Refactor code structure and remove redundant sections for improved readability and maintainability

Remove outdated project documentation and heart dataset files
Edit uv.lock
2026-03-16 05:11:40 +01:00 · 2026-03-10 12:23:50 +01:00 · 2026-03-10 12:08:37 +01:00 · 2026-03-10 12:08:19 +01:00 · 2026-03-10 11:39:11 +01:00
34 changed files with 271 additions and 1280319 deletions
--- a/Regression/Projet_MRC_DANJOU_LEGRAND_MERIC_VONSIEMENS.html
+++ b/Regression/Projet_MRC_DANJOU_LEGRAND_MERIC_VONSIEMENS.html
--- a/Regression/Projet_MRC_DANJOU_LEGRAND_MERIC_VONSIEMENS.qmd
+++ b/Regression/Projet_MRC_DANJOU_LEGRAND_MERIC_VONSIEMENS.qmd
--- a/Regression/README.md
+++ b/Regression/README.md
@@ -1,310 +0,0 @@
 # Implied Volatility Prediction from Options Data
 [![R](https://img.shields.io/badge/R-4.0+-276DC3.svg)](https://www.r-project.org/)
 [![Course](https://img.shields.io/badge/Course-Classification%20%26%20Regression-orange.svg)]()
 [![License](https://img.shields.io/badge/License-Academic-blue.svg)]()
 > **M2 Master's Project** – Predicting implied volatility using advanced regression techniques and machine learning models on financial options data.
 This project explores the prediction of **implied volatility** from options market data, combining classical statistical methods with modern machine learning approaches. The analysis covers data preprocessing, feature engineering, model benchmarking, and interpretability analysis using real-world financial panel data.
 ---
 ## 📋 Project Overview
 ### Problem Statement
 Implied volatility represents the market's forward-looking expectation of an asset's future volatility. Accurate prediction is crucial for:
 - **Option pricing** and valuation
 - **Risk management** and hedging strategies
 - **Trading strategies** based on volatility arbitrage
 ### Dataset
 The project uses a comprehensive panel dataset tracking **3,887 assets** across **544 observation dates** (2019-2022):
 | File | Description | Shape |
 |------|-------------|-------|
 | `Train_ISF.csv` | Training data with target variable | 1,909,465 rows × 21 columns |
 | `Test_ISF.csv` | Test data for prediction | 1,251,308 rows × 18 columns |
 | `hat_y.csv` | Final predictions from both models | 1,251,308 rows × 2 columns |
 ### Key Variables
 **Target Variable:**
 - `implied_vol_ref` – The implied volatility to predict
 **Feature Categories:**
 - **Identifiers:** `asset_id`, `obs_date`
 - **Market Activity:** `call_volume`, `put_volume`, `call_oi`, `put_oi`, `total_contracts`
 - **Volatility Metrics:** `realized_vol_short`, `realized_vol_mid1-3`, `realized_vol_long1-4`, `market_vol_index`
 - **Option Structure:** `strike_dispersion`, `maturity_count`
 ---
 ## 🏗️ Methodology
 ### Data Pipeline
 ```
 Raw Data
    ↓
 ┌─────────────────────────────────────────────────────────┐
 │  Data Splitting (Chronological 80/20)                  │
 │  - Training: 2019-10 to 2021-07                         │
 │  - Validation: 2021-07 to 2022-03                     │
 └─────────────────────────────────────────────────────────┘
    ↓
 ┌─────────────────────────────────────────────────────────┐
 │  Feature Engineering                                   │
 │  - Aggregation of volatility horizons                 │
 │  - Creation of financial indicators                   │
 └─────────────────────────────────────────────────────────┘
    ↓
 ┌─────────────────────────────────────────────────────────┐
 │  Data Preprocessing (tidymodels)                       │
 │  - Winsorization (99.5th percentile)                  │
 │  - Log/Yeo-Johnson transformations                    │
 │  - Z-score normalization                              │
 │  - PCA (95% variance retention)                       │
 └─────────────────────────────────────────────────────────┘
    ↓
 Three Datasets Generated:
 ├── Tree-based (raw, scale-invariant)
 ├── Linear (normalized, winsorized)
 └── PCA (dimensionality-reduced)
 ```
 ### Feature Engineering
 New financial indicators created to capture market dynamics:
 | Feature | Description | Formula |
 |---------|-------------|---------|
 | `pulse_ratio` | Volatility trend direction | RV_short / RV_long |
 | `stress_spread` | Asset vs market stress | RV_short - Market_VIX |
 | `put_call_ratio_volume` | Immediate market stress | Put_Volume / Call_Volume |
 | `put_call_ratio_oi` | Long-term risk structure | Put_OI / Call_OI |
 | `liquidity_ratio` | Market depth | Total_Volume / Total_OI |
 | `option_dispersion` | Market uncertainty | Strike_Dispersion / Total_Contracts |
 | `put_low_strike` | Downside protection density | Strike_Dispersion / Put_OI |
 | `put_proportion` | Hedging vs speculation | Put_Volume / Total_Volume |
 ---
 ## 🤖 Models Implemented
 ### Linear Models
 | Model | Description | Best RMSE |
 |-------|-------------|-----------|
 | **OLS** | Ordinary Least Squares | 11.26 |
 | **Ridge** | L2 regularization | 12.48 |
 | **Lasso** | L1 regularization (variable selection) | 12.03 |
 | **Elastic Net** | L1 + L2 combined | ~12.03 |
 | **PLS** | Partial Least Squares (on PCA) | 12.79 |
 ### Linear Mixed-Effects Models (LMM)
 Advanced panel data models accounting for asset-specific effects:
 | Model | Features | RMSE |
 |-------|----------|------|
 | LMM Baseline | All variables + Random Intercept | 8.77 |
 | LMM Reduced | Collinearity removal | ~8.77 |
 | LMM Interactions | Financial interaction terms | ~8.77 |
 | LMM + Quadratic | Convexity terms (vol of vol) | 8.41 |
 | **LMM + Random Slopes (mod_lmm_5)** | Asset-specific betas | **8.10** ⭐ |
 ### Tree-Based Models
 | Model | Strategy | Validation RMSE | Training RMSE |
 |-------|----------|-----------------|---------------|
 | **XGBoost** | Level-wise, Bayesian tuning | 10.70 | 0.57 |
 | **LightGBM** | Leaf-wise, feature regularization | **10.61** ⭐ | 10.90 |
 | Random Forest | Bagging | DNF* | - |
 *DNF: Did Not Finish (computational constraints)
 ### Neural Networks
 | Model | Architecture | Status |
 |-------|--------------|--------|
 | MLP | 128-64 units, tanh activation | Failed to converge |
 ---
 ## 📊 Results Summary
 ### Model Comparison
 ```
 RMSE Performance (Lower is Better)
 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 Linear Mixed-Effects (LMM5)     8.38 ████████████████████ Best Linear
 Linear Mixed-Effects (LMM4)     8.41 ███████████████████
 Linear Mixed-Effects (Baseline) 8.77 ██████████████████
 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 LightGBM                       10.61 ███████████████ Best Non-Linear
 XGBoost                        10.70 ██████████████
 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 OLS (with interactions)        11.26 █████████████
 Lasso                          12.03 ███████████
 OLS (baseline)                 12.01 ███████████
 Ridge                          12.48 ██████████
 PLS                            12.79 █████████
 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 ```
 ### Key Findings
 1. **Best Linear Model:** LMM with Random Slopes (RMSE = 8.38)
   - Captures asset-specific volatility sensitivities
   - Includes quadratic terms for convexity effects
 2. **Best Non-Linear Model:** LightGBM (RMSE = 10.61)
   - Superior generalization vs XGBoost
   - Feature regularization prevents overfitting
 3. **Interpretability Insights (SHAP Analysis):**
   - `realized_vol_mid` dominates (57% of gain)
   - Volatility clustering confirmed as primary driver
   - Non-linear regime switching in stress_spread
 ---
 ## 📁 Repository Structure
 ```
 PROJECT/
 ├── Projet_MRC_DANJOU_LEGRAND_MERIC_VONSIEMENS.qmd    # Main analysis (Quarto)
 ├── Projet_MRC_DANJOU_LEGRAND_MERIC_VONSIEMENS.html    # Rendered report
 ├── packages.R                                         # R dependencies installer
 ├── Train_ISF.csv                                      # Training data (~1.9M rows)
 ├── Test_ISF.csv                                       # Test data (~1.25M rows)
 ├── hat_y.csv                                          # Final predictions
 ├── README.md                                          # This file
 └── results/
    ├── lightgbm/                                      # LightGBM model outputs
    └── xgboost/                                       # XGBoost model outputs
 ```
 ---
 ## 🚀 Getting Started
 ### Prerequisites
 - **R** ≥ 4.0
 - Required packages (auto-installed via `packages.R`)
 ### Installation
 ```r
 # Install all dependencies
 source("packages.R")
 ```
 Or manually install key packages:
 ```r
 install.packages(c(
  "tidyverse", "tidymodels", "caret", "glmnet",
  "lme4", "lmerTest", "xgboost", "lightgbm",
  "ranger", "pls", "shapviz", "rBayesianOptimization"
 ))
 ```
 ### Running the Analysis
 1. **Open the Quarto document:**
   ```r
   # In RStudio
   rstudioapi::navigateToFile("Projet_MRC_DANJOU_LEGRAND_MERIC_VONSIEMENS.qmd")
   ```
 2. **Render the document:**
   ```r
   quarto::quarto_render("Projet_MRC_DANJOU_LEGRAND_MERIC_VONSIEMENS.qmd")
   ```
 3. **Or run specific sections interactively** using the code chunks in the `.qmd` file
 ---
 ## 🛠️ Technical Details
 ### Data Split Strategy
 - **Chronological split** at 80th percentile of dates
 - Prevents look-ahead bias and data leakage
 - Training: ~1.53M observations
 - Validation: ~376K observations
 ### Hyperparameter Tuning
 - **Method:** Bayesian Optimization (Gaussian Processes)
 - **Acquisition:** Expected Improvement (UCB)
 - **Goal:** Maximize negative RMSE
 ### Evaluation Metric
 **Exponential RMSE** on original scale:
 $$
 RMSE_{real} = \sqrt{\frac{1}{n} \sum_{i=1}^{n} \left( \exp(\hat{y}_{\log, i}) - y_i \right)^2}
 $$
 Models trained on log-transformed target for variance stabilization.
 ---
 ## 📖 Key Concepts
 ### Financial Theories Applied
 1. **Volatility Clustering** – Past volatility predicts future volatility
 2. **Variance Risk Premium** – Spread between implied and realized volatility
 3. **Fear Gauge** – Put-call ratio as sentiment indicator
 4. **Mean Reversion** – Volatility tends to return to long-term average
 5. **Liquidity Premium** – Illiquid assets command higher volatility
 ### Statistical Methods
 - Panel data modeling with fixed and random effects
 - Principal Component Analysis (PCA)
 - Bayesian hyperparameter optimization
 - SHAP values for model interpretability
 ---
 ## 👥 Authors
 **Team:**
 - Arthur DANJOU
 - Camille LEGRAND  
 - Axelle MERIC
 - Moritz VON SIEMENS
 **Course:** Classification and Regression (M2)
 **Academic Year:** 2025-2026
 ---
 ## 📝 Notes
 - **Computational Constraints:** Some models (Random Forest, MLP) failed due to hardware limitations (16GB RAM, CPU-only)
 - **Reproducibility:** Set `seed = 2025` for consistent results
 - **Language:** Analysis documented in English, course materials in French
 ---
 ## 📚 References
 Key R packages used:
 - `tidymodels` – Modern modeling framework
 - `glmnet` – Regularized regression
 - `lme4` / `lmerTest` – Mixed-effects models
 - `xgboost` / `lightgbm` – Gradient boosting
 - `shapviz` – Model interpretability
 - `rBayesianOptimization` – Hyperparameter tuning
--- a/Regression/hat_y.csv
+++ b/Regression/hat_y.csv
--- a/Regression/packages.R
+++ b/Regression/packages.R
@@ -1,44 +0,0 @@
 # Liste des packages nécessaires
 packages <- c(
  "tidyverse",
  "rsample",
  "scales",
  "dplyr",
  "tidyr",
  "glue",
  "corrplot",
  "ggfortify",
  "carData",
  "car",
  "MASS",
  "ggplot2",
  "DataExplorer",
  "skimr",
  "plotly",
  "gridExtra",
  "grid",
  "rlang",
  "caret",
  "reshape2",
  "class",
  "ROCR",
  "randomForest",
  "fitdistrplus",
  "hexbin",
  "paletteer"
 )
 # Fonction pour installer les packages manquants
 install_if_missing <- function(p) {
  if (!require(p, character.only = TRUE)) {
    install.packages(p, dependencies = TRUE)
  }
 }
 # Application de la fonction sur toute la liste
 invisible(sapply(packages, install_if_missing))
 # Chargement de toutes les librairies
 invisible(lapply(packages, library, character.only = TRUE))
 message("Tous les packages ont été installés et chargés avec succès !")
