giacomoV giacomoV - 1 month ago 5x
R Question

R merge back List after computation

I am having trouble with a simple problem and I can't find a simple solution to it. (This question is probably a

but I can't find it!)

What I need is to
back a list to its original list after a computation.

I need to
because the computation I am doing is too complicated to
them directly to the list. So, I have to do it separately and somehow to put it back to the original dataset. (I can't use
directly here because of this problem).

Because I can't reproduce my data, I will use
to demonstrate my problem.

I have an original list and I am applying a computation to it (it doesn't matter which one), so for example :


My original dataset is a list

dt = mtcars %>%
group_by(gear) %>%

Then, on this list, I do a computation, for example :

dt %>%
map(~summarise(., cluster = mean(disp)))

And I am ending up with a

The (real) structure of my data end up looking like this

gear cluster
1 3 326.3

gear cluster
1 4 123

and so on. What I need is simply to
merge back
this list to the original list.
How can I do this ?

What I need (output wanted) is to end up with (it's difficult to reproduce here) my original
and the
computed values.

Something like


mpg cyl disp hp drat wt qsec vs am gear carb cluster
1 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 XXX
2 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 XXX
3 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 XXX
4 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 XXX

and so on for all the lists (df)

I emphasise again that my original dataset is a list not a data.frame. What I need is to merge
, not

I thought of something like

dt = mtcars %>% # my data is a list
group_by(gear) %>%

fmerge = function(x) x %>% lapply(dt, ., by = 'gear')

dt %>%
map(~summarise(., cluster = mean(disp))) %>%


dt %>%
map(~summarise(., cluster = mean(disp))) %>%
join_all(dt, ., by = 'gear')

But it doesn't work well.

Any clue ?


We can use bind_rows to rbind the list elements and then do a right_join or left_join

mtcars %>% 
   group_by(gear) %>% 
   split(.$gear) %>% 
   map(~summarise(., cluster = mean(disp))) %>%
   bind_rows() %>%
   right_join(., mtcars, by = "gear")

However, this can be done without the split/map/bind_rows/right_join by just creating the 'cluster' with mutate after we group_by 'gear'

mtcars %>% 
     group_by(gear) %>%
     mutate(cluster = mean(disp))

However, we assume that this simplified process may not work in the OP's original dataset.


Based on the OP's comments, we can use map2 to do the left_join for corresponding elements of list

dt %>%
    map(~summarise(., cluster = mean(disp))) %>% 
    map2(dt, ., left_join, by = "gear")

Or if we need a single data.frame, then use map2df

dt %>%
    map(~summarise(., cluster = mean(disp))) %>% 
    map2_df(dt, ., left_join, by = "gear")