Date: 2026-05-18

# RastaRocketVignette

#### 2026-05-18

    library(RastaRocket)
    library(dplyr)
    library(tidyr)
    library(labelled)
    library(rlang)
    library(gtsummary)
    library(forcats)

# Introduction

This vignette demonstrates the different options available for the `desc_var` function, accompanied by examples to illustrate its usage.

# Toy dataset

We will generate a sample dataset to apply the `desc_var` function.

    # Charger le package nécessaire
    set.seed(123)  # Pour garantir la reproductibilité

    # Création du data frame
    data <- data.frame(
      Age = c(rnorm(45, mean = 50, sd = 10), rep(NA, 5)),  # Renommée Age
      sexe = sample(c(0, 1), 50, replace = TRUE, prob = c(0.4, 0.6)),  # Renommée sexe
      quatre_modalites = sample(c("A", "B", "C"), 50, replace = TRUE, prob = c(0.2, 0.5, 0.3)),  # Modalités sans "D"
      traitement = sample(c("BRAS-A", "BRAS-B"), 50, replace = TRUE, prob = c(0.55, 0.45)),  # Nouvelle variable traitement
      echelle = sample(0:5, 50, replace = TRUE)  # Nouvelle variable entière de 0 à 5
    )

    # Ajouter la modalité "D" comme niveau sans effectif
    data$quatre_modalites <- factor(data$quatre_modalites, levels = c("A", "B", "C", "D"))

    # Ajouter des labels à la variable sexe
    data$sexe <- factor(data$sexe, levels = c(0, 1), labels = c("Femme", "Homme"))

    # Aperçu des données

    data <- data |>  labelled::set_variable_labels( Age = "Age",
                                                    sexe = "sexe",
                                                    traitement  = "traitement",
                                                    quatre_modalites = "quatres niveaux",
                                                    echelle = "Echelle")

# Basic usage

Below, we describe the options used in the example.

The dataset is passed to the `desc_var` function for analysis.

- `table_title`: Title of the descriptive table. Here, it is “test.”
- `by_group`: Logical indicating whether the descriptive table should be stratified by the grouping variable (var_group). If TRUE, the table is grouped by var_group; if FALSE, the grouping variable is ignored and not described in the table.
- `var_group`: The variable used for grouping the data. Here, it is “traitement.”
- `group_title`: Title of the grouping variable column. Here, it is “traitement.”
- `add_total`: Add a Total column when `var_group` is specified
- `show_n_per_group`: Should the ‘N’ appears in the column header of the groups. if TRUE, `N` is shown; if FALSE, No `N` is shown ( Default to `FALSE`)

    data |>  RastaRocket::desc_var(table_title = "test",
                                by_group = TRUE,
                                var_group = "traitement",
                                group_title = "Traitement",
                                add_total = TRUE,
                                show_n_per_group = TRUE)

test
Characteristic | Traitement
--- | ---
Overall N = 50 | BRAS-A N = 29 | BRAS-B N = 21
Age n (d.m.) | 45 (5) | 26 (3) | 19 (2)
Mean (SD) | 50.7 (9.5) | 51.0 (9.6) | 50.2 (9.5)
Median (Q1 ; Q3) | 50.7 (44.4 ; 57.0) | 51.3 (44.4 ; 57.0) | 49.4 (43.1 ; 58.8)
Min ; Max | 30.3 ; 71.7 | 30.3 ; 71.7 | 33.1 ; 67.9
sexe n (d.m.) | 50 (0) | 29 (0) | 21 (0)
Femme | 22 (44%) | 16 (55%) | 6 (29%)
Homme | 28 (56%) | 13 (45%) | 15 (71%)
quatres niveaux n (d.m.) | 50 (0) | 29 (0) | 21 (0)
A | 7 (14%) | 2 (7%) | 5 (24%)
B | 28 (56%) | 18 (62%) | 10 (48%)
C | 15 (30%) | 9 (31%) | 6 (29%)
Echelle n (d.m.) | 50 (0) | 29 (0) | 21 (0)
0 | 13 (26%) | 6 (21%) | 7 (33%)
1 | 11 (22%) | 6 (21%) | 5 (24%)
2 | 5 (10%) | 4 (14%) | 1 (5%)
3 | 3 (6%) | 3 (10%) | 0 (0%)
4 | 12 (24%) | 7 (24%) | 5 (24%)
5 | 6 (12%) | 3 (10%) | 3 (14%)

# Quantitative and qualitative feature

The package support the user specification of feature type as quantitative or qualitative features. For instance, you could chose to describe a quantitative features as a qualitative one if it has few values. For instance, we can do this for `Age` after we round it.

