1-555-CONFIDE

人民力量拉一塊布，建制派接招，也拉來了一塊布。那塊布是紅色的，封閉了我們的雙眼。
封閉了雙眼，人們竟看到幸福，感到舒服，一切現實中的痛苦都忘記了。
蒙著雙眼的人們，被問到你想怎樣，人們答不如由別人作主吧。

R 有個 function 叫 paste() 。
如

paste(“The current date and time is”, Sys.time(), “and you current working directory is”, getwd(), “. Mr Stark.”)

Output 是

“The current date and time is 2012-05-18 13:05:39 and you current working directory is /home/chainsaw/Stat . Mr Stark.”

基本上就是將一大堆的 String 串成一個大 String 。此 paste() 為我常用的功能之一。
但是，此 paste function 有一個問題。例如我想將存有 1-4 string 的 vector 串成 “1 2 3 4″ ，如下 code :

paste(as.character(1:4))

出來的結果仍是 vector ，不是如我所預料那樣。解決方法是用 Higher-order function Reduce() ，如此:

Reduce(paste, as.character(1:4))

此 Reduce 是將 paste 變成這樣：

paste(paste(paste(1,2),3),4)

此類的 function 還有 Map, Filter, Find, Position 和 Negate 。最近工作上要常常用到如此 function 去除去 for loop 。

A task that come up regularly during data munging is like this: you have a list of user ID (uid). Then, you have another table that has the user ID(lookup.uid) and the value of interest(lookup.value). You would like to fetch the list of value (lookup.value) with the uid = lookup.uid and retaining the order of uid.
I don’t know the terminology for this task. I usually call it “lookup”.
Programmatically, lookup can be done with a simple for-loop to loop through the uid and match the uid with the lookup.uid one by one. In R, the following is the usual pattern for this for-loop.

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#< -
forlooplookup <- function(uid, lookup) {
  uid.value <- rep(NA, length(uid))
  for (i in 1:length(uid)) {
    uid.value[uid == lookup[i,1]] <- lookup[i,2]
  }
  return(uid.value)
}

#< -
forlooplookup <- function(uid, lookup) {
  uid.value <- rep(NA, length(uid))
  for (i in 1:length(uid)) {
    uid.value[uid == lookup[i,1]] <- lookup[i,2]
  }
  return(uid.value)
}

However, for loop is slow. The whole point of R programming is vectorization¹ . The above for-loop can be rewritten using the vectorized match() function.

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vectorlookup <- function(uid, lookup) {
  return(lookup[match(uid, lookup[,1]),2])
}

vectorlookup <- function(uid, lookup) {
  return(lookup[match(uid, lookup[,1]),2])
}

Let’s benchmark the for-loop version and vectorized version of lookup.

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lookup <- data.frame(ID=1:30000, value=rnorm(30000))
uid <- sample(1:30000)
system.time(forlooplookup(uid, lookup))
system.time(vectorlookup(uid, lookup))

lookup <- data.frame(ID=1:30000, value=rnorm(30000))
uid <- sample(1:30000)
system.time(forlooplookup(uid, lookup))
system.time(vectorlookup(uid, lookup))

For-loop version took 29.4s to complete but only 0.007s for vectorized version. The performance was increased by 4200 folds.
We can also assert two functions are doing the same thing to check for correctness.

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sum(forlooplookup(uid, lookup) - vectorlookup(uid, lookup)) == 0

sum(forlooplookup(uid, lookup) - vectorlookup(uid, lookup)) == 0

1. I would say the whole point of R programming is functional programming, but I guess OO people may not agree. [↩]