RastaRocketVignette

2025-11-04

Source: vignettes/RastaRocketVignette.Rmd


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(0.6, 0.4)),  # 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
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 Overall
N = 50
Traitement
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 28 (56.0%) 13 (44.8%) 15 (71.4%)
    Homme 22 (44.0%) 16 (55.2%) 6 (28.6%)
quatres niveaux n (d.m.) 50 (0) 29 (0) 21 (0)
    A 7 (14.0%) 2 (6.9%) 5 (23.8%)
    B 28 (56.0%) 18 (62.1%) 10 (47.6%)
    C 15 (30.0%) 9 (31.0%) 6 (28.6%)
Echelle n (d.m.) 50 (0) 29 (0) 21 (0)
    0 13 (26.0%) 6 (20.7%) 7 (33.3%)
    1 11 (22.0%) 6 (20.7%) 5 (23.8%)
    2 5 (10.0%) 4 (13.8%) 1 (4.8%)
    3 3 (6.0%) 3 (10.3%) 0 (0.0%)
    4 12 (24.0%) 7 (24.1%) 5 (23.8%)
    5 6 (12.0%) 3 (10.3%) 3 (14.3%)

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 Overall
traitement
BRAS-A BRAS-B
Age n (d.m.) 45 (5) 26 (3) 19 (2)
    30 1 (2.2%) 1 (3.8%) 0 (0.0%)
    33 1 (2.2%) 0 (0.0%) 1 (5.3%)
    37 2 (4.4%) 1 (3.8%) 1 (5.3%)
    39 2 (4.4%) 1 (3.8%) 1 (5.3%)
    40 1 (2.2%) 1 (3.8%) 0 (0.0%)
    43 3 (6.7%) 1 (3.8%) 2 (10.5%)
    44 3 (6.7%) 3 (11.5%) 0 (0.0%)
    45 1 (2.2%) 1 (3.8%) 0 (0.0%)
    46 2 (4.4%) 0 (0.0%) 2 (10.5%)
    47 2 (4.4%) 0 (0.0%) 2 (10.5%)
    48 3 (6.7%) 3 (11.5%) 0 (0.0%)
    49 1 (2.2%) 0 (0.0%) 1 (5.3%)
    51 3 (6.7%) 1 (3.8%) 2 (10.5%)
    52 1 (2.2%) 1 (3.8%) 0 (0.0%)
    54 3 (6.7%) 1 (3.8%) 2 (10.5%)
    55 2 (4.4%) 2 (7.7%) 0 (0.0%)
    56 1 (2.2%) 1 (3.8%) 0 (0.0%)
    57 2 (4.4%) 2 (7.7%) 0 (0.0%)
    58 2 (4.4%) 2 (7.7%) 0 (0.0%)
    59 2 (4.4%) 0 (0.0%) 2 (10.5%)
    62 2 (4.4%) 2 (7.7%) 0 (0.0%)
    63 1 (2.2%) 0 (0.0%) 1 (5.3%)
    66 1 (2.2%) 0 (0.0%) 1 (5.3%)
    67 1 (2.2%) 1 (3.8%) 0 (0.0%)
    68 1 (2.2%) 0 (0.0%) 1 (5.3%)
    72 1 (2.2%) 1 (3.8%) 0 (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 Overall
Species
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 Overall
Species
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 Overall
traitement
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 28 (56.0%) 13 (44.8%) 15 (71.4%)
    Homme 22 (44.0%) 16 (55.2%) 6 (28.6%)
quatres niveaux n (d.m.) 50 (0) 29 (0) 21 (0)
    B 28 (56.0%) 18 (62.1%) 10 (47.6%)
    C 15 (30.0%) 9 (31.0%) 6 (28.6%)
    A 7 (14.0%) 2 (6.9%) 5 (23.8%)
Echelle n (d.m.) 50 (0) 29 (0) 21 (0)
    0 13 (26.0%) 6 (20.7%) 7 (33.3%)
    1 11 (22.0%) 6 (20.7%) 5 (23.8%)
    2 5 (10.0%) 4 (13.8%) 1 (4.8%)
    3 3 (6.0%) 3 (10.3%) 0 (0.0%)
    4 12 (24.0%) 7 (24.1%) 5 (23.8%)
    5 6 (12.0%) 3 (10.3%) 3 (14.3%)

