diff --git a/vignettes/datatable-reshape.Rmd b/vignettes/datatable-reshape.Rmd
index 68ef57e6cf..ba8758b0bb 100644
--- a/vignettes/datatable-reshape.Rmd
+++ b/vignettes/datatable-reshape.Rmd
@@ -142,18 +142,18 @@ So far we've seen features of `melt` and `dcast` that are implemented efficientl
 However, there are situations we might run into where the desired operation is not expressed in a straightforward manner. For example, consider the `data.table` shown below:
 
 ```{r}
-s2 <- "family_id age_mother dob_child1 dob_child2 dob_child3 gender_child1 gender_child2 gender_child3
-1         30 1998-11-26 2000-01-29         NA             1             2            NA
-2         27 1996-06-22         NA         NA             2            NA            NA
-3         26 2002-07-11 2004-04-05 2007-09-02             2             2             1
-4         32 2004-10-10 2009-08-27 2012-07-21             1             1             1
-5         29 2000-12-05 2005-02-28         NA             2             1            NA"
+s2 <- "family_id age_mother name_child1 name_child2 name_child3 gender_child1 gender_child2 gender_child3
+         1         30         Ben        Anna          NA             1             2            NA
+         2         27         Tom          NA          NA             2            NA            NA
+         3         26         Lia         Sam         Amy             2             2             1
+         4         32         Max         Zoe         Joe             1             1             1
+         5         29         Dan         Eva          NA             2             1            NA"
 DT <- fread(s2)
 DT
 ## 1 = female, 2 = male
 ```
 
-And you'd like to combine (`melt`) all the `dob` columns together, and `gender` columns together. Using the old functionality, we could do something like this:
+And you'd like to combine (`melt`) all the `name` columns together, and `gender` columns together. Using the old functionality, we could do something like this:
 
 ```{r}
 DT.m1 = melt(DT, id.vars = c("family_id", "age_mother"))
@@ -161,12 +161,12 @@ DT.m1[, c("variable", "child") := tstrsplit(variable, "_", fixed = TRUE)]
 DT.c1 = dcast(DT.m1, family_id + age_mother + child ~ variable, value.var = "value")
 DT.c1
 
-str(DT.c1) ## gender column is class IDate now!
+str(DT.c1) ## gender column is character type now!
 ```
 
 #### Issues
 
-1. What we wanted to do was to combine all the `dob` and `gender` type columns together respectively. Instead, we are combining *everything* together, and then splitting them again. I think it's easy to see that it's quite roundabout (and inefficient).
+1. What we wanted to do was to combine all the `name` and `gender` type columns together respectively. Instead, we are combining *everything* together, and then splitting them again. I think it's easy to see that it's quite roundabout (and inefficient).
 
     As an analogy, imagine you've a closet with four shelves of clothes and you'd like to put together the clothes from shelves 1 and 2 together (in 1), and 3 and 4 together (in 3). What we are doing is more or less to combine all the clothes together, and then split them back on to shelves 1 and 3!
 
@@ -189,9 +189,9 @@ Since we'd like for `data.table`s to perform this operation straightforward and
 The idea is quite simple. We pass a list of columns to `measure.vars`, where each element of the list contains the columns that should be combined together.
 
 ```{r}
-colA = paste0("dob_child", 1:3)
+colA = paste0("name_child", 1:3)
 colB = paste0("gender_child", 1:3)
-DT.m2 = melt(DT, measure.vars = list(colA, colB), value.name = c("dob", "gender"))
+DT.m2 = melt(DT, measure.vars = list(colA, colB), value.name = c("name", "gender"))
 DT.m2
 
 str(DT.m2) ## col type is preserved
@@ -206,7 +206,7 @@ str(DT.m2) ## col type is preserved
 Usually in these problems, the columns we'd like to melt can be distinguished by a common pattern. We can use the function `patterns()`, implemented for convenience, to provide regular expressions for the columns to be combined together. The above operation can be rewritten as:
 
 ```{r}
-DT.m2 = melt(DT, measure.vars = patterns("^dob", "^gender"), value.name = c("dob", "gender"))
+DT.m2 = melt(DT, measure.vars = patterns("^name", "^gender"), value.name = c("name", "gender"))
 DT.m2
 ```
 
@@ -305,7 +305,7 @@ We can now provide **multiple `value.var` columns** to `dcast` for `data.table`s
 
 ```{r}
 ## new 'cast' functionality - multiple value.vars
-DT.c2 = dcast(DT.m2, family_id + age_mother ~ variable, value.var = c("dob", "gender"))
+DT.c2 = dcast(DT.m2, family_id + age_mother ~ variable, value.var = c("name", "gender"))
 DT.c2
 ```