    data |>
      dplyr::select(Age, traitement) |>
      dplyr::mutate(Age = round(Age)) |>
      RastaRocket::desc_var(table_title = "test",
                         by_group = TRUE,
                         var_group = "traitement",
                         group_title = "traitement",
                         quali = c("Age"))

test
Characteristic | traitement
--- | ---
Overall | BRAS-A | BRAS-B
Age n (d.m.) | 45 (5) | 26 (3) | 19 (2)
30 | 1 (2%) | 1 (4%) | 0 (0%)
33 | 1 (2%) | 0 (0%) | 1 (5%)
37 | 2 (4%) | 1 (4%) | 1 (5%)
39 | 2 (4%) | 1 (4%) | 1 (5%)
40 | 1 (2%) | 1 (4%) | 0 (0%)
43 | 3 (7%) | 1 (4%) | 2 (11%)
44 | 3 (7%) | 3 (12%) | 0 (0%)
45 | 1 (2%) | 1 (4%) | 0 (0%)
46 | 2 (4%) | 0 (0%) | 2 (11%)
47 | 2 (4%) | 0 (0%) | 2 (11%)
48 | 3 (7%) | 3 (12%) | 0 (0%)
49 | 1 (2%) | 0 (0%) | 1 (5%)
51 | 3 (7%) | 1 (4%) | 2 (11%)
52 | 1 (2%) | 1 (4%) | 0 (0%)
54 | 3 (7%) | 1 (4%) | 2 (11%)
55 | 2 (4%) | 2 (8%) | 0 (0%)
56 | 1 (2%) | 1 (4%) | 0 (0%)
57 | 2 (4%) | 2 (8%) | 0 (0%)
58 | 2 (4%) | 2 (8%) | 0 (0%)
59 | 2 (4%) | 0 (0%) | 2 (11%)
62 | 2 (4%) | 2 (8%) | 0 (0%)
63 | 1 (2%) | 0 (0%) | 1 (5%)
66 | 1 (2%) | 0 (0%) | 1 (5%)
67 | 1 (2%) | 1 (4%) | 0 (0%)
68 | 1 (2%) | 0 (0%) | 1 (5%)
72 | 1 (2%) | 1 (4%) | 0 (0%)

# Missing data

The display of missing data is controlled by the `show_missing_data` argument in the `RastaRocket::desc_var` function. By default, if `anyNA(data1)` returns `TRUE`, missing data will be displayed. If no missing data is detected, it will be hidden. Users can override this behavior by explicitly setting `show_missing_data` to `TRUE` or `FALSE`.

    iris |>  RastaRocket::desc_var(table_title = "test",
                                by_group = TRUE,
                                var_group = "Species",
                                group_title = "Species",
                                show_missing_data = TRUE)

test
Characteristic | Species
--- | ---
Overall | setosa | versicolor | virginica
Sepal.Length n (d.m.) | 150 (0) | 50 (0) | 50 (0) | 50 (0)
Mean (SD) | 5.8 (0.8) | 5.0 (0.4) | 5.9 (0.5) | 6.6 (0.6)
Median (Q1 ; Q3) | 5.8 (5.1 ; 6.4) | 5.0 (4.8 ; 5.2) | 5.9 (5.6 ; 6.3) | 6.5 (6.2 ; 6.9)
Min ; Max | 4.3 ; 7.9 | 4.3 ; 5.8 | 4.9 ; 7.0 | 4.9 ; 7.9
Sepal.Width n (d.m.) | 150 (0) | 50 (0) | 50 (0) | 50 (0)
Mean (SD) | 3.1 (0.4) | 3.4 (0.4) | 2.8 (0.3) | 3.0 (0.3)
Median (Q1 ; Q3) | 3.0 (2.8 ; 3.3) | 3.4 (3.2 ; 3.7) | 2.8 (2.5 ; 3.0) | 3.0 (2.8 ; 3.2)
Min ; Max | 2.0 ; 4.4 | 2.3 ; 4.4 | 2.0 ; 3.4 | 2.2 ; 3.8
Petal.Length n (d.m.) | 150 (0) | 50 (0) | 50 (0) | 50 (0)
Mean (SD) | 3.8 (1.8) | 1.5 (0.2) | 4.3 (0.5) | 5.6 (0.6)
Median (Q1 ; Q3) | 4.4 (1.6 ; 5.1) | 1.5 (1.4 ; 1.6) | 4.4 (4.0 ; 4.6) | 5.6 (5.1 ; 5.9)
Min ; Max | 1.0 ; 6.9 | 1.0 ; 1.9 | 3.0 ; 5.1 | 4.5 ; 6.9
Petal.Width n (d.m.) | 150 (0) | 50 (0) | 50 (0) | 50 (0)
Mean (SD) | 1.2 (0.8) | 0.2 (0.1) | 1.3 (0.2) | 2.0 (0.3)
Median (Q1 ; Q3) | 1.3 (0.3 ; 1.8) | 0.2 (0.2 ; 0.3) | 1.3 (1.2 ; 1.5) | 2.0 (1.8 ; 2.3)
Min ; Max | 0.1 ; 2.5 | 0.1 ; 0.6 | 1.0 ; 1.8 | 1.4 ; 2.5

    iris |>  RastaRocket::desc_var(table_title = "test",
                                by_group = TRUE,
                                var_group = "Species",
                                group_title = "Species",
                                show_missing_data = FALSE)

