Group-wise subsetting where feasible

I would like to subset rows of my data

library(data.table); set.seed(333); n <- 100 
dat <- data.table(id=1:n, group=rep(1:2,each=n/2), x=runif(n,100,120), y=runif(n,200,220), z=runif(n,300,320))

> head(dat)
   id group        x        y        z
1:  1     1 109.3400 208.6732 308.7595
2:  2     1 101.6920 201.0989 310.1080
3:  3     1 119.4697 217.8550 313.9384
4:  4     1 111.4261 205.2945 317.3651
5:  5     1 100.4024 212.2826 305.1375
6:  6     1 114.4711 203.6988 319.4913

in several stages within each group. I need to automate this and it might happen that the subset is empty. For example, focusing only on group 1,

dat1 <- dat[1:50]
> s <-subset(dat1,x>119)
> s
   id group        x        y        z
1:  3     1 119.4697 217.8550 313.9384
2: 50     1 119.2519 214.2517 318.8567

the second step subset(s, y>219) would come up empty but I would still want to apply the third step subset(s,z>315). If I were to set the threshold manually, Frank has provided an excellent solution here that outputs

> f(dat1, x>119, y>219, z>315)
      cond  skip
1: x > 119 FALSE
2: y > 219  TRUE
3: z > 315 FALSE
   id group        x        y        z
1: 50     1 119.2519 214.2517 318.8567

and reports which parts were skipped.

My problem is that I need to apply this to different groups simultaneously, where the thresholds for each group are given in a separate data.table. The goal is to have at least one id per group. For example, if my thresholds were

c <- data.table(group=1:2, x=c(119,119), y=c(219,219), z=c(315,319))
> c
   group   x   y   z
1:     1 119 219 315
2:     2 119 219 319

I would like to end up with

> res
   id group        x        y        z
1: 50     1 119.2519 214.2517 318.8567
2: 55     2 119.2634 219.0044 315.6556

I could apply Frank's function repeatedly within a for-loop but I am sure there are cleverer ways that save time. I wonder, for instance, whether the function can be applied to each group within data.table. Or perhaps there is a way within the tidyverse, which I am not really familiar with yet.

Another possible approach using standard evaluation:

#convert conditions into long format, storing operator in data.table as well
cond <- data.table(group=1:2, bop=c(`>`, `>`), x=c(119,119), y=c(219,219), z=c(315,319))
thres <- melt(cond, id.vars=c("group","bop"))

#convert data into long format and lookup filter and thresholds
mdat <- melt(dat, id.vars=c("id", "group"))[
    thres, on=.(group, variable), c("bop","thres") := mget(c("bop","i.value"))]

#apply filtering
ss <- mdat[mapply(function(f, x, y) f(x, y), bop, value, thres)]

#apply sequential subsetting
dat[id %in% ss[, {
        idx <- id
        ans <- .SD[, {
                x <- intersect(idx, id)
                if(length(x) > 0) {
                    idx <- x
                }
                idx
            }, .(variable)]

        ans[variable==last(variable), V1]
    }, .(group)]$V1
]

output:

   id group        x        y        z
1: 50     1 119.2519 214.2517 318.8567
2: 55     2 119.2634 219.0044 315.6556
3: 58     2 119.2211 214.0305 319.3097
4: 72     2 114.0802 217.7402 313.3655
5: 90     2 116.8115 215.1576 317.0261
6: 99     2 119.2964 212.9973 308.9360

data:

library(data.table)
set.seed(333)
n <- 100
dat <- data.table(id=1:n, group=rep(1:2,each=n/2),
    x=runif(n,100,120), y=runif(n,200,220), z=runif(n,300,320))