R how to "divide" into combined subsets?

Question

Let's say I have a factor in which there are tons of levels. And I grouped some of those levels that are represented by the grps variable.

I can use “split” to split my data frame, but is it possible to split the data frame so that the merged layers represented by grps are in the same split?

set.seed(42)
fooLevels <- c(115,119,156,120,158,219)
foo <- fooLevels[round(runif(20, min=1, max=6))]
doo <- rnorm(20)
df <- data.frame(foo,doo)

grps <- c("{115}","{119}","{156}{120}{158}{219}")
splits <- split(df, f = df$foo)

I would like the result to look like this:

>splits
$`{115}`
   foo         doo
8  115  0.08983289
9  115 -2.99309008
12 115  0.18523056

$`{119}`
   foo        doo
2  119 -0.7838389
7  119  0.6792888
13 119  0.5818237

$`{156}{120}{158}{219}`
   foo        doo
1  120  0.3219253
4  120  0.6428993
6  120  0.2765507
11 120 -0.3672346
18 120  1.0385061
20 120  0.7208782
3  156 1.5757275
10 156 0.2848830
17 156 0.3358481
5  158 0.08976065
16 158 1.30254263
19 158 0.92072857
14 219  1.3997368
15 219 -0.7272921

The order of the lines in the list (data.frame) is irrelevant.

Answer 1

You can set list names and change the expression str_splitto anything that works for you.

lapply(
    strsplit(
        grps, 
        '}\\{|\\{|}'
    ), 
    function(x) {
        df[df$foo %in% x,]
    }
)
[[1]]
[1] foo doo
<0 rows> (or 0-length row.names)

[[2]]
   foo       doo
3  119 -1.388861
8  119 -2.656455
14 119  1.214675
18 119 -1.763163

[[3]]
   foo        doo
1  219  1.3048697
2  219  2.2866454
4  158 -0.2787888
5  120 -0.1333213
6  120  0.6359504
7  158 -0.2842529
9  120 -2.4404669
10 158  1.3201133
11 156 -0.3066386
12 158 -1.7813084
13 219 -0.1719174
15 156  1.8951935
16 219 -0.4304691
17 219 -0.2572694
19 156  0.4600974
20 120 -0.6399949

Answer 2

grp , -

x = split(df, df$foo)
y = Reduce(`rbind`, x[names(x)> 120])
o = c(x[names(x) <= 120], setNames(list(y), paste(unique(y$foo), collapse = ' ')))

#> o
#$`119`
#   foo       doo
#3  119 -1.388861
#8  119 -2.656455
#14 119  1.214675
#18 119 -1.763163

#$`120`
#   foo        doo
#5  120 -0.1333213
#6  120  0.6359504
#9  120 -2.4404669
#20 120 -0.6399949

#$`156 158 219`
#   foo        doo
#11 156 -0.3066386
#15 156  1.8951935
#19 156  0.4600974
#4  158 -0.2787888
#7  158 -0.2842529
#10 158  1.3201133
#12 158 -1.7813084
#1  219  1.3048697
#2  219  2.2866454
#13 219 -0.1719174
#16 219 -0.4304691
#17 219 -0.2572694