[英]How do I sum the values of columns in several tables if tables have different lengths?
Alright, this should be an easy one but I'm looking for a solution that's as fast as possible. 好吧,这应该是一个简单的,但我正在寻找一个尽可能快的解决方案。
Let's say I have 3 tables (the number of tables will be much larger): 假设我有3个表(表的数量会大得多):
tab1 <- table(c(1, 1, 1, 2, 2, 3, 3, 3))
tab2 <- table(c(1, 1, 4, 4, 4))
tab3 <- table(c(1, 1, 2, 3, 5))
This is what we get: 这就是我们得到的:
> tab1
1 2 3
3 2 3
> tab2
1 4
2 3
> tab3
1 2 3 5
2 1 1 1
What I want to have in a fast way so that it works with many big tables is this: 我希望以快速的方式拥有它以便它适用于许多大表是:
1 2 3 4 5
7 3 4 3 1
So, basically the tables get aggregated over all names
. 因此,基本上表格会聚合在所有
names
。 Is there an elementary function that does this which I am missing? 是否有基本功能可以解决这个问题? Thanks for your help!
谢谢你的帮助!
We concatenate ( c
) the tab
output to create 'v1', use tapply
to get the sum
of the elements grouped by the names
of that object. 我们连接(
c
) tab
输出以创建'v1',使用tapply
来获取按该对象的names
分组的元素的sum
。
v1 <- c(tab1, tab2, tab3)
tapply(v1, names(v1), FUN=sum)
#1 2 3 4 5
#7 3 4 3 1
You could use rowsum()
. 你可以使用
rowsum()
。 The output will be slightly different than what you show, but you can always restructure it after the calculations. 输出与您显示的输出略有不同,但您可以在计算后重新进行重组。
rowsum()
is known to be very efficient. 已知
rowsum()
非常有效。
x <- c(tab1, tab2, tab3)
rowsum(x, names(x))
# [,1]
# 1 7
# 2 3
# 3 4
# 4 3
# 5 1
Here's a benchmark with akrun's data.table suggestion added in as well. 这里是akrun的data.table建议的基准。
library(microbenchmark)
library(data.table)
xx <- rep(x, 1e5)
microbenchmark(
tapply = tapply(xx, names(xx), FUN=sum),
rowsum = rowsum(xx, names(xx)),
data.table = data.table(xx, names(xx))[, sum(xx), by = V2]
)
# Unit: milliseconds
# expr min lq mean median uq max neval
# tapply 150.47532 154.80200 176.22410 159.02577 204.22043 233.34346 100
# rowsum 41.28635 41.65162 51.85777 43.33885 45.43370 109.91777 100
# data.table 21.39438 24.73580 35.53500 27.56778 31.93182 92.74386 100
you can try this 你可以试试这个
df <- rbind(as.matrix(tab1), as.matrix(tab2), as.matrix(tab3))
aggregate(df, by=list(row.names(df)), FUN=sum)
Group.1 V1
1 1 7
2 2 3
3 3 4
4 4 3
5 5 1
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