编辑：在我的第一个示例中有很多问题，因此我在这里对其进行了重新设计。这主要是为了向最初的响应者表示感谢，即使我的例子很差，他们也将我的处理时间减少了约180倍。这个问题因不清楚或不够笼统而被冻结，但我认为它具有价值，因为data.table可以使用正确的语法来完成令人惊奇的事情，但是即使使用可用的小插图，该语法也难以捉摸。根据我自己的经验，有更多关于如何使用data.table的示例将很有帮助。特别是对于那些开始使用Excel的人来说，这里的VLOOKUP之类的行为填补了并非总是容易找到的空白。
此示例中可能引起普遍关注的特定事情是：
修改后的原始问题（有限行）示例：
我正在寻找神秘的data.table世界，传递函数以及在多个表之间快速使用查找方面的帮助。我有一个更大的函数，当我对其进行概要分析时，似乎将其所有时间都花在了这一方面，以进行一些相当简单的查找和求和操作。我没有足够的能力进行概要分析，无法准确找出导致该问题的调用的哪些子区域，但是我的猜测是我无意间在执行不需要执行的计算量大的操作。 Data.table语法对我来说仍然是个谜，因此我在这里寻求帮助以加快此过程。
小型示例：
library(data.table)
set.seed(seed = 911)
##Other parts of the analysis generate all of these data.tables
#A data table containing id values (the real version has other things too)
whoamI<-data.table(id=1:5)
#The result of another calculation it tells me how many neighbors I will be interested in
#the real version has many more columns in it.
howmanyneighbors<-data.table(id=1:5,toCount=round(runif(5,min=1,max=3),0))
#Who the first three neighbors are for each id
#real version has a hundreds of neighbors
myneighborsare<-data.table(id=1:5,matrix(1:5,ncol=3,nrow=5,byrow = TRUE))
colnames(myneighborsare)<-c("id","N1","N2","N3")
#How many of each group live at each location?
groupPops<-data.table(id=1:5,matrix(floor(runif(25,min=0,max=10)),ncol=5,nrow=5))
colnames(groupPops)<-c("id","ape","bat","cat","dog","eel")
whoamI
howmanyneighbors
myneighborsare
groupPops
> whoamI
id
1: 1
2: 2
3: 3
4: 4
5: 5
> howmanyneighbors
id toCount
1: 1 2
2: 2 1
3: 3 3
4: 4 3
5: 5 2
> myneighborsare
id N1 N2 N3
1: 1 1 2 3
2: 2 4 5 1
3: 3 2 3 4
4: 4 5 1 2
5: 5 3 4 5
> groupPops
id ape bat cat dog eel
1: 1 9 8 6 8 1
2: 2 9 8 0 9 8
3: 3 6 1 9 1 2
4: 4 6 1 9 0 3
5: 5 6 2 2 2 5
##At any given time I will only want the group populations for some of the groups
#I will always want 'ape' but other groups will vary. Here I have picked two
#I retain this because passing the column names by variable along with the pass of 'ape' was tricky
#and I don't want to lose that syntax in any new answer
animals<-c("bat","eel")
i<-2 #similarly, howmanyneighbors has many more columns in it and I need to pass a reference to one of them which I call i here
##Functions I will call on the above data
#Get the ids of my neighbors from myneighborsare. The number of ids returned will vary based on value in howmanyneighbors
getIDs<-function(a){myneighborsare[id==a,2:(as.numeric(howmanyneighbors[id==a,..i])+1)]} #so many coding fails here it pains me to put this in public view
#Sum the populations of my neighbors for groups I am interested in.
sumVals<-function(b){colSums(groupPops[id%in%b,c("ape",..animals)])} #cringe
#Wrap the first two together and put them into a format that works well with being returned as a row in a data.table
doBoth<-function(a){
ro.ws<-getIDs(a)
su.ms<-sumVals(ro.ws)
answer<-lapply(split(su.ms,names(su.ms)),unname) #not too worried about this as it just mimics some things that happen in the original code at little time cost
return(answer)
}
#Run the above function on my data
result<-data.table(whoamI)
result[,doBoth(id),by=id]
id ape bat eel
1: 1 18 16 9
2: 2 6 1 3
3: 3 21 10 13
4: 4 24 18 14
5: 5 12 2 5
答案 0 :(得分：1)
这涉及重塑和非等距联接。
library(data.table)
# reshape to long and add a grouping ID for a non-equi join later
molten_neighbors <- melt(myneighborsare, id.vars = 'id')[, grp_id := .GRP, by = variable]
#regular join by id
whoamI[howmanyneighbors,
on = .(id)
#non-equi join - replaces getIDs(a)
][molten_neighbors,
on = .(id, toCount >= grp_id),
nomatch = 0L
#regular join - next steps replace sumVals(ro.ws)
][groupPops[, c('id','ape', ..animals)],
on = .(value = id),
.(id, ape, bat, eel),
nomatch = 0L,
][,
lapply(.SD, sum),
keyby = id
]
我强烈建议简化以后的问题。使用10行可让您在问题中发布表格。照原样，这有点难以理解。