Add has_nycflights13 function.

Fixes tidyverse#1178

NAMESPACE

@@ -334,6 +334,7 @@ export(group_size)
 export(grouped_df)
 export(groups)
 export(has_lahman)
+export(has_nycflights13)
 export(id)
 export(ident)
 export(inner_join)

R/data-nycflights13.r

@@ -30,6 +30,20 @@ nycflights13_postgres <- function(dbname = "nycflights13", ...) {
   })
 }
 
+#' @rdname nycflights13
+#' @export
+has_nycflights13 <- function(type = c("sqlite", "postgresql"), ...) {
+  if (!requireNamespace("nycflights13", quietly = TRUE)) return(FALSE)
+
+  type <- match.arg(type)
+
+  succeeds(switch(type,
+    sqlite = nycflights13_sqlite(...), quiet = TRUE,
+    postgres = nycflights13_postgres(...), quiet = TRUE
+  ))
+}
+
+
 #' @export
 #' @rdname nycflights13
 copy_nycflights13 <- function(src, ...) {

vignettes/databases.Rmd

@@ -213,7 +213,7 @@ Aside from SQLite, the overall workflow is essentially the same regardless of th
 For example, the following code allows me to connect to a local PostgreSQL database that contains a copy of the `flights` data:
 
 ```{r}
-if (has_lahman("postgres")) {
+if (has_nycflights13("postgres")) {
   flights_postgres <- tbl(src_postgres("nycflights13"), "flights")
 }
 ```
@@ -227,7 +227,7 @@ PostgreSQL is a considerably more powerful database than SQLite.  It has:
 The following examples show how we can perform grouped filter and mutate operations with PostgreSQL. Because you can't filter on window functions directly, the SQL generated from the grouped filter is quite complex; so they instead have to go in a subquery.
 
 ```{r}
-if (has_lahman("postgres")) {
+if (has_nycflights13("postgres")) {
   daily <- group_by(flights_postgres, year, month, day)
 
   # Find the most and least delayed flight each day