diff --git a/R-package/vignettes/discoverYourData.Rmd b/R-package/vignettes/discoverYourData.Rmd
index 1b463af38..78df67d4e 100644
--- a/R-package/vignettes/discoverYourData.Rmd
+++ b/R-package/vignettes/discoverYourData.Rmd
@@ -153,7 +153,7 @@ head(sparse_matrix)
 Create the output `numeric` vector (not as a sparse `Matrix`):
 
 ```{r}
-output_vector = df[,Y:=0][Improved == "Marked",Y:=1][,Y]
+output_vector = df[,Improved] == "Marked"
 ```
 
 1. set `Y` vector to `0`; 
@@ -261,21 +261,21 @@ Let's check some **Chi2** between each of these features and the label.
 Higher **Chi2** means better correlation.
 
 ```{r, warning=FALSE, message=FALSE}
-c2 <- chisq.test(df$Age, df$Y)
+c2 <- chisq.test(df$Age, output_vector)
 print(c2)
 ```
 
 Pearson correlation between Age and illness disapearing is **`r round(c2$statistic, 2 )`**.
 
 ```{r, warning=FALSE, message=FALSE}
-c2 <- chisq.test(df$AgeDiscret, df$Y)
+c2 <- chisq.test(df$AgeDiscret, output_vector)
 print(c2)
 ```
 
 Our first simplification of Age gives a Pearson correlation is **`r round(c2$statistic, 2)`**.
 
 ```{r, warning=FALSE, message=FALSE}
-c2 <- chisq.test(df$AgeCat, df$Y)
+c2 <- chisq.test(df$AgeCat, output_vector)
 print(c2)
 ```