--- a/Visualisation/Exemple
+++ b/Visualisation/Exemple
--- a/Projet/Application-projet.html
+++ b/Projet/Application-projet.html
--- a/Visualisation/Exemple
+++ b/Visualisation/Exemple
--- a/Visualisation/Exemple
+++ b/Visualisation/Exemple
@@ -1,304 +0,0 @@
 age,sex,cp,trestbps,chol,fbs,restecg,thalach,exang,oldpeak,slope,ca,thal,target
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--- a/Visualisation/Project/.Rprofile
+++ b/Visualisation/Project/.Rprofile
@@ -1 +0,0 @@
 source("renv/activate.R")
--- a/Visualisation/Project/NoticeTechnique.Rmd
+++ b/Visualisation/Project/NoticeTechnique.Rmd
@@ -1,801 +0,0 @@
 ---
 output: 
  pdf_document:
    number_sections: true
    toc: false
    toc_depth: 2
    fig_caption: true
    highlight: tango
    latex_engine: xelatex
 geometry: "left=2cm,right=2cm,top=2cm,bottom=2cm"
 header-includes:
  - \usepackage{titling}
  - \usepackage{graphicx}
  - \usepackage{fancyhdr}
  - \pagestyle{fancy}
  - \fancyhead[L]{Notice Technique - Tuberculose}
  - \fancyhead[R]{Arthur DANJOU}
  - \fancyfoot[C]{\thepage}
 ---
 \begin{titlepage}
    \begin{center}
        \vspace*{1cm}
        % --- En-tête Université ---
        {\Large \textsc{Université Paris-Dauphine -- PSL}} \\
        \vspace{0.2cm}
        {\large Master 280 -- Ingénierie Statistique et Financière}
        \vspace{1.5cm}
        % --- Bloc Titre ---
        \hrulefill
        \vspace{0.4cm}
        {\bfseries \Huge \uppercase{Monitorage et Segmentation \\[0.3cm] de la Tuberculose (OMS)}}
        \vspace{0.4cm}
        \hrulefill
        \vspace{1.5cm}
        % --- AJOUT : Problématique ---
        % Utilisation d'une minipage pour contrôler la largeur du texte (80% de la page)
        \begin{minipage}{0.85\textwidth}
            \centering
            \Large \textit{«~Au-delà des agrégats nationaux : comment l'analyse multivariée permet-elle de révéler une typologie opérationnelle des risques sanitaires mondiaux face à la tuberculose ?~»}
        \end{minipage}
        \vspace{2cm}
        % --- Auteur et Enseignant ---
        {\Large \textsc{Arthur DANJOU}} \\ 
        \vspace{1.5cm}
        {\large Enseignant :} \\ [0.2cm]
        {\large Quentin GUIBERT}
        \vfill % Pousse le logo vers le bas de page automatiquement
        % --- Logo ---
        \includegraphics[height=25mm]{logo_dauphine.jpg}
        \vspace{0.5cm}
        \hrulefill
        \vspace{0.2cm}
        % --- Pied de page ---
        {\textsc{Data Visualisation \\ Année Universitaire 2025-2026}}
    \end{center}
 \end{titlepage}
 \newpage
 \tableofcontents
 \newpage
 ```{r setup, include=FALSE}
 knitr::opts_chunk$set(
  echo = FALSE,
  warning = FALSE,
  message = FALSE,
  fig.align = "center",
  out.width = "75%"
 )
 library(tidyverse)
 library(sf)
 library(rnaturalearth)
 library(rnaturalearthdata)
 library(knitr)
 library(kableExtra)
 library(gridExtra)
 library(moments)
 library(factoextra)
 ```
 > * **Application déployée :** [https://go.arthurdanjou.fr/datavis-app](https://go.arthurdanjou.fr/datavis-app)
 > * **Code Source de(GitHub) :** [https://go.arthurdanjou.fr/datavis-code](https://go.arthurdanjou.fr/datavis-code)
 # Introduction
 ## Contexte et enjeux sanitaires
 Avec 1,6 million de décès annuels et plus de 10 millions de nouveaux cas estimés en 2022, la tuberculose (TB) demeure la deuxième maladie infectieuse la plus meurtrière au monde après le COVID-19 (OMS, 2025). Pourtant, derrière ces chiffres globaux se cache une épidémie profondément inégalitaire. Alors que certains pays rapportent une incidence maîtrisée inférieure à 10 cas pour 100 000 habitants, d'autres font face à des taux critiques dépassant les 500 cas, révélant des fractures sanitaires majeures entre les nations.
 Pour piloter la réponse mondiale, l'Organisation Mondiale de la Santé produit le *Global Tuberculosis Report*, une base de données exhaustive comptant plus de 200 pays et une quarantaine d'indicateurs. Cependant, la richesse même de ces données pose un défi d'analyse : face à la multitude de variables (incidence, notification, mortalité, co-infection), les tableaux statistiques traditionnels échouent à offrir une vision synthétique et opérationnelle. Ils ne permettent ni d'identifier rapidement les profils à risque, ni de visualiser les dynamiques temporelles complexes.
 ## Problématique : Au-delà des agrégats nationaux : comment l'analyse multivariée permet-elle de révéler une typologie opérationnelle des risques sanitaires mondiaux face à la tuberculose ?
 Ce projet déploie une chaîne de traitement Data Science complète reposant sur trois piliers. Premièrement, une rationalisation de la donnée par sélection de variables et analyse exploratoire (EDA) pour isoler les signaux pertinents. Deuxièmement, une segmentation intelligente (Clustering K-Means) pour identifier des profils de risque homogènes au-delà des simples zones géographiques. Enfin, une opérationnalisation interactive via une application R Shiny, offrant aux décideurs une interface dynamique pour visualiser les tendances 2000-2024.
 ## Périmètre et Structure
 L'étude se concentre sur les indicateurs épidémiologiques "durs" pour garantir la robustesse du modèle, les facteurs exogènes (PIB, dépenses) étant considérés comme contextuels.
 La suite de cette notice détaille la méthodologie : la préparation des données (Section 2) et la modélisation mathématique (Section 3) précèdent l'analyse des profils identifiés (Section 4). L'architecture de l'application R Shiny est décrite en Section 5, suivie de l'exploitation des résultats et du benchmarking (Section 6). Le document se clôt sur les perspectives d'évolution (Section 7) et le cadre d'intégrité académique (Section 8).
 # Analyse Exploratoire des Données
 ## Source et structure des données
 ### Origine et portée des données
 Le socle empirique repose sur les données du *Global Tuberculosis Report* 2024 de l'OMS, référence internationale couvrant 25 ans (2000-2024) pour 215 territoires. Le fichier brut de 50 variables s'articule autour de trois dimensions complémentaires : **épidémiologique** (morbidité, mortalité, prise en charge), **démographique** (structure de population nécessaire à la standardisation des taux) et **géopolitique** (métadonnées spatiales et codes ISO-3) dédiées à l'analyse spatiale.
 ### Convention de nommage et sémantique
 L'analyse requiert la maîtrise d'une nomenclature rigoureuse distinguant les **Cas notifiés** (préfixe `c_`, données brutes administratives) des **Estimations modélisées** (préfixe `e_`), par lesquelles l'OMS corrige les biais de sous-déclaration et intègre les incertitudes. Pour cette étude, nous privilégierons exclusivement ces variables estimées (`e_`) : ce choix méthodologique permet de neutraliser l'hétérogénéité des performances administratives locales afin de garantir une comparabilité internationale stricte des dynamiques épidémiques.
 ### Qualité des données et limites
 Bien qu'offrant une profondeur spatio-temporelle unique appuyée par une méthodologie standardisée, ce jeu de données présente des hétérogénéités inhérentes à la surveillance mondiale. Les biais de mesure restent prégnants pour les pays à faibles revenus ou en conflit, où les estimations reposent sur l'extrapolation statistique plutôt que sur un comptage exhaustif, sans compter le caractère provisoire des données récentes (2023-2024). Ces limites intrinsèques justifient l'adoption d'une approche méthodologique prudente, privilégiant l'exclusion des variables incertaines et le rejet de l'imputation pour les observations incomplètes.
 ### Importation et aperçu initial
 ```{r}
 data_raw <- read.csv("data/TB_burden_countries_2025-12-09.csv")
 ```
 Le jeu de données importé contient $5347$ observations et $50$ variables. Le tableau ci-dessous présente les dix premières lignes du jeu de données, illustrant la structure longitudinale pour le premier pays par ordre alphabétique (Afghanistan) au début de la période d'étude (de 2000 à 2009).
 ```{r}
 data_raw |>
  select(year, country, g_whoregion, e_inc_100k, e_mort_exc_tbhiv_100k) |>
  head(10) |>
  kable(
    col.names = c(
      "Année",
      "Pays",
      "Région",
      "Incidence (/100k)",
      "Mortalité (/100k)"
    ),
    caption = "Aperçu des premières lignes du jeu de données brut",
    booktabs = TRUE
  ) |>
  kable_styling(latex_options = c("striped", "hold_position"))
 ```
 ## Sélection de variables
 La qualité d'une segmentation non-supervisée étant tributaire de la pertinence des entrées, l'injection brute des 50 variables initiales a été écartée pour prévenir deux écueils méthodologiques. D'une part, le **fléau de la dimension** (*Curse of Dimensionality*) qui tend à uniformiser les distances euclidiennes et flouter les clusters et d'autre part, le **biais de redondance**, où la colinéarité des variables risque de surpondérer artificiellement un même phénomène. Nous avons donc déployé une stratégie de réduction de dimension en deux temps : un filtrage structurel (approche par entonnoir) consolidé par un arbitrage statistique des corrélations.
 ### Approche par entonnoir : élimination des métadonnées, des bornes d'incertitude et des valeurs absolues
 Une stratégie de réduction de dimension en quatre étapes successives a été appliquée pour isoler les variables pertinentes. Dans un premier temps, le **nettoyage structurel** et la simplification ont permis d'écarter les métadonnées techniques (ex: `iso_numeric`) ainsi que les bornes d'incertitude (`_lo`, `_hi`), jugées non pertinentes pour le calcul de distances euclidiennes ou redondantes avec l'estimation centrale. Ensuite, l'étape de **standardisation** a exclu les valeurs absolues (`_num`) afin de neutraliser tout biais démographique et permettre la comparaison directe entre pays de tailles hétérogènes. Enfin, un **filtrage de la colinéarité** a supprimé les indicateurs redondants (corrélation > 0,8), tels que les notifications brutes, pour éviter de biaiser la pondération des dimensions dans l'algorithme de clustering.
 ### Arbitrage méthodologique : traitement de la colinéarité (Incidence vs Notifications) et de la redondance (Mortalité vs Mortalité VIH)
 À l'issue du filtrage structurel, il subsiste plusieurs candidats potentiels pour mesurer la charge épidémique. Pour éviter la redondance (colinéarité), nous analysons la matrice de corrélation de Pearson entre ces candidats.
