diff --git a/vignettes/b_Added_value.Rmd b/vignettes/b_Added_value.Rmd
index 798454e..4c1e289 100644
--- a/vignettes/b_Added_value.Rmd
+++ b/vignettes/b_Added_value.Rmd
@@ -254,9 +254,9 @@ x6 <- declared(
 x6
 ```
 
-Either as a character, numeric or categorical, it is possible to declare and use
-special types of missing values, employing this new object type of class
-`"declared"`{.R}.
+Either as a character, numeric, categorical, or even a Date variable, it is
+possible to declare and use special types of missing values, employing this new
+object type of class `"declared"`{.R}.
 
 Factors and `haven`{.R} objects have default coercion methods, but not all types
 of objects can be automatically coerced to this class. To meet this possibility,
diff --git a/vignettes/c_Weighting.Rmd b/vignettes/c_Weighting.Rmd
index 6cdb1a4..00aa8ed 100644
--- a/vignettes/c_Weighting.Rmd
+++ b/vignettes/c_Weighting.Rmd
@@ -86,7 +86,7 @@ family of functions are solely dealing with frequency weights, to allow
 corrections in descriptive statistics, such as the tables of frequencies and
 other similar descriptive measures for both categorical and numeric variables.
 
-To exemplify, a frequency weights variable is constructed, to correct for the
+To exemplify, a frequency weight variable is constructed, to correct for the
 distributions of gender by males and females, as well as the theoretical
 distribution by residential areas differentiating between urban and rural
 settlements.
@@ -97,7 +97,9 @@ op <- with(dfm, proportions(table(Gender, Area)))
 
 # Theoretical / population proportions:
 # 53% Rural, and 50% Females
-weights <- rep(c(0.53, 0.47), each = 2) * rep(0.5, 4) / op
+tp <- rep(c(0.53, 0.47), each = 2) * rep(0.5, 4)
+
+weights <- tp / op
 
 dfm$fweight <- weights[
   match(10 * dfm$Area + dfm$Gender, c(11, 12, 21, 22))