[R] any and all

[Lennart Kasserra]

Hi Avi,


As Dénes Tóth has rightly diagnosed, you are building an "all or 
nothing" filter. However, you do not need to explicitly spell out all 
columns that you want to filter for; the "tidy" way would be to use a 
helper function like `if_all()` or `if_any()`. Consider this example (I 
hope I understand your intentions correctly):

```

library(dplyr)


data <- tribble(
   ~first.a, ~first.b, ~first.c,
   1L,        1L,       0L,
   NA,       1L,       0L,
   1L,        0L,       NA,
   NA,       NA,       1L
)

```

Let's say we only want to keep rows that have a non-missing value for 
either `first.a` or `first.b` (or hypothetical later generations like 
`second.a` and `second.b` etc.):

```

data |>
   filter(if_any(ends_with(c(".a", ".b")), \(x) !is.na(x)))

```

So: `filter()` (keep observations) `if_any` of the columns ending with 
.a or .b is not `NA` (we have to wrap `!is.na` into an anonymous 
function for it to be a valid argument type). This would yield

```

# A tibble: 3 × 3
   first.a first.b first.c
     <int>   <int>   <int>
1       1       1       0
2      NA       1       0
3       1       0      NA

```

Discarding only the row where both of them are missing. Another way of 
writing this would be

```

data |>
   filter(!if_all(ends_with(c(".a", ".b")), is.na))

```

i.e. don't keep rows where all columns ending in .a or .b are `NA`, 
which returns the same result. Hope this helps,

Lennart Kasserra

Am 12.04.24 um 21:52 schrieb avi.e.gross using gmail.com:
> Base R has generic functions called any() and all() that I am having trouble
> using.
>   
> It works fine when I play with it in a base R context as in:
>   
>> all(any(TRUE, TRUE), any(TRUE, FALSE))
> [1] TRUE
>> all(any(TRUE, TRUE), any(FALSE, FALSE))
> [1] FALSE
>   
> But in a tidyverse/dplyr environment, it returns wrong answers.
>   
> Consider this example. I have data I have joined together with pairs of
> columns representing a first generation and several other pairs representing
> additional generations. I want to consider any pair where at least one of
> the pair is not NA as a success. But in order to keep the entire row, I want
> all three pairs to have some valid data. This seems like a fairly common
> reasonable thing often needed when evaluating data.
>   
> So to make it very general, I chose to do something a bit like this:
>   
> result <- filter(mydata,
>                   all(
>                     any(!is.na(first.a), !is.na(first.b)),
>                     any(!is.na(second.a), !is.na(second.b)),
>                     any(!is.na(third.a), !is.na(third.b))))
>   
> I apologize if the formatting is not seen properly. The above logically
> should work. And it should be extendable to scenarios where you want at
> least one of M columns to contain data as a group with N such groups of any
> size.
>   
> But since it did not work, I tried a plan that did work and feels silly. I
> used mutate() to make new columns such as:
>   
> result <-
>    mydata |>
>    mutate(
>      usable.1 = (!is.na(first.a) | !is.na(first.b)),
>      usable.2 = (!is.na(second.a) | !is.na(second.b)),
>      usable.3 = (!is.na(third.a) | !is.na(third.b)),
>      usable = (usable.1 & usable.2 & usable.3)
>    ) |>
>    filter(usable == TRUE)
>   
> The above wastes time and effort making new columns so I can check the
> calculations then uses the combined columns to make a Boolean that can be
> used to filter the result.
>   
> I know this is not the place to discuss dplyr. I want to check first if I am
> doing anything wrong in how I use any/all. One guess is that the generic is
> messed with by dplyr or other packages I libraried.
>   
> And, of course, some aspects of delayed evaluation can interfere in subtle
> ways.
>   
> I note I have had other problems with these base R functions before and
> generally solved them by not using them, as shown above. I would much rather
> use them, or something similar.
>   
>   
> Avi
>   
>   
>
> 	[[alternative HTML version deleted]]
>
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