maj

Module_correlation.Rmd

@@ -3,32 +3,6 @@ title: '"Module d''analyse des corrélations des annotations des échantillons"'
 output: html_document
 ---
 
-
-```{r input_parameters, include=FALSE}
-# Chemin  vers le fichier de design
-design_file <- "C:/Users/User/Desktop/projet_visualisation/design_WS3.csv"
-
-# Liste des noms de variables à considérer comme catégorielles.
-categorical_vars <- c("sample", "condition", "animal", "experiment", "extraction")
-
-# Liste des noms de variables à considérer comme quantitatives.
-quantitative_vars <- c( "volume", "quantity")
-
-# Liste des noms de variables à afficher pour la figure "matrice de plots" (advanced pairs plot).
-# Ici, on exclut par exemple 'sample' pour ne pas surcharger la visualisation.
-display_vars <- c("condition", "animal", "experiment", "extraction")
-```
-
-
-```{r setup, include=FALSE}
-
-# Global options: hide code, warnings and messages in the final report.
-knitr::opts_chunk$set(echo = FALSE, warning = FALSE, message = FALSE)
-
-```
-
-
-
 ```{r load_packages, include=FALSE}
 # Installer et charger les packages nécessaires 
 if(!require(GGally)) install.packages("GGally")
@@ -60,50 +34,60 @@ library(DT)
 library(plotly)
 library(lsr)  
 library(knitr)
+```
 
+```{r setup, include=FALSE}
 
-
-
+# Global options: hide code, warnings and messages in the final report.
+knitr::opts_chunk$set(echo = FALSE, warning = FALSE, message = FALSE)
 
 ```
 
 
-1. Data Import and Exploration
-```{r}
-# Import the design file (CSV)
+## 1. Data Import and Interactive Overview
+
+```{r data_import, echo=FALSE}
+# Import the design file (CSV) using the path specified in the input parameters.
 annotations <- read.csv(design_file, sep = ",", stringsAsFactors = FALSE)
 
-# Convert specified categorical variables to factors.
-annotations <- annotations %>%
-  mutate(across(all_of(categorical_vars), as.factor))
+# Display an interactive table to review all variables in the dataset.
+DT::datatable(annotations,
+              options = list(pageLength = 10, autoWidth = TRUE),
+              caption = "Interactive Data Table: Review the design file")
 
-# Overview of data structure and summary.
-glimpse(annotations)
-summary(annotations)
 
-# Check missing values per column.
-missing_values <- colSums(is.na(annotations))
-print(missing_values)
+```
 
 
-```
 
-2. Variable Separation and Conversion
+### Manual Data Classification and Modification
 
+## 2. Manual Data Classification and Modification
 
-```{r}
-# Variables numériques d'origine (pour certaines analyses)
-num_data <- annotations %>% select(where(is.numeric))
+```{r data_classification, echo=TRUE}
+# After reviewing the dataset via the interactive table, you can decide which variables 
+# should be treated as categorical, which as quantitative, and which ones to display 
+# in the advanced pairs plot.
 
-# Variables catégorielles d'origine (caractère ou facteur)
-cat_data <- annotations %>% select(where(~ is.character(.) | is.factor(.)))
 
-cat("Variables numériques détectées : ", paste(colnames(num_data), collapse = ", "), "\n")
-cat("Variables catégorielles détectées : ", paste(colnames(cat_data), collapse = ", "), "\n")
+# Print the default classification lists defined in the input parameters:
+cat("Default Categorical Variables: ", paste(categorical_vars, collapse = ", "), "\n")
+cat("Default Quantitative Variables: ", paste(quantitative_vars, collapse = ", "), "\n")
+cat("Default Variables for Advanced Pairs Plot: ", paste(display_vars, collapse = ", "), "\n")
 
+# If needed, manually modify the classification lists below:
+# (For example, if you decide that a variable should be reclassified, update the lists here.)
+# categorical_vars <- c("sample", "condition", "animal", "experiment", "extraction")
+# quantitative_vars <- c("volume", "quantity")
+# display_vars <- c("condition", "animal", "experiment", "extraction")
 
 ```
 
+
+
+
+
+
 2.1 Automated Conversion for Correlation Analysis
 
 ```{r}