diff --git a/module2/exo1/toy_document_en.Rmd b/module2/exo1/toy_document_en.Rmd
index 4132751c503eb3773ecc8a74e3d458dd9071218f..8bf06f5bfd5af8f649a61505af939fe1ed04a4e8 100644
--- a/module2/exo1/toy_document_en.Rmd
+++ b/module2/exo1/toy_document_en.Rmd
@@ -47,3 +47,59 @@ It is then straightforward to obtain a (not really good) approximation to $\pi$
 ```{r}
 4*mean(df$Accept)
 ```
+
+```{r}
+
+data <- c(
+  14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0  
+)
+
+```
+
+```{r}
+mean_value <- mean(data)
+mean_value
+```
+
+```{r}
+
+minimum <- min(data)
+minimum
+
+```
+
+```{r}
+maximum <- max(data)
+maximum
+```
+
+\
+
+```{r}
+
+median <- median(data)
+median
+```
+
+\
+
+```{r}
+sd <- sd(data)
+sd
+
+```
+
+```{r}
+# Line plot
+plot(data, type = "o", col = "blue", 
+     xlab = "Index", ylab = "Value", 
+     main = "Line Plot of Data")
+```
+
+```{r}
+# Histogram
+hist(data, breaks = 10, col = "lightgreen", 
+     xlab = "Value", main = "Histogram of Data")
+```
+
+\