结合列表列表与数组和属性R [英] Combine list of list with arrays and attributes R

问题描述

我想合并一个列表列表，该列表包含的元素是数组和数字，数组具有我要保留的属性以及数组的尺寸.

I would like to combine a list of list which has elements that are arrays and numbers, the array has attributes that I want to conserve and also the dimensions of the array.

# names of rows
pn <-as.list(paste0("P",1:10))

pa <-matrix(runif(100),nrow=10)
rownames(pa)<-pn

pb <-matrix(runif(100),nrow=10)
rownames(pb)<-pn

a<-list(list(nsim=runif(1),param=pa),list(nsim=runif(1),param=pb))

我可以将此列表合并为一个这样的列表

I can combine this list into one list like this

a1 <- do.call(Map, c(c, a))

但这不是我需要的结果，行名丢失了，我想对每个组件进行不同的操作，对于nsim我想求和，对于param我想按列进行组合，例如cbind .我认为可以使用for循环完成此操作，但我想知道是否可以使用函数而不是Map更快地完成此操作. 我期望的结果是这样的:

but this is not the result I need, the names of the rows are lost and I want to make different operations for each component, for nsim I want to make a sum, and for param I want to combine by columns like cbind. I think that this could be done with a for loop but I wonder if it could be done faster using a function instead of Map. The result I expect is like this:

pc <-matrix(runif(200),nrow=10)
rownames(pc)<-pn
res<-list(nsim=runif(1)+runif(1),param=pa)

res 


$nsim
[1] 0.3104111

$param
[,1]       [,2]       [,3]       [,4]       [,5]        [,6]      [,7]      [,8]      [,9]
P1  0.2239347 0.06278946 0.98205058 0.61853458 0.02383296 0.002134358 0.8513100 0.5719191 0.5900750
P2  0.8692000 0.12358855 0.16389523 0.70464981 0.28209890 0.595703930 0.1770708 0.7762373 0.5342873
P3  0.3812888 0.78675818 0.58067408 0.75473889 0.26632764 0.665318861 0.4758030 0.4214253 0.1549026
...
[,11]     [,12]     [,13]       [,14]       [,15]     [,16]     [,17]     [,18]      [,19]
P1  0.7276528 0.6331882 0.8154552 0.034903094 0.384146499 0.1222392 0.1048753 0.5124031 0.57319354
P2  0.9089065 0.4261310 0.4903192 0.680854370 0.331368439 0.0239536 0.4988695 0.2384391 0.28938142
P3  0.2615411 0.6219749 0.8196556 0.611008178 0.012309896 0.1656148 0.1684677 0.8212866 0.96902755
...

推荐答案

根据您的描述，Map和Reduce的以下组合应该起作用:

Based on your description, the following combination of Map and Reduce should work:

 Map(function(f, i) Reduce(f, lapply(a, "[[", i)), list("+", "cbind"), as.list(1:2))

此想法是将适当的功能(+和cbind)映射到每个列表的适当元素.使用lapply，[[和所需的索引提取适当的列表元素.然后，通过Reduce使用适当的函数合并这些提取的结果.

The idea is to map the appropriate functions (+ and cbind) to the appropriate elements of each list. The appropriate list elements are extracted using lapply, [[ and the desired index. These extracted results are then comined using the appropriate function through Reduce.

种子为1234时，返回

With a seed of 1234, this returns

[[1]]
[1] 1.189114

[[2]]
           [,1]      [,2]       [,3]      [,4]      [,5]       [,6]       [,7]       [,8]      [,9]     [,10]      [,11]
P1  0.113703411 0.6935913 0.31661245 0.4560915 0.5533336 0.07377988 0.86483383 0.12148019 0.9264005 0.1663938 0.03545673
P2  0.622299405 0.5449748 0.30269337 0.2651867 0.6464061 0.30968660 0.04185728 0.89283638 0.4719097 0.9004246 0.56507611
P3  0.609274733 0.2827336 0.15904600 0.3046722 0.3118243 0.71727174 0.31718216 0.01462726 0.1426153 0.1340782 0.28025778
P4  0.623379442 0.9234335 0.03999592 0.5073069 0.6218192 0.50454591 0.01374994 0.78312110 0.5442698 0.1316141 0.20419632
P5  0.860915384 0.2923158 0.21879954 0.1810962 0.3297702 0.15299896 0.23902573 0.08996133 0.1961747 0.1052875 0.13373890
P6  0.640310605 0.8372956 0.81059855 0.7596706 0.5019975 0.50393349 0.70649462 0.51918998 0.8985805 0.5115836 0.32568192
P7  0.009495756 0.2862233 0.52569755 0.2012480 0.6770945 0.49396092 0.30809476 0.38426669 0.3894998 0.3001991 0.15506197
P8  0.232550506 0.2668208 0.91465817 0.2588098 0.4849912 0.75120020 0.50854757 0.07005250 0.3108708 0.0267169 0.12996214
P9  0.666083758 0.1867228 0.83134505 0.9921504 0.2439288 0.17464982 0.05164662 0.32064442 0.1600287 0.3096474 0.43553106
P10 0.514251141 0.2322259 0.04577026 0.8073523 0.7654598 0.84839241 0.56456984 0.66849540 0.8961858 0.7421197 0.03864265
        [,12]     [,13]     [,14]     [,15]      [,16]      [,17]     [,18]      [,19]     [,20]
P1  0.7133016 0.7442772 0.9622228 0.5027498 0.43904205 0.42944396 0.7572890 0.47880269 0.6389113
P2  0.1007690 0.9159742 0.6340994 0.9836351 0.22859970 0.57587778 0.5842715 0.34483055 0.9925160
P3  0.9503049 0.9945982 0.1274334 0.3243860 0.08215807 0.43250740 0.7088394 0.60071414 0.1282698
P4  0.1218178 0.9423607 0.4230470 0.4813749 0.85026492 0.22484576 0.4269758 0.07608332 0.8832396
P5  0.2196566 0.4861354 0.9143169 0.3569871 0.23466126 0.08498474 0.3435727 0.95599261 0.8100834
P6  0.9130878 0.2834595 0.4677923 0.6274777 0.98816745 0.63729826 0.7591200 0.02220682 0.8218512
P7  0.9458531 0.2515457 0.9081691 0.7416002 0.60189755 0.43101637 0.4240302 0.84171063 0.8347027
P8  0.2791562 0.5032552 0.5977433 0.5659668 0.99874081 0.07271609 0.5608873 0.63244245 0.7327322
P9  0.1234711 0.4969662 0.6317428 0.9807865 0.37559938 0.80240202 0.1161358 0.31009417 0.9830440
P10 0.7971605 0.3184458 0.8691583 0.5768127 0.55512663 0.32527830 0.3030218 0.74256937 0.6392046

请注意，您可以通过将其包装在setNames中来为每个列表元素添加名称.

Note that you can add names to each list element by wrapping this in setNames.

alexis-laz建议使用此方法的两个更简洁的版本，一个具有do.call，Map，...和嵌套列表.

alexis-laz suggests two cleaner versions of this method, one with do.call, Map , ..., and nested lists.

# put the functions into their own list.
funs = list(`+`, cbind)
do.call(Map, c(function(f, ...) f(...), list(funs), a))

，另一个使用.mapply，...和嵌套列表.

and the other using .mapply, ... and nested lists.

.mapply(function(f, ...) f(...), c(list(funs), a), NULL)

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