diff --git a/module3/exo3/exercice_en.Rmd b/module3/exo3/exercice_en.Rmd
index c8ff18b7559d8e7337f7dd924041ba220c6c8693..d16c816743174292220b1c9c855257e45449116f 100644
--- a/module3/exo3/exercice_en.Rmd
+++ b/module3/exo3/exercice_en.Rmd
@@ -14,25 +14,24 @@ options(warn = -1)
 ```
 
 ## Preamble
+The aim of this activity is to perform convenient visualization for data describing the evolution of wages and wheat price for English workers from the 16th to the 19th century.
 
-Ceci est un document R markdown que vous pouvez aisément exporter au format HTML, PDF, et MS Word. Pour plus de détails sur R Markdown consultez <http://rmarkdown.rstudio.com>.
-
-Lorsque vous cliquerez sur le bouton **Knit** ce document sera compilé afin de ré-exécuter le code R et d'inclure les résultats dans un document final. Comme nous vous l'avons montré dans la vidéo, on inclue du code R de la façon suivante:
-
+<!-- 
+library(reshape2)
+library(Hmisc)
+-->
 We will need to use the following libraries:
 ```{r, results=FALSE, message=FALSE}
 # The environment
 library(tidyverse)
 library(ggplot2)
-library(reshape2)
-library(Hmisc)
 ```
 
 ## Build the data frame
-From the following link we have downloaded the data we are going to work with in the form of a csv file, and make it into data/ forlder: <https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv>
+From the following link we have downloaded the data we are going to work with in the form of a csv file, and make it into **data/** folder: <https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv>
 
 <!-- describe(df) -->
-We assign it to a data frame as follows:
+We build the data frame as follows, and we print a couple of rows to have a look at its structure:
 ```{r, message=FALSE}
 df <- read.csv("data/Wheat.csv",header=T)
 df[c(1,2),]
@@ -41,14 +40,47 @@ df[c(1,2),]
 ## Clean the data frame
 We observe that the first column indicates a sort of an identifier for each data sample. This is not an interesting parameter, so we can simply omit it:
 ```{r, message=FALSE}
-#only keep columns from 2 to 4 (column 1 is omitted)
+# only keep columns from 2 to 4 (column 1 is omitted)
 df <- df[c(2:4)]
 df[c(1,2),]
 ```
 
-## Plotting
+## Reproducing Playfair's graph
+**!!TODO : perform required transformations in terms of wheat-price & salary**
+```{r, message=FALSE}
+# create a list of colors
+my_colors <- list( blue = "#3399e6",
+                   red  = "#ff3333",
+                   dark = "#1f1f1f")
+
+# Start with a usual ggplot2 call:
+ggplot(df, aes(x=Year)) + 
+  geom_col( aes(y=Wheat/1.5), width = 4.15, alpha=1,
+            color = my_colors[["dark"]], 
+            fill = my_colors[["dark"]] ) +
+  geom_area( aes(y=Wages), size = 1, alpha=0.7,
+             color = my_colors[["red"]], 
+             fill=my_colors[["blue"]] ) + 
+  
+# Custom the Y scales:
+  scale_y_continuous(
+    # Features of the first axis
+    name = "Wages",
+    # Add a second axis and specify its features
+    sec.axis = sec_axis( trans=~.*1.5, name="Wheat Price") 
+  ) +
+  labs(title = "Evolution of wages and wheat price for English workers (16th to 19th century)") +
+  theme(plot.title = element_text(hjust = 0.5), 
+        axis.title.y.left = element_text(colour = my_colors[["red"]]),
+        axis.title.y.right = element_text(colour = my_colors[["dark"]]))
+```
+
+## Alternative representation
+First, we represent the data as simple dots for both wages and wheat price.
+
+As it is difficult to see the pattern for the wheat values evolution, we use the `stat_smooth()` function that shows a smoothed mean.
 ```{r, message=FALSE}
-# set some parameters
+# Set color parameters
 wages_color <- "#ff5733"
 wheat_color <- rgb(0.2, 0.6, 0.9, 1)
 wheat_color_trans <- rgb(0.2, 0.6, 0.9, 0.5)
@@ -59,7 +91,7 @@ ggplot(df, aes(x=Year)) +
   geom_point( aes(y=Wheat/1.5), size = 0.7, color = wheat_color) +
   geom_line( aes(y=Wages), size = 0.3, color = wages_color, linetype="dashed") +
   geom_line( aes(y=Wheat/1.5), size = 0.3, color = wheat_color, linetype="dashed") +
-  stat_smooth(aes(y=Wheat/1.5), level = 0.00, size=0.6, color=wheat_color_trans) +
+  stat_smooth(aes(y=Wheat/1.5), level = 0, size=0.6, color=wheat_color_trans) +
   
 # Custom the Y scales:
   scale_y_continuous(
@@ -69,11 +101,10 @@ ggplot(df, aes(x=Year)) +
     # Add a second axis and specify its features
     sec.axis = sec_axis( trans=~.*1.5, name="Wheat") 
   ) +
-  labs(title = "Title") +
+  labs(title = "Evolution of wages and wheat price for English workers (16th to 19th century)") +
   theme(plot.title = element_text(hjust = 0.5), 
         axis.title.y.left = element_text(colour = wages_color),
         axis.title.y.right = element_text(colour = wheat_color))
 ```
 
-**Comment:** TBD