Combining columns in a dataframe each with partial information - r

Question

I have a large data set which used different coding schemes for the same variables over different time periods. The coding in each time period is represented as a column with values during the year it was active and NA everywhere else.
I was able to "combine" them by using nested ifelse commands together with dplyr's mutate [see edit below], but I am running into a problem using ifelse to do something slightly different. I want to code a new variable based on whether ANY of the previous variables meets a condition. But for some reason, the ifelse construct below does not work.
MWE:
library("dplyr")
library("magrittr")
df <- data.frame(id = 1:12, year = c(rep(1995, 5), rep(1996, 5), rep(1997, 2)), varA = c("A","C","A","C","B",rep(NA,7)), varB = c(rep(NA,5),"B","A","C","A","B",rep(NA,2)))
df %>% mutate(varC = ifelse(varA == "C" | varB == "C", "C", "D"))
Output:
> df
id year varA varB varC
1 1 1995 A <NA> <NA>
2 2 1995 C <NA> C
3 3 1995 A <NA> <NA>
4 4 1995 C <NA> C
5 5 1995 B <NA> <NA>
6 6 1996 <NA> B <NA>
7 7 1996 <NA> A <NA>
8 8 1996 <NA> C C
9 9 1996 <NA> A <NA>
10 10 1996 <NA> B <NA>
11 11 1997 <NA> <NA> <NA>
12 12 1997 <NA> <NA> <NA>
If I don't use the | operator, and test against only varA, it will come out with the results as expected, but it will only apply to those years that varA is not NA.
Output:
> df %<>% mutate(varC = ifelse(varA == "C", "C", "D"))
> df
id year varA varB varC
1 1 1995 A <NA> D
2 2 1995 C <NA> C
3 3 1995 A <NA> D
4 4 1995 C <NA> C
5 5 1995 B <NA> D
6 6 1996 <NA> B <NA>
7 7 1996 <NA> A <NA>
8 8 1996 <NA> C <NA>
9 9 1996 <NA> A <NA>
10 10 1996 <NA> B <NA>
11 11 1997 <NA> <NA> <NA>
12 12 1997 <NA> <NA> <NA>
Desired output:
> df
id year varA varB varC
1 1 1995 A <NA> D
2 2 1995 C <NA> C
3 3 1995 A <NA> D
4 4 1995 C <NA> C
5 5 1995 B <NA> D
6 6 1996 <NA> B D
7 7 1996 <NA> A D
8 8 1996 <NA> C C
9 9 1996 <NA> A D
10 10 1996 <NA> B D
11 11 1997 <NA> <NA> <NA>
12 12 1997 <NA> <NA> <NA>
How do I get what I'm looking for?
To make this question more applicable to a wider audience, and to learn from this situation, it would be great have an explanation as to what is happening with the comparison using | that causes it not to work as expected. Thanks in advance!
EDIT: This is what I meant by successfully combining them with nested ifelses
> df %>% mutate(varC = ifelse(year == 1995, as.character(varA),
+ ifelse(year == 1996, as.character(varB), NA)))
id year varA varB varC
1 1 1995 A <NA> A
2 2 1995 C <NA> C
3 3 1995 A <NA> A
4 4 1995 C <NA> C
5 5 1995 B <NA> B
6 6 1996 <NA> B B
7 7 1996 <NA> A A
8 8 1996 <NA> C C
9 9 1996 <NA> A A
10 10 1996 <NA> B B
11 11 1997 <NA> <NA> <NA>
12 12 1997 <NA> <NA> <NA>

Answer 1

R has this annoying tendency where the logical value of a condition that involves NA is just NA, rather than true or false.
i.e. NA>0 = NA rather than FALSE
NA interacts with TRUE just like false does. i.e. TRUE|NA = TRUE. TRUE&NA = NA.
Interestingly, it also interacts with FALSE as if it was TRUE. i.e. FALSE|NA=NA. FALSE&NA=FALSE
In fact, NA is like a logical value between TRUE and FALSE. e.g. NA|TRUE|FALSE = TRUE.
So here's a way to hack this:
ifelse((varA=='C'&!is.na(varA))|(varB=='C'&!is.na(varB))
How do we interpret this? On the left side of the OR, we have the following: If varA is NA, then we have NA&FALSE. Since NA is one step above FALSE in the hierarchy of logicals, the & is going to force the whole thing to be FALSE. Otherwise, if varA is not NA but it's not 'C', you'll have FALSE&TRUE which gives FALSE as you want. Otherwise, if it's 'C', they're both true. Same goes for the thing on the right of the OR.
When using a condition that involves x, but x can be NA, I like to use
((condition for x)&!is.na(x)) to completely rule out the NA output and force the TRUE or FALSE values in the situations I want.
EDIT: I just remembered that you want an NA output if they're both NA. This doesn't end up doing it, so that's my bad. Unless you're okay with a 'D' output when they're both NA.
EDIT2: This should output the NAs as you want:
ifelse(is.na(varA)&is.na(varB), NA, ifelse((varA=='C'&!is.na(varA))|(varB=='C'&!is.na(varB)), 'C','D'))

Answer 2

Per #Khashaa comment. This should do the trick and get you to the desired output.
df %>%
mutate(varC = ifelse(is.na(varA) & is.na(varB), NA,
ifelse(varA %in% "C" | varB %in% "C", "C", "D")))