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 Overall
traitement
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 28 (56.0%) 13 (44.8%) 15 (71.4%)
    Homme 22 (44.0%) 16 (55.2%) 6 (28.6%)
quatres niveaux n (d.m.) 50 (0) 29 (0) 21 (0)
    A 7 (14.0%) 2 (6.9%) 5 (23.8%)
    C 15 (30.0%) 9 (31.0%) 6 (28.6%)
    B 28 (56.0%) 18 (62.1%) 10 (47.6%)
Echelle n (d.m.) 50 (0) 29 (0) 21 (0)
    0 13 (26.0%) 6 (20.7%) 7 (33.3%)
    1 11 (22.0%) 6 (20.7%) 5 (23.8%)
    2 5 (10.0%) 4 (13.8%) 1 (4.8%)
    3 3 (6.0%) 3 (10.3%) 0 (0.0%)
    4 12 (24.0%) 7 (24.1%) 5 (23.8%)
    5 6 (12.0%) 3 (10.3%) 3 (14.3%)

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 Overall
traitement
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 28 (56.0%) 13 (44.8%) 15 (71.4%)
    Homme 22 (44.0%) 16 (55.2%) 6 (28.6%)
quatres niveaux n (d.m.) 50 (0) 29 (0) 21 (0)
    A 7 (14.0%) 2 (6.9%) 5 (23.8%)
    B 28 (56.0%) 18 (62.1%) 10 (47.6%)
    C 15 (30.0%) 9 (31.0%) 6 (28.6%)
    D 0 (0.0%) 0 (0.0%) 0 (0.0%)
Echelle n (d.m.) 50 (0) 29 (0) 21 (0)
    0 13 (26.0%) 6 (20.7%) 7 (33.3%)
    1 11 (22.0%) 6 (20.7%) 5 (23.8%)
    2 5 (10.0%) 4 (13.8%) 1 (4.8%)
    3 3 (6.0%) 3 (10.3%) 0 (0.0%)
    4 12 (24.0%) 7 (24.1%) 5 (23.8%)
    5 6 (12.0%) 3 (10.3%) 3 (14.3%)

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 Overall
traitement
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 28 (56.0%) 13 (44.8%) 15 (71.4%)
    Homme 22 (44.0%) 16 (55.2%) 6 (28.6%)
quatres niveaux n (d.m.) 50 (0) 29 (0) 21 (0)
    A 7 (14.0%) 2 (6.9%) 5 (23.8%)
    B 28 (56.0%) 18 (62.1%) 10 (47.6%)
    C 15 (30.0%) 9 (31.0%) 6 (28.6%)
Echelle n (d.m.) 50 (0) 29 (0) 21 (0)
    0 13 (26.0%) 6 (20.7%) 7 (33.3%)
    1 11 (22.0%) 6 (20.7%) 5 (23.8%)
    2 5 (10.0%) 4 (13.8%) 1 (4.8%)
    3 3 (6.0%) 3 (10.3%) 0 (0.0%)
    4 12 (24.0%) 7 (24.1%) 5 (23.8%)
    5 6 (12.0%) 3 (10.3%) 3 (14.3%)

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 N 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
28 (56.0%)
    Homme
22 (44.0%)
quatres niveaux n (d.m.) 50 (0)
    A
7 (14.0%)
    B
28 (56.0%)
    C
15 (30.0%)
Echelle n (d.m.) 50 (0)
    0
13 (26.0%)
    1
11 (22.0%)
    2
5 (10.0%)
    3
3 (6.0%)
    4
12 (24.0%)
    5
6 (12.0%)

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 N 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
28 (56.0%)
    Homme
22 (44.0%)
Title B
quatres niveaux 50
    A
7 (14.0%)
    B
28 (56.0%)
    C
15 (30.0%)

Number of Digits in Quantitative and Qualitative Features

You can specify the number of digits for quantitative and qualitative features using the digits argument.