test
Characteristic | Species
--- | ---
Overall | setosa | versicolor | virginica
Sepal.Length | 150 | 50 | 50 | 50
Mean (SD) | 5.8 (0.8) | 5.0 (0.4) | 5.9 (0.5) | 6.6 (0.6)
Median (Q1 ; Q3) | 5.8 (5.1 ; 6.4) | 5.0 (4.8 ; 5.2) | 5.9 (5.6 ; 6.3) | 6.5 (6.2 ; 6.9)
Min ; Max | 4.3 ; 7.9 | 4.3 ; 5.8 | 4.9 ; 7.0 | 4.9 ; 7.9
Sepal.Width | 150 | 50 | 50 | 50
Mean (SD) | 3.1 (0.4) | 3.4 (0.4) | 2.8 (0.3) | 3.0 (0.3)
Median (Q1 ; Q3) | 3.0 (2.8 ; 3.3) | 3.4 (3.2 ; 3.7) | 2.8 (2.5 ; 3.0) | 3.0 (2.8 ; 3.2)
Min ; Max | 2.0 ; 4.4 | 2.3 ; 4.4 | 2.0 ; 3.4 | 2.2 ; 3.8
Petal.Length | 150 | 50 | 50 | 50
Mean (SD) | 3.8 (1.8) | 1.5 (0.2) | 4.3 (0.5) | 5.6 (0.6)
Median (Q1 ; Q3) | 4.4 (1.6 ; 5.1) | 1.5 (1.4 ; 1.6) | 4.4 (4.0 ; 4.6) | 5.6 (5.1 ; 5.9)
Min ; Max | 1.0 ; 6.9 | 1.0 ; 1.9 | 3.0 ; 5.1 | 4.5 ; 6.9
Petal.Width | 150 | 50 | 50 | 50
Mean (SD) | 1.2 (0.8) | 0.2 (0.1) | 1.3 (0.2) | 2.0 (0.3)
Median (Q1 ; Q3) | 1.3 (0.3 ; 1.8) | 0.2 (0.2 ; 0.3) | 1.3 (1.2 ; 1.5) | 2.0 (1.8 ; 2.3)
Min ; Max | 0.1 ; 2.5 | 0.1 ; 0.6 | 1.0 ; 1.8 | 1.4 ; 2.5

# Feature Data Management

In the previous example, no specific data management operations were applied.

## Order of categorical features

### Order by frequency

In this example, we add `freq_relevel = TRUE`, which orders the categories of categorical variables in descending order based on their counts.

    data |>  desc_var(table_title = "test",
                 by_group = TRUE,
                 var_group = "traitement",
                 group_title = "traitement",
                 freq_relevel = TRUE)

test
Characteristic | traitement
--- | ---
Overall | BRAS-A | BRAS-B
Age n (d.m.) | 45 (5) | 26 (3) | 19 (2)
Mean (SD) | 50.7 (9.5) | 51.0 (9.6) | 50.2 (9.5)
Median (Q1 ; Q3) | 50.7 (44.4 ; 57.0) | 51.3 (44.4 ; 57.0) | 49.4 (43.1 ; 58.8)
Min ; Max | 30.3 ; 71.7 | 30.3 ; 71.7 | 33.1 ; 67.9
sexe n (d.m.) | 50 (0) | 29 (0) | 21 (0)
Homme | 28 (56%) | 13 (45%) | 15 (71%)
Femme | 22 (44%) | 16 (55%) | 6 (29%)
quatres niveaux n (d.m.) | 50 (0) | 29 (0) | 21 (0)
B | 28 (56%) | 18 (62%) | 10 (48%)
C | 15 (30%) | 9 (31%) | 6 (29%)
A | 7 (14%) | 2 (7%) | 5 (24%)
Echelle n (d.m.) | 50 (0) | 29 (0) | 21 (0)
0 | 13 (26%) | 6 (21%) | 7 (33%)
4 | 12 (24%) | 7 (24%) | 5 (24%)
1 | 11 (22%) | 6 (21%) | 5 (24%)
5 | 6 (12%) | 3 (10%) | 3 (14%)
2 | 5 (10%) | 4 (14%) | 1 (5%)
3 | 3 (6%) | 3 (10%) | 0 (0%)

### Custom order

The default order of categorical features is determined by their levels. If you want to customize this order, you can modify the levels using a library such as `forcats`.

    data |>
      dplyr::mutate(quatre_modalites = forcats::fct_relevel(quatre_modalites,
                                                           "A", "C", "D", "B")) |>
      desc_var(table_title = "test",
               by_group = TRUE,
               var_group = "traitement",
               group_title = "traitement")

test
Characteristic | traitement
--- | ---
Overall | BRAS-A | BRAS-B
Age n (d.m.) | 45 (5) | 26 (3) | 19 (2)
Mean (SD) | 50.7 (9.5) | 51.0 (9.6) | 50.2 (9.5)
Median (Q1 ; Q3) | 50.7 (44.4 ; 57.0) | 51.3 (44.4 ; 57.0) | 49.4 (43.1 ; 58.8)
Min ; Max | 30.3 ; 71.7 | 30.3 ; 71.7 | 33.1 ; 67.9
sexe n (d.m.) | 50 (0) | 29 (0) | 21 (0)
Femme | 22 (44%) | 16 (55%) | 6 (29%)
Homme | 28 (56%) | 13 (45%) | 15 (71%)
quatres niveaux n (d.m.) | 50 (0) | 29 (0) | 21 (0)
A | 7 (14%) | 2 (7%) | 5 (24%)
C | 15 (30%) | 9 (31%) | 6 (29%)
B | 28 (56%) | 18 (62%) | 10 (48%)
Echelle n (d.m.) | 50 (0) | 29 (0) | 21 (0)
0 | 13 (26%) | 6 (21%) | 7 (33%)
1 | 11 (22%) | 6 (21%) | 5 (24%)
2 | 5 (10%) | 4 (14%) | 1 (5%)
3 | 3 (6%) | 3 (10%) | 0 (0%)
4 | 12 (24%) | 7 (24%) | 5 (24%)
5 | 6 (12%) | 3 (10%) | 3 (14%)

