Multiple unions

I am trying to do unions on several lists (these are actually GRanges objects not integer lists but the priciple is the same), basically one big union.

x<-sort(sample(1:20, 9))
y<-sort(sample(10:30, 9))
z<-sort(sample(20:40, 9))
mylists<-c("x","y","z")
emptyList<-list()
sapply(mylists,FUN=function(x){emptyList<-union(emptyList,get(x))})

That is just returning the list contents. I need the equivalent of

union(x,union(y,z))
[1]  2  3  5  6  7 10 13 15 20 14 19 21 24 27 28 29 26 3开发者_如何学Go1 36 39

but written in an extensible and non-"variable explicit" form

---

A not necessarily memory efficient paradigm that will work with GRanges is

Reduce(union, list(x, y, z))

The argument might also be a GRangesList(x, y, z) for appropriate values of x etc.

---

x<-sort(sample(1:20, 9))
y<-sort(sample(10:30, 9))
z<-sort(sample(20:40, 9))

Both of the below produce the same output

unique(c(x,y,z))
[1]  1  2  4  6  7  8 11 15 17 14 16 18 21 23 26 28 29 20 22 25 31 32 35

union(x,union(y,z))
[1]  1  2  4  6  7  8 11 15 17 14 16 18 21 23 26 28 29 20 22 25 31 32 35

---

unique(unlist(mget(mylists, globalenv())))

will do the trick. (Possibly changing the environment given in the call to mget, as required.)

---

I think it would be cleaner to separate the "dereference" part from the n-ary union part, e.g.

dereflist <- function(l) lapply(a,get)
nunion <- function(l) Reduce(union,l)

But if you look at how union works, you'll see that you could also do

nunion <- function(l) unique(do.call(c,l))

which is faster in all the cases I've tested (much faster for long lists).

-s

---

This can be done by using the reduce function in the purrr package.

purrr::reduce(list(x, y, z),union)

---

ok this works but I am curious why sapply seems to have its own scope

x<-sort(sample(1:20, 9))
y<-sort(sample(10:30, 9))
z<-sort(sample(20:40, 9))
mylists<-c("x","y","z")
emptyList<-vector()
for(f in mylists){emptyList<-union(emptyList,get(f))}