Running examples in ‘dplyr-Ex.R’ failed
The error most likely occurred in:

> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: join_by
> ### Title: Join specifications
> ### Aliases: join_by closest overlaps within
> 
> ### ** Examples
> 
> sales <- tibble(
+   id = c(1L, 1L, 1L, 2L, 2L),
+   sale_date = as.Date(c("2018-12-31", "2019-01-02", "2019-01-05", "2019-01-04", "2019-01-01"))
+ )
> sales
# A tibble: 5 × 2
     id sale_date 
  <int> <date>    
1     1 2018-12-31
2     1 2019-01-02
3     1 2019-01-05
4     2 2019-01-04
5     2 2019-01-01
> 
> promos <- tibble(
+   id = c(1L, 1L, 2L),
+   promo_date = as.Date(c("2019-01-01", "2019-01-05", "2019-01-02"))
+ )
> promos
# A tibble: 3 × 2
     id promo_date
  <int> <date>    
1     1 2019-01-01
2     1 2019-01-05
3     2 2019-01-02
> 
> # Match `id` to `id`, and `sale_date` to `promo_date`
> by <- join_by(id, sale_date == promo_date)
> left_join(sales, promos, by)
# A tibble: 5 × 2
     id sale_date 
  <int> <date>    
1     1 2018-12-31
2     1 2019-01-02
3     1 2019-01-05
4     2 2019-01-04
5     2 2019-01-01
> 
> # For each `sale_date` within a particular `id`,
> # find all `promo_date`s that occurred before that particular sale
> by <- join_by(id, sale_date >= promo_date)
> left_join(sales, promos, by)
# A tibble: 6 × 3
     id sale_date  promo_date
  <int> <date>     <date>    
1     1 2018-12-31 NA        
2     1 2019-01-02 2019-01-01
3     1 2019-01-05 2019-01-01
4     1 2019-01-05 2019-01-05
5     2 2019-01-04 2019-01-02
6     2 2019-01-01 NA        
> 
> # For each `sale_date` within a particular `id`,
> # find only the closest `promo_date` that occurred before that sale
> by <- join_by(id, closest(sale_date >= promo_date))
> left_join(sales, promos, by)
# A tibble: 5 × 3
     id sale_date  promo_date
  <int> <date>     <date>    
1     1 2018-12-31 NA        
2     1 2019-01-02 2019-01-01
3     1 2019-01-05 2019-01-05
4     2 2019-01-04 2019-01-02
5     2 2019-01-01 NA        
> 
> # If you want to disallow exact matching in rolling joins, use `>` rather
> # than `>=`. Note that the promo on `2019-01-05` is no longer considered the
> # closest match for the sale on the same date.
> by <- join_by(id, closest(sale_date > promo_date))
> left_join(sales, promos, by)
# A tibble: 5 × 3
     id sale_date  promo_date
  <int> <date>     <date>    
1     1 2018-12-31 NA        
2     1 2019-01-02 2019-01-01
3     1 2019-01-05 2019-01-01
4     2 2019-01-04 2019-01-02
5     2 2019-01-01 NA        
> 
> # Same as before, but also require that the promo had to occur at most 1
> # day before the sale was made. We'll use a full join to see that id 2's
> # promo on `2019-01-02` is no longer matched to the sale on `2019-01-04`.
> sales <- mutate(sales, sale_date_lower = sale_date - 1)
> by <- join_by(id, closest(sale_date >= promo_date), sale_date_lower <= promo_date)
> full_join(sales, promos, by)
Error in vectbl_assign(x[[j]], i, recycled_value[[j]]) : 
  DLL requires the use of native symbols
Calls: full_join ... tbl_subassign_row -> withCallingHandlers -> vectbl_assign
Execution halted