我的生成目标的函数不仅依赖于data.frame，还依赖于其他参数.喜欢:
# _targets.R
library(targets)
mydat <- function() data.frame(time=1901:2020, val=letters[1:10])
myfun <- function(dat, from, to) subset(dat, time>from & time<to)
list(
tar_target(dat, mydat()),
tar_target(dat_of_interest, myfun(dat, from=2019, to=2020)
)
现在我想知道如何定义目标管道，使它知道参数的变化？是否有最佳做法？
targets也足够智能，可以判断全局变量的依赖关系，因此您只需编写:
library(targets)
mydat <- function() data.frame(time = 1901:2020, val = letters[1:10])
myfun <- function(dat, from, to) subset(dat, time >= from & time <= to)
