r - which(vector1 < vector2) -

let's make small example first, computes in r:

x<- c(1,3,1,4,2) max(which(x<2)) [1] 3 

now, not 1 value 2, many values simultaneously. should give me that:

max(which(x<c(1,2,3,4,5,6))) [1] na 3 5 5 5 5 

of course run for loop, slow:

for(i in c(1,2,3,4,5,6)){     test[i]<-max(which(x<i)) } 

is there fast way this?

find max index of each value seen in x:

xvals    <- unique(x) xmaxindx <- length(x) - match(xvals,rev(x)) + 1l 

rearrange

xvals    <- xvals[order(xmaxindx,decreasing=true)] xmaxindx <- xmaxindx[order(xmaxindx,decreasing=true)]    # 2 4 1 3  # 5 4 3 2 

select those:

xmaxindx[vapply(1:6,function(z){   ok <- xvals < z   if(length(ok)) which(ok)[1] else na_integer_ },integer(1))] # <na>    1    2    2    2    2  #   na    3    5    5    5    5  

it handily reports values (in first row) along indices (second row).

---

the sapply way simpler , not slower:

xmaxindx[sapply(1:6,function(z) which(xvals < z)[1])]     

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benchmarks. op's case not described, here benchmarks anyway:

# setup nicola <- function() max.col(outer(y,x,">"),ties.method="last")*na^(y<=min(x)) frank  <- function(){     xvals    <- unique(x)     xmaxindx <- length(x) - match(xvals,rev(x)) + 1l      xvals    <- xvals[order(xmaxindx,decreasing=true)]     xmaxindx <- xmaxindx[order(xmaxindx,decreasing=true)]        xmaxindx[vapply(y,function(z){       ok <- xvals < z       if(length(ok)) which(ok)[1] else na_integer_     },integer(1))] } beauvel <- function()      vectorize(function(u) ifelse(length(which(x<u))==0,na,max(which(x<u))))(y) davida <- function() vapply(y, function(i) c(max(which(x < i)),na)[1], double(1)) hallo <- function(){     test <- vector("integer",length(y))     for(i in y){             test[i]<-max(which(x<i))     }     test } josho <- function(){     xo <- sort(unique(x))     xi <- cummax(1l + length(x) - match(xo, rev(x)))     xi[cut(y, c(xo, inf))] } require(microbenchmark) 

(@mrhallo's , @davidarenburg's throw bunch of warnings way have them written now, fixed.) here results:

> x <- sample(1:4,1e6,replace=true) > y <- 1:6  > microbenchmark(nicola(),frank(),beauvel(),davida(),hallo(),josho(),times=10) unit: milliseconds       expr      min       lq     mean   median        uq       max neval   nicola() 76.17992 78.01171 99.75596 98.43919 120.81776 127.63058    10    frank() 25.27245 25.44666 36.41508 28.44055  45.32306  73.66652    10  beauvel() 47.70081 59.47828 67.44918 68.93808  74.12869  95.20936    10   davida() 26.52582 26.55827 33.93855 30.00990  35.55436  57.24119    10    hallo() 26.58186 26.63984 32.68850 28.68163  33.54364  50.49190    10    josho() 25.69634 26.28724 37.95341 30.50828  47.90526  68.30376    10 there 20 warnings (use warnings() see them) >   >  > x <- sample(1:80,1e6,replace=true) > y <- 1:60 > microbenchmark(nicola(),frank(),beauvel(),davida(),hallo(),josho(),times=10) unit: milliseconds       expr        min         lq       mean     median         uq       max neval   nicola() 2341.96795 2395.68816 2446.60612 2481.14602 2496.77128 2504.8117    10    frank()   25.67026   25.81119   42.80353   30.41979   53.19950  123.7467    10  beauvel()  665.26904  686.63822  728.48755  734.04857  753.69499  784.7280    10   davida()  326.79072  359.22803  390.66077  397.50163  420.66266  456.8318    10    hallo()  330.10586  349.40995  380.33538  389.71356  397.76407  443.0808    10    josho()   26.06863   30.76836   35.04775   31.05701   38.84259   57.3946    10 there 20 warnings (use warnings() see them) >   >  > x <- sample(sample(1e5,1e1),1e6,replace=true) > y <- sample(1e5,1e4) > microbenchmark(frank(),josho(),times=10) unit: milliseconds     expr      min       lq     mean   median       uq       max neval  frank() 69.41371 74.53816 94.41251 89.53743 107.6402 134.01839    10  josho() 35.70584 37.37200 56.42519 54.13120  63.3452  90.42475    10 

of course, comparisons might come out differently op's true case.