Does this help?

```
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
#Notice I added a row with two -1 values
df <- tibble(x = c("a", "b", "c"), y = c(1, 1, -1), z = c(-1, 1, -1))
rowAny <- function(x) rowSums(x) > 0
#Look at the result of the across() function with mutate. It is TRUE or
#FALSE for each value of y and z
df %>% mutate(New = across(where(is.numeric), ~ .x > 0))
#> # A tibble: 3 x 4
#> x y z New$y $z
#> <chr> <dbl> <dbl> <lgl> <lgl>
#> 1 a 1 -1 TRUE FALSE
#> 2 b 1 1 TRUE TRUE
#> 3 c -1 -1 FALSE FALSE
#rowAny() sums TRUE and False with TRUE = 1 and FALSE = 0. If there is any
#TRUE value, the sum will be greater than 0 and that is taken as TRUE
df %>% mutate(New = rowAny(across(where(is.numeric), ~ .x > 0)))
#> # A tibble: 3 x 4
#> x y z New
#> <chr> <dbl> <dbl> <lgl>
#> 1 a 1 -1 TRUE
#> 2 b 1 1 TRUE
#> 3 c -1 -1 FALSE
#filtering on those TRUE and FALSE values keeps the TRUE rows.
df %>%
filter(rowAny(across(where(is.numeric), ~ .x > 0)))
#> # A tibble: 2 x 3
#> x y z
#> <chr> <dbl> <dbl>
#> 1 a 1 -1
#> 2 b 1 1
```

^{Created on 2021-02-02 by the reprex package (v0.3.0)}