data.table 中的逐行操作和更新 [英] row-by-row operations and updates in data.table
问题描述
我最终得到了一个大 data.table，我必须对每行进行操作.(是的......我知道这显然不是 data.table 的用途)
I ended up with a big data.table and I have to do operations per row. (yes... I know that this is clearly not what data.table are for)
R) set.seed(1)
R) DT=data.table(matrix(rnorm(100),nrow=10))
R) DT[,c('a','b'):=list(1:10,2:11)]
R) DT
V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 a b
1: -0.6264538107 1.51178116845 0.91897737161 1.35867955153 -0.1645235963 0.3981058804 2.40161776050 0.475509528900 -0.5686687328 -0.5425200310 1 2
2: 0.1836433242 0.38984323641 0.78213630073 -0.10278772734 -0.2533616801 -0.6120263933 -0.03924000273 -0.709946430922 -0.1351786151 1.2078678060 2 3
3: -0.8356286124 -0.62124058054 0.07456498337 0.38767161156 0.6969633754 0.3411196914 0.68973936245 0.610726353489 1.1780869966 1.1604026157 3 4
4: 1.5952808021 -2.21469988718 -1.98935169586 -0.05380504058 0.5566631987 -1.1293630961 0.02800215878 -0.934097631644 -1.5235668004 0.7002136495 4 5
5: 0.3295077718 1.12493091814 0.61982574789 -1.37705955683 -0.6887556945 1.4330237017 -0.74327320888 -1.253633400239 0.5939461876 1.5868334545 5 6
6: -0.8204683841 -0.04493360902 -0.05612873953 -0.41499456330 -0.7074951570 1.9803998985 0.18879229951 0.291446235517 0.3329503712 0.5584864256 6 7
7: 0.4874290524 -0.01619026310 -0.15579550671 -0.39428995371 0.3645819621 -0.3672214765 -1.80495862889 -0.443291873218 1.0630998373 -1.2765922085 7 8
8: 0.7383247051 0.94383621069 -1.47075238390 -0.05931339671 0.7685329245 -1.0441346263 1.46555486156 0.001105351632 -0.3041839236 -0.5732654142 8 9
9: 0.5757813517 0.82122119510 -0.47815005511 1.10002537198 -0.1123462122 0.5697196274 0.15325333821 0.074341324152 0.3700188099 -1.2246126149 9 10
10: -0.3053883872 0.59390132122 0.41794156020 0.76317574846 0.8811077265 -0.1350546039 2.17261167036 -0.589520946188 0.2670987908 -0.4734006364 10 11
假设我想要逐行遍历所有 Vi 列的 min，我曾经在使用 时使用 .apply>data.frame
Say I want the min across of all the Vi columns row by row, I used to use apply when I was using data.frame.
apply(DT[,paste0('V',1:10),with=FALSE],FUN=min,MAR=1)
[1] -0.6264538107 -0.7099464309 -0.8356286124 -2.2146998872 -1.3770595568 -0.8204683841 -1.8049586289 -1.4707523839 -1.2246126149 -0.5895209462
这样我就可以轻松更新了.
So I can update easily.
好的，现在说我想一次更新 min 和 max (当然这是一个例子，所以我只做了两件事，但在现实生活中将是 10...)
Ok, now say that I want to update the min and max at once (off course this is an example so I took just 2 things but in real life that would be 10...)
f = function(x){return(c(max=max(x),min=min(x)))}
new=apply(DT[,paste0('V',1:10),with=FALSE],FUN=f,MAR=1)
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
max 2.4016177605 1.2078678060 1.1780869966 1.595280802 1.586833455 1.9803998985 1.063099837 1.465554862 1.100025372 2.1726116704
min -0.6264538107 -0.7099464309 -0.8356286124 -2.214699887 -1.377059557 -0.8204683841 -1.804958629 -1.470752384 -1.224612615 -0.5895209462
我想写
DT[,rownames(new):=new]
但这不起作用，所以这是我的问题
but this does not work, so here are my questions
- 使用我的方法，如何转换
new以便我可以立即更新DT? - 有没有更好的方法(可以让我一次更新多个列，结果是逐行计算)
- Using my method, how can I transform
newsuch that I can updateDTat once ? - Are there some better approach (that would allow me to update multiple columns at once, with the result of a by-row calculation)
EDIT:我找到了 1 的解决方案，但这很丑，实际上 := 不处理 matrix 很奇怪，我很确定以前是这样的
EDIT: I found a solution for 1 but that's UGLY, actually It is strange that := do not handle matrix, I am pretty sure it used to be the case
DT[,c('a1','a2'):=data.table(matrix(apply(DT[,paste0('V',1:10),with=FALSE],FUN=f,MAR=1),byrow=T,nrow=10))]
R) DT
V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 a b
