
species | island | bill | flipper | ||||
length | depth | length | |||||
avg | sd | avg | sd | avg | sd | ||
Adelie | Biscoe | 38.97500 | 2.480916 | 18.37045 | 1.1888199 | 188.7955 | 6.729247 |
Adelie | Dream | 38.50179 | 2.465359 | 18.25179 | 1.1336171 | 189.7321 | 6.585083 |
Adelie | Torgersen | 38.95098 | 3.025318 | 18.42941 | 1.3394468 | 191.1961 | 6.232238 |
Chinstrap | Dream | 48.83382 | 3.339256 | 18.42059 | 1.1353951 | 195.8235 | 7.131894 |
Gentoo | Biscoe | 47.50488 | 3.081857 | 14.98211 | 0.9812198 | 217.1870 | 6.484976 |
Ce tableau peut maintenant être personnalisé afin d’obtenir un meilleur rendu final.
ft <- ft |>
theme_booktabs(bold_header = TRUE) |>
footnote(i = 3, j = grep("_avg$", colnames(dat), value = TRUE),
part = "header",
ref_symbols = "1", value = as_paragraph("Arithmetic Mean")) |>
footnote(i = 3, j = grep("_sd$", colnames(dat), value = TRUE),
part = "header",
ref_symbols = "2", value = as_paragraph("Standard Deviation")) |>
align(align = "center", part = "all", j = 3:8) |>
merge_v(j = 1) |>
valign(j = 1, valign = "top") |>
colformat_double(digits = 2)
ft
species | island | bill | flipper | ||||
length | depth | length | |||||
avg1 | sd2 | avg1 | sd2 | avg1 | sd2 | ||
Adelie | Biscoe | 38.98 | 2.48 | 18.37 | 1.19 | 188.80 | 6.73 |
Dream | 38.50 | 2.47 | 18.25 | 1.13 | 189.73 | 6.59 | |
Torgersen | 38.95 | 3.03 | 18.43 | 1.34 | 191.20 | 6.23 | |
Chinstrap | Dream | 48.83 | 3.34 | 18.42 | 1.14 | 195.82 | 7.13 |
Gentoo | Biscoe | 47.50 | 3.08 | 14.98 | 0.98 | 217.19 | 6.48 |
1Arithmetic Mean | |||||||
2Standard Deviation |