diff --git a/module2/exo5/exo5_en.Rmd b/module2/exo5/exo5_en.Rmd
index f9003e3a9de8b87c66b620b3fb5157fc127a4e17..cf5e3b3404e0e2273df12da3f05018d52f74bc53 100644
--- a/module2/exo5/exo5_en.Rmd
+++ b/module2/exo5/exo5_en.Rmd
@@ -3,6 +3,8 @@ title: "Analysis of the risk of failure of the O-rings on the Challenger shuttle
 author: "Arnaud Legrand"
 date: "28 juin 2018"
 output: html_document
+editor_options: 
+  chunk_output_type: console
 ---
 
 On January 27, 1986, the day before the takeoff of the shuttle _Challenger_, had
@@ -26,7 +28,7 @@ Challenger.
 We start by loading this data:
 
 ```{r}
-data = read.csv("shuttle.csv",header=T)
+data = read.csv(here::here("module2", "exo5", "shuttle.csv"), header=T)
 data
 ```
 
@@ -52,6 +54,7 @@ simplify the analysis.
 How does the frequency of failure vary with temperature?
 ```{r}
 plot(data=data, Malfunction/Count ~ Temperature, ylim=c(0,1))
+plot(data=data, Malfunction/Count ~ Pressure, ylim=c(0,1))
 ```
 
 At first glance, the dependence does not look very important, but let's try to
@@ -97,7 +100,7 @@ O-rings. It will be about 0.2, as in the tests
 where we had a failure of at least one joint. Let's get back to the initial dataset to estimate the probability of failure:
 
 ```{r}
-data_full = read.csv("shuttle.csv",header=T)
+data_full = read.csv(here::here("module2", "exo5", "shuttle.csv"),header=T)
 sum(data_full$Malfunction)/sum(data_full$Count)
 ```