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.gr...@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
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