 L'objectif est de conserver les variables les plus représentatives tout en maximisant l'orthogonalité (l'indépendance) des informations fournies au modèle. La figure ci-dessous visualise la matrice de corrélation de Pearson entre les quatre variables candidates : l'incidence (estimée et notifiée) et la mortalité (avec et hors VIH).
 ```{r}
 vars_candidates <- data_raw |>
  select(
    "Incidence (Estimée)" = e_inc_100k,
    "Incidence (Notifiée)" = c_newinc_100k,
    "Mortalité (Hors VIH)" = e_mort_exc_tbhiv_100k,
    "Mortalité (Avec VIH)" = e_mort_tbhiv_100k
  )
 cor_mat <- cor(vars_candidates, use = "pairwise.complete.obs")
 ```
 ```{r}
 cor_df <- as.data.frame(cor_mat) |>
  tibble::rownames_to_column(var = "Var1") |>
  pivot_longer(-Var1, names_to = "Var2", values_to = "r") |>
  mutate(
    Var1 = factor(Var1, levels = unique(Var1)),
    Var2 = factor(Var2, levels = rev(unique(Var1)))
  )
 ggplot(cor_df, aes(x = Var1, y = Var2, fill = r)) +
  geom_tile(color = "white") +
  geom_text(aes(label = round(r, 2)), size = 3) +
  scale_fill_gradient2(
    low = "#313695",
    mid = "white",
    high = "#a50026",
    midpoint = 0,
    limits = c(-1, 1),
    name = "r"
  ) +
  coord_fixed() +
  theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    axis.title = element_blank(),
    panel.grid = element_blank()
  )
 ```
 #### Analyse et décisions de modélisation :
 L'analyse de la matrice de corrélation a imposé deux arbitrages majeurs. Premièrement, l'**Incidence Estimée** (`e_inc_100k`) a été préférée aux cas notifiés. En effet, ces derniers souffrent d'un biais administratif : un faible taux de notification peut refléter un manque de médecins plutôt qu'une absence de malades, alors que l'estimation de l'OMS corrige ces sous-diagnostics pour refléter la charge réelle.
 Deuxièmement, nous avons retenu la **Mortalité hors VIH** (`e_mort_exc_tbhiv_100k`) malgré sa redondance avec la mortalité globale. Inclure la mortalité liée au VIH aurait risqué de biaiser la segmentation en isolant un "cluster SIDA" (spécifique à l'Afrique Australe), ce qui aurait masqué notre objectif principal : évaluer la performance des programmes antituberculeux indépendamment de l'accès aux antirétroviraux.
 #### Synthèse des variables retenues :
 Le modèle de clustering reposera donc sur un couple de variables actives parcimonieux et complémentaire :
 - Variable Active 1 : Incidence (Diffusion de la maladie) - `e_inc_100k`.
 - Variable Active 2 : Mortalité (Sévérité / Échec du traitement) - `e_mort_exc_tbhiv_100k`
 Ces deux dimensions, bien que corrélées ($r \approx 0.73$), ne sont pas redondantes : la variance non expliquée par la corrélation correspond justement à la différence d'efficacité des systèmes de soins (capacité à guérir les malades identifiés), ce qui est le cœur de notre segmentation.
 ### Variables illustratives et contextuelles
 En complément des variables actives, cinq variables illustratives sont conservées pour éclairer l'interprétation a posteriori sans biaiser le calcul des distances euclidiennes. Le contexte démographique est porté par la Population (`e_pop_num`), indispensable aux pondérations, tandis que le volet géopolitique repose sur la **Région OMS** (`g_whoregion`), structurant l'analyse spatiale en six zones administratives (AFR, AMR, EMR, EUR, SEA, WPR). Enfin, les identifiants techniques — **Pays, Code ISO et Année** — assurent les fonctions supports : étiquetage, jointure cartographique et filtrage dynamique des trajectoires temporelles.
 #### Création du sous-ensemble de travail :
 Nous appliquons cette sélection au jeu de données brut pour ne conserver que les 7 colonnes d'intérêt.
 ```{r, echo=TRUE}
 tb_clean <- data_raw |>
  select(
    iso3,
    country,
    year,
    g_whoregion,
    e_inc_100k,
    e_mort_exc_tbhiv_100k,
    e_pop_num
  )
 ```
 ## Traitement des valeurs manquantes
 La gestion des valeurs manquantes (NA) est une étape critique en analyse de données, particulièrement pour les méthodes de partitionnement comme les K-Means qui reposent sur des calculs de distance euclidienne et ne tolèrent aucune incomplétude vectorielle.
 Cette étape ne relève pas du simple "nettoyage" technique mais constitue un choix méthodologique qui influence la représentativité de l'échantillon final.
 ### Diagnostic de la structure des manquants
 Nous analysons la distribution spatio-temporelle des valeurs manquantes sur la variable de mortalité (`e_mort_exc_tbhiv_100k`), l'incidence étant complète par construction (filtrage préalable).
 ```{r}
 ggplot(tb_clean, aes(x = year, y = e_inc_100k)) +
  geom_point(
    aes(color = is.na(e_mort_exc_tbhiv_100k)),
    alpha = 0.6,
    size = 1.5
  ) +
  scale_color_manual(
    values = c("TRUE" = "red", "FALSE" = "blue"),
    labels = c("FALSE" = "Donnée Complète", "TRUE" = "Donnée Manquante")
  ) +
  labs(
    subtitle = "Les points rouges indiquent les observations exclues de l'analyse",
    x = "Année",
    y = "Incidence (log scale)",
    color = "Statut"
  ) +
  scale_y_log10() +
  theme_minimal()
 ```
 ### Analyse d'impact de l'exclusion
 Le tableau ci-dessous identifie les territoires les plus affectés :
 ```{r}
 missing_profiles <- tb_clean |>
  filter(is.na(e_mort_exc_tbhiv_100k)) |>
  group_by(country, g_whoregion) |>
  summarise(
    n_missing = n(),
    avg_incidence = mean(e_inc_100k, na.rm = TRUE),
    total_pop_affected = mean(e_pop_num, na.rm = TRUE),
    .groups = "drop"
  ) |>
  arrange(desc(n_missing)) |>
  head(10)
 kable(
  missing_profiles,
  col.names = c(
    "Territoire",
    "Région",
    "Années manquantes",
    "Incidence Moyenne",
    "Population Moy."
  ),
  caption = "Top 10 des territoires exclus pour données manquantes",
  digits = 0,
  format.args = list(big.mark = " "),
  booktabs = TRUE
 ) |>
  kable_styling(latex_options = c("striped", "hold_position"))
 ```
 Ce tableau confirme que les données manquantes concernent quasi-exclusivement des micro-états et territoires insulaires à très faible démographie (souvent inférieure à 100 000 habitants), validant ainsi leur exclusion sans impact significatif sur la représentativité mondiale de l'étude.
 ### Justification méthodologique
 L'exclusion des données manquantes se fonde sur trois justifications méthodologiques. D'un point de vue **géographique**, ces lacunes concernent quasi-exclusivement des micro-états ou territoires insulaires (ex: Monaco, Anguilla) dont la faible démographie induit une volatilité statistique excessive rendant les estimations peu fiables. Sur le plan **épidémiologique**, cette suppression est sans impact stratégique : ces territoires, bien que représentant 15% des observations, ne cumulent que 0,1% de la population mondiale et affichent une incidence marginale (17 cas/100k contre 125 pour l'échantillon conservé). Enfin, l'i**ntégrité statistique** a prévalu sur l'exhaustivité artificielle : le recours à l'imputation a été écarté car la génération de valeurs synthétiques pour ces profils atypiques risquerait de bruiter le calcul des distances euclidiennes et d'introduire des artefacts mathématiques préjudiciables au clustering.
 ### Finalisation de l'échantillon
 Nous appliquons donc le filtre définitif pour générer le jeu de données d'analyse.
 ```{r, results='asis', echo=TRUE}
 tb_clean <- tb_clean |> drop_na(e_inc_100k, e_mort_exc_tbhiv_100k)
 ```
 L'exclusion des observations incomplètes réduit la taille de l'échantillon de 15% (de 5 322 à 4 532 observations valides), couvrant 183 pays sur la période 2000-2024
 ## Analyse et Transformation
 Cette étape vise à caractériser la structure distributionnelle des variables actives (`tb_clean`). L'objectif est double : comprendre la dynamique épidémique sous-jacente et préparer les données pour satisfaire les hypothèses de l'algorithme K-Means (sensibilité aux valeurs extrêmes et aux variances inégales).
 ### Statistiques descriptives et asymétrie
 Le tableau ci-dessous résume les moments statistiques des deux variables actives sur l'ensemble de la période ($n = 4 532$ observations).
 ```{r}
 desc_stats <- tb_clean |>
  summarise(
    across(
      c(e_inc_100k, e_mort_exc_tbhiv_100k),
      list(
        Min = ~ min(.x),
        Q1 = ~ quantile(.x, 0.25),
        Med = ~ median(.x),
        Mean = ~ mean(.x),
        Q3 = ~ quantile(.x, 0.75),
        Max = ~ max(.x),
        Skew = ~ moments::skewness(.x)
      ),
      .names = "{.col}__{.fn}"
    )
  )
 desc_long <- desc_stats |>
  pivot_longer(
    everything(),
    names_to = c("Var", "Stat"),
    names_sep = "__"
  ) |>
  pivot_wider(names_from = Stat, values_from = value) |>
  mutate(
    Var = case_when(
      Var == "e_inc_100k" ~ "Incidence",
      Var == "e_mort_exc_tbhiv_100k" ~ "Mortalité",
      TRUE ~ Var
    )
  )
 kable(
  desc_long,
  digits = 2,
  caption = "Statistiques descriptives des variables actives (2000-2024)",
  booktabs = TRUE
 ) |>
  kable_styling(latex_options = c("striped", "hold_position"))
 ```
 L'écart considérable entre la médiane et la moyenne, couplé à des coefficients d'asymétrie (Skewness) largement supérieurs à 1, indique des distributions fortement asymétriques à droite (Lognormales ou de Pareto). Concrètement, la majorité des pays présentent une charge épidémique faible, tandis qu'une minorité d'observations "extrêmes" tire la moyenne vers le haut. Cette structure est typique des phénomènes épidémiques mais problématique pour le K-Means, qui risque de créer des clusters uniquement pour isoler ces valeurs extrêmes.
 ### Dynamiques temporelles et spatiales
 L'analyse visuelle permet de contextualiser ces statistiques globales.
 ```{r}
 p_time <- ggplot(tb_clean, aes(x = year, y = e_inc_100k)) +
  geom_line(aes(group = country), alpha = 0.05, color = "#2c3e50") +
  geom_smooth(method = "loess", color = "#d73027", se = FALSE) +
  scale_y_log10() +
  labs(
    title = "Trajectoires (2000-2024)",
    subtitle = "Échelle Log",
    y = "Incidence",
    x = "Année"
  ) +
  theme_minimal()
 p_region <- ggplot(
  tb_clean,
  aes(x = g_whoregion, y = e_inc_100k, fill = g_whoregion)
 ) +
  geom_boxplot(outlier.size = 0.5, alpha = 0.8) +
  scale_y_log10() +
  scale_fill_brewer(palette = "Set3") +
  labs(
    title = "Disparités Régionales",
    y = "Incidence",
    x = ""
  ) +
  theme_minimal() +
  theme(
    legend.position = "none",
    axis.text.x = element_text(angle = 45, hjust = 1)
  )
 grid.arrange(p_time, p_region, ncol = 2)
 ```
 L'analyse visuelle révèle une double dynamique. D'une part, la **tendance globale** montre une lente érosion de l'incidence moyenne mondiale (courbe rouge), malgré la forte inertie des trajectoires individuelles. D'autre part, les boxplots confirment une **fracture Nord-Sud** structurelle : les médianes logarithmiques de l'Afrique (AFR) et de l'Asie du Sud-Est (SEA) sont nettement supérieures à celles de l'Europe ou des Amériques. Cette hétérogénéité spatiale valide la pertinence d'inclure la région comme variable illustrative pour l'interprétation post-clustering.
