row-by-row operations and updates in data.table

data.table, r

Solution

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).

Data:

set.seed(1)
require(data.table)
DT1 <- data.table(matrix(rnorm(1e6),ncol=10))
DT1[, a := 1:1e5]
DT2 <- copy(DT1)
DT3 <- copy(DT1)

Functions:

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)]
}

Benchmarking:

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

--

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))}]

Problem

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 ``` 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 ``` So I can update easily. 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 ``` I would like to write ``` DT[,rownames(new):=new] ``` but this does not work, so here are my questions - Using my method, how can I transform `new` such that I can update `DT` at 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: 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: 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 ?

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