---
title: "RastaRocketVignette"
date: "`r Sys.Date()`"
output:
  html_vignette:
    toc: true
    toc_depth: 2
    keep_md: true
vignette: >
  %\VignetteIndexEntry{RastaRocketVignette}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
editor_options: 
  chunk_output_type: console
---

```{r,include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  warning=FALSE,
  message=FALSE,
  results='asis'
)
```

\


```{r setup}
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.


```{r}
# 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
- `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`)

```{r}
data %>% RastaRocket::desc_var(table_title = "test",
                            by_group = TRUE,
                            var_group = "traitement",
                            group_title = "Traitement",
                            add_total = TRUE,
                            show_n_per_group = TRUE)
```

# 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.

```{r}
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"))
```


# 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`.

```{r}
iris %>% RastaRocket::desc_var(table_title = "test",
                            by_group = TRUE,
                            var_group = "Species",
                            group_title = "Species",
                            show_missing_data = TRUE)
```

```{r}
iris %>% RastaRocket::desc_var(table_title = "test",
                            by_group = TRUE,
                            var_group = "Species",
                            group_title = "Species",
                            show_missing_data = FALSE)
```

# 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.

```{r second example}
data %>% desc_var(table_title = "test",
             by_group = TRUE,
             var_group = "traitement",
             group_title = "traitement",
             freq_relevel = TRUE)
```

### 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`.

```{r}
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")
```


## Remove zero-count levels


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

```{r third example}
data %>% desc_var(table_title = "test",
             by_group = TRUE,
             var_group = "traitement",
             group_title = "traitement",
             drop_levels = FALSE)
```

# Overall and Per-Group Descriptions

## Per-Group Description

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

```{r }
data %>% desc_var(table_title = "test",
             by_group = TRUE,
             var_group = "traitement",
             group_title = "traitement")
```

## Overall Description

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

```{r}
data %>% RastaRocket::desc_var(table_title = "test",
             by_group = FALSE,
             var_group = "traitement",
             group_title = "traitement")
```

# 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.

```{r}
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"))

```

# 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.



```{r}
data %>%
  RastaRocket::desc_var(table_title = "test",
                     by_group = TRUE,
                     var_group = "traitement",
                     digits = list(r_quanti = 0, r_quali = 1))
```



## 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`.




```{r}
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 = 0, r_quali = 1))

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



# 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.

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

## 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.

```{r}
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 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`.

```{r}
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()
```

This also works when using stacked tables.



```{r}
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()
```



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

```{r}
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)))
```

# 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.


```{r}
# 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")

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

# back to default format
gtsummary::reset_gtsummary_theme()
```

