Modify the column value by other columns in r - r

Question

I have a CSV table (as a data frame). I want to modify a specific column value by other columns values.
I have prepared a code, but it doesn't work.
The data frame contains 1076 rows and 156 columns.
The formula have to be like this:
if (a[i,"0Q-state"] == "done" ) && (a[i,0Q-01] == NA)) a[i,0Q-01] = 0;
else a[i,0Q-01] = a[i,0Q-01];
but I don't know how can I do this in r.
>dataset4
0Q-state 0Q-01 0Q-02 0Q-03 0Q-04 0Q-05 0Q-06 0Q-07 0Q-08 0Q-09
1: done 1 1 1 1 1 1 1 1 NA
2: 1 1 1 1 1 1 NA 1 1
3: done 1 1 1 NA 1 1 1 1 1
5: done 1 1 1 1 0 0 0 1 0
6: done 1 1 1 1 0 0 0 1 0
7: 1 1 NA 1 0 0 0 1 0
8: done 1 1 1 1 0 0 0 1 0
sapply(c("0Q-01","0Q-02","0Q-03","0Q-04","0Q-05","0Q-06","0Q-07","0Q-08","0Q-09"),
function(y) {
dataset4[,y] <- sapply(c(1:1076), function(x)
ifelse (((is.na(dataset4[x,y])) && (dataset4[x,c("0Q-state")] == "done"))
,0, dataset4[x,y]))}
)
Output has to be:
>dataset4
0Q-state 0Q-01 0Q-02 0Q-03 0Q-04 0Q-05 0Q-06 0Q-07 0Q-08 0Q-09
1: done 1 1 1 1 1 1 1 1 0
2: 1 1 1 1 1 1 NA 1 1
3: done 1 1 1 0 1 1 1 1 1
5: done 1 1 1 1 0 0 0 1 0
6: done 1 1 1 1 0 0 0 1 0
7: 1 1 NA 1 0 0 0 1 0
8: done 1 1 1 1 0 0 0 1 0

Answer 1

we could try:
df[rep(df[, 1] == "done", ncol(df)) & is.na(df)] <- 0
df
1 done 1 1 1 1 1 1 1 1 0
2 1 1 1 1 1 1 NA 1 1
3 done 1 1 1 0 1 1 1 1 1
4 done 1 1 1 1 0 0 0 1 0
5 done 1 1 1 1 0 0 0 1 0
6 1 1 NA 1 0 0 0 1 0
7 done 1 1 1 1 0 0 0 1 0
or using sapply():
myFunc <- function(x, y) ifelse(is.na(x) & y == "done", 1, x)
data.frame(df[, 1], sapply(df[, -1], myFunc, y = df[, 1]))
1 done 1 1 1 1 1 1 1 1 NA
2 1 1 1 1 1 1 NA 1 1
3 done 1 1 1 NA 1 1 1 1 1
4 done 1 1 1 1 0 0 0 1 0
5 done 1 1 1 1 0 0 0 1 0
6 1 1 NA 1 0 0 0 1 0
7 done 1 1 1 1 0 0 0 1 0
where you can always substitute df[, 1] with df[, "0Q-state"] and df[, -1] with df[, namesOfDummyVars]

Answer 2

The question has been tagged with data.table and the printed output of dataset4 suggests that dataset4 already is a data.table object.
Here are three variants in data.table syntax to replace NAs in rows which are marked as "done".
# create vector of names of columns to be changed
cols <- sprintf("0Q-%02i", 1:9)
# variant 1
dataset4[`0Q-state` == "done",
(cols) := lapply(.SD, function(x) replace(x, is.na(x), 0L)),
.SDcols = cols][]
0Q-state 0Q-01 0Q-02 0Q-03 0Q-04 0Q-05 0Q-06 0Q-07 0Q-08 0Q-09
1: done 1 1 1 1 1 1 1 1 0
2: 1 1 1 1 1 NA 1 1 NA
3: done 1 1 1 0 1 1 1 1 1
4: done 1 1 1 1 0 0 0 1 0
5: done 1 1 1 1 0 0 0 1 0
6: 1 NA 1 0 0 0 1 0 NA
7: done 1 1 1 1 0 0 0 1 0
or
# variant 2
lapply(cols, function(i) dataset4[`0Q-state` == "done" & is.na(get(i)), (i) := 0L])
dataset4
returning the same as above
or
# variant 3 --- data.table development version 1.10.5
for (i in cols)
set(dataset4, which(dataset4[, "0Q-state"] == "done" & is.na(dataset4[, ..i])), i, 0L)
dataset4