使用：=在data.table中按組分配多列

Question

使用data.table分配到多列的最佳方式是什麼？例如：

f <- function(x) {c("hi", "hello")} 
x <- data.table(id = 1:10) 

我願做這樣的事情（當然這個語法是不正確的）：

x[ , (col1, col2) := f(), by = "id] 

，並延長我可能有名稱的列在一個變量（比如column_names），我想這樣做：

x[ , col_names := another_f(), by = "id", with = FALSE] 

什麼是做這樣的事情的正確方法？

Answer 1

這現在可以在R-Forge的v1.8.3上使用。感謝您的突出！

x <- data.table(a = 1:3, b = 1:6) 
f <- function(x) {list("hi", "hello")} 
x[ , c("col1", "col2") := f(), by = a][] 
# a b col1 col2 
# 1: 1 1 hi hello 
# 2: 2 2 hi hello 
# 3: 3 3 hi hello 
# 4: 1 4 hi hello 
# 5: 2 5 hi hello 
# 6: 3 6 hi hello 

x[ , c("mean", "sum") := list(mean(b), sum(b)), by = a][] 
# a b col1 col2 mean sum 
# 1: 1 1 hi hello 2.5 5 
# 2: 2 2 hi hello 3.5 7 
# 3: 3 3 hi hello 4.5 9 
# 4: 1 4 hi hello 2.5 5 
# 5: 2 5 hi hello 3.5 7 
# 6: 3 6 hi hello 4.5 9 

mynames = c("Name1", "Longer%") 
x[ , (mynames) := list(mean(b) * 4, sum(b) * 3), by = a] 
#  a b col1 col2 mean sum Name1 Longer% 
# 1: 1 1 hi hello 2.5 5 10  15 
# 2: 2 2 hi hello 3.5 7 14  21 
# 3: 3 3 hi hello 4.5 9 18  27 
# 4: 1 4 hi hello 2.5 5 10  15 
# 5: 2 5 hi hello 3.5 7 14  21 
# 6: 3 6 hi hello 4.5 9 18  27 

x[ , mynames := list(mean(b) * 4, sum(b) * 3), by = a, with = FALSE][] # same 
# a b col1 col2 mean sum Name1 Longer% 
# 1: 1 1 hi hello 2.5 5 10  15 
# 2: 2 2 hi hello 3.5 7 14  21 
# 3: 3 3 hi hello 4.5 9 18  27 
# 4: 1 4 hi hello 2.5 5 10  15 
# 5: 2 5 hi hello 3.5 7 14  21 
# 6: 3 6 hi hello 4.5 9 18  27 

x[ , get("mynames") := list(mean(b) * 4, sum(b) * 3), by = a][] # same 
# a b col1 col2 mean sum Name1 Longer% 
# 1: 1 1 hi hello 2.5 5 10  15 
# 2: 2 2 hi hello 3.5 7 14  21 
# 3: 3 3 hi hello 4.5 9 18  27 
# 4: 1 4 hi hello 2.5 5 10  15 
# 5: 2 5 hi hello 3.5 7 14  21 
# 6: 3 6 hi hello 4.5 9 18  27 

x[ , eval(mynames) := list(mean(b) * 4, sum(b) * 3), by = a][] # same 
# a b col1 col2 mean sum Name1 Longer% 
# 1: 1 1 hi hello 2.5 5 10  15 
# 2: 2 2 hi hello 3.5 7 14  21 
# 3: 3 3 hi hello 4.5 9 18  27 
# 4: 1 4 hi hello 2.5 5 10  15 
# 5: 2 5 hi hello 3.5 7 14  21 
# 6: 3 6 hi hello 4.5 9 18  27