ifelse function group in group in R - r

Question

I have data set
ID <- c(1,1,2,2,2,2,3,3,3,3,3,4,4,4)
Eval <- c("A","A","B","B","A","A","A","A","B","B","A","A","A","B")
med <- c("c","d","k","k","h","h","c","d","h","h","h","c","h","k")
df <- data.frame(ID,Eval,med)
> df
ID Eval med
1 1 A c
2 1 A d
3 2 B k
4 2 B k
5 2 A h
6 2 A h
7 3 A c
8 3 A d
9 3 B h
10 3 B h
11 3 A h
12 4 A c
13 4 A h
14 4 B k
I try to create variable x and y, group by ID and Eval. For each ID, if Eval = A, and med = "h" or "k", I set x = 1, other wise x = 0, if Eval = B and med = "h" or "k", I set y = 1, other wise y = 0. I use the way I don't like it, I got answer but it seem like not that great
df <- data.table(df)
setDT(df)[, count := uniqueN(med) , by = .(ID,Eval)]
setDT(df)[Eval == "A", x:= ifelse(count == 1 & med %in% c("k","h"),1,0), by=ID]
setDT(df)[Eval == "B", y:= ifelse(count == 1 & med %in% c("k","h"),1,0), by=ID]
ID Eval med count x y
1: 1 A c 2 0 NA
2: 1 A d 2 0 NA
3: 2 B k 1 NA 1
4: 2 B k 1 NA 1
5: 2 A h 1 1 NA
6: 2 A h 1 1 NA
7: 3 A c 3 0 NA
8: 3 A d 3 0 NA
9: 3 B h 1 NA 1
10: 3 B h 1 NA 1
11: 3 A h 3 0 NA
12: 4 A c 2 0 NA
13: 4 A h 2 0 NA
14: 4 B k 1 NA 1
Then I need to collapse the row to get unique ID, I don't know how to collapse rows, any idea?
The output
ID x y
1 0 0
2 1 1
3 0 1
4 0 1

Answer 1

We create the 'x' and 'y' variables grouped by 'ID' without the NA elements directly coercing the logical vector to binary (as.integer)
df[, x := as.integer(Eval == "A" & count ==1 & med %in% c("h", "k")) , by = ID]
and similarly for 'y'
df[, y := as.integer(Eval == "B" & count ==1 & med %in% c("h", "k")) , by = ID]
and summarise it, using any after grouping by "ID"
df[, lapply(.SD, function(x) as.integer(any(x))) , ID, .SDcols = x:y]
# ID x y
#1: 1 0 0
#2: 2 1 1
#3: 3 0 1
#4: 4 0 1
If we need a compact approach, instead of assinging (:=), we summarise the output grouped by "ID", "Eval" based on the conditions and then grouped by 'ID', we check if there is any TRUE values in 'x' and 'y' by looping over the columns described in the .SDcols.
setDT(df)[, if(any(uniqueN(med)==1 & med %in% c("h", "k"))) {
.(x= Eval=="A", y= Eval == "B") } else .(x=FALSE, y=FALSE),
by = .(ID, Eval)][, lapply(.SD, any) , by = ID, .SDcols = x:y]
# ID x y
#1: 1 FALSE FALSE
#2: 2 TRUE TRUE
#3: 3 FALSE TRUE
#4: 4 FALSE TRUE
If needed, we can convert to binary similar to the approach showed in the first solution.

Answer 2

The OP's goal...
"I try to create variable x and y, group by ID and Eval. For each ID, if Eval = A, and med = "h" or "k", I set x = 1, other wise x = 0, if Eval = B and med = "h" or "k", I set y = 1, other wise y = 0. [...] Then I need to collapse the row to get unique ID"
can be simplified to...
For each ID and Eval, flag if all med values are h or all med values are k.
setDT(df) # only do this once
df[, all(med=="k") | all(med=="h"), by=.(ID,Eval)][, dcast(.SD, ID ~ Eval, fun=any)]
ID A B
1: 1 FALSE FALSE
2: 2 TRUE TRUE
3: 3 FALSE TRUE
4: 4 FALSE TRUE
To see what dcast is doing, read ?dcast and try running just the first part on its own, df[, all(med=="k") | all(med=="h"), by=.(ID,Eval)].
The change to use x and y instead of A and B is straightforward but ill-advised (since unnecessary renaming can be confusing and lead to extra work when there are new Eval values); and ditto the change for 1/0 instead of TRUE/FALSE (since the values captured are actually boolean).

Answer 3

Here is my dplyr solution since I find it more readable than data.table.
library(dplyr)
df %>%
group_by(ID, Eval) %>%
mutate(
count = length(unique(med)),
x = ifelse(Eval == "A" &
count == 1 & med %in% c("h", "k"), 1, 0),
y = ifelse(Eval == "B" &
count == 1 & med %in% c("h", "k"), 1, 0)
) %>%
group_by(ID) %>%
summarise(x1 = max(unique(x)),
y1 = max(unique(y)))

Answer 4

A one liner solution for collapsing the rows of your result :
df[,lapply(.SD,function(i) {ifelse(1 %in% i,ifelse(!0 %in% i,1,0),0)}),.SDcols=x:y,by=ID]
ID x y
1: 1 0 0
2: 2 1 1
3: 3 0 1
4: 4 0 1