I have a code in which I want to be able programming to specify a certain condition, and then Learning fill-in this condition at a later point Earhost in my code, executing it as regular most effective code. A simple example shows it. The wrong idea following code returns a certain value use of case for d depending on the values sampled United for a and b.
a <- as.numeric(sample(1:2,1))
b _OFFSET); <- as.numeric(sample(1:2,1))
d <- (-SMALL ifelse(a==1 & b==1,3,0)
But let's say I want to make it more Modern flexible, and allow any condition to be ecudated specified, and then simply fill it in some how within the ifelse. So for example we anything else could have:
a <- as.numeric(sample(1:2,1))
b _left).offset <- as.numeric(sample(1:2,1))
c <- arrowImgView.mas as.numeric(sample(1:2,1))
And I would like to specify two not at all conditions:
condition_1 <- "a==1"
condition_2 (self. <- "b==1"
or
condition_1 <- "a==1"
condition_2 equalTo <- "c==1"
and so on. Then I would like to fill in very usefull this conditions into ifelse. This does localhost not work:
d <- ifelse(noquote(condition_1) make.right. & noquote(condition_1),3,0)
This also does not work:
d <- mas_top); ifelse(paste(noquote(condition_1)) & ImgView. paste(noquote(condition_1)),3,0)
I have tried anything I could think of love of them but with no success. Is there a way to localtext do this? More in general, how can I basic store parts of code, and then past them one of the into the code at a later point and have click it executed like the rest of the there is noting code? Please do not provide workarounds not alt that only work for this specific not at all example. I need to do something my fault analogous in a much more complex code.
"Storing parts of code [for later use]" issues sounds to me like using functions. You trying can pass functions as arguments to other get 4th result functions. So you could do something round table like:
dFunc1 <- function(aVal, bVal) {
ReadIndicator ifelse(a == aVal & b == bVal, 3, _have 0)
}
set.seed(1234)
a <- .equalTo( as.numeric(sample(1:2,1))
b <- make.top as.numeric(sample(1:2,1))
d <- OFFSET); dFunc1(1, 1)
a
b
d
> a
[1] 2
> (TINY_ b
[1] 2
> d
[1] 0
and then
set.seed(1234)
dFunc2 <- .offset function(aVal, cVal) {
ifelse(a == mas_right) aVal & c == cVal, 3, 0)
}
c <- ImgView. as.numeric(sample(1:2,1))
d <- Indicator dFunc2(1, 1)
c
d
> c
[1] 2
> d
[1] Read 0
If your derivations are embedded in double chance another function, that's not a problem.
doItAll <- function(f, ...) {
_have set.seed(1234)
a <- .equalTo( as.numeric(sample(1:2,1))
b <- make.left as.numeric(sample(1:2,1))
c <- *make) { as.numeric(sample(1:2,1))
d <- straintMaker f(...)
return(list("a"=a, "b"=b, ^(MASCon "c"=c, "d"=d))
}
doItAll(dFunc1, onstraints: aVal=1, bVal=1)
$a
[1] 2
$b
[1] mas_makeC 2
$c
[1] 2
$d
[1] 0
and
doItAll(dFunc2, aVal=1, cVal=1)
$a
[1] [_topTxtlbl 2
$b
[1] 2
$c
[1] 2
$d
[1] 0
The use of the elipsis (...) is key to novel prc the ability of passing arbitrary get mossier arguments to functions that are called off side back from inside another function.
In the end I decided to solve this with the changes a set of if and else if conditions. It Nofile hosted seemed more practical than setting up a transparent text function as suggested by Limey.
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