extract the row having column values length equal to 1 in R - r

Question

my input data is
df
anger sad joy happy trust disgust
1 1 0 1 2 3 0
2 2 0 0 2 0 3
3 2 2 1 1 1 1
4 0 1 1 1 0 1
I want output like this
mydata
anger sad joy happy trust disgust col
1 1 0 1 2 3 0 trust
2 2 0 0 2 0 3 disgust
I want to extract max value colname from each row but output only those rows having only one max value colname and discard all other row with more than one colname.
i tried this
d1 <- df[!apply(df[-1], 1, function(x) anyDuplicated(x[x == max(x)])),]
but i am getting this
anger sad joy happy trust disgust
1 1 0 1 2 3 0
2 2 0 0 2 0 3
3 2 2 1 1 1 1
I don't want third row in the output.
Thanks for help in advance.

Answer 1

We can use max.col to get the index of columns for each row after subsetting the rows
d1 <- mydata[!apply(mydata[-1], 1, anyDuplicated),]
d1$out <- names(d1)[-1][max.col(d1[-1], 'first')]
d1
# zone_id v1 v2 v3 v4 out
#1 1 12 15 18 20 v4
#3 3 31 28 14 2 v1
#4 4 12 16 9 5 v2
#5 5 5 18 10 12 v2
Update
If the OP wanted to remove only the duplicate values of max values, then replace the first line with
d1 <- mydata[!apply(mydata[-1], 1, function(x) anyDuplicated(x[x == max(x)])),]
Update2
Based on the newdataset by the OP, we don't need to remove the first column as it is not an id column
d2 <- mydata1[!apply(mydata1, 1, function(x) anyDuplicated(x[x == max(x)])),]
d2$out <- names(d2)[max.col(d2, 'first')]
d2
# anger sad joy happy trust disgust out
#1 1 0 1 2 3 0 trust
#2 2 0 0 2 0 3 disgust
data
mydata1 <- structure(list(anger = c(1L, 2L, 2L, 0L), sad = c(0L, 0L, 2L,
1L), joy = c(1L, 0L, 1L, 1L), happy = c(2L, 2L, 1L, 1L), trust = c(3L,
0L, 1L, 0L), disgust = c(0L, 3L, 1L, 1L)), .Names = c("anger", "sad",
"joy", "happy", "trust", "disgust"), row.names = c(NA, 4L),
class = "data.frame")

Answer 2

you can try:
mydata %>%
select(-zone_id) %>%
mutate(mx = do.call(pmax, (.))) %>%
select(mx) %>%
cbind(mydata) %>%
mutate( flg = rowSums(. == mx)) %>%
filter(flg ==2) %>%
select(-flg) %>%
gather(key = out, value= v, -mx, -zone_id) %>%
filter(mx == v) %>%
select(zone_id, mx, out) %>%
left_join(mydata)
which gives:
zone_id mx out v1 v2 v3 v4
1 3 31 v1 31 28 2 2
2 4 16 v2 1 16 9 1
3 5 18 v2 5 18 10 12
4 1 20 v4 12 15 18 20