 ### Relation Bivariée et Transformation
 La relation entre l'Incidence et la Mortalité est le cœur de notre modélisation.
 ```{r}
 p_raw <- ggplot(tb_clean, aes(x = e_inc_100k, y = e_mort_exc_tbhiv_100k)) +
  geom_point(alpha = 0.3, color = "#2c3e50") +
  labs(
    title = "Espace Naturel (Asymétrique)",
    x = "Incidence",
    y = "Mortalité"
  ) +
  theme_minimal()
 tb_ready <- tb_clean |>
  mutate(
    log_inc = log1p(e_inc_100k),
    log_mort = log1p(e_mort_exc_tbhiv_100k)
  )
 p_log <- ggplot(tb_ready, aes(x = log_inc, y = log_mort)) +
  geom_point(alpha = 0.3, color = "#4575b4") +
  geom_smooth(method = "lm", color = "#d73027", se = FALSE) +
  labs(
    title = "Espace Log-Transformé (Symétrique)",
    subtitle = "Transformation log(1+x)",
    x = "Log-Incidence",
    y = "Log-Mortalité"
  ) +
  theme_minimal()
 grid.arrange(p_raw, p_log, ncol = 2)
 ```
 Le graphique supérieur met en évidence une forte concentration à l'origine et une hétéroscédasticité marquée, risquant de biaiser les distances euclidiennes par les seules valeurs extrêmes. L'application de la transformation $f(x)=ln(1+x)$ corrige ces biais structurels : elle **symétrise les distributions** pour optimiser l'occupation de l'espace vectoriel et **linéarise la relation** entre les variables, facilitant la détection de groupes naturels. De plus, contrairement au logarithme népérien standard, cette fonction assure une gestion **robuste des zéros** (évitant le cas $ln(0)=−\infty$ pour les pays sans décès), garantissant ainsi la stabilité numérique du modèle.
 ## Synthèse de l'exploration, du nettoyage et des transformations
 À l'issue de cette phase de préparation, nous disposons d'un jeu de données optimisé pour la modélisation.
 Le tableau ci-dessous synthétise les caractéristiques du dataset final `tb_ready` qui sera injecté dans l'algorithme :
 ```{r}
 summary_final <- data.frame(
  Metrique = c(
    "Observations totales",
    "Pays couverts",
    "Plage Temporelle",
    "Variables Actives (Transformées)",
    "Variables Illustratives"
  ),
  Valeur = c(
    nrow(tb_ready),
    length(unique(tb_ready$iso3)),
    "2000 - 2024",
    "log_inc, log_mort",
    "Population, Région, Année"
  )
 )
 kable(
  summary_final,
  caption = "Fiche d'identité du jeu de données final",
  booktabs = TRUE
 ) |>
  kable_styling(latex_options = "hold_position")
 ```
 La validation de ce socle de données clôture la phase exploratoire. L'absence de valeurs manquantes, la réduction de la dimensionnalité et la normalisation des distributions nous permettent désormais de procéder au partitionnement (Clustering) avec une robustesse statistique garantie.
 # Stratégie de Modélisation (Clustering)
 La préparation des données ayant abouti à un espace vectoriel cohérent et symétrisé (`tb_ready`), nous procédons désormais à la segmentation proprement dite. Nous avons retenu l'algorithme des K-Means (Nuées dynamiques), une méthode de partitionnement non-supervisé privilégiée pour sa robustesse sur des jeux de données de dimension modérée et pour la lisibilité géométrique de ses résultats.
 ## Prétraitement : Centrage et Réduction
 Bien que nous ayons appliqué une transformation logarithmique pour corriger l'asymétrie, les variables d'Incidence et de Mortalité possèdent des plages de variation distinctes. L'algorithme K-Means reposant sur la distance euclidienne isotrope, il est impératif que chaque dimension contribue de manière équitable au calcul de similarité.
 Nous appliquons donc une standardisation (Z-score) : $z = \frac{x - \mu}{\sigma}$
 ```{r}
 data_scaled <- tb_ready |>
  select(log_inc, log_mort) |>
  scale()
 check_table <- data.frame(
  Variable = c("Incidence (Log)", "Mortalité (Log)"),
  Moyenne = apply(data_scaled, 2, mean),
  Ecart_Type = apply(data_scaled, 2, sd)
 )
 kable(
  check_table,
  digits = 2,
  col.names = c("Variable", "Moyenne (Z)", "Écart-Type (Z)"),
  caption = "Validation du Centrage-Réduction",
  booktabs = TRUE
 ) |>
  kable_styling(
    latex_options = c("striped", "hold_position"),
    font_size = 10
  )
 ```
 ## Détermination du nombre de clusters ($k$)
 L'algorithme K-Means nécessite de fixer a priori le nombre de classes k. Ce choix résulte d'un arbitrage entre performance statistique (minimisation de l'inertie intra-classe) et pertinence opérationnelle (interprétabilité métier).
 ### Approche statistique (Méthode du Coude)
 Nous calculons l'inertie intra-classe totale pour des valeurs de k allant de 1 à 10. Le point d'inflexion ("coude") indique le seuil au-delà duquel l'ajout d'un cluster n'apporte plus de gain significatif en compacité. Sur la figure ci-dessous, le coude se situe entre $k=2$ et $k=3$.
 ```{r}
 fviz_nbclust(data_scaled, kmeans, method = "wss") +
  geom_vline(xintercept = 3, linetype = 2, color = "#d73027") +
  labs(
    title = "Optimisation du nombre de clusters",
    x = "Nombre de clusters k",
    y = "Inertie Intra-classe totale"
  ) +
  theme_minimal()
 ```
 ### Arbitrage
 L'analyse graphique révèle une rupture de pente franche à $k=3$, seuil au-delà duquel les gains d'inertie deviennent marginaux (rendements décroissants). Ce choix statistique est corroboré par une pertinence opérationnelle majeure : une segmentation ternaire permet d'adopter une logique de signalisation intuitive type Traffic Light (Vert/Contrôle, Orange/Surveillance, Rouge/Critique). Nous retenons donc $k=3$ afin de garantir des clusters à la fois statistiquement denses et immédiatement actionnables par les décideurs.
 ## Paramétrage et Exécution de l'algorithme
 L'algorithme K-Means étant sensible à l'initialisation des centroïdes (risque d'optimum local), nous avons configuré une exécution robuste : le modèle opère 25 initialisations aléatoires différentes (`nstart = 25`) pour ne conserver que la partition minimisant l'inertie globale sur les 3 classes définies (`centers = 3`). Enfin, la fixation de la graine aléatoire (`set.seed(123)`) garantit la stricte reproductibilité des résultats présentés.
 ```{r, echo=TRUE}
 set.seed(123)
 km_res <- kmeans(data_scaled, centers = 3, nstart = 25)
 var_totale <- round(km_res$betweenss / km_res$totss * 100, 1)
 ```
 Avec **83,9 % de variance expliquée**, le modèle valide la robustesse statistique de la segmentation ternaire. Ce score élevé traduit une séparation nette des profils épidémiologiques, corroborant ainsi la forte structuration spatiale pressentie lors de l'analyse exploratoire.
 ## Intégration des résultats
 Nous réintégrons les labels de clusters dans le jeu de données principal pour l'analyse.
 ```{r}
 tb_clustered <- tb_ready |>
  mutate(cluster = as.factor(km_res$cluster))
 table(tb_clustered$cluster) |>
  kable(
    col.names = c("Cluster ID", "Nombre d'observations"),
    caption = "Répartition des observations par cluster (k=3)",
    booktabs = TRUE
  ) |>
  kable_styling(latex_options = "hold_position")
 ```
 Le partitionnement étant validé avec 3 classes, nous abordons désormais l'étape de labellisation visant à traduire ces clusters statistiques en profils épidémiologiques intelligibles.
 # Analyse des Profils Épidémiques
 L'analyse mathématique ayant validé la qualité de la partition, nous procédons ici à la caractérisation "métier" des clusters pour les transformer en outils d'aide à la décision.
 ## Caractérisation et Labellisation
 Nous calculons les moyennes d'incidence et de mortalité par groupe, ordonnons les clusters du moins au plus sévère et leur attribuons des étiquettes sémantiques explicites.
 ```{r}
 cluster_stats <- tb_clustered |>
  group_by(cluster) |>
  summarise(
    n_obs = n(),
    mean_inc = mean(e_inc_100k),
    mean_mort = mean(e_mort_exc_tbhiv_100k)
  ) |>
  arrange(mean_inc)
 labels_map <- c("1. Impact Faible", "2. Impact Modéré", "3. Impact Critique")
 tb_clustered <- tb_clustered |>
  mutate(
    rank_severity = match(cluster, cluster_stats$cluster),
    label = factor(labels_map[rank_severity], levels = labels_map)
  )
 tb_clustered |>
  select(country, year, e_inc_100k, label) |>
  head(10) |>
  kable(
    col.names = c("Pays", "Année", "Incidence (pour 100k)", "Classification"),
    digits = 1,
    align = c("l", "c", "r", "l"),
    caption = "Aperçu de la segmentation sanitaire (Échantillon)"
  ) |>
  kable_styling(latex_options = c("striped", "hold_position"))
 ```
 ## Analyse des Profils Épidémiques
 Le tableau ci-dessous synthétise les caractéristiques moyennes de chaque profil type identifié par le modèle.
 ```{r}
 final_summary <- tb_clustered |>
  group_by(label) |>
  summarise(
    `Nombre d'observations` = n(),
    `Incidence Moyenne` = round(mean(e_inc_100k), 0),
    `Mortalité Moyenne` = round(mean(e_mort_exc_tbhiv_100k), 1),
    `Ratio Mort/Inc (%)` = round(
      mean(e_mort_exc_tbhiv_100k) / mean(e_inc_100k) * 100,
      1
    )
  )
 kable(
  final_summary,
  caption = "Typologie des clusters de Tuberculose (k=3)",
  booktabs = TRUE
 ) |>
  kable_styling(latex_options = c("striped", "hold_position"))
 ```
 ### Interprétation de la typologie
 L'analyse des centroïdes révèle une hiérarchisation sanitaire nette. Le cluster **Impact Faible** (`n=1 416`), représentatif des standards occidentaux (Europe, Amérique du Nord), affiche une incidence marginale (14 cas/100k) et une mortalité résiduelle (<1 décès/100k). Le faible ratio de létalité (~6 %) témoigne d'une prise en charge thérapeutique efficace où la maladie est rarement fatale.
 Le cluster **Impact Modéré** (`n=1 570`) regroupe des pays en transition (Maghreb, Amérique Latine) confrontés à une circulation active du bacille (79 cas/100k). Toutefois, la mortalité contenue (7 décès/100k) indique que si le contrôle de la transmission reste un défi, les systèmes de santé parviennent à traiter la majorité des patients diagnostiqués.
 Enfin, le cluster **Impact Critique** (`n=1 546`), centré sur l'Afrique subsaharienne, concentre la charge mondiale avec une incidence massive (374 cas/100k) et une mortalité très élevée (57 décès/100k). Le taux de létalité y atteint un niveau alarmant de 15,3 %, révélant des défaillances systémiques graves (retards de diagnostic, résistances) : dans cette zone, la tuberculose ne se contente pas de circuler, elle tue massivement.
 ## Visualisation de la Segmentation
 La projection des clusters sur le plan bivarié illustre la logique de séparation opérée par l'algorithme.