## Remove zero-count levels

By default, zero-count levels are removed but we can explicitly specify we do not want to drop them.

    data |>  desc_var(table_title = "test",
                 by_group = TRUE,
                 var_group = "traitement",
                 group_title = "traitement",
                 drop_levels = FALSE)

test
Characteristic | traitement
--- | ---
Overall | BRAS-A | BRAS-B
Age n (d.m.) | 45 (5) | 26 (3) | 19 (2)
Mean (SD) | 50.7 (9.5) | 51.0 (9.6) | 50.2 (9.5)
Median (Q1 ; Q3) | 50.7 (44.4 ; 57.0) | 51.3 (44.4 ; 57.0) | 49.4 (43.1 ; 58.8)
Min ; Max | 30.3 ; 71.7 | 30.3 ; 71.7 | 33.1 ; 67.9
sexe n (d.m.) | 50 (0) | 29 (0) | 21 (0)
Femme | 22 (44%) | 16 (55%) | 6 (29%)
Homme | 28 (56%) | 13 (45%) | 15 (71%)
quatres niveaux n (d.m.) | 50 (0) | 29 (0) | 21 (0)
A | 7 (14%) | 2 (7%) | 5 (24%)
B | 28 (56%) | 18 (62%) | 10 (48%)
C | 15 (30%) | 9 (31%) | 6 (29%)
D | 0 (0%) | 0 (0%) | 0 (0%)
Echelle n (d.m.) | 50 (0) | 29 (0) | 21 (0)
0 | 13 (26%) | 6 (21%) | 7 (33%)
1 | 11 (22%) | 6 (21%) | 5 (24%)
2 | 5 (10%) | 4 (14%) | 1 (5%)
3 | 3 (6%) | 3 (10%) | 0 (0%)
4 | 12 (24%) | 7 (24%) | 5 (24%)
5 | 6 (12%) | 3 (10%) | 3 (14%)

# Overall and Per-Group Descriptions

## Per-Group Description

Here, we use a per-group description for the variables.

    data |>  desc_var(table_title = "test",
                 by_group = TRUE,
                 var_group = "traitement",
                 group_title = "traitement")

test
Characteristic | traitement
--- | ---
Overall | BRAS-A | BRAS-B
Age n (d.m.) | 45 (5) | 26 (3) | 19 (2)
Mean (SD) | 50.7 (9.5) | 51.0 (9.6) | 50.2 (9.5)
Median (Q1 ; Q3) | 50.7 (44.4 ; 57.0) | 51.3 (44.4 ; 57.0) | 49.4 (43.1 ; 58.8)
Min ; Max | 30.3 ; 71.7 | 30.3 ; 71.7 | 33.1 ; 67.9
sexe n (d.m.) | 50 (0) | 29 (0) | 21 (0)
Femme | 22 (44%) | 16 (55%) | 6 (29%)
Homme | 28 (56%) | 13 (45%) | 15 (71%)
quatres niveaux n (d.m.) | 50 (0) | 29 (0) | 21 (0)
A | 7 (14%) | 2 (7%) | 5 (24%)
B | 28 (56%) | 18 (62%) | 10 (48%)
C | 15 (30%) | 9 (31%) | 6 (29%)
Echelle n (d.m.) | 50 (0) | 29 (0) | 21 (0)
0 | 13 (26%) | 6 (21%) | 7 (33%)
1 | 11 (22%) | 6 (21%) | 5 (24%)
2 | 5 (10%) | 4 (14%) | 1 (5%)
3 | 3 (6%) | 3 (10%) | 0 (0%)
4 | 12 (24%) | 7 (24%) | 5 (24%)
5 | 6 (12%) | 3 (10%) | 3 (14%)

## Overall Description

In this example, we generate a global description of the variables.

    data |>  RastaRocket::desc_var(table_title = "test",
                 by_group = FALSE,
                 var_group = "traitement",
                 group_title = "traitement")

test
Characteristic | Overall
--- | ---
Age n (d.m.) | 45 (5)
Mean (SD) | 50.7 (9.5)
Median (Q1 ; Q3) | 50.7 (44.4 ; 57.0)
Min ; Max | 30.3 ; 71.7
sexe n (d.m.) | 50 (0)
Femme | 22 (44%)
Homme | 28 (56%)
quatres niveaux n (d.m.) | 50 (0)
A | 7 (14%)
B | 28 (56%)
C | 15 (30%)
Echelle n (d.m.) | 50 (0)
0 | 13 (26%)
1 | 11 (22%)
2 | 5 (10%)
3 | 3 (6%)
4 | 12 (24%)
5 | 6 (12%)

# Intermediate titles

To insert intermediate titles, you can use the `intermediate_header` function which takes a list of sub-tables generated by `desc_var` and a vector of titles.

