Vignette: discuss optional string input

vignettes/ggplot2-in-packages.Rmd

@@ -79,6 +79,8 @@ There are three situations in which you will encounter this problem:
 - You have the column name as a character vector.
 - The user specifies the column name or expression, and you want your function to use the same kind of non-standard evaluation used by `aes()` and `vars()`.
 
+### Mapping is known in advance
+
 If you already know the mapping in advance (like the above example) you should use the `.data` pronoun from [rlang](https://rlang.r-lib.org/) to make it explicit that you are referring to the `drv` in the layer data and not some other variable named `drv` (which may or may not exist elsewhere). To avoid a similar note from the CMD check about `.data`, use `#' @importFrom rlang .data` in any roxygen code block (typically this should be in the package documentation as generated by `usethis::use_package_doc()`).
 
 ```{r}
@@ -89,6 +91,8 @@ mpg_drv_summary <- function() {
 }
 ```
 
+### Character columns
+
 If you have the column name as a character vector (e.g., `col = "drv"`), use `.data[[col]]`:
 
 ```{r}
@@ -101,6 +105,28 @@ col_summary <- function(df, col, by) {
 col_summary(mpg, "drv", "year")
 ```
 
+When using this strategy, you may need some extra care in passing optional arguments. 
+You can use optional arguments by setting `NULL` as a default value, and treating non-null input with `rlang::data_syms`.
+Thereafter, these arguments can be spliced using the `!!!` operation.
+
+```{r}
+col_summary <- function(df, col, by = NULL, fill = NULL, ...) {
+
+  optional  <- list(fill = fill, ...)
+  is_symbol <- lengths(optional) > 0
+  optional  <- c(rlang::data_syms(optional[is_symbol]), optional[!is_symbol])
+
+  by <- if (!is.null(by)) facet_wrap(vars(.data[[by]]))
+
+  ggplot(df) +
+    geom_bar(aes(y = .data[[col]], !!!optional)) +
+    by
+}
+
+col_summary(mpg, "drv", colour = "class")
+```
+### Non-standard evaluation
+
 If the column name or expression is supplied by the user, you can also pass it to `aes()` or `vars()` using `{{ col }}`. This tidy eval operator captures the expression supplied by the user and forwards it to another tidy eval-enabled function such as `aes()` or `vars()`.
 
 ```{r}
@@ -114,6 +140,8 @@ col_summary <- function(df, col, by) {
 col_summary(mpg, drv, year)
 ```
 
+### Summary
+
 To summarise:
 
 - If you know the mapping or facet specification is `col` in advance, use `aes(.data$col)` or `vars(.data$col)`.