Specify Number of Digits

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

data %>%
  RastaRocket::desc_var(table_title = "test",
                     by_group = TRUE,
                     var_group = "traitement",
                     digits = list(mean_sd = 0,
                                   median_q1_q3_min_max = 0,
                                   pct = 1))
test
Characteristic Overall
traitement
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 28 (56.0%) 13 (44.8%) 15 (71.4%)
    Homme 22 (44.0%) 16 (55.2%) 6 (28.6%)
quatres niveaux n (d.m.) 50 (0) 29 (0) 21 (0)
    A 7 (14.0%) 2 (6.9%) 5 (23.8%)
    B 28 (56.0%) 18 (62.1%) 10 (47.6%)
    C 15 (30.0%) 9 (31.0%) 6 (28.6%)
Echelle n (d.m.) 50 (0) 29 (0) 21 (0)
    0 13 (26.0%) 6 (20.7%) 7 (33.3%)
    1 11 (22.0%) 6 (20.7%) 5 (23.8%)
    2 5 (10.0%) 4 (13.8%) 1 (4.8%)
    3 3 (6.0%) 3 (10.3%) 0 (0.0%)
    4 12 (24.0%) 7 (24.1%) 5 (23.8%)
    5 6 (12.0%) 3 (10.3%) 3 (14.3%)

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(mean_sd = 2,
                                   median_q1_q3_min_max = 2,
                                   pct = 2))

tb2 <- data %>%
  dplyr::select(quatre_modalites, traitement) %>%
  RastaRocket::desc_var(table_title = "test",
                     by_group = TRUE,
                     var_group = "traitement",
                     digits = list(mean_sd = 0,
                                   median_q1_q3_min_max = 0,
                                   pct = 1))

gtsummary::tbl_stack(list(tb1, tb2))
test
Characteristic Overall
traitement
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 28 (56.00%) 13 (44.83%) 15 (71.43%)
    Homme 22 (44.00%) 16 (55.17%) 6 (28.57%)
quatres niveaux 50 29 21
    A 7 (14.0%) 2 (6.9%) 5 (23.8%)
    B 28 (56.0%) 18 (62.1%) 10 (47.6%)
    C 15 (30.0%) 9 (31.0%) 6 (28.6%)

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 Overall
traitement
 p-value
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 28 (56.0%) 13 (44.8%) 15 (71.4%)
    Homme 22 (44.0%) 16 (55.2%) 6 (28.6%)
quatres niveaux n (d.m.) 50 (0) 29 (0) 21 (0) 0.33
    A 7 (14.0%) 2 (6.9%) 5 (23.8%)
    B 28 (56.0%) 18 (62.1%) 10 (47.6%)
    C 15 (30.0%) 9 (31.0%) 6 (28.6%)
Echelle n (d.m.) 50 (0) 29 (0) 21 (0) 0.63
    0 13 (26.0%) 6 (20.7%) 7 (33.3%)
    1 11 (22.0%) 6 (20.7%) 5 (23.8%)
    2 5 (10.0%) 4 (13.8%) 1 (4.8%)
    3 3 (6.0%) 3 (10.3%) 0 (0.0%)
    4 12 (24.0%) 7 (24.1%) 5 (23.8%)
    5 6 (12.0%) 3 (10.3%) 3 (14.3%)
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 Overall
traitement
 p-value
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 28 (56.0%) 13 (44.8%) 15 (71.4%)
    Homme 22 (44.0%) 16 (55.2%) 6 (28.6%)
quatres niveaux n (d.m.) 50 (0) 29 (0) 21 (0) 0.33
    A 7 (14.0%) 2 (6.9%) 5 (23.8%)
    B 28 (56.0%) 18 (62.1%) 10 (47.6%)
    C 15 (30.0%) 9 (31.0%) 6 (28.6%)
Echelle n (d.m.) 50 (0) 29 (0) 21 (0) 0.63
    0 13 (26.0%) 6 (20.7%) 7 (33.3%)
    1 11 (22.0%) 6 (20.7%) 5 (23.8%)
    2 5 (10.0%) 4 (13.8%) 1 (4.8%)
    3 3 (6.0%) 3 (10.3%) 0 (0.0%)
    4 12 (24.0%) 7 (24.1%) 5 (23.8%)
    5 6 (12.0%) 3 (10.3%) 3 (14.3%)
1 Welch Two Sample t-test
2 Pearson’s Chi-squared test
3 Fisher’s exact test