    tb1 <- data |>
      dplyr::select(Age, sexe) |>
      RastaRocket::desc_var(table_title = "test")

    tb2 <- data |>
      dplyr::select(quatre_modalites) |>
      RastaRocket::desc_var(table_title = "test")

    RastaRocket::intermediate_header(tbls = list(tb1, tb2),
                                  group_header = c("Title A", "Title B"))

test
Characteristic | Overall
--- | ---
Title A
Age n (d.m.) | 45 (5)
Mean (SD) | 50.7 (9.5)
Median (Q1 ; Q3) | 50.7 (44.4 ; 57.0)
Min ; Max | 30.3 ; 71.7
sexe n (d.m.) | 50 (0)
Femme | 22 (44%)
Homme | 28 (56%)
Title B
quatres niveaux | 50
A | 7 (14%)
B | 28 (56%)
C | 15 (30%)

# Number of Digits in Quantitative and Qualitative Features

You can specify the number of digits for quantitative and qualitative features using `r_quanti` and `r_quali` in the `digits` argument. It specifies the number of digits statistics are rounded to.
The values passed can be a single integer or a vector of integers. If a single integer or a vector is passed, it rearranged to the length of the number of statistics presented.

## Specify Number of Digits

- In the example below, quantitative values are rounded to 1 decimal places, while qualitative values percentage are rounded to 0 decimal place.

    data |>
      RastaRocket::desc_var(table_title = "test",
                         by_group = TRUE,
                         var_group = "traitement",
                         digits = list(r_quanti = 1, r_quali = 0))

test
Characteristic | traitement
--- | ---
Overall | BRAS-A | BRAS-B
Age n (d.m.) | 45 (5) | 26 (3) | 19 (2)
Mean (SD) | 50.7 (9.5) | 51.0 (9.6) | 50.2 (9.5)
Median (Q1 ; Q3) | 50.7 (44.4 ; 57.0) | 51.3 (44.4 ; 57.0) | 49.4 (43.1 ; 58.8)
Min ; Max | 30.3 ; 71.7 | 30.3 ; 71.7 | 33.1 ; 67.9
sexe n (d.m.) | 50 (0) | 29 (0) | 21 (0)
Femme | 22 (44%) | 16 (55%) | 6 (29%)
Homme | 28 (56%) | 13 (45%) | 15 (71%)
quatres niveaux n (d.m.) | 50 (0) | 29 (0) | 21 (0)
A | 7 (14%) | 2 (7%) | 5 (24%)
B | 28 (56%) | 18 (62%) | 10 (48%)
C | 15 (30%) | 9 (31%) | 6 (29%)
Echelle n (d.m.) | 50 (0) | 29 (0) | 21 (0)
0 | 13 (26%) | 6 (21%) | 7 (33%)
1 | 11 (22%) | 6 (21%) | 5 (24%)
2 | 5 (10%) | 4 (14%) | 1 (5%)
3 | 3 (6%) | 3 (10%) | 0 (0%)
4 | 12 (24%) | 7 (24%) | 5 (24%)
5 | 6 (12%) | 3 (10%) | 3 (14%)

- In the example below, we used a vector of integer to round quantitative and qualitative features.

    RastaRocket:: desc_var(
         data1 = iris,
         quanti = "Sepal.Length",
         stat_var_quanti = c("{sum}", "{mean} ({sd})"),
         digits = list(r_quanti = c(1, 3, 2), r_quali = c(0, 2))
     )

Characteristic | Overall
--- | ---
Sepal.Length | 150
Sum | 876.5
Mean (SD) | 5.843 (0.83)
Sepal.Width | 150
Sum | 458.6
Mean (SD) | 3.057 (0.44)
Petal.Length | 150
Sum | 563.7
Mean (SD) | 3.758 (1.77)
Petal.Width | 150
Sum | 179.9
Mean (SD) | 1.199 (0.76)
Species | 150
setosa | 50 (33.33%)
versicolor | 50 (33.33%)
virginica | 50 (33.33%)

## Combine Subtables with Different Rounding

To have more control over rounding, you can create subtables with different numbers of digits and combine them into a single table using `gtsummary::tbl_stack`.

    tb1 <- data %>%
      dplyr::select(Age, sexe, traitement) %>%
      RastaRocket::desc_var(table_title = "test",
                         by_group = TRUE,
                         var_group = "traitement",
                         digits = list(r_quanti = 2, r_quali = 2))

    tb2 <- data %>%
      dplyr::select(quatre_modalites, traitement) %>%
      RastaRocket::desc_var(table_title = "test",
                         by_group = TRUE,
                         var_group = "traitement",
                         digits = list(r_quanti = 1, r_quali = 0))

    gtsummary::tbl_stack(list(tb1, tb2))

test
Characteristic | traitement
--- | ---
Overall | BRAS-A | BRAS-B
Age n (d.m.) | 45 (5) | 26 (3) | 19 (2)
Mean (SD) | 50.67 (9.49) | 51.03 (9.63) | 50.17 (9.53)
Median (Q1 ; Q3) | 50.71 (44.40 ; 57.01) | 51.32 (44.40 ; 57.01) | 49.38 (43.05 ; 58.78)
Min ; Max | 30.33 ; 71.69 | 30.33 ; 71.69 | 33.13 ; 67.87
sexe n (d.m.) | 50 (0) | 29 (0) | 21 (0)
Femme | 22.00 (44.00%) | 16.00 (55.17%) | 6.00 (28.57%)
Homme | 28.00 (56.00%) | 13.00 (44.83%) | 15.00 (71.43%)
quatres niveaux | 50 | 29 | 21
A | 7 (14%) | 2 (7%) | 5 (24%)
B | 28 (56%) | 18 (62%) | 10 (48%)
C | 15 (30%) | 9 (31%) | 6 (29%)

