diff --git a/module2/exo1/toy_document_orgmode_R_en.org b/module2/exo1/toy_document_orgmode_R_en.org
index 2b73d64e82dad485ab562f8bda6434ffa7b68d27..a232e76e72a97b5542969bac63110a08c6e4b880 100644
--- a/module2/exo1/toy_document_orgmode_R_en.org
+++ b/module2/exo1/toy_document_orgmode_R_en.org
@@ -1,6 +1,6 @@
-#+TITLE:  Your title
-#+AUTHOR: Your name
-#+DATE:   Today's date
+#+TITLE:  On the computation of pi
+#+AUTHOR: Louis Boulanger
+#+DATE:   October 13th 2020
 #+LANGUAGE: en
 # #+PROPERTY: header-args :eval never-export
 
@@ -11,71 +11,55 @@
 #+HTML_HEAD: <script type="text/javascript" src="http://www.pirilampo.org/styles/lib/js/jquery.stickytableheaders.js"></script>
 #+HTML_HEAD: <script type="text/javascript" src="http://www.pirilampo.org/styles/readtheorg/js/readtheorg.js"></script>
 
-* Some explanations
-
-This is an org-mode document with code examples in R.  Once opened in
-Emacs, this document can easily be exported to HTML, PDF, and Office
-formats. For more information on org-mode, see
-https://orgmode.org/guide/.
-
-When you type the shortcut =C-c C-e h o=, this document will be
-exported as HTML. All the code in it will be re-executed, and the
-results will be retrieved and included into the exported document. If
-you do not want to re-execute all code each time, you can delete the #
-and the space before ~#+PROPERTY:~ in the header of this document.
-
-Like we showed in the video, R code is included as follows (and is
-exxecuted by typing ~C-c C-c~):
+* Asking the maths library
 
+My computer tells me that $\pi$ is /approximately/
 #+begin_src R :results output :exports both
-print("Hello world!")
+pi
 #+end_src
 
 #+RESULTS:
-: [1] "Hello world!"
+: [1] 3.141593
 
-And now the same but in an R session. This is the most frequent
-situation, because R is really an interactive language. With a
-session, R's state, i.e. the values of all the variables, remains
-persistent from one code block to the next. The code is still executed
-using ~C-c C-c~.
+* Buffon's needle
+Applying the method of [[https://en.wikipedia.org/wiki/Buffon%27s_needle_problem][Buffon's needle]], we get the *approximation*
 
-#+begin_src R :results output :session *R* :exports both
-summary(cars)
+#+begin_src R :results output :exports both
+set.seed(42)
+N = 100000
+x = runif(N)
+theta = pi/2*runif(N)
+2/(mean(x+sin(theta)>1))
 #+end_src
 
 #+RESULTS:
-:      speed           dist       
-:  Min.   : 4.0   Min.   :  2.00  
-:  1st Qu.:12.0   1st Qu.: 26.00  
-:  Median :15.0   Median : 36.00  
-:  Mean   :15.4   Mean   : 42.98  
-:  3rd Qu.:19.0   3rd Qu.: 56.00  
-:  Max.   :25.0   Max.   :120.00
-
-Finally, an example for graphical output:
-#+begin_src R :results output graphics :file "./cars.png" :exports results :width 600 :height 400 :session *R* 
-plot(cars)
+: [1] 3.14327
+
+* Using a surface fraction argument
+A method that is easier to understand and does not make use of the sin
+function is base on the fact that if $X \sim U(0,1)$ and $Y \sim
+U(0,1)$, then $P\left[X^2 + Y^2 \le 1 \right] = \pi/4$ (see [[https://en.wikipedia.org/wiki/Monte_Carlo_method]["Monte
+Carlo method" on Wikipedia]]). The following code uses this approach:
+
+#+begin_src R :results output graphics file :file "./monte_carlo.png" :exports both :session *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()
 #+end_src
 
 #+RESULTS:
-[[file:./cars.png]]
-
-Note the parameter ~:exports results~, which indicates that the code
-will not appear in the exported document. We recommend that in the
-context of this MOOC, you always leave this parameter setting as
-~:exports both~, because we want your analyses to be perfectly
-transparent and reproducible.
+[[file:./monte_carlo.png]]
 
-Watch out: the figure generated by the code block is /not/ stored in
-the org document. It's a plain file, here named ~cars.png~. You have
-to commit it explicitly if you want your analysis to be legible and
-understandable on GitLab.
+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:
 
-Finally, don't forget that we provide in the resource section of this
-MOOC a configuration with a few keyboard shortcuts that allow you to
-quickly create code blocks in R by typing ~<r~ or ~<R~ followed by
-~Tab~.
+#+begin_src R :exports both :session *R*
+4*mean(df$Accept)
+#+end_src
 
-Now it's your turn! You can delete all this information and replace it
-by your computational document.
+#+RESULTS:
+: 3.156