I have the following data.table
我有以下data.table
x = structure(list(f1 = 1:3, f2 = 3:5), .Names = c("f1", "f2"), row.names = c(NA, -3L), class = c("data.table", "data.frame"))
I would like to apply a function to each row of the data.table. The function func.test uses args f1 and f2 and does something with it and returns a computed value. Assume (as an example)
我想将一个函数应用于data.table的每一行。函数func.test使用args f1和f2并对其执行某些操作并返回计算值。假设（作为例子）
func.text <- function(arg1,arg2){ return(arg1 + exp(arg2))}
but my real function is more complex and does loops and all, but returns a computed value. What would be the best way to accomplish this?
但我的真实函数更复杂，并且循环和所有，但返回计算值。实现这一目标的最佳方法是什么？
3 个解决方案
#1
32
The best way is to write a vectorized function, but if you can't, then perhaps this will do:
最好的方法是编写一个矢量化函数，但如果你不能，那么也许这样做：
x[, func.text(f1, f2), by = 1:nrow(x)]
#2
7
We can define rows with .I function.
我们可以使用.I函数定义行。
dt_iris <- data.table(iris)
dt_iris[, ..I := .I]
## Let's define some function
some_fun <- function(dtX) {
print('hello')
return(dtX[, Sepal.Length / Sepal.Width])
}
## by row
dt_iris[, some_fun(.SD), by = ..I] # or simply: dt_iris[, some_fun(.SD), by = .I]
## vectorized calculation
some_fun(dt_iris)
#3
6
The most elegant way I've found is with mapply:
我找到的最优雅的方式是与mapply：
x[, value := mapply(func.text, f1, f2)]
x
# f1 f2 value
# 1: 1 3 21.08554
# 2: 2 4 56.59815
# 3: 3 5 151.4132
#1
32
The best way is to write a vectorized function, but if you can't, then perhaps this will do:
最好的方法是编写一个矢量化函数，但如果你不能，那么也许这样做：
x[, func.text(f1, f2), by = 1:nrow(x)]
#2
7
We can define rows with .I function.
我们可以使用.I函数定义行。
dt_iris <- data.table(iris)
dt_iris[, ..I := .I]
## Let's define some function
some_fun <- function(dtX) {
print('hello')
return(dtX[, Sepal.Length / Sepal.Width])
}
## by row
dt_iris[, some_fun(.SD), by = ..I] # or simply: dt_iris[, some_fun(.SD), by = .I]
## vectorized calculation
some_fun(dt_iris)
#3
6
The most elegant way I've found is with mapply:
我找到的最优雅的方式是与mapply：
x[, value := mapply(func.text, f1, f2)]
x
# f1 f2 value
# 1: 1 3 21.08554
# 2: 2 4 56.59815
# 3: 3 5 151.4132