diff --git a/module2/exo1/toy_document_en.Rmd b/module2/exo1/toy_document_en.Rmd
index db540b403c29bfbf9bb5ee45e657c0ed94ba1df6..7e65b6718e2d56387c27ac1091c63b5c123dbe7c 100644
--- a/module2/exo1/toy_document_en.Rmd
+++ b/module2/exo1/toy_document_en.Rmd
@@ -5,22 +5,20 @@ date: "November 1st, 2022"
 output: html_document
 ---
 
-
 ```{r setup, include=FALSE}
 knitr::opts_chunk$set(echo = TRUE)
 ```
 
-
 ## Asking the maths library
-My computer tells me that $\pi$ is approximatively
-```{r pi}
+My computer tells me that $\pi$ is *approximatively*
+```{r}
 pi
 ```
 
 
-##Buffon’s needle
-Applying the method of [Buffon’s needle](https://en.wikipedia.org/wiki/Buffon%27s_needle_problem), we get the approssimazione
-```{r Buffon}
+## Buffon's needle
+Applying the method of [Buffon's needle](https://en.wikipedia.org/wiki/Buffon%27s_needle_problem), we get the __approximation__
+```{r}
 set.seed(42)
 N = 100000
 x = runif(N)
@@ -29,30 +27,20 @@ theta = pi/2*runif(N)
 ```
 
 
-##Using a surface fraction argument
-A method that is easier to understand and does not make use of the $\sin$ function is based on the fact that if $X \sim U(0,1)$ and $Y \sim U(0,1)$, then $P[X^2+Y^2 \le 1]=\pi / 4 (see [“Monte Carlo method” on Wikipedia](https://en.wikipedia.org/wiki/Monte_Carlo_method)). The following code uses this approach:
-```{r surface-frac}
+## Using a surface fraction argument
+A method that is easier to understand and does not make use of the $\sin$ function is based on the fact that if $X\sim U(0,1)$ and $Y\sim U(0,1)$, then $P[X^2+Y^2 \leq 1]=\pi/4$ (see ["Monte Carlo method" on Wikipedia](https://en.wikipedia.org/wiki/Monte_Carlo_method)). The following code uses this approach:
+
+```{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()
+ggplot(df, aes(x=X,y=Y,color=Accept)) 
++ geom_point(alpha=.2) + coord_fixed() 
++ theme_bw()
 ```
-It is therefore straightforward to obtain a (not really good) approximation to \pi by counting how many times, on average, $X^2 + Y^2$ is smaller than 1 :
-```{r surface-frac2}
+It is then straightforward to obtain a (not really good) approximation to $\pi$ by counting how many times, on average, $X^2 + Y^2$ is smaller than 1 :
+```{r}
 4*mean(df$Accept)
-```
-
-
-It is also straightforward to include figures. For example:
-
-```{r pressure, echo=FALSE}
-plot(pressure)
-```
-
-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.
-
-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.
-
-Now it's your turn! You can delete all this information and replace it by your computational document.
+```
\ No newline at end of file