# Custom appearance

To have a nicer appearance of the table, it is possible to customize it as a `gt` table. A dedicated function is implemented: `custom_format`.

    data %>%
      RastaRocket::desc_var(table_title = "test",
                         by_group = TRUE,
                         var_group = "traitement",
                         tests = list(Age = "t.test",
                                      sexe = "chisq.test",
                                      echelle = "fisher.test")) %>%
      custom_format()

test
Characteristic | traitement | p-value
--- | --- | ---
Overall | BRAS-A | BRAS-B
Age n (d.m.) | 45 (5) | 26 (3) | 19 (2) | 1 0.8
Mean (SD) | 50.7 (9.5) | 51.0 (9.6) | 50.2 (9.5) |
Median (Q1 ; Q3) | 50.7 (44.4 ; 57.0) | 51.3 (44.4 ; 57.0) | 49.4 (43.1 ; 58.8) |
Min ; Max | 30.3 ; 71.7 | 30.3 ; 71.7 | 33.1 ; 67.9 |
sexe n (d.m.) | 50 (0) | 29 (0) | 21 (0) | 2 0.11
Femme | 22 (44%) | 16 (55%) | 6 (29%) |
Homme | 28 (56%) | 13 (45%) | 15 (71%) |
quatres niveaux n (d.m.) | 50 (0) | 29 (0) | 21 (0) | 3 0.3
A | 7 (14%) | 2 (7%) | 5 (24%) |
B | 28 (56%) | 18 (62%) | 10 (48%) |
C | 15 (30%) | 9 (31%) | 6 (29%) |
Echelle n (d.m.) | 50 (0) | 29 (0) | 21 (0) | 3 0.6
0 | 13 (26%) | 6 (21%) | 7 (33%) |
1 | 11 (22%) | 6 (21%) | 5 (24%) |
2 | 5 (10%) | 4 (14%) | 1 (5%) |
3 | 3 (6%) | 3 (10%) | 0 (0%) |
4 | 12 (24%) | 7 (24%) | 5 (24%) |
5 | 6 (12%) | 3 (10%) | 3 (14%) |
1 Welch Two Sample t-test
2 Pearson’s Chi-squared test
3 Fisher’s exact test

This also works when using stacked tables.

    tb1 <- data %>%
      dplyr::select(Age, sexe, traitement) %>%
      RastaRocket::desc_var(table_title = "test",
                         by_group = TRUE,
                         var_group = "traitement",
                         digits = list(r_quanti = 0, r_quali = 0))

    tb2 <- data %>%
      dplyr::select(quatre_modalites, traitement) %>%
      RastaRocket::desc_var(table_title = "test",
                         by_group = TRUE,
                         var_group = "traitement",
                         digits = list(r_quanti = 0, r_quali = 1))

    gtsummary::tbl_stack(list(tb1, tb2)) %>%
      custom_format()

test
Characteristic | traitement
--- | ---
Overall | BRAS-A | BRAS-B
Age n (d.m.) | 45 (5) | 26 (3) | 19 (2)
Mean (SD) | 51 (9) | 51 (10) | 50 (10)
Median (Q1 ; Q3) | 51 (44 ; 57) | 51 (44 ; 57) | 49 (43 ; 59)
Min ; Max | 30 ; 72 | 30 ; 72 | 33 ; 68
sexe n (d.m.) | 50 (0) | 29 (0) | 21 (0)
Femme | 22 (44%) | 16 (55%) | 6 (29%)
Homme | 28 (56%) | 13 (45%) | 15 (71%)
quatres niveaux | 50 | 29 | 21
A | 7.0 (14.0%) | 2.0 (6.9%) | 5.0 (23.8%)
B | 28.0 (56.0%) | 18.0 (62.1%) | 10.0 (47.6%)
C | 15.0 (30.0%) | 9.0 (31.0%) | 6.0 (28.6%)

You can customize the format by specifying the column size and the alignment.