 ```{r}
 ggplot(
  tb_clustered,
  aes(x = e_inc_100k, y = e_mort_exc_tbhiv_100k, color = label)
 ) +
  geom_point(alpha = 0.5, size = 1.5) +
  scale_x_log10() +
  scale_y_log10() +
  scale_color_manual(values = c("#66bd63", "#fdae61", "#d73027")) +
  labs(
    title = "Projection des Clusters de Risque",
    subtitle = "k=3 : Une segmentation claire du risque sanitaire",
    x = "Incidence (Log scale)",
    y = "Mortalité (Log scale)",
    color = "Niveau de Risque"
  ) +
  theme_minimal() +
  theme(legend.position = "bottom")
 ```
 Le graphique confirme que le score de 83,9 % d'inertie expliquée se traduit visuellement par des frontières nettes entre les groupes, avec très peu de chevauchement. La segmentation en "feux tricolores" est donc statistiquement robuste et opérationnellement pertinente.
 ## Préparation pour l'Application
 Nous sauvegardons le jeu de données final enrichi des labels, qui servira de socle à l'application R Shiny.
 ```{r, echo=TRUE}
 save(tb_clustered, file = "data/TB_analysis_ready.RData")
 ```
 # Application R Shiny
 L'étape finale de ce projet consiste à transformer les résultats de la segmentation (K-Means) en un outil de pilotage interactif. Nous avons développé une application web via le framework R Shiny, permettant aux décideurs de santé publique d'explorer les données, de visualiser les disparités géographiques et de monitorer l'évolution des profils de risque en temps réel.
 ## Architecture technique : Structure UI/Server et flux de données réactif
 Fondée sur une architecture client-serveur réactive, l'application mobilise un écosystème de librairies R spécialisées pour garantir fluidité et interactivité. L'interface utilisateur, structurée de manière modulaire via `shinydashboard`, articule la cartographie vectorielle de `leaflet` avec les graphiques dynamiques du couple `ggplot2` / `plotly` (survol, zoom). En amont, la manipulation des données et le filtrage en temps réel reposent sur la performance des packages `dplyr` et `tidyr`, assurant une réactivité immédiate aux interactions de l'utilisateur.
 ### Flux de Données Réactif
 Le coeur de l'application réside dans son graphe de dépendance réactif qui, contrairement à un script statique, optimise les ressources en ne recalculant les éléments qu'à la demande. Le flux suit une logique séquentielle : toute interaction sur un **Input** (sélection d'une année ou d'un pays) déclenche une **Expression Réactive** chargée de filtrer le jeu de données `tb_clustered`. Ce nouveau sous-ensemble propage alors instantanément la mise à jour vers les **Outputs** (cartes, tableaux et courbes) sans nécessiter de rechargement de la page.
 ## Fonctionnalités décisionnelles :
 L'interface a été conçue pour répondre à trois besoins analytiques majeurs : la vision globale, le suivi temporel et l'analyse comparative.
 ### Cartographie Interactive des Risques (Vision Globale)
 La page d'accueil déploie une carte mondiale interactive (`leaflet`) où chaque pays est coloré selon son cluster d'appartenance : **Vert** (Impact Faible), **Orange** (Modéré) ou **Rouge** (Critique). Cette visualisation offre une lecture immédiate de la géographie sanitaire, permettant d'identifier les foyers épidémiques structurels (telle la ceinture rouge subsaharienne) tout en repérant rapidement les anomalies locales (pays critiques isolés au sein d'une zone préservée).
 ### Monitorage Temporel (Analyse Dynamique)
 Un curseur temporel (Slider Input) permet de naviguer sur la période 2000-2024. L'animation de ce curseur permet de visualiser les transitions de clusters (trajectoires). On peut ainsi observer les succès de certains pays passant du statut "Critique" à "Modéré" suite à l'amélioration de leur système de soins, ou inversement, les dégradations liées à des conflits ou crises sanitaires.
 ### Analyse Comparative
 Un module dédié permet de sélectionner un pays spécifique (ex: Nigeria) pour générer son Bulletin de Santé complet. Celui-ci articule l'affichage des **KPIs clés** (valeurs brutes d'incidence, mortalité, cluster) avec une analyse de **positionnement relatif**. En confrontant la trajectoire du pays sélectionné aux moyennes régionales et mondiales, ce graphique permet d'objectiver sa performance réelle et de déterminer s'il sous-performe par rapport à son voisinage direct, indépendamment de la tendance globale.
 ## Implémentation et logique applicative
 L'application a été développée selon une architecture modulaire, séparant distinctement l'interface utilisateur (Frontend) de la logique de calcul (Backend), conformément au paradigme du framework Shiny.
 ### Stack Technologique et Dépendances
 Le développement repose sur une stack technique optimisée pour l'interactivité. L'orchestration de l'interface est assurée par le couple `shiny` et `shinydashboard`, garantissant une structure modulaire et responsive. La couche géospatiale combine la précision vectorielle de `sf` à la fluidité de rendu de `leaflet`, tandis que la visualisation des résultats exploite les capacités dynamiques de `plotly` (pour les graphiques interactifs) et la puissance de tri de `DT` (pour les tableaux). Enfin, `dplyr` agit comme moteur de calcul en temps réel, assurant le filtrage réactif et l'agrégation instantanée des données en arrière-plan.
 ### Architecture de l'Interface Utilisateur (UI)
 L'interface guide l'utilisateur du général au particulier via une structure en trois volets. Le **Dashboard**, véritable cœur décisionnel, orchestre via une grille fluide l'affichage de KPIs dynamiques, d'une double visualisation interactive (Carte/Nuage de points) et d'un module de comparaison des trajectoires. Il est complété par un **Explorateur de Données** pour l'accès aux chiffres bruts et une section **Méthodologie** garantissant l'auto-portance de l'outil. Transversalement, la navigation latérale assure le pilotage global des graphiques via un filtrage régional et un contrôle temporel animé (2000-2024).
 ### Logique Serveur et Réactivité
 Le script serveur orchestre l'intelligence applicative via deux leviers. D'une part, le **filtrage réactif** optimise la performance : contrairement à une approche statique, les données ne sont chargées qu'une fois puis segmentées dynamiquement par une expression (`filtered_data`) qui joint instantanément le sous-ensemble aux polygones géographiques (`world_map`) à chaque modification des entrées.
 D'autre part, la gestion d'état centralisée permet un **Cross-Filtering** avancé. Une variable réactive (`reactiveVal`), stockant l'identifiant du pays actif, est mise à jour indifféremment par trois interactions distinctes : un clic sur la carte, le nuage de points ou le graphique de densité. Cette interconnexion totale assure une exploration fluide, où l'investigation d'un point aberrant sur un graphique projette immédiatement l'information sur l'ensemble des autres vues.
 ### Rendu Conditionnel et Comparaison 
 Le graphique de tendance (`trend_plot`) transforme la simple série temporelle en un outil d'analyse comparative en construisant dynamiquement trois courbes à la volée : **la trajectoire du pays sélectionné** (mise en évidence), confrontée à la **moyenne de sa région** (calculée en temps réel) et à la **référence mondiale** fixe. Cette logique de calcul à la demande permet ainsi de situer instantanément la performance de n'importe quel territoire vis-à-vis de son contexte géographique immédiat.
 # Exploitation et Analyse des Résultats
 Au-delà de l'implémentation technique, l'application R Shiny permet d'objectiver les dynamiques épidémiologiques mondiales. L'exploration interactive des données (2000-2024) met en lumière trois niveaux de lecture.
 ## Analyse Macroscopique : La fracture Nord-Sud
 La cartographie interactive confirme que la segmentation ternaire obéit à une logique géopolitique structurante. Le **Cluster 1 (Faible Impact - Vert)** se superpose quasi-intégralement aux pays de l'OCDE, caractérisant une maladie devenue résiduelle. Il se distingue du **Cluster 2 (Intermédiaire - Orange)**, véritable zone tampon hétérogène (Amérique Latine, Europe de l'Est) où les infrastructures de santé font face à des défis de résistance. Enfin, le **Cluster 3 (Critique - Rouge)** dessine une ceinture épidémique continue en Afrique Subsaharienne et sur certains foyers asiatiques, dont la superposition avec les zones de forte prévalence du VIH et d'instabilité politique apparaît frappante.
 ## Dynamiques Régionales et Temporelles
 L'outil de monitorage (2000-2024) objective une baisse mondiale de l'incidence à géométrie variable. Tandis que l'**Europe** et les **Amériques** affichent une stagnation ou une décroissance marginale caractéristique d'une épidémie maîtrisée, l'**Afrique** se distingue par la chute la plus rapide en valeur absolue depuis 2010, témoignant du succès des campagnes contre la co-infection TB-VIH. À l'opposé, l'**Asie du Sud-Est** manifeste une inertie inquiétante et demeure, par la densité démographique de l'Inde et de l'Indonésie, le principal réservoir volumique mondial de nouveaux cas.
 ## Cas d'usage : la France
 Pour illustrer la puissance analytique de l'outil, nous prenons le cas de la France. L'analyse du cas français illustre la puissance de l'outil pour situer un territoire. Solidement ancrée dans le **Cluster 1 (Faible Impact)** avec une incidence de 8 cas/100k en 2024, la France affiche une performance remarquable sur trois échelles : elle se situe un facteur 15 sous la moyenne mondiale et surperforme nettement la moyenne européenne (~24 cas/100k), cette dernière étant grevée par les pays de l'Est du Cluster 2. La confrontation avec un représentant du Cluster 3 comme l'Afrique du Sud (> 389 cas/100k) objective une fracture sanitaire vertigineuse : maladie du passé pour l'Hexagone, la tuberculose demeure une urgence vitale ailleurs. Ce diagnostic valide l'efficacité de la stratégie nationale tout en rappelant l'impératif de vigilance face aux risques de réintroduction depuis les zones critiques (Orange et Rouge).
 # Conclusion et Perspectives
 Ce projet s'est attaché à transformer une base de données brute et complexe, issue du rapport mondial de l'OMS, en un outil d'aide à la décision sanitaire opérationnel. En combinant une approche statistique rigoureuse (analyse exploratoire, réduction de dimension) et une modélisation non-supervisée (Clustering K-Means), nous avons pu objectiver les disparités mondiales face à l'épidémie de tuberculose.
 ## Synthèse des résultats
 L'analyse de la période 2000-2024 valide trois enseignements majeurs. D'abord, la **pertinence d'une segmentation ternaire** ($k=3$) qui, forte d'une robustesse statistique de 83,9 %, dépasse le simple clivage Nord-Sud pour cartographier le risque selon une gradation opérationnelle (Faible, Modéré, Critique). Ensuite, la **polarisation de l'épidémie** : le cluster Critique concentre une létalité disproportionnée (> 15 %), dictant un ciblage prioritaire des efforts sur l'Afrique subsaharienne. Enfin, la valeur ajoutée du **monitorage dynamique** : l'application R Shiny a permis d'objectiver la mobilité des trajectoires, identifiant les pays en transition pour fournir des signaux d'alerte précoce ou valider l'efficacité des politiques publiques.
 ## Limites méthodologiques
 Dans une démarche critique, trois limites méthodologiques doivent être soulignées. Premièrement, le **biais déclaratif** persiste malgré l'usage des estimations OMS (`e_`) : les données restent tributaires de la qualité de la surveillance nationale, induisant un paradoxe où l'amélioration du diagnostic peut être confondue avec une dégradation épidémique (hausse mécanique de l'incidence détectée). Deuxièmement, la **parcimonie du modèle**, restreinte à deux variables pour garantir la robustesse, confine l'étude à un rôle descriptif qui occulte les déterminants causaux (pauvreté, VIH). Enfin, la **suppression des données manquantes** (15 % des observations), impérative pour la stabilité du K-Means, rend de facto le modèle inopérant pour les micro-états insulaires exclus.
 ## Perspectives d'évolution
 Pour enrichir cet outil de pilotage, trois axes de développement majeurs se dessinent. D'abord, le passage vers une **modélisation explicative** : l'intégration de variables socio-économiques (PIB, Gini) via une ACP permettrait d'identifier les déterminants structurels du cluster Critique. Ensuite, le déploiement d'une **approche prédictive** (via ARIMA ou Prophet) transformerait ce tableau de bord analytique en outil prospectif, capable d'évaluer l'atteinte des objectifs onusiens à l'horizon 2030. Enfin, l'adoption d'une **granularité infra-nationale** s'avérerait pertinente pour les grands états fédéraux (Brésil, Inde) où la moyenne nationale masque de fortes disparités. En somme, ce projet offre une boussole efficace et constitue la première pierre d'une épidémiologie de précision guidée par la donnée.
