More efficient ways to use R than 'for' loops - r

Question

I'm a relative newcomer to R so I'm sorry if there's an obvious answer to this. I've looked at other questions and I think 'apply' is the answer but I can't work out how to use it in this case.
I've got a longitudinal survey where participants are invited every year. In some years they fail to take part, and sometimes they die. I need to identify which participants have taken part for a consistent 'streak' since from the start of the survey (i.e. if they stop, they stop for good).
I've done this with a 'for' loop, which works fine in the example below. But I have many years and many participants, and the loop is very slow. Is there a faster approach I could use?
In the example, TRUE means they participated in that year. The loop creates two vectors - 'finalyear' for the last year they took part, and 'streak' to show if they completed all years before the finalyear (i.e. cases 1, 3 and 5).
dat <- data.frame(ids = 1:5, "1999" = c(T, T, T, F, T), "2000" = c(T, F, T, F, T), "2001" = c(T, T, T, T, T), "2002" = c(F, T, T, T, T), "2003" = c(F, T, T, T, F))
finalyear <- NULL
streak <- NULL
for (i in 1:nrow(dat)) {
x <- as.numeric(dat[i,2:6])
y <- max(grep(1, x))
finalyear[i] <- y
streak[i] <- sum(x) == y
}
dat$finalyear <- finalyear
dat$streak <- streak
Thanks!

Answer 1

We could use max.col and rowSums as a vectorized approach.
dat$finalyear <- max.col(dat[-1], 'last')
If there are rows without TRUE values, we can make sure to return 0 for that row by multiplying with the double negation of rowSums. The FALSE will be coerced to 0 and multiplying with 0 returns 0 for that row.
dat$finalyear <- max.col(dat[-1], 'last')*!!rowSums(dat[-1])
Then, we create the 'streak' column by comparing the rowSums of columns 2:6 with that of 'finalyear'
dat$streak <- rowSums(dat[,2:6])==dat$finalyear
dat
# ids X1999 X2000 X2001 X2002 X2003 finalyear streak
#1 1 TRUE TRUE TRUE FALSE FALSE 3 TRUE
#2 2 TRUE FALSE TRUE TRUE TRUE 5 FALSE
#3 3 TRUE TRUE TRUE TRUE TRUE 5 TRUE
#4 4 FALSE FALSE TRUE TRUE TRUE 5 FALSE
#5 5 TRUE TRUE TRUE TRUE FALSE 4 TRUE
Or a one-line code (it could fit in one-line, but decided to make it obvious by 2-lines ) suggested by #ColonelBeauvel
library(dplyr)
mutate(dat, finalyear=max.col(dat[-1], 'last'),
streak=rowSums(dat[-1])==finalyear)

Answer 2

For-loops are not inherently bad in R, but they are slow if you grow vectors iteratively (like you are doing). There are often better ways to do things. Example of a solution with only apply-functions:
dat$finalyear <- apply(dat[,2:6],MARGIN=1,function(x){max(which(x))})
dat$streak <- apply(dat[,2:7],MARGIN=1,function(x){sum(x[1:5])==x[6]})
Or option 2, based on comment by #Spacedman:
dat$finalyear <- apply(dat[,2:6],MARGIN=1,function(x){max(which(x))})
dat$streak <- apply(dat[,2:6],MARGIN=1,function(x){max(which(x))==sum(x)})
> dat
ids X1999 X2000 X2001 X2002 X2003 finalyear streak
1 1 TRUE TRUE TRUE FALSE FALSE 3 TRUE
2 2 TRUE FALSE TRUE TRUE TRUE 5 FALSE
3 3 TRUE TRUE TRUE TRUE TRUE 5 TRUE
4 4 FALSE FALSE TRUE TRUE TRUE 5 FALSE
5 5 TRUE TRUE TRUE TRUE FALSE 4 TRUE

Answer 3

Here is a solution with dplyr and tidyr.
gather(data = dat,year,value,-ids) %>%
mutate(year=as.integer(gsub("X","",year))) %>%
group_by(ids) %>%
summarize(finalyear=last(year[value]),
streak=!any(value[first(year):finalyear] == FALSE))
output
ids finalyear streak
1 1 2001 TRUE
2 2 2003 FALSE
3 3 2003 TRUE
4 4 2003 FALSE
5 5 2002 TRUE

Answer 4

Here's a base version using apply to loop over rows and rle to see how often the state changes. Your condition seems to be equivalent to the state starting as TRUE and only ever changing to FALSE at most once, so I test the rle as being shorter than 3 and the first value being TRUE:
> dat$streak = apply(dat[,2:6],1,function(r){r[1] & length(rle(r)$length)<=2})
>
> dat
ids X1999 X2000 X2001 X2002 X2003 streak
1 1 TRUE TRUE TRUE FALSE FALSE TRUE
2 2 TRUE FALSE TRUE TRUE TRUE FALSE
3 3 TRUE TRUE TRUE TRUE TRUE TRUE
4 4 FALSE FALSE TRUE TRUE TRUE FALSE
5 5 TRUE TRUE TRUE TRUE FALSE TRUE
There's probably loads of ways of working out finalyear, this just finds the last element of each row which is TRUE:
> dat$finalyear = apply(dat[,2:6], 1, function(r){max(which(r))})
> dat
ids X1999 X2000 X2001 X2002 X2003 streak finalyear
1 1 TRUE TRUE TRUE FALSE FALSE TRUE 3
2 2 TRUE FALSE TRUE TRUE TRUE FALSE 5
3 3 TRUE TRUE TRUE TRUE TRUE TRUE 5
4 4 FALSE FALSE TRUE TRUE TRUE FALSE 5
5 5 TRUE TRUE TRUE TRUE FALSE TRUE 4