    data |>
      RastaRocket::desc_var(table_title = "test",
                         by_group = TRUE,
                         var_group = "traitement") |>
      custom_format(align = "left",
                    column_size = list(label ~ gt::pct(50),
                                       gt::starts_with("stat") ~ gt::pct(25)))

test
Characteristic | traitement
--- | ---
Overall | BRAS-A | BRAS-B
Age n (d.m.) | 45 (5) | 26 (3) | 19 (2)
Mean (SD) | 50.7 (9.5) | 51.0 (9.6) | 50.2 (9.5)
Median (Q1 ; Q3) | 50.7 (44.4 ; 57.0) | 51.3 (44.4 ; 57.0) | 49.4 (43.1 ; 58.8)
Min ; Max | 30.3 ; 71.7 | 30.3 ; 71.7 | 33.1 ; 67.9
sexe n (d.m.) | 50 (0) | 29 (0) | 21 (0)
Femme | 22 (44%) | 16 (55%) | 6 (29%)
Homme | 28 (56%) | 13 (45%) | 15 (71%)
quatres niveaux n (d.m.) | 50 (0) | 29 (0) | 21 (0)
A | 7 (14%) | 2 (7%) | 5 (24%)
B | 28 (56%) | 18 (62%) | 10 (48%)
C | 15 (30%) | 9 (31%) | 6 (29%)
Echelle n (d.m.) | 50 (0) | 29 (0) | 21 (0)
0 | 13 (26%) | 6 (21%) | 7 (33%)
1 | 11 (22%) | 6 (21%) | 5 (24%)
2 | 5 (10%) | 4 (14%) | 1 (5%)
3 | 3 (6%) | 3 (10%) | 0 (0%)
4 | 12 (24%) | 7 (24%) | 5 (24%)
5 | 6 (12%) | 3 (10%) | 3 (14%)

## Text indentation

- You can specify the indentation for columns by using a numeric value correspinding the space to indent text. A dedicated function is implemented: `indent_gtsummary_table()`.
- You can create subtables with different indentation and combine them into a single table using `gtsummary::tbl_stack`.

    tb1 <- data %>%
         dplyr::select(Age, traitement) %>%
         RastaRocket::desc_var(table_title = "tb1 test",
                               by_group = TRUE,
                               var_group = "traitement",
                               digits = list(r_quanti = 2, r_quali = 2)) |> RastaRocket::indent_gtsummary_table(indent = 4)

    tb2 <- data %>%
         dplyr::select(sexe, traitement) %>%
         RastaRocket::desc_var(table_title = "tb2 test",
                               by_group = TRUE,
                               var_group = "traitement",
                               digits = list(r_quanti = 2, r_quali = 0)) |> RastaRocket::indent_gtsummary_table(indent = 8)

    tb3 <- data %>%
         dplyr::select(quatre_modalites, traitement) %>%
         RastaRocket::desc_var(table_title = "tb3 test",
                               by_group = TRUE,
                               var_group = "traitement",
                               digits = list(r_quanti = 2, r_quali = 1)) |> RastaRocket::indent_gtsummary_table(indent = 16)

    tb4 <- data %>%
         dplyr::select(echelle, traitement) %>%
         RastaRocket::desc_var(table_title = "tb4 test",
                               by_group = TRUE,
                               var_group = "traitement",
                               digits = list(r_quanti = 2, r_quali = 1))

    gtsummary::tbl_stack(list(tb1, tb2, tb3, tb4))

tb1 test
Characteristic | traitement
--- | ---
Overall | BRAS-A | BRAS-B
Age n (d.m.) | 45 (5) | 26 (3) | 19 (2)
Mean (SD) | 50.67 (9.49) | 51.03 (9.63) | 50.17 (9.53)
Median (Q1 ; Q3) | 50.71 (44.40 ; 57.01) | 51.32 (44.40 ; 57.01) | 49.38 (43.05 ; 58.78)
Min ; Max | 30.33 ; 71.69 | 30.33 ; 71.69 | 33.13 ; 67.87
sexe | 50 | 29 | 21
Femme | 22 (44%) | 16 (55%) | 6 (29%)
Homme | 28 (56%) | 13 (45%) | 15 (71%)
quatres niveaux | 50 | 29 | 21
A | 7.0 (14.0%) | 2.0 (6.9%) | 5.0 (23.8%)
B | 28.0 (56.0%) | 18.0 (62.1%) | 10.0 (47.6%)
C | 15.0 (30.0%) | 9.0 (31.0%) | 6.0 (28.6%)
Echelle | 50 | 29 | 21
0 | 13.0 (26.0%) | 6.0 (20.7%) | 7.0 (33.3%)
1 | 11.0 (22.0%) | 6.0 (20.7%) | 5.0 (23.8%)
2 | 5.0 (10.0%) | 4.0 (13.8%) | 1.0 (4.8%)
3 | 3.0 (6.0%) | 3.0 (10.3%) | 0.0 (0.0%)
4 | 12.0 (24.0%) | 7.0 (24.1%) | 5.0 (23.8%)
5 | 6.0 (12.0%) | 3.0 (10.3%) | 3.0 (14.3%)

# Statistical tests

## Add Default Statistical Tests

You can include statistical tests in your summary table using the `tests = TRUE` argument. This automatically applies default statistical tests for the grouped variables.

