Question

在data.table中使用R，我正在尝试对子集进行操作，不包括所选元素。我正在使用by运算符，但我不知道这是否是正确的方法。

这是一个例子。例如。 Delta中IAH:SNA的值为（3 + 3）/ 2，这是Stops IAH:SNA的平均值，Delta被排除。{/ 1}} p>

library(data.table)
s1 <- "Market   Carrier Stops
IAH:SNA     Delta     1
IAH:SNA     Delta     1
IAH:SNA Southwest     3
IAH:SNA Southwest     3
MSP:CLE Southwest     2
MSP:CLE Southwest     2
MSP:CLE  American     2
MSP:CLE   JetBlue     1"

d <- data.table(read.table(textConnection(s1), header=TRUE))

setkey(d, Carrier, Market)

f <- function(x, y){
         subset(d, !(Carrier %in% x) & Market == y, Stops)[, mean(Stops)]}

d[, s := f(.BY[[1]], .BY[[2]]), by=list(Carrier, Market)]

##     Market   Carrier Stops     s
## 1: MSP:CLE  American     2  1.666667
## 2: IAH:SNA     Delta     1  3.000000
## 3: IAH:SNA     Delta     1  3.000000
## 5: IAH:SNA Southwest     3  1.000000
## 6: IAH:SNA Southwest     3  1.000000
## 7: MSP:CLE Southwest     2  1.500000
## 8: MSP:CLE Southwest     2  1.500000

上述解决方案在大型数据集上执行非常（它基本上是mapply），但我不确定如何在快速data.table中执行此操作 - 喜欢的方式。

也许人们可以（动态地）生成一个能够做到这一点的因素？我只是不确定如何。。

有没有办法改善它？

编辑：只是为了它，这是一种获得上述更大版本的方法

library(data.table)
dl.dta <- function(...){
      ## input years ..
      years <- gsub("\\.", "_", c(...))
      baseurl <- "http://www.transtats.bts.gov/Download/"
      names <- paste("Origin_and_Destination_Survey_DB1BMarket", years, sep="_")
      info <- t(sapply(names, function(x) file.exists(paste(x, c("zip", "csv"), sep="."))))
      to.download <- paste(baseurl, names, ".zip", sep="")[!apply(info, 1, any)]
      if (length(to.download) > 0){
          message("starting download...")
          sapply(to.download,
                 function(x) download.file(x, rev(strsplit(x, "/")[[1]])[1]))}

      to.unzip <- paste(names,  "zip", sep=".")[!info[, 2]]
      if (length(to.unzip > 0)){
          message("starting to unzip...")
          sapply(to.unzip, unzip)}
      paste(names, "csv", sep=".")}

countWords.split <- function(x, s=":"){
    ## Faster on my machine than grep for some reanon
    sapply(strsplit(as.character(x), s), length)}

countWords.grep <- function(x){
    sapply(gregexpr("\\W+", x), length)+1}

fname <- dl.dta(2013.1)
cols <- rep("NULL", 41)
## Columns to keep: 9 is Origin, 18 is Dest, 24 is groups of airports in travel
## 30 is RPcarrier (reporting carrier).  
## For more columns: 35 is market fare and 36 is distance.
cols[9] <- cols[18] <- cols[24] <- cols[30] <- NA
d <- data.table(read.csv(file=fname,  colClasses=cols))
d[, Market := paste(Origin, Dest, sep=":")]
## should probably
d[, Stops := -2 + countWords.split(AirportGroup)]
d[, Carrier := RPCarrier]
d[, c("RPCarrier", "Origin", "Dest", "AirportGroup") := NULL]

Answer 1

使用一点基本数学：

d[, c("tmp.mean", "N") := list(mean(Stops), .N), by = Market]
d[, exep.mean := (tmp.mean * N - sum(Stops)) / (N - .N), by = list(Market,Carrier)]

#     Market   Carrier Stops tmp.mean N exep.mean
# 1: IAH:SNA     Delta     1     2.00 4  3.000000
# 2: IAH:SNA     Delta     1     2.00 4  3.000000
# 3: IAH:SNA Southwest     3     2.00 4  1.000000
# 4: IAH:SNA Southwest     3     2.00 4  1.000000
# 5: MSP:CLE Southwest     2     1.75 4  1.500000
# 6: MSP:CLE Southwest     2     1.75 4  1.500000
# 7: MSP:CLE  American     2     1.75 4  1.666667
# 8: MSP:CLE   JetBlue     1     1.75 4  2.000000

R：关于子集的Data.table，不包括值

1 个答案: