Revert list structure

Question

GOAL

Given a list of lists my goal is to reverse its structure (R language).

So, I want to bring the elements of the nested lists to be elements of the tier one list.

Probably an example better specifies my purpose. Given:

z <- list(z1 = list(a = 1, b = 2, c = 3), z2 = list(b = 4, a = 1, c = 0)) 

I want an output equivalent to the subsequent R object:

o <- list(a = list(z1 = 1, z2 = 1), b = list(z1 = 2, z2 = 4), c = list(z1 = 3, z2 = 0)) 

SOLUTIONS

MY SOLUTION

I created my own solution, which I am attaching below, but let me know if there is some better.

revert_list_str_1 <- function(ls) {   res <- lapply(names(ls[[1]]), function(n, env) {     name <- paste(n, 'elements', sep = '_')     assign(name, vector('list', 0))     inner <- sapply(ls, function(x) {       assign(name, c(get(name), x[which(names(x) == n)]))     })     names(inner) <- names(ls)      inner   })   names(res) <- names(ls[[1]])    res } 

Executing str(revert_list_str_1(z)) I obtain the subsequent output, corresponding to what I wanted.

List of 3  $ a:List of 2   ..$ z1: num 1   ..$ z2: num 1  $ b:List of 2   ..$ z1: num 2   ..$ z2: num 4  $ c:List of 2   ..$ z1: num 3   ..$ z2: num 0 

But as I said I'd like to investigate (and learn) the existence of a more elegant and dynamic solution.

In fact my solution works fully only if all the nested lists have the same names (also in different order). This because of names(ls[[1]]). I would also point out that it acts only on lists of 2 levels, like the one reported.

So, do you know other solutions that are more dynamic? Can rapply and/or Filter functions be useful for this task?

end edit 1.

ANALYSIS OF PROPOSED SOLUTIONS

I've done a little analysis of the proposes solutions, thans you all !. The analysis consists of verifying the following points for all functions:

1. accepted classes (nested list elements)
   1. type preserved also if there are elements with different types (if they are atomic)
   2. object contained in the elements preserved (e.g. a matrix)
2. columns considered (for columns I mean the names of the nested lists)
   1. not common columns ignored (the classification 'not' is understood positively in this case)
   2. not common columns preserved
   3. it works also when columns do not match (based only on the names of the first nested list)

In all this cases the classification 'yes' is understood positively execept for point 2.1.

This are all the functions I've considered (the comments relate to the analysis items mentioned above):

# yes 1.1 # yes 1.2 # yes 2.1, not 2.2, not 2.3 revert_list_str_1 <- function(ls) { # @leodido     # see above }  # not 1.1 # not 1.2 # not 2.1, not 2.2, not 2.3 revert_list_str_2 <- function(ls) { # @mnel   # convert each component of list to a data.frame   # so rbind.data.frame so named elements are matched   x <- data.frame((do.call(rbind, lapply(ls, data.frame))))   # convert each column into an appropriately named list   o <- lapply(as.list(x), function(i, nam) as.list(`names<-`(i, nam)), nam = rownames(x))    o }  # yes 1.1 # yes 1.2 # yes 2.1, not 2.2, yes 2.3 revert_list_str_3 <- function(ls) { # @mnel   # unique names   nn <- Reduce(unique, lapply(ls, names))   # convert from matrix to list `[` used to ensure correct ordering   as.list(data.frame(do.call(rbind,lapply(ls, `[`, nn)))) }  # yes 1.1 # yes 1.2 # yes 2.1, not 2.2, yes 2.3 revert_list_str_4 <- function(ls) { # @Josh O'Brien   # get sub-elements in same order   x <- lapply(ls, `[`, names(ls[[1]]))   # stack and reslice   apply(do.call(rbind, x), 2, as.list)  }  # not 1.1 # not 1.2 # not 2.1, not 2.2, not 2.3 revert_list_str_5 <- function(ls) { # @mnel   apply(data.frame((do.call(rbind, lapply(ls, data.frame)))), 2, as.list) }  # not 1.1 # not 1.2 # not 2.1, yes 2.2, yes 2.3 revert_list_str_6 <- function(ls) { # @baptiste + @Josh O'Brien   b <- recast(z, L2 ~ L1)   apply(b, 1, as.list) }  # yes 1.1 # yes 1.2 # not 2.1, yes 2.2, yes 2.3 revert_list_str_7 <-  function(ll) { # @Josh O'Brien   nms <- unique(unlist(lapply(ll, function(X) names(X))))   ll <- lapply(ll, function(X) setNames(X[nms], nms))   ll <- apply(do.call(rbind, ll), 2, as.list)   lapply(ll, function(X) X[!sapply(X, is.null)]) } 

CONSIDERATIONS

From this analysis emerges that:

• functions revert_list_str_7 and revert_list_str_6 are the most flexible regarding the names of the nested list
• functions revert_list_str_4, revert_list_str_3 followed by my own function are complete enough, good trade-offs.
• the most complete in absolute function is revert_list_str_7.

BENCHMARKS

To complete the work I've done some little benchmarks (with microbenchmark R package) on this 4 functions (times = 1000 for each benchmark).

BENCHMARK 1

Input:

list(z1 = list(a = 1, b = 2, c = 3), z2 = list(a = 0, b = 3, d = 22, f = 9)).

Results:

Unit: microseconds     expr       min         lq     median         uq       max 1 func_1   250.069   467.5645   503.6420   527.5615  2028.780 2 func_3   204.386   393.7340   414.5485   429.6010  3517.438 3 func_4    89.922   173.7030   189.0545   194.8590  1669.178 4 func_6 11295.463 20985.7525 21433.8680 21934.5105 72476.316 5 func_7   348.585   387.0265   656.7270   691.2060  2393.988 

Winner: revert_list_str_4.

BENCHMARK 2

Input:

list(z1 = list(a = 1, b = 2, c = 'ciao'), z2 = list(a = 0, b = 3, c = 5)).

revert_list_str_6 excluded because it does not support different type of nested child elements.

Results:

Unit: microseconds     expr     min       lq   median       uq      max 1 func_1 249.558 483.2120 502.0915 550.7215 2096.978 2 func_3 210.899 387.6835 400.7055 447.3785 1980.912 3 func_4  92.420 170.9970 182.0335 192.8645 1857.582 4 func_7 257.772 469.9280 477.8795 487.3705 2035.101 

Winner: revert_list_str_4.

BENCHMARK 3

Input:

list(z1 = list(a = 1, b = m, c = 'ciao'), z2 = list(a = 0, b = 3, c = m)).

m is a matrix 3x3 of integers and revert_list_str_6 has been excluded again.

Results:

Unit: microseconds expr     min       lq   median       uq      max 1 func_1 261.173 484.6345 503.4085 551.6600 2300.750 2 func_3 209.322 393.7235 406.6895 449.7870 2118.252 3 func_4  91.556 174.2685 184.5595 196.2155 1602.983 4 func_7 252.883 474.1735 482.0985 491.9485 2058.306 

Winner: revert_list_str_4. Again!

end edit 2.

CONCLUSION

First of all: thanks to all, wonderful solutions.

In my opinion if you know in advance that you list will have nested list with the same names reverse_str_4 is the winner as best compromise between performances and support for different types.

The most complete solution is revert_list_str_7 although the full flexibility induces an average of about 2.5 times a worsening of performances compared to reverse_str_4 (useful if your nested list have different names).

Answer 1

Edit:

Here's a more flexible version that will work on lists whose elements don't necessarily contain the same set of sub-elements.

fun <-  function(ll) {     nms <- unique(unlist(lapply(ll, function(X) names(X))))     ll <- lapply(ll, function(X) setNames(X[nms], nms))     ll <- apply(do.call(rbind, ll), 2, as.list)     lapply(ll, function(X) X[!sapply(X, is.null)]) }  ## An example of an 'unbalanced' list z <- list(z1 = list(a = 1, b = 2),            z2 = list(b = 4, a = 1, c = 0)) ## Try it out fun(z) 

---

Original answer

z <- list(z1 = list(a = 1, b = 2, c = 3), z2 = list(b = 4, a = 1, c = 0))  zz <- lapply(z, `[`, names(z[[1]]))   ## Get sub-elements in same order apply(do.call(rbind, zz), 2, as.list) ## Stack and reslice 

Answer 2

---

EDIT -- working from @Josh O'Briens suggestion and my own improvemes

The problem was that do.call rbind was not calling rbind.data.frame which does some matching of names. rbind.data.frame should work, because data.frames are lists and each sublist is a list, so we could just call it directly.

apply(do.call(rbind.data.frame, z), 1, as.list) 

However, while this may be succicint, it is slow because do.call(rbind.data.frame, ...) is inherently slow.

---

Something like (in two steps)

 # convert each component of z to a data.frame  # so rbind.data.frame so named elements are matched  x <- data.frame((do.call(rbind, lapply(z, data.frame))))  # convert each column into an appropriately named list  o <- lapply(as.list(x), function(i,nam) as.list(`names<-`(i, nam)), nam = rownames(x))  o $a $a$z1 [1] 1  $a$z2 [1] 1   $b $b$z1 [1] 2  $b$z2 [1] 4   $c $c$z1 [1] 3  $c$z2 [1] 0 

And an alternative

# unique names nn <- Reduce(unique,lapply(z, names)) # convert from matrix to list `[` used to ensure correct ordering as.list(data.frame(do.call(rbind,lapply(z, `[`, nn))))