 # Déclaration d'Intégrité et Usage de l'IA
 Conformément aux consignes académiques relatives au plagiat et à l'utilisation des assistants numériques, cette section explicite le cadre de réalisation de ce projet.
 ## Originalité de la démarche
 Le jeu de données utilisé (*Global Tuberculosis Report*) est public et largement étudié. Cependant, l'approche développée dans ce projet est originale et personnelle.
 Disposant d'un **profil d'ingénieur logiciel**, j'ai fait le choix stratégique de concentrer mon effort technique sur l'architecture et l'interactivité de l'application **R Shiny** (Section 5), afin de produire un outil de qualité professionnelle. Cette notice technique assure la couverture rigoureuse de la partie Data Science, justifiant les choix mathématiques implémentés dans l'application.
 ## Usage des outils d'IA Générative
 L'utilisation d'outils d'intelligence artificielle générative s'est inscrite dans une démarche d'assistance ponctuelle et rigoureusement contrôlée. Sur le volet **rédactionnel**, l'IA a contribué à l'optimisation syntaxique et à la fluidité des transitions, le raisonnement et les interprétations demeurant strictement personnels. Sur le plan **technique**, elle a servi d'outil de diagnostic pour le débogage de l'application R Shiny (gestion de la réactivité, conflits). L'intégralité du code a été vérifiée et maîtrisée : aucune partie de l'analyse n'a été déléguée sans supervision humaine.
 \newpage
 # Bibliographie
 ## Rapports et Encyclopédies
 - [1] Organisation Mondiale de la Santé (OMS). (2024). Global Tuberculosis Report 2024. Disponible sur : https://www.who.int/teams/global-programme-on-tuberculosis-and-lung-health/tb-reports/global-tuberculosis-report-2024
 - [2] Wikipédia. (s.d.). Tuberculose. Disponible sur : https://fr.wikipedia.org/wiki/Tuberculose
 ## Supports de Cours - Master 2 ISF (2025-2026)
 - [3] Ochoa, J. (2025-2026). *Les algorithmes non supervisés.* Support de cours : Machine Learning. Université Paris-Dauphine - PSL.
 - [4] Bertrand, P. (2025-2026). *K-Means.* Support de cours : Apprentissage non supervisé et clustering. Université Paris-Dauphine - PSL.
 - [5] Guibert, Q. (2025-2026). *Data Visualisation.* Support de cours : Visualisation des données avec R. Université Paris-Dauphine - PSL.
--- a/Visualisation/Project/NoticeTechnique.pdf
+++ b/Visualisation/Project/NoticeTechnique.pdf
--- a/Visualisation/Project/app.R
+++ b/Visualisation/Project/app.R
@@ -1,863 +0,0 @@
 # Chargement des bibliothèques
 library(shiny)
 library(shinydashboard)
 library(leaflet)
 library(plotly)
 library(dplyr)
 library(sf)
 library(RColorBrewer)
 library(DT)
 library(rnaturalearth)
 library(rnaturalearthdata)
 # Chargement des données
 load("data/TB_analysis_ready.RData")
 # Définition des labels pour les clusters
 labels <- c("1. Faible Impact", "2. Impact Modéré", "3. Impact Critique")
 # Application des labels aux données
 tb_clustered$label <- factor(tb_clustered$label)
 levels(tb_clustered$label) <- labels
 # Création de la carte du monde
 world_map <- ne_countries(scale = "medium", returnclass = "sf")
 # Définition des couleurs pour les clusters
 green <- "#66bd63"
 orange <- "#f48a43"
 red <- "#d73027"
 # Interface utilisateur
 ui <- shinydashboard::dashboardPage(
  skin = "black",
  # Header
  dashboardHeader(title = "Tuberculose"),
  # Sidebar
  dashboardSidebar(
    sidebarMenu(
      menuItem(
        "Méthodologie & Définitions",
        tabName = "methodo",
        icon = icon("info-circle")
      ),
      menuItem(
        "Vue d'Ensemble",
        tabName = "dashboard",
        icon = icon("dashboard")
      ),
      menuItem("Données Brutes", tabName = "data", icon = icon("table")),
      # Footer - Informations et crédits
      div(
        style = "position: absolute; bottom: 10px; width: 100%; text-align: center; font-size: 12px; color: #b8c7ce;",
        p("© 2026 Arthur Danjou"),
        p("M2 ISF - Dauphine PSL"),
        p(
          a(
            "Code Source",
            href = "https://go.arthurdanjou.fr/datavis",
            target = "_blank",
            style = "color: #3c8dbc;"
          )
        )
      ),
      hr(),
      # Filtre par Région
      selectInput(
        "region_select",
        "Filtrer par Région :",
        choices = c("Toutes", unique(tb_clustered$g_whoregion)),
        selected = "Toutes"
      ),
      # Sélecteur d'année
      sliderInput(
        "year_select",
        "Année :",
        min = min(tb_clustered$year),
        max = max(tb_clustered$year),
        value = max(tb_clustered$year),
        step = 1,
        sep = "",
        animate = animationOptions(interval = 5000, loop = FALSE)
      )
    )
  ),
  # Body
  dashboardBody(
    tabItems(
      # Page 1 - Vue d'Ensemble
      tabItem(
        tabName = "dashboard",
        # KPI - Total des cas
        fluidRow(
          valueBoxOutput("kpi_total_cases", width = 4),
          valueBoxOutput("kpi_worst_country", width = 4),
          valueBoxOutput("kpi_critical_count", width = 4)
        ),
        # Carte Épidémiologique
        fluidRow(
          box(
            width = 7,
            title = "Carte Épidémiologique",
            status = "primary",
            solidHeader = TRUE,
            radioButtons(
              "metric_select",
              "Indicateur :",
              choices = c(
                "Incidence" = "e_inc_100k",
                "Mortalité" = "e_mort_exc_tbhiv_100k",
                "Clusters K-Means" = "label"
              ),
              inline = TRUE
            ),
            p(
              class = "text-muted",
              "Cliquer sur un point pour filtrer par pays."
            ),
            leafletOutput("map_plot", height = "500px")
          ),
          # Scatter Plot des Clusters
          box(
            width = 5,
            title = "Analyse des Clusters (Incidence vs Mortalité)",
            status = "success",
            solidHeader = TRUE,
            p(
              class = "text-muted",
              style = "font-size:0.9em",
              "Chaque point est un pays. Les couleurs correspondent aux groupes de risque identifiés par l'algorithme K-Means."
            ),
            p(
              class = "text-muted",
              "Cliquer sur un point pour filtrer par pays."
            ),
            plotlyOutput("cluster_scatter", height = "530px")
          )
        ),
        fluidRow(
          # Plot des tendances
          box(
            width = 7,
            title = "Comparaison : Pays vs Moyenne Régionale vs Moyenne Mondiale",
            status = "warning",
            solidHeader = TRUE,
            plotlyOutput("trend_plot", height = "400px")
          ),
          # Distribution des Clusters
          box(
            width = 5,
            title = "Distribution des Clusters",
            status = "info",
            solidHeader = TRUE,
            p(
              class = "text-muted",
              "Cliquer sur un point du rug pour filtrer par pays."
            ),
            plotlyOutput("density_plot", height = "400px")
          )
        )
      ),
      # Page 2 - Données Brutes
      tabItem(
        tabName = "data",
        fluidRow(
          box(
            width = 12,
            title = "Explorateur de Données",
            status = "primary",
            p("Tableau filtrable et exportable des données utilisées."),
            DTOutput("raw_table")
          )
        )
      ),
      # Page 3 - Méthodologie
      tabItem(
        tabName = "methodo",
        fluidRow(
          # Indicateurs OMS
          box(
            width = 12,
            title = "Définitions des Indicateurs OMS",
            status = "info",
            solidHeader = TRUE,
            column(
              width = 6,
              h4(icon("lungs"), "Incidence de la Tuberculose"),
              p(
                "Correspond à la variable ",
                code("e_inc_100k"),
                " dans le jeu de données de l'OMS."
              ),
              p(
                "Il s'agit du nombre estimé de ",
                strong("nouveaux cas"),
                " de tuberculose (toutes formes confondues) survenus au cours d'une année donnée, rapporté pour 100 000 habitants."
              ),
              p(
                "Cet indicateur mesure la ",
                em("propagation"),
                " de la maladie dans la population."
              ),
            ),
            column(
              width = 6,
              h4(icon("skull"), "Mortalité (hors VIH)"),
              p(
                "Correspond à la variable ",
                code("e_mort_exc_tbhiv_100k"),
                "."
              ),
              p(
                "Il s'agit du nombre estimé de décès dus à la tuberculose chez les personnes non infectées par le VIH, rapporté pour 100 000 habitants."
              ),
              p(
                "Cet indicateur mesure la ",
                em("sévérité"),
                " et l'efficacité de l'accès aux soins (un taux élevé signale souvent un système de santé défaillant)."
              )
            ),
          ),
        ),
        # Choix des Variables
        fluidRow(
          column(
            width = 6,
            box(
              width = 12,
              title = "Pourquoi seulement 2 variables actives ?",
              status = "warning",
              solidHeader = TRUE,
              p(
                "Le modèle de clustering repose uniquement sur l'Incidence et la Mortalité. Ce choix de parcimonie est justifié par 4 contraintes techniques :"
              ),
              br(),
              column(
                width = 6,
                h4(
                  icon("ruler-combined"),
                  "1. Robustesse Mathématique",
                  class = "text-warning"
                ),
                p(
                  "Évite le 'fléau de la dimension'. Avec trop de variables, les distances euclidiennes perdent leur sens et les groupes deviennent flous."
                ),
                br(),
                h4(
                  icon("project-diagram"),
                  "2. Non-Colinéarité",
                  class = "text-warning"
                ),
                p(
                  "Évite de compter deux fois la même information (ex: Incidence vs Nombre de cas) qui fausserait le poids des indicateurs."
                ),
              ),
              column(
                width = 6,
                h4(
                  icon("filter"),
                  "3. Qualité des Données",
                  class = "text-warning"
                ),
                p(
                  "Le K-Means ne tolère pas les données manquantes. Ajouter des variables socio-économiques aurait réduit la taille de l'échantillon de 30% à 50%."
                ),
                br(),
                h4(icon("eye"), "4. Lisibilité", class = "text-warning"),
                p(
                  "Permet une visualisation directe en 2D (Scatterplot) sans déformation, rendant l'outil accessible aux non-statisticiens."
                )
              )
            ),
            # Source des Données
            box(
              width = 12,
              title = "Source des Données",
              status = "danger",
              solidHeader = TRUE,
              p(
                icon("database"),
                "Les données sont issues du ",
                a(
                  "Global Tuberculosis Report",
                  href = "https://www.who.int/teams/global-programme-on-tuberculosis-and-lung-health/data",
                  target = "_blank"
                ),
                " de l'Organisation Mondiale de la Santé (OMS)."
              ),
              p("Dernière mise à jour du dataset : Octobre 2024.")
            ),
          ),
          column(
            width = 6,
            box(
              width = 12,
              title = "Algorithme de Classification (Clustering)",
              status = "success",
              solidHeader = TRUE,
              h4("Pourquoi un Clustering K-Means ?"),
              p(
                "Afin de synthétiser l'information et de faciliter la prise de décision, j'ai appliqué un algorithme d'apprentissage non supervisé (K-Means) pour regrouper les pays ayant des profils épidémiques similaires sous 4 clusters."
              ),
              h4("Méthodologie"),
              tags$ul(
                tags$li(
                  strong("Variables :"),
                  " Incidence et Mortalité (centrées et réduites pour assurer un poids équivalent)."
