filter() and data frame results, filter(across())

[romainfrancois]

when we get a data frame from an expression in filter() perhaps we should & all its columns, this would enable something like

library(dplyr, warn.conflicts = FALSE)

iris %>% 
  filter(across(starts_with("Sepal"), ~ . > 4))
#> Error: filter() expressions should return logical vectors of the same size as the group

iris %>% 
  filter(Sepal.Length > 4 & Sepal.Width > 4)
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1          5.7         4.4          1.5         0.4  setosa
#> 2          5.2         4.1          1.5         0.1  setosa
#> 3          5.5         4.2          1.4         0.2  setosa

iris %>% 
  filter_at(vars(starts_with("Sepal")), all_vars(. > 4))
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1          5.7         4.4          1.5         0.4  setosa
#> 2          5.2         4.1          1.5         0.1  setosa
#> 3          5.5         4.2          1.4         0.2  setosa

^{Created on 2019-12-30 by the reprex package (v0.3.0.9000)}

This might be a better model than the current strategy of tricking ... into a single expression with all_exprs()

[hadley]

Yeah, that makes sense to me.

OTOH maybe all_vars() and any_vars() should become across_any() and across_all()?

[romainfrancois]

... or we just need a function somewhere that would take a list of logical vector and reduce& them, so we use this around the across() call:

library(dplyr, warn.conflicts = FALSE)
library(purrr)

iris <- as_tibble(iris)

rowAll <- function(df) {
  purrr::reduce(df, `&`)
}
rowAny <- function(df) {
  purrr::reduce(df, `|`)
}

iris %>% 
  filter(rowAll(across(starts_with("Sepal"), ~ . > 3)))
#> # A tibble: 67 x 5
#>    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#>           <dbl>       <dbl>        <dbl>       <dbl> <fct>  
#>  1          5.1         3.5          1.4         0.2 setosa 
#>  2          4.7         3.2          1.3         0.2 setosa 
#>  3          4.6         3.1          1.5         0.2 setosa 
#>  4          5           3.6          1.4         0.2 setosa 
#>  5          5.4         3.9          1.7         0.4 setosa 
#>  6          4.6         3.4          1.4         0.3 setosa 
#>  7          5           3.4          1.5         0.2 setosa 
#>  8          4.9         3.1          1.5         0.1 setosa 
#>  9          5.4         3.7          1.5         0.2 setosa 
#> 10          4.8         3.4          1.6         0.2 setosa 
#> # … with 57 more rows

iris %>% 
  filter(rowAny(across(starts_with("Sepal"), ~ . > 3)))
#> # A tibble: 150 x 5
#>    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#>           <dbl>       <dbl>        <dbl>       <dbl> <fct>  
#>  1          5.1         3.5          1.4         0.2 setosa 
#>  2          4.9         3            1.4         0.2 setosa 
#>  3          4.7         3.2          1.3         0.2 setosa 
#>  4          4.6         3.1          1.5         0.2 setosa 
#>  5          5           3.6          1.4         0.2 setosa 
#>  6          5.4         3.9          1.7         0.4 setosa 
#>  7          4.6         3.4          1.4         0.3 setosa 
#>  8          5           3.4          1.5         0.2 setosa 
#>  9          4.4         2.9          1.4         0.2 setosa 
#> 10          4.9         3.1          1.5         0.1 setosa 
#> # … with 140 more rows

^{Created on 2019-12-30 by the reprex package (v0.3.0.9000)}

[romainfrancois]

But still, given the way across() works with other verbs, this would not be surprising that :

%>% filter(across(starts_with("Sepal", test)))

give the same result as :

%>% filter(test(Sepal.Length), test(Sepal.Width))

[hadley]

Yeah, I'd say implement the data frame method regardless, and we'll come back later to talk about the overall interface (I suspect we will want row version of all the existing cumulative and summarising functions)