轉換mapply輸出到數據幀變量

Question

我有這樣一個數據幀：

df <- data.frame(x=c(7,5,4),y=c(100,100,100),w=c(170,170,170),z=c(132,720,1256)) 

我使用mapply創建新列：

set.seed(123) 
library(truncnorm) 
df$res <- mapply(rtruncnorm,df$x,df$y,df$w,df$z,25) 

所以，我得到：

> df 
#x y w z   res 
#1 7 100 170 132 117.9881, 126.2456, 133.7627, 135.2322, 143.5229, 100.3735, 114.8287 
#2 5 100 170 720      168.8581, 169.4955, 169.6461, 169.8998, 169.0343 
#3 4 100 170 1256        169.7245, 167.6744, 169.7025, 169.4441 

#dput(df) 
df <- structure(list(x = c(7, 5, 4), y = c(100, 100, 100), w = c(170, 
170, 170), z = c(132, 720, 1256), res = list(c(117.988108836195, 
126.245562762918, 133.762709785614, 135.232193379024, 143.52290514973, 
100.373469134837, 114.828678702662), c(168.858147661715, 169.495493758985, 
169.646123183828, 169.899849943838, 169.034333943479), c(169.724470294466, 
167.674371713068, 169.70250974042, 169.444134892323))), .Names = c("x", 
"y", "w", "z", "res"), row.names = c(NA, -3L), class = "data.frame") 

但我真正需要的是根據df$res結果重複df數據幀的每一行，如下所示：

> df2 
# x y w z  res 
#1 7 100 170 132 117.9881 
#2 7 100 170 132 126.2456 
#3 7 100 170 132 133.7627 
#4 7 100 170 132 135.2322 
#5 7 100 170 132 143.5229 
#6 7 100 170 132 100.3735 
#7 7 100 170 132 114.8287 
#8 5 100 170 720 168.8581 
#9 5 100 170 720 169.4955 
#10 5 100 170 720 169.6461 
#11 5 100 170 720 169.8998 
#12 5 100 170 720 169.0343 
#13 4 100 170 1256 169.7245 
#14 4 100 170 1256 167.6744 
#15 4 100 170 1256 169.7025 
#16 4 100 170 1256 169.4441 

如何實現這個效率？我需要將此應用到大數據幀

Answer 1

df <- data.frame(x=c(7,5,4),y=c(100,100,100),w=c(170,170,170),z=c(132,720,1256)) 
set.seed(123) 
l <- mapply(rtruncnorm,df$x,df$y,df$w,df$z,25) 
cbind.data.frame(df[rep(seq_along(l), lengths(l)),], 
       res = unlist(l)) 
#  x y w z  res 
# 1 7 100 170 132 117.9881 
# 1.1 7 100 170 132 126.2456 
# 1.2 7 100 170 132 133.7627 
# 1.3 7 100 170 132 135.2322 
# 1.4 7 100 170 132 143.5229 
# 1.5 7 100 170 132 100.3735 
# 1.6 7 100 170 132 114.8287 
# 2 5 100 170 720 168.8581 
# 2.1 5 100 170 720 169.4955 
# 2.2 5 100 170 720 169.6461 
# 2.3 5 100 170 720 169.8998 
# 2.4 5 100 170 720 169.0343 
# 3 4 100 170 1256 169.7245 
# 3.1 4 100 170 1256 167.6744 
# 3.2 4 100 170 1256 169.7025 
# 3.3 4 100 170 1256 169.4441 

Answer 2

儘量此基礎上給出的df：

df$res <- sapply(df$res, paste0, collapse=",") 
do.call(rbind, apply(df, 1, function(x) do.call(expand.grid, strsplit(x, ",")))) 

    # x y w z    res 
# 1 7 100 170 132 117.988108836195 
# 2 7 100 170 132 126.245562762918 
# 3 7 100 170 132 133.762709785614 
# 4 7 100 170 132 135.232193379024 
# 5 7 100 170 132 143.52290514973 
# 6 7 100 170 132 100.373469134837 
# 7 7 100 170 132 114.828678702662 
# 8 5 100 170 720 168.858147661715 
# 9 5 100 170 720 169.495493758985 
# 10 5 100 170 720 169.646123183828 
# 11 5 100 170 720 169.899849943838 
# 12 5 100 170 720 169.034333943479 
# 13 4 100 170 1256 169.724470294466 
# 14 4 100 170 1256 167.674371713068 
# 15 4 100 170 1256 169.70250974042 
# 16 4 100 170 1256 169.444134892323