                ),
                tags$li(
                  strong("Nombre de Clusters (k) :"),
                  " Fixé à 3 pour obtenir une segmentation tricolore lisible (Faible, Modéré, Critique)."
                ),
                tags$li(
                  strong("Stabilité :"),
                  "Utilisation de `set.seed(123)` pour garantir la reproductibilité des résultats."
                )
              ),
              h4("Interprétation des 3 Groupes"),
              # Tableau des Groupes
              tags$table(
                class = "table table-striped",
                tags$thead(
                  tags$tr(
                    tags$th("Cluster"),
                    tags$th("Description"),
                    tags$th("Profil Type")
                  )
                ),
                tags$tbody(
                  tags$tr(
                    tags$td(span(
                      style = paste0(
                        "background-color:",
                        green,
                        "; color: black; font-weight: bold; padding: 5px; border-radius: 5px;"
                      ),
                      labels[1]
                    )),
                    tags$td("Incidence et mortalité très basses."),
                    tags$td("Europe de l'Ouest, Amérique du Nord")
                  ),
                  tags$tr(
                    tags$td(span(
                      style = paste0(
                        "background-color:",
                        orange,
                        "; color: black; font-weight: bold; padding: 5px; border-radius: 5px;"
                      ),
                      labels[2]
                    )),
                    tags$td("Incidence significative mais mortalité contenue."),
                    tags$td("Amérique Latine, Maghreb, Europe de l'Est")
                  ),
                  tags$tr(
                    tags$td(span(
                      style = paste0(
                        "background-color:",
                        red,
                        "; color: black; font-weight: bold; padding: 5px; border-radius: 5px;"
                      ),
                      labels[3]
                    )),
                    tags$td("Incidence massive et forte létalité."),
                    tags$td("Afrique Subsaharienne, Zones de conflit")
                  )
                )
              )
            ),
            # Code & Documentation
            box(
              title = "Code & Documentation",
              status = "primary",
              solidHeader = TRUE,
              width = 12,
              tags$p(
                "Ce projet suit une approche Open Science.",
                style = "font-style: italic;"
              ),
              tags$p(
                "L'intégralité du code source (Rmd, App) ainsi que la notice technique (PDF) sont disponibles en libre accès sur le dépôt GitHub."
              ),
              tags$a(
                href = "https://go.arthurdanjou.fr/datavis-code",
                target = "_blank",
                class = "btn btn-block btn-social btn-github",
                style = "color: white; background-color: #333; border-color: #333;",
                icon("github"),
                " Accéder au Code"
              )
            )
          ),
        ),
      )
    )
  )
 )
 # Serveur
 server <- function(input, output, session) {
  # Filtrage des données
  filtered_data <- shiny::reactive({
    req(input$year_select)
    data <- tb_clustered |> filter(year == input$year_select)
    if (input$region_select != "Toutes") {
      data <- data |> filter(g_whoregion == input$region_select)
    }
    return(data)
  })
  # Données pour la carte
  map_data_reactive <- shiny::reactive({
    req(filtered_data())
    world_map |> inner_join(filtered_data(), by = c("adm0_a3" = "iso3"))
  })
  # Définition des couleurs pour les clusters
  cluster_colors <- setNames(
    c(green, orange, red),
    labels
  )
  # Fonction pour dessiner les polygones sur la carte
  dessiner_polygones <- function(map_object, data, metric) {
    if (metric == "label") {
      values_vec <- data$label
      pal_fun <- leaflet::colorFactor(
        as.character(cluster_colors),
        domain = names(cluster_colors)
      )
      fill_vals <- pal_fun(values_vec)
      legend_title <- "Cluster"
      label_txt <- paste0(data$name, " - ", data$label)
      legend_vals <- names(cluster_colors)
    } else {
      values_vec <- data[[metric]]
      pal_fun <- leaflet::colorNumeric(
        palette = c(green, orange, red),
        domain = values_vec
      )
      fill_vals <- pal_fun(values_vec)
      legend_title <- "Taux / 100k"
      label_txt <- paste0(data$name, ": ", round(values_vec, 1))
      legend_vals <- values_vec
    }
    map_object |>
      leaflet::addPolygons(
        data = data,
        fillColor = ~fill_vals,
        weight = 1,
        color = ifelse(metric == "label", "gray", "black"),
        fillOpacity = 0.7,
        layerId = ~adm0_a3,
        label = ~label_txt,
        highlightOptions = highlightOptions(
          weight = 3,
          color = "#666",
          bringToFront = TRUE
        )
      ) |>
      leaflet::addLegend(
        pal = pal_fun,
        values = legend_vals,
        title = legend_title,
        position = "bottomright"
      )
  }
  # Carte
  output$map_plot <- leaflet::renderLeaflet({
    isolate({
      data <- map_data_reactive()
      metric <- input$metric_select
      leaflet(options = leafletOptions(minZoom = 2, maxZoom = 6)) |>
        addProviderTiles(
          providers$CartoDB.Positron,
          options = providerTileOptions(noWrap = TRUE)
        ) |>
        setMaxBounds(lng1 = -180, lat1 = -90, lng2 = 180, lat2 = 90) |>
        setView(lat = 20, lng = 0, zoom = 2) |>
        dessiner_polygones(data, metric)
    })
  })
  # KPI - Total des cas
  output$kpi_total_cases <- shinydashboard::renderValueBox({
    data <- filtered_data()
    val <- round(mean(data$e_inc_100k, na.rm = TRUE))
    valueBox(
      val,
      "Incidence Moyenne (cas/100k)",
      icon = icon("chart-area"),
      color = "green"
    )
  })
  # KPI - Pire pays
  output$kpi_worst_country <- shinydashboard::renderValueBox({
    data <- filtered_data()
    worst <- data |>
      arrange(desc(e_inc_100k)) |>
      slice(1)
    if (nrow(worst) > 0) {
      valueBox(
        worst$country,
        paste("Max Incidence :", round(worst$e_inc_100k)),
        icon = icon("exclamation-triangle"),
        color = "red"
      )
    } else {
      valueBox("N/A", "Pas de données", icon = icon("ban"), color = "red")
    }
  })
  # KPI - Pays en phase 'Critique'
  output$kpi_critical_count <- shinydashboard::renderValueBox({
    data <- filtered_data()
    count <- sum(data$label == labels[3], na.rm = TRUE)
    valueBox(
      count,
      "Pays en phase 'Critique'",
      icon = icon("hospital"),
      color = "orange"
    )
  })
  # Plot des tendances
  output$trend_plot <- plotly::renderPlotly({
    req(selected_country())
    country_hist <- tb_clustered |> filter(iso3 == selected_country())
    if (nrow(country_hist) == 0) {
      return(NULL)
    }
    nom_pays <- unique(country_hist$country)[1]
    region_du_pays <- unique(country_hist$g_whoregion)[1]
    region_benchmark <- tb_clustered |>
      filter(g_whoregion == region_du_pays) |>
      group_by(year) |>
      summarise(mean_inc = mean(e_inc_100k, na.rm = TRUE))
    global_benchmark <- tb_clustered |>
      group_by(year) |>
      summarise(mean_inc = mean(e_inc_100k, na.rm = TRUE))
    p <- ggplot() +
      geom_area(
        data = country_hist,
        aes(
          x = year,
          y = e_inc_100k,
          group = 1,
          text = paste0("<b>Pays : ", nom_pays, "</b>")
        ),
        fill = red,
        alpha = 0.1
      ) +
      geom_line(
        data = region_benchmark,
        aes(
          x = year,
          y = mean_inc,
          group = 1,
          color = "Moyenne Régionale",
          text = paste0(
            "<b>Moyenne ",
            region_du_pays,
            "</b><br>Année : ",
            year,
            "<br>Incidence : ",
            round(mean_inc, 1)
          )
        ),
        size = 0.5,
        linetype = "dashed"
      ) +
      geom_line(
        data = global_benchmark,
        aes(
          x = year,
          y = mean_inc,
          group = 1,
          color = "Moyenne Mondiale",
          text = paste0(
            "<b>Moyenne Mondiale</b><br>Année : ",
            year,
            "<br>Incidence : ",
            round(mean_inc, 1)
          )
        ),
        size = 0.75,
        linetype = "dashed"
      ) +
      geom_line(
        data = country_hist,
        aes(
          x = year,
          y = e_inc_100k,
          group = 1,
          color = "Pays Sélectionné",
          text = paste0(
            "<b>Pays : ",
            nom_pays,
            "</b><br>Incidence : ",
            round(e_inc_100k, 1),
            "<br>Mortalité : ",
            round(e_mort_exc_tbhiv_100k, 1)
          )
        ),
        size = 1
      ) +
      geom_vline(
        xintercept = as.numeric(input$year_select),
        linetype = "dotted",
        color = "black",
        alpha = 0.6
      ) +
      scale_color_manual(
        name = "",
        values = c(
          "Moyenne Régionale" = "grey30",
          "Pays Sélectionné" = red,
          "Moyenne Mondiale" = "orange"
        )
      ) +
      labs(
        title = paste(
          "Trajectoire :",
          nom_pays,
          "vs",
          region_du_pays,
          "vs Monde"
        ),
        x = "Année",
        y = "Incidence (pour 100k)"
      ) +
      theme_minimal() +
      theme(legend.position = "bottom")
    ggplotly(p, tooltip = "text") |>
      layout(
        legend = list(orientation = "h", x = 0.1, y = -0.2),
        hovermode = "x unified"
      )
  })
  # Densité des cas
  output$density_plot <- plotly::renderPlotly({
    data <- filtered_data()
    sel_iso <- selected_country()
    highlight_data <- data |> filter(iso3 == sel_iso)
    p <- ggplot(data, aes(x = e_inc_100k, fill = label)) +
      geom_density(
        aes(text = paste0("<b>Cluster : </b>", label)),
        alpha = 0.6,
        color = NA
      ) +
      geom_rug(
        aes(
          color = label,
          customdata = iso3,
          text = paste0(
            "<b>Pays : </b>",
            country,
            "<br><b>Incidence : </b>",
            round(e_inc_100k),
            " (cas/100k)<br><b>Cluster : </b>",
            label
          )
        ),
        sides = "b",
        length = unit(0.2, "npc"),
        size = 1.2,
        alpha = 0.9
      ) +
      geom_point(
        data = highlight_data,
        aes(
          x = e_inc_100k,
          y = 0,
          text = paste0(
            "<b>PAYS SÉLECTIONNÉ</b><br><b>",
            country,
            "</b><br>Incidence : ",
            round(e_inc_100k)
          )
        ),
        color = "black",
        fill = "white",
        shape = 21,
        size = 4
      ) +
      scale_fill_manual(values = cluster_colors) +
      scale_color_manual(values = cluster_colors) +
      scale_x_log10() +
      labs(
        title = "Distribution des Risques",
        x = "Incidence (Échelle Log)",
        y = NULL
      ) +
      theme_minimal() +
      theme(
        legend.position = "none",
        axis.text.y = element_blank(),
        axis.ticks.y = element_blank(),
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank()
      )
    ggplotly(p, tooltip = "text", source = "density_click") |>
      layout(hovermode = "closest")
  })
  # Nuage de points des clusters
  output$cluster_scatter <- plotly::renderPlotly({
    data <- filtered_data()
    sel_iso <- selected_country()
    highlight_data <- data |> filter(iso3 == sel_iso)
    p <- ggplot(data, aes(x = e_inc_100k, y = e_mort_exc_tbhiv_100k)) +
      geom_point(
        aes(
          color = label,
          customdata = iso3,
          text = paste(
            "Pays:",
            country,
            "<br>Cluster:",
            label,
            "<br>Pop:",
            round(e_pop_num / 1e6, 1),
            "M",
            "<br>Incidence:",
            round(e_inc_100k),
            "<br>Mortalité:",
            round(e_mort_exc_tbhiv_100k)
          )
        ),
        size = 3,
        alpha = 0.6
      ) +
      geom_point(
        data = highlight_data,
        aes(
          fill = label,
          text = paste(
            "<b>PAYS SÉLECTIONNÉ</b>",
            "<br>Pays:",
            country,
            "<br>Cluster:",
            label,
            "<br>Incidence:",
            round(e_inc_100k),
            "<br>Mortalité:",
            round(e_mort_exc_tbhiv_100k)
          )
        ),
        shape = 21,
        color = "black",
        stroke = 1,
        size = 5,
        alpha = 1,
        show.legend = FALSE
      ) +
      scale_x_log10() +
      scale_y_log10() +
      scale_color_manual(values = cluster_colors) +
      scale_fill_manual(values = cluster_colors) +
      labs(title = "Incidence vs Mortalité", x = "Incidence", y = "Mortalité") +
      theme_minimal() +
      theme(legend.position = "bottom")
    ggplotly(p, tooltip = "text", source = "scatter_click")
  })
  # Tableau des données brutes
  output$raw_table <- DT::renderDT({
    data <- filtered_data() |>
      select(
        country,
        year,
        g_whoregion,
        e_inc_100k,
        e_mort_exc_tbhiv_100k,
        label
      )
    datatable(
      data,
      rownames = FALSE,
      options = list(pageLength = 15, scrollX = TRUE),
      colnames = c(
        "Pays",
        "Année",
        "Région",
        "Incidence",
        "Mortalité",
        "Cluster"
      )
    )
  })
  # Mise à jour de la carte
  shiny::observe({
    data <- map_data_reactive()
    metric <- input$metric_select
    leafletProxy("map_plot", data = data) |>
      clearShapes() |>
      clearControls() |>
      dessiner_polygones(data, metric)
  })
  # Sélection du pays
  selected_country <- shiny::reactiveVal("FRA")
  # Sélection du pays sur la carte
  shiny::observeEvent(input$map_plot_shape_click, {
    click <- input$map_plot_shape_click
    if (!is.null(click$id)) {
      selected_country(click$id)
    }
  })
  # Sélection du pays dans le nuage de points
  shiny::observeEvent(event_data("plotly_click", source = "scatter_click"), {
    click_info <- event_data("plotly_click", source = "scatter_click")
    if (!is.null(click_info$customdata)) {
      selected_country(click_info$customdata)
    }
  })
  # Sélection du pays dans la densité
  shiny::observeEvent(event_data("plotly_click", source = "density_click"), {
    click_info <- event_data("plotly_click", source = "density_click")
    if (!is.null(click_info$customdata)) {
      selected_country(click_info$customdata)
    }
  })
 }
 # Lancement de l'application Shiny
 shiny::shinyApp(ui, server)
--- a/Visualisation/Project/data/TB_analysis_ready.RData
+++ b/Visualisation/Project/data/TB_analysis_ready.RData
--- a/Visualisation/Project/data/TB_burden_countries_2025-12-09.csv
+++ b/Visualisation/Project/data/TB_burden_countries_2025-12-09.csv
--- a/Visualisation/Project/logo_dauphine.jpg
+++ b/Visualisation/Project/logo_dauphine.jpg
--- a/Visualisation/Project/renv.lock
+++ b/Visualisation/Project/renv.lock
--- a/Learning/project/Project_RL_DANJOU_VON-SIEMENS.ipynb
+++ b/Learning/project/Project_RL_DANJOU_VON-SIEMENS.ipynb
--- a/Learning/project/README.md
+++ b/Learning/project/README.md
@@ -1,91 +0,0 @@
 # RL Project: Atari Tennis Tournament
 Comparison of Reinforcement Learning algorithms on Atari Tennis (`ALE/Tennis-v5` via Gymnasium/PettingZoo).