The following example adds statistical tests for all features, grouped by the `traitement` variable.

    data %>%
      RastaRocket::desc_var(table_title = "test",
                         by_group = TRUE,
                         var_group = "traitement",
                         tests = TRUE)

test
Characteristic | traitement | p-value
--- | --- | ---
Overall | BRAS-A | BRAS-B
Age n (d.m.) | 45 (5) | 26 (3) | 19 (2) | 0.71
Mean (SD) | 50.7 (9.5) | 51.0 (9.6) | 50.2 (9.5) |
Median (Q1 ; Q3) | 50.7 (44.4 ; 57.0) | 51.3 (44.4 ; 57.0) | 49.4 (43.1 ; 58.8) |
Min ; Max | 30.3 ; 71.7 | 30.3 ; 71.7 | 33.1 ; 67.9 |
sexe n (d.m.) | 50 (0) | 29 (0) | 21 (0) | 0.0612
Femme | 22 (44%) | 16 (55%) | 6 (29%) |
Homme | 28 (56%) | 13 (45%) | 15 (71%) |
quatres niveaux n (d.m.) | 50 (0) | 29 (0) | 21 (0) | 0.33
A | 7 (14%) | 2 (7%) | 5 (24%) |
B | 28 (56%) | 18 (62%) | 10 (48%) |
C | 15 (30%) | 9 (31%) | 6 (29%) |
Echelle n (d.m.) | 50 (0) | 29 (0) | 21 (0) | 0.63
0 | 13 (26%) | 6 (21%) | 7 (33%) |
1 | 11 (22%) | 6 (21%) | 5 (24%) |
2 | 5 (10%) | 4 (14%) | 1 (5%) |
3 | 3 (6%) | 3 (10%) | 0 (0%) |
4 | 12 (24%) | 7 (24%) | 5 (24%) |
5 | 6 (12%) | 3 (10%) | 3 (14%) |
1 Wilcoxon rank sum exact test
2 Pearson’s Chi-squared test
3 Fisher’s exact test

## Specify Statistical Tests for Each Feature

For greater control, you can specify the test to use for each feature by passing a named list to the tests argument. The example below applies:

- t-test for Age,
- Chi-squared test for sexe, and
- Fisher’s exact test for echelle.

    data %>%
      RastaRocket::desc_var(table_title = "test",
                         by_group = TRUE,
                         var_group = "traitement",
                         tests = list(Age = "t.test",
                                      sexe = "chisq.test",
                                      echelle = "fisher.test"))

test
Characteristic | traitement | p-value
--- | --- | ---
Overall | BRAS-A | BRAS-B
Age n (d.m.) | 45 (5) | 26 (3) | 19 (2) | 0.81
Mean (SD) | 50.7 (9.5) | 51.0 (9.6) | 50.2 (9.5) |
Median (Q1 ; Q3) | 50.7 (44.4 ; 57.0) | 51.3 (44.4 ; 57.0) | 49.4 (43.1 ; 58.8) |
Min ; Max | 30.3 ; 71.7 | 30.3 ; 71.7 | 33.1 ; 67.9 |
sexe n (d.m.) | 50 (0) | 29 (0) | 21 (0) | 0.112
Femme | 22 (44%) | 16 (55%) | 6 (29%) |
Homme | 28 (56%) | 13 (45%) | 15 (71%) |
quatres niveaux n (d.m.) | 50 (0) | 29 (0) | 21 (0) | 0.33
A | 7 (14%) | 2 (7%) | 5 (24%) |
B | 28 (56%) | 18 (62%) | 10 (48%) |
C | 15 (30%) | 9 (31%) | 6 (29%) |
Echelle n (d.m.) | 50 (0) | 29 (0) | 21 (0) | 0.63
0 | 13 (26%) | 6 (21%) | 7 (33%) |
1 | 11 (22%) | 6 (21%) | 5 (24%) |
2 | 5 (10%) | 4 (14%) | 1 (5%) |
3 | 3 (6%) | 3 (10%) | 0 (0%) |
4 | 12 (24%) | 7 (24%) | 5 (24%) |
5 | 6 (12%) | 3 (10%) | 3 (14%) |
1 Welch Two Sample t-test
2 Pearson’s Chi-squared test
3 Fisher’s exact test

# French format

You can customize the output format to french using the `gtsummary::theme_gtsummary_language` function. The `gtsummary::reset_gtsummary_theme()` reset the format to the default behavior (i.e English). You can set the format once at the beginning of the document, no need to specify it multiple times.

    # reset theme to default
    gtsummary::reset_gtsummary_theme()
    # switch to French format
    gtsummary::theme_gtsummary_language(language = "fr", decimal.mark = ",", big.mark = " ")

    iris %>%
      RastaRocket::desc_var(table_title = "test")

test
Caractéristique | Total
--- | ---
Sepal.Length | 150
Moyenne (ET) | 5,8 (0,8)
Médiane (Q1 ; Q3) | 5,8 (5,1 ; 6,4)
Min ; Max | 4,3 ; 7,9
Sepal.Width | 150
Moyenne (ET) | 3,1 (0,4)
Médiane (Q1 ; Q3) | 3,0 (2,8 ; 3,3)
Min ; Max | 2,0 ; 4,4
Petal.Length | 150
Moyenne (ET) | 3,8 (1,8)
Médiane (Q1 ; Q3) | 4,4 (1,6 ; 5,1)
Min ; Max | 1,0 ; 6,9
Petal.Width | 150
Moyenne (ET) | 1,2 (0,8)
Médiane (Q1 ; Q3) | 1,3 (0,3 ; 1,8)
Min ; Max | 0,1 ; 2,5
Species | 150
setosa | 50 (33%)
versicolor | 50 (33%)
virginica | 50 (33%)

    # you can put several tables here, it will keep French format

    # back to default format
    gtsummary::reset_gtsummary_theme()