为什么dplyr比plyr慢于数据聚合？

Question

背景问题:

假设我们有一个数据集,如:

ID DRIVE_NUM FLAG
 1         A PASS
 2         A FAIL
 3         A PASS
-----------------
 4         B PASS
 5         B PASS
 6         B PASS
-----------------
 7         C PASS
 8         C FAIL
 9         C FAIL

我想通过以下规则聚合DRIVE_NUM的这个数据集:

对于特定的DRIVE_NUM组,

如果DRIVE_NUM组中有任何FAIL标志,我希望第一行带有FAIL标志.

如果组中没有FAIL标志,只需占用组中的第一行.

所以,我将得到以下集合:

  ID DRIVE_NUM FLAG
   2         A FAIL
   4         B PASS
   8         C FAIL

---

更新:

似乎dplyr解决方案甚至比plyr慢.我不正确地使用任何东西吗？

#Simulate Data

X = data.frame(
  group = rep(paste0("NO",1:10000),each=2),
  flag = sample(c("F","P"),20000,replace = TRUE),
  var = rnorm(20000)
)



library(plyr)
library(dplyr)

#plyr

START = proc.time()
X2 = ddply(X,.(flag),function(df) {
  if( sum(df$flag=="F")> 0){
    R = df[df$flag=="F",]
    if(nrow(R)>1) {R = R[1,]} else {R = R}
  } else{
    R = df[1,]
  }
  R
})
proc.time() - START   

#user  system elapsed 
#0.03    0.00    0.03 

#dplyr method 1

START = proc.time()
X %>%
  group_by(group) %>% 
  slice(which.min(flag))
proc.time() - START  

#user  system elapsed 
#0.22    0.02    0.23 

#dplyr method 2

START = proc.time()
X %>%
  group_by(group, flag) %>%
  slice(1) %>%
  group_by(group) %>% 
  slice(which.min(flag))
proc.time() - START  

#user  system elapsed 
#0.28    0.00    0.28 

是否有data.table版本可以比plyr快得多？

Answer 1

运用 data.table

library(data.table)
START = proc.time()
 X3 = as.data.table(X)[X[, .I[which.min(flag)] , by = group]$V1]
proc.time() - START
#   user  system elapsed 
#  0.00    0.02    0.02 

或者使用 order

START = proc.time()
 X4 = as.data.table(X)[order(flag), .SD[1L] , by = group]
proc.time() - START
#    user  system elapsed 
#    0.02    0.00    0.01 

与对应的定时dplyr和plyr使用OP的代码是

#   user  system elapsed 
#  0.28    0.04    2.68 

#   user  system elapsed 
#  0.01    0.06    0.67 

同样由@Frank评论,base R方法时间是

START = proc.time()
Z = X[order(X$flag),]
X5 = with(Z, Z[tapply(seq(nrow(X)), group, head, 1), ])
proc.time() - START
#    user  system elapsed 
#    0.15    0.03    0.65 

我猜这slice是在减速dplyr.