Faster way to subset data table instead of a for loop R

问题

I have a data table (you'll need the data table package installed) in R generated with X and Y coordinates and random data values from both normal and uniform distributions. The coordinates represent points on a 2000x1600 array and has to be divided into 16 smaller "sectors" each 500x400. These sectors need their mean of Normal Distribution values taken, divided by the min^2 of the Uniform Distribution values. I also created two variables x and y using a provided function startstop, that have the coordinates for the 16 sectors and a function that calculates the numbers for each sector.

library(data.table)
DT <- data.table(X = rep(1:2000, times = 1600), Y = rep(1:1600, each = 2000), Norm =rnorm(1600*2000), Unif = runif(1600*2000))

sectorCalc <- function(x,y,DT) {
    sector <- numeric(length = 16)
    for (i in 1:length(sector)) {
        sect <- DT[X %between% c(x[[1]][i],x[[2]][i]) & Y %between% c(y[[1]][i],y[[2]][i])]
        sector[i] <- sCalc(sect)
    }
    return(sector)
 }

startstop <- function(width, y = FALSE) {
    startend <- width - (width/4 - 1)
    start <- round(seq(0, startend, length.out = 4))
    stop <- round(seq(width/4, width, length.out = 4))
    if  (length(c(start,stop)[anyDuplicated(c(start,stop))]) != 0) {
        dup <- anyDuplicated(c(start,stop))
        stop[which(stop == c(start,stop)[dup])] <- stop[which(stop == c(start,stop)[dup])] - 1
}
    if (y == TRUE) {
        coord <- list(rep(start, each = 4), rep(stop, each = 4))
  } else if (y == FALSE) {
        coord <- list(rep(start, times = 4), rep(stop, times = 4))
  }
  return(coord)
}

x <- startstop(2000)
y <- startstop(1600, T)

sectorNos <- sectorCalc(x,y,DT)

The startstop function isn't really an issue but I need a faster way to subset the data table. Some modifications have to be made to the 'sectorCalc' function. The for loop was the best way I could think of but I don't have too much experience with data tables. Any ideas on a faster method of breaking up the data table?

回答1:

A solution using not only the package data.table but also the cut function to build the interval "groups":

# Create your test data
library(data.table)

set.seed(123)      # make random numbers reproducible to allow comparison of different answers
DT <- data.table(X = rep(1:2000, times = 1600), Y = rep(1:1600, each = 2000), Norm =rnorm(1600*2000), Unif = runif(1600*2000))

# calculate the sector by cutting the x and y values into groups defined by the interval breaks
DT[, x.sect := cut(DT[, X], c(0, 499, 1000, 1500, 2000), dig.lab=10)] # Intervals should be: seq(0, 2000, by=500) lower bound is less one since it is not included in the interval (see help for cut function)
DT[, y.sect := cut(DT[, Y], c(0, 399, 800, 1200, 1600), dig.lab=10)] # Intervals should be: seq(0, 1600, by=400)

# Now calculate per group (calculation logic "stolen" from the working answer of user "Symbolix"
DT[, .(sect = mean(Norm)/min(Unif)^2), by=.(x.sect, y.sect)]

Please note: I think the size of the first and second interval is wrong in the original solution (499 instead of 500 for x and 399 instead of 400 for y so that I could not use the seq function to reproduce your desired intervals but had to enumerate the interval breaks manually).

Edit 1: I have replaced the original code that adds the x.sect and y.sect columns by an improved solution that adds columns by reference (:=).

Edit 2: If you want to order the result you have (at least) two options:

# "Chaining" (output is input of next)
DT[, .(sect = mean(Norm)/min(Unif)^2), by=.(x.sect, y.sect)][order(x.sect, y.sect),]
# Or: Use the "keyby" param instead of "by"
DT[, .(sect = mean(Norm)/min(Unif)^2), keyby=.(x.sect, y.sect)]

Edit 3: Added dig.lab=10 param to cut function in code above to avoid scientific notation of the interval breaks.

回答2:

To replace your sectorCalc function I think we can make use of data.tables joins

As you are looping over each row of sector, you just have to create a data.table to join onto that is your sector data, specify a column to join (here I'm using key_col), and specify a 'group' variable for each row, to enable us to do a the calculation at the end:

x <- startstop(2000)
y <- startstop(1600, T)
## copy the original DT
dt <- copy(DT)


dt_xy <- data.table(x_1 = x[[1]],
                    x_2 = x[[2]],
                    y_1 = y[[1]],
                    y_2 = y[[2]])


dt[, key_col := 1]
dt_xy[, `:=`(key_col = 1, xy_grp = seq(1,.N))]

## Use a data.table join, allowing cartesian, then filter out results.
dt_res <- dt[ dt_xy, on="key_col", allow.cartesian=T][x_1 <= X & X <= x_2 & y_1 <= Y & Y <= y_2]

## calculate 'sect' as required.
dt_sect <- dt_res[, .(sect = mean(Norm)/min(Unif)^2)   , by=.(xy_grp)]

来源：https://stackoverflow.com/questions/35618570/faster-way-to-subset-data-table-instead-of-a-for-loop-r

标签

for-loop

indexing

data.table

subset