From d2aaa064f216ea5f7593c09cd90ade8bde33dd8e Mon Sep 17 00:00:00 2001
From: cff7102486a296f3268a16457e94c80a
 <cff7102486a296f3268a16457e94c80a@app-learninglab.inria.fr>
Date: Wed, 9 Jul 2025 11:13:42 +0000
Subject: [PATCH] Update toy_document_fr.Rmd

---
 module2/exo1/toy_document_fr.Rmd | 54 +++++++++-----------------------
 1 file changed, 15 insertions(+), 39 deletions(-)

diff --git a/module2/exo1/toy_document_fr.Rmd b/module2/exo1/toy_document_fr.Rmd
index bbfa405..13b258d 100644
--- a/module2/exo1/toy_document_fr.Rmd
+++ b/module2/exo1/toy_document_fr.Rmd
@@ -1,57 +1,33 @@
 ---
-title: "Calcul de Pi"
-author: "Andriambola"
-date: "09/07/2025"
+title: "Your title"
+author: "Your name"
+date: "Today's date"
 output: html_document
 ---
 
+
 ```{r setup, include=FALSE}
 knitr::opts_chunk$set(echo = TRUE)
 ```
 
-# À propos du calcul de pi
-
-*Arnaud Legrand 25 juin 2018*
+## Some explanations
 
-# En demandant à la lib maths
+This is an R Markdown document that you can easily export to HTML, PDF, and MS Word formats. For more information on R Markdown, see <http://rmarkdown.rstudio.com>.
 
-Mon ordinateur m'indique que $\pi$ vaut *approximativement* :
+When you click on the button **Knit**, the document will be compiled in order to re-execute the R code and to include the results into the final document. As we have shown in the video, R code is inserted as follows:
 
-```{r}
-pi
+```{r cars}
+summary(cars)
 ```
 
-# En utilisant la méthode des aiguilles de Buffon
-
-Mais calculé avec la **méthode** des [aiguilles de Buffon](https://fr.wikipedia.org/wiki/Aiguille_de_Buffon) :
-
-```{r}
-
+It is also straightforward to include figures. For example:
 
-
-set.seed(42)
-N = 100000
-x = runif(N)
-theta = pi:2*runif(N)
-2/(mean(x=sin(theta)>1))
+```{r pressure, echo=FALSE}
+plot(pressure)
 ```
 
-# Avec un argument "fréquentiel" de surface
-
-Sinon, ume méthode plus simple à comprendre et ne faisant pas intervenir d'appel à la fonction sinus se base sur le fait que $X \sim {\sf U}(0, 1)$ et $Y \sim {\sf U}(0, 1)$ alors \$P [X\^{2} + Y\^{2} \$\le 1] = \$\pi/4 (voir [méthode de Monte Carlo sur Wikipedia](https://fr.wikipedia.org/wiki/M%C3%A9thode_de_Monte-Carlo#D%C3%A9termination_de_la_valeur_de_%CF%80)). Le code suivant illustre ce fait:
-
-```{r}
-set.seed(42)
-N = 1000
-df = data.frame(X = runif(N), Y = runif(N))
-df$Accept = (df$X**2 + df$Y**2 <=1)
-library(ggplot2)
-ggplot(df, aes(x=X,y=Y,color=Accept)) + geom_point(alpha=.2) + coord_fixed() + theme_bw()
-
-```
+Note the parameter `echo = FALSE` that indicates that the code will not appear in the final version of the document. We recommend not to use this parameter in the context of this MOOC, because we want your data analyses to be perfectly transparent and reproducible.
 
-Il est alors aisé d'obtenir une approximation (pas terrible) de \$\pi en comptant combien de fois, en moyenne, X\^{2} + Y\^{2} est inférieur à 1:
+Since the results are not stored in Rmd files, you should generate an HTML or PDF version of your exercises and commit them. Otherwise reading and checking your analysis will be difficult for anyone else but you.
 
-```{r}
-4*mean(df$Accept)
-`
+Now it's your turn! You can delete all this information and replace it by your computational document.
-- 
2.18.1