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 Overall
traitement
 p-value
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 28 (56.0%) 13 (44.8%) 15 (71.4%)
    Homme 22 (44.0%) 16 (55.2%) 6 (28.6%)
quatres niveaux n (d.m.) 50 (0) 29 (0) 21 (0) 0.33
    A 7 (14.0%) 2 (6.9%) 5 (23.8%)
    B 28 (56.0%) 18 (62.1%) 10 (47.6%)
    C 15 (30.0%) 9 (31.0%) 6 (28.6%)
Echelle n (d.m.) 50 (0) 29 (0) 21 (0) 0.63
    0 13 (26.0%) 6 (20.7%) 7 (33.3%)
    1 11 (22.0%) 6 (20.7%) 5 (23.8%)
    2 5 (10.0%) 4 (13.8%) 1 (4.8%)
    3 3 (6.0%) 3 (10.3%) 0 (0.0%)
    4 12 (24.0%) 7 (24.1%) 5 (23.8%)
    5 6 (12.0%) 3 (10.3%) 3 (14.3%)
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(mean_sd = 0,
                                     median_q1_q3_min_max = 0,
                                     pct = 0))

tb2 <- data %>%
  dplyr::select(quatre_modalites, traitement) %>%
  RastaRocket::desc_var(table_title = "test",
                     by_group = TRUE,
                     var_group = "traitement",
                     digits = list(mean_sd = 2,
                                   median_q1_q3_min_max = 2,
                                   pct = 2))

gtsummary::tbl_stack(list(tb1, tb2)) %>%
  custom_format()
test
Characteristic Overall
traitement
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 28 (56%) 13 (45%) 15 (71%)
    Homme 22 (44%) 16 (55%) 6 (29%)
quatres niveaux 50 29 21
    A 7 (14.00%) 2 (6.90%) 5 (23.81%)
    B 28 (56.00%) 18 (62.07%) 10 (47.62%)
    C 15 (30.00%) 9 (31.03%) 6 (28.57%)

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 Overall
traitement
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 28 (56.0%) 13 (44.8%) 15 (71.4%)
    Homme 22 (44.0%) 16 (55.2%) 6 (28.6%)
quatres niveaux n (d.m.) 50 (0) 29 (0) 21 (0)
    A 7 (14.0%) 2 (6.9%) 5 (23.8%)
    B 28 (56.0%) 18 (62.1%) 10 (47.6%)
    C 15 (30.0%) 9 (31.0%) 6 (28.6%)
Echelle n (d.m.) 50 (0) 29 (0) 21 (0)
    0 13 (26.0%) 6 (20.7%) 7 (33.3%)
    1 11 (22.0%) 6 (20.7%) 5 (23.8%)
    2 5 (10.0%) 4 (13.8%) 1 (4.8%)
    3 3 (6.0%) 3 (10.3%) 0 (0.0%)
    4 12 (24.0%) 7 (24.1%) 5 (23.8%)
    5 6 (12.0%) 3 (10.3%) 3 (14.3%)

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 N 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 (33,3%)
    versicolor
50 (33,3%)
    virginica
50 (33,3%) 

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

# back to default format
gtsummary::reset_gtsummary_theme()