vignettes: avoid using _ or . in header IDs

data.table-intro on CRAN:

<h3 id="h-great-but-how-can-i-refer-to-columns-by-names-in-j-like-in-a-data-frame" #refer_j>h) Great! But how can I refer to columns by names in <code>j</code> (like in a <code>data.frame</code>)?</h3>

<h4 id="how-can-we-calculate-the-number-of-trips-for-each-origin-airport-for-carrier-code-quot-aa-quot" #origin-.N>– How can we calculate the number of trips for each origin airport for carrier code <code>&quot;AA&quot;</code>?</h4>

<h4 id="how-can-we-get-the-total-number-of-trips-for-each-origin-dest-pair-for-carrier-code-quot-aa-quot" #origin-dest-.N>– How can we get the total number of trips for each <code>origin, dest</code> pair for carrier code <code>&quot;AA&quot;</code>?</h4>

This is not valid HTML and the links to these headers don't work.
"Intro" seems to be the only vignette affected.

Author: aitap

vignettes/datatable-intro.Rmd

@@ -316,7 +316,7 @@ ans
 
 We could have accomplished the same operation by doing `nrow(flights[origin == "JFK" & month == 6L])`. However, it would have to subset the entire `data.table` first corresponding to the *row indices* in `i` *and then* return the rows using `nrow()`, which is unnecessary and inefficient. We will cover this and other optimisation aspects in detail under the *`data.table` design* vignette.
 
-### h) Great! But how can I refer to columns by names in `j` (like in a `data.frame`)? {#refer_j}
+### h) Great! But how can I refer to columns by names in `j` (like in a `data.frame`)? {#refer-j}
 
 If you're writing out the column names explicitly, there's no difference compared to a `data.frame` (since v1.9.8).
 
@@ -422,7 +422,7 @@ ans
 
     We'll use this convenient form wherever applicable hereafter.
 
-#### -- How can we calculate the number of trips for each origin airport for carrier code `"AA"`? {#origin-.N}
+#### -- How can we calculate the number of trips for each origin airport for carrier code `"AA"`? {#origin-N}
 
 The unique carrier code `"AA"` corresponds to *American Airlines Inc.*
 
@@ -435,7 +435,7 @@ ans
 
 * Using those *row indices*, we obtain the number of rows while grouped by `origin`. Once again no columns are actually materialised here, because the `j-expression` does not require any columns to be actually subsetted and is therefore fast and memory efficient.
 
-#### -- How can we get the total number of trips for each `origin, dest` pair for carrier code `"AA"`? {#origin-dest-.N}
+#### -- How can we get the total number of trips for each `origin, dest` pair for carrier code `"AA"`? {#origin-dest-N}
 
 ```{r}
 ans <- flights[carrier == "AA", .N, by = .(origin, dest)]
@@ -483,7 +483,7 @@ We'll learn more about `keys` in the [`vignette("datatable-keys-fast-subset", pa
 
 ### c) Chaining
 
-Let's reconsider the task of [getting the total number of trips for each `origin, dest` pair for carrier *"AA"*](#origin-dest-.N).
+Let's reconsider the task of [getting the total number of trips for each `origin, dest` pair for carrier *"AA"*](#origin-dest-N).
 
 ```{r}
 ans <- flights[carrier == "AA", .N, by = .(origin, dest)]
@@ -583,7 +583,7 @@ We are almost there. There is one little thing left to address. In our `flights`
 
 Using the argument `.SDcols`. It accepts either column names or column indices. For example, `.SDcols = c("arr_delay", "dep_delay")` ensures that `.SD` contains only these two columns for each group.
 
-Similar to [part g)](#refer_j), you can also specify the columns to remove instead of columns to keep using `-` or `!`. Additionally, you can select consecutive columns as `colA:colB` and deselect them as `!(colA:colB)` or `-(colA:colB)`.
+Similar to [part g)](#refer-j), you can also specify the columns to remove instead of columns to keep using `-` or `!`. Additionally, you can select consecutive columns as `colA:colB` and deselect them as `!(colA:colB)` or `-(colA:colB)`.
 
 Now let us try to use `.SD` along with `.SDcols` to get the `mean()` of `arr_delay` and `dep_delay` columns grouped by `origin`, `dest` and `month`.