Iteratively and hierarchically cycle through rows till a condition is met

Question

I'm trying to solve a data management problem in R.

Suppose my data looks as follows:

id <- c("123", "414", "606")
next.up <- c("414", "606", "119")
is.cond.met <- as.factor(c("FALSE", "FALSE", "TRUE"))
df <- data.frame(id, next.up, is.cond.met)

> df
   id next.up is.cond.met
1 123     414       FALSE
2 414     606       FALSE
3 606     119        TRUE

And I'd like to obtain is the following:

id <- c("123", "414", "606")
next.up <- c("414", "606", "119")
is.cond.met <- as.factor(c("FALSE", "FALSE", "TRUE"))
origin <- c("606", "606", "119")
df.result <- data.frame(id, next.up, is.cond.met, origin)

> df.result
   id next.up is.cond.met origin
1 123     414       FALSE    606
2 414     606       FALSE    606
3 606     119        TRUE    119

In other words: I want to match each ID to its "origin" when a given condition (is.met) is true. The difficulty I'm having is that this is iterative and hierarchical: to find the origin I may have to go through multiple degrees of separations. the logical steps are illustrated below. I'm really not sure how to tackle this in R.

UPDATE
One of the comments propose a data.frame solution which works for sorted data, as in the minimal example above. In truth, my data is not sorted in such a manner. A better example is as follows:

id <- c("961980", "14788", "902460", "900748", "728912", "141726", "1041190", "692268")
next.up <- c("20090", "655036", "40375164", "40031850", "40368996", "961980", "141726", "760112")
is.cond.met <- c(TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE)
df <- data.frame(id, next.up, is.cond.met, stringsAsFactors = FALSE)

glimpse(df)

Observations: 8
Variables: 3
$ id          <chr> "961980", "14788", "902460", "900748", "728912", "141726", "1041190", "692268"
$ next.up     <chr> "20090", "655036", "40375164", "40031850", "40368996", "961980", "141726", "760112"
$ is.cond.met <lgl> TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE
> df
       id  next.up is.cond.met
1  961980    20090        TRUE
2   14788   655036       FALSE
3  902460 40375164       FALSE
4  900748 40031850       FALSE
5  728912 40368996       FALSE
6  141726   961980       FALSE
7 1041190   141726       FALSE
8  692268   760112       FALSE

UPDATE 2: the end result should look like this:

> df.end.result
       id  next.up is.cond.met origin
1  961980    20090        TRUE   <NA>
2   14788   655036       FALSE   <NA>
3  902460 40375164       FALSE   <NA>
4  900748 40031850       FALSE   <NA>
5  728912 40368996       FALSE   <NA>
6  141726   961980       FALSE 961980
7 1041190   141726       FALSE 961980
8  692268   760112       FALSE   <NA>

Answer 1

I have extended your example data a bit to show what happens with more TRUE values in is.cond.met. Using the data.table package, you could do:

library(data.table)
setDT(df)[, grp := shift(cumsum(is.cond.met), fill=0)
          ][, origin := ifelse(is.cond.met, next.up, id[.N]), by = grp][]

which gives:

> df
    id next.up is.cond.met grp origin
1: 123     414       FALSE   0    606
2: 414     606       FALSE   0    606
3: 606     119        TRUE   0    119
4: 119     321       FALSE   1    321
5: 321     507        TRUE   1    507
6: 507     185        TRUE   2    185

Explanation:

1. Create a grouping variable first with shift(cumsum(is.cond.met), fill=0).
2. With ifelse(is.cond.met, next.up, id[.N]) you assign the correct values to origin.

Note: The id and next.up columns should be of class character for the above to work (for that reason I used stringsAsFactors = FALSE in constructing the extended example data). If they are factors, convert them first with as.character. If is.cond.met isn't already a logical, convert it with as.logical.

---

On the updated example data, the above code gives:

        id  next.up is.cond.met grp origin
1:  961980    20090        TRUE   0  20090
2:   14788   655036       FALSE   1 692268
3:  902460 40375164       FALSE   1 692268
4:  900748 40031850       FALSE   1 692268
5:  728912 40368996       FALSE   1 692268
6:  141726   961980       FALSE   1 692268
7: 1041190   141726       FALSE   1 692268
8:  692268   760112       FALSE   1 692268

---

Used data:

id <- c("123", "414", "606", "119", "321", "507")
next.up <- c("414", "606", "119", "321", "507", "185")
is.cond.met <- c(FALSE, FALSE, TRUE, FALSE, TRUE, TRUE)

df <- data.frame(id, next.up, is.cond.met, stringsAsFactors = FALSE)