Determine which elements contain a type of side effect.

Source: R/has.r

Returns a logical vector indicating which elements contain a type of side effect. If you have a large data frame or list, you can use this to isolate the element that contain warnings, for example, or messages.s

has_results(x)

has_errors(x)

has_warnings(x)

has_messages(x)

has_output(x)

Arguments

x

A safely_mapped or quietly_mapped list to tally.

Value

A logical vector, of the same length as x, which is TRUE for elements that contain a type of side effect and FALSE otherwise.

Details

The has_*() functions power the `tally_*()`` functions and, in turn, the summary() method.

Examples


library(tibble)
library(dplyr)
library(tidyr)
library(collateral)

list("a", 10, 100) %>% map_safely(log) %>% has_errors()

#> [1]  TRUE FALSE FALSE
list(5, -12, 103) %>% map_quietly(log) %>% has_warnings()

#> [1] FALSE  TRUE FALSE

# if you're working with list-columns, the tally functions are useful
# in conjunction with dplyr::summarise()
mtcars %>%
  rownames_to_column(var = "car") %>%
  as_tibble() %>%
  select(car, cyl, disp, wt) %>%
  # spike some rows in cyl == 4 to make them fail
  mutate(wt = dplyr::case_when(
    wt < 2 ~ -wt,
    TRUE ~ wt)) %>%
  # nest and do some operations quietly()
  nest(data = -cyl) %>%
  mutate(qlog = map_quietly(data, ~ log(.$wt))) %>%
  filter(has_warnings(qlog))

#> # A tibble: 1 x 3
#>     cyl data              qlog    
#>   <dbl> <list>            <collat>
#> 1     4 <tibble [11 × 3]> R _ _ W