1: -0.6264538107 1.51178116845 0.91897737161 1.35867955153 -0.1645235963 0.3981058804 2.40161776050 0.475509528900 -0.5686687328 -0.5425200310 1 2
2: 0.1836433242 0.38984323641 0.78213630073 -0.10278772734 -0.2533616801 -0.6120263933 -0.03924000273 -0.709946430922 -0.1351786151 1.2078678060 2 3
3: -0.8356286124 -0.62124058054 0.07456498337 0.38767161156 0.6969633754 0.3411196914 0.68973936245 0.610726353489 1.1780869966 1.1604026157 3 4
4: 1.5952808021 -2.21469988718 -1.98935169586 -0.05380504058 0.5566631987 -1.1293630961 0.02800215878 -0.934097631644 -1.5235668004 0.7002136495 4 5
5: 0.3295077718 1.12493091814 0.61982574789 -1.37705955683 -0.6887556945 1.4330237017 -0.74327320888 -1.253633400239 0.5939461876 1.5868334545 5 6
6: -0.8204683841 -0.04493360902 -0.05612873953 -0.41499456330 -0.7074951570 1.9803998985 0.18879229951 0.291446235517 0.3329503712 0.5584864256 6 7
7: 0.4874290524 -0.01619026310 -0.15579550671 -0.39428995371 0.3645819621 -0.3672214765 -1.80495862889 -0.443291873218 1.0630998373 -1.2765922085 7 8
8: 0.7383247051 0.94383621069 -1.47075238390 -0.05931339671 0.7685329245 -1.0441346263 1.46555486156 0.001105351632 -0.3041839236 -0.5732654142 8 9
9: 0.5757813517 0.82122119510 -0.47815005511 1.10002537198 -0.1123462122 0.5697196274 0.15325333821 0.074341324152 0.3700188099 -1.2246126149 9 10
10: -0.3053883872 0.59390132122 0.41794156020 0.76317574846 0.8811077265 -0.1350546039 2.17261167036 -0.589520946188 0.2670987908 -0.4734006364 10 11
a1 a2
1: 2.401617761 -0.6264538107
2: 1.207867806 -0.7099464309
3: 1.178086997 -0.8356286124
4: 1.595280802 -2.2146998872
5: 1.586833455 -1.3770595568
6: 1.980399899 -0.8204683841
7: 1.063099837 -1.8049586289
8: 1.465554862 -1.4707523839
9: 1.100025372 -1.2246126149
10: 2.172611670 -0.5895209462
EDIT2:它使用 DT[, (newColnames):=f(.DT), by=IDX, .SDcols= 查看我的数据someIdx] 比 apply 方式慢很多，这是预期的吗?
EDIT2: It looks on my data that using DT[, (newColnames):=f(.DT), by=IDX, .SDcols=someIdx] is much slower than the apply way, is that expected ?
推荐答案
在每一行上创建 .SD 可能是一项非常昂贵的操作，特别是如果您的 data.table 包含 rows >>列.我建议使用 pmin 和 pmax 跨列循环.我将用更大的数据(沿行)来说明这一点.
Creating .SD on each row could be a very costly operation, especially if your data.table consists of rows >> columns. I'd advice using pmin and pmax across columns with a loop. I'll illustrate this with a bigger data (along the rows).
set.seed(1)
require(data.table)
DT1 <- data.table(matrix(rnorm(1e6),ncol=10))
DT1[, a := 1:1e5]
DT2 <- copy(DT1)
DT3 <- copy(DT1)
功能:
arun <- function(DT) {
# assign first column (dummy)
DT[, `:=`(min = DT[, V1], max = DT[, V1])]
# get all other column names and use pmin and pmax
# and replace min and max columns
cols <- names(DT)[2:10]
for (i in cols) {
DT[, `:=`(min = pmin(min, DT[[i]]), max = pmax(max, DT[[i]]))]
}
DT
}
eddi <- function(DT) {
DT[, `:=`(min = min(.SD), max = max(.SD)), by = a, .SDcols = paste0("V", 1:10)]
}
frank <- function(DT) {
cols <- names(DT)[grepl('^V[[:digit:]]+$',names(DT))]
newcols <- c("min","max")
myfun <- range
DT[,(newcols):=as.list(myfun(.SD)),.SDcols=cols,by=1:nrow(DT)]
}
基准测试:
require(microbenchmark)
microbenchmark(o1 <- arun(DT1), o2 <- eddi(DT2), o3 <- frank(DT3), times=2)
Unit: milliseconds
expr min lq median uq max neval
o1 <- arun(DT1) 204.4417 204.4417 250.5205 296.5992 296.5992 2
o2 <- eddi(DT2) 92343.5321 92343.5321 96706.1622 101068.7923 101068.7923 2
o3 <- frank(DT3) 49083.7000 49083.7000 49521.9296 49960.1592 49960.1592 2
identical(o1, o2) # TRUE
identical(o1, o3) # TRUE
--
正如@Frank 在评论下指出的那样，您可以将 for 循环替换为 do.call 为:
As @Frank points out under comments, you could replace the for-loop with do.call as:
DT[, c("min", "max") := { z <- dt[, 1:10];
list(do.call(pmin, z), do.call(pmax, z))}]
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