 ## Overview
 This project implements and compares five RL agents playing Atari Tennis against the built-in AI and in head-to-head tournaments.
 ## Algorithms
 | Agent | Type | Policy | Update Rule |
 |-------|------|--------|-------------|
 | **Random** | Baseline | Uniform random | None |
 | **SARSA** | TD(0), on-policy | ε-greedy | $W_a \leftarrow W_a + \alpha \cdot (r + \gamma \hat{q}(s', a') - \hat{q}(s, a)) \cdot \phi(s)$ |
 | **Q-Learning** | TD(0), off-policy | ε-greedy | $W_a \leftarrow W_a + \alpha \cdot (r + \gamma \max_{a'} \hat{q}(s', a') - \hat{q}(s, a)) \cdot \phi(s)$ |
 | **Monte Carlo** | First-visit MC | ε-greedy | $W_a \leftarrow W_a + \alpha \cdot (G_t - \hat{q}(s, a)) \cdot \phi(s)$ |
 | **DQN** | Deep Q-Network | ε-greedy | MLP (256→256) with experience replay & target network |
 ## Architecture
 - **Linear agents** (SARSA, Q-Learning, Monte Carlo): $\hat{q}(s, a; \mathbf{W}) = \mathbf{W}_a^\top \phi(s)$ with $\phi(s) \in \mathbb{R}^{128}$ (RAM observation)
 - **DQN**: MLP network (128 → 128 → 64 → 18) trained with Adam optimizer, Huber loss, and periodic target network sync
 ## Environment
 - **Game**: Atari Tennis via PettingZoo (`tennis_v3`)
 - **Observation**: RAM state (128 features)
 - **Action Space**: 18 discrete actions
 - **Agents**: 2 players (`first_0` and `second_0`)
 ## Project Structure
 ```
 .
 ├── Project_RL_DANJOU_VON-SIEMENS.ipynb   # Main notebook
 ├── README.md                              # This file
 ├── checkpoints/                           # Saved agent weights
 │   ├── sarsa.pkl
 │   ├── q_learning.pkl
 │   ├── montecarlo.pkl
 │   └── dqn.pkl
 └── plots/                                 # Training & evaluation plots
    ├── SARSA_training_curves.png
    ├── Q-Learning_training_curves.png
    ├── MonteCarlo_training_curves.png
    ├── DQN_training_curves.png
    ├── evaluation_results.png
    └── championship_matrix.png
 ```
 ## Key Results
 ### Win Rate vs Random Baseline
 | Agent | Win Rate |
 |-------|----------|
 | SARSA | 88.9% |
 | Q-Learning | 41.2% |
 | Monte Carlo | 47.1% |
 | DQN | 6.2% |
 ### Championship Tournament
 Full round-robin tournament where each agent faces every other agent in both positions (first_0/second_0).
 ## Notebook Sections
 1. **Configuration & Checkpoints** — Incremental training workflow with pickle serialization
 2. **Utility Functions** — Observation normalization, ε-greedy policy
 3. **Agent Definitions** — `RandomAgent`, `SarsaAgent`, `QLearningAgent`, `MonteCarloAgent`, `DQNAgent`
 4. **Training Infrastructure** — `train_agent()`, `plot_training_curves()`
 5. **Evaluation** — Match system, random baseline, round-robin tournament
 6. **Results & Visualization** — Win rate plots, matchup matrix heatmap
 ## Known Issues
 - **Monte Carlo & DQN**: Checkpoint loading issues — saved weights may not restore properly during evaluation (training works correctly)
 ## Dependencies
 - Python 3.13+
 - `numpy`, `matplotlib`
 - `torch`
 - `gymnasium`, `ale-py`
 - `pettingzoo`
 - `tqdm`
 ## Authors
 - Arthur DANJOU
 - Moritz VON SIEMENS
--- a/Learning/project/checkpoints/dqn.pkl
+++ b/Learning/project/checkpoints/dqn.pkl
--- a/Learning/project/checkpoints/montecarlo.pkl
+++ b/Learning/project/checkpoints/montecarlo.pkl
--- a/Learning/project/checkpoints/q_learning.pkl
+++ b/Learning/project/checkpoints/q_learning.pkl
--- a/Learning/project/checkpoints/sarsa.pkl
+++ b/Learning/project/checkpoints/sarsa.pkl
--- a/Learning/project/plots/DQN_training_curves.png
+++ b/Learning/project/plots/DQN_training_curves.png
--- a/Learning/project/plots/MonteCarlo_training_curves.png
+++ b/Learning/project/plots/MonteCarlo_training_curves.png
--- a/Learning/project/plots/Q-Learning_training_curves.png
+++ b/Learning/project/plots/Q-Learning_training_curves.png
--- a/Learning/project/plots/SARSA_training_curves.png
+++ b/Learning/project/plots/SARSA_training_curves.png
--- a/Learning/project/plots/championship_matrix.png
+++ b/Learning/project/plots/championship_matrix.png
--- a/Learning/project/plots/championship_results.png
+++ b/Learning/project/plots/championship_results.png
--- a/Learning/project/plots/evaluation_results.png
+++ b/Learning/project/plots/evaluation_results.png
--- a/M2/VBA/Course1.xlsm
+++ b/M2/VBA/Course1.xlsm
--- a/README.md
+++ b/README.md
@@ -37,7 +37,6 @@
 | Course | Description |
 |--------|-------------|
 | `Advanced Machine Learning` | Advanced ML techniques |
 | `Classification and Regression` | Supervised learning methods |
 | `Clustering In Practice` | Unsupervised learning and clustering |
 | `Data Visualisation` | Data visualization principles and tools |
 | `Deep Learning` | Neural networks and deep architectures |
@@ -51,7 +50,6 @@
 | `Statistiques Non Paramétrique` | Non-parametric statistics |
 | `Time Series` | Time series analysis and forecasting |
 | `Unsupervised Learning` | Unsupervised learning methods |
 | `VBA` | Visual Basic for Applications |
 ## 🛠️ Technologies & Tools
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,34 +5,31 @@ description = "A curated collection of mathematics and data science projects dev
 readme = "README.md"
 requires-python = ">= 3.12,<3.14"
 dependencies = [
    "ale-py>=0.11.2",
    "catboost>=1.2.10",
    "ipykernel>=7.2.0",
    "ipywidgets>=8.1.8",
    "langchain>=1.2.10",
-    "langchain-core>=1.2.17",
+    "langchain-core>=1.2.18",
    "langchain-huggingface>=1.2.1",
    "langchain-mistralai>=1.1.1",
    "matplotlib>=3.10.8",
-    "numpy>=2.4.2",
+    "numpy>=2.4.3",
    "opencv-python>=4.13.0.92",
    "pandas>=3.0.1",
    "pandas-stubs>=3.0.0.260204",
    "pettingzoo[atari]>=1.24.3",
    "plotly>=6.6.0",
    "scikit-learn>=1.8.0",
    "scipy>=1.17.1",
    "seaborn>=0.13.2",
    # "sequenzo>=0.1.20",
-    "shap>=0.50.0",
+    "shap>=0.51.0",
    "spacy>=3.8.11",
-    "tensorflow>=2.20.0",
+    "tensorflow>=2.21.0",
    "tf-keras>=2.20.1",
    "tiktoken>=0.12.0",
    "todoist-python>=8.1.4",
    "torch>=2.10.0",
    "umap-learn>=0.5.11",
-    "uv>=0.10.7",
+    "uv>=0.10.9",
    "xgboost>=3.2.0",
 ]
--- a/uv.lock
+++ b/uv.lock
@@ -3,12 +3,12 @@ revision = 3
 requires-python = ">=3.12, <3.14"
 resolution-markers = [
    "python_full_version >= '3.13' and sys_platform == 'win32'",
    "python_full_version >= '3.13' and sys_platform == 'emscripten'",
    "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform == 'darwin'",
    "(python_full_version >= '3.13' and platform_machine != 'x86_64' and sys_platform == 'darwin') or (python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32')",
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 [[package]]
Author	SHA1	Message	Date
Arthur DANJOU	28e4e72c28	Refactor code structure and remove redundant sections for improved readability and maintainability	2026-03-10 12:23:50 +01:00
Arthur DANJOU	b468e08350	Remove outdated project documentation and heart dataset files	2026-03-10 12:08:37 +01:00
Arthur DANJOU	658c0d3072	Edit uv.lock	2026-03-10 12:08:19 +01:00
Arthur DANJOU	895463e9e9	Refactor code structure for improved readability and maintainability	2026-03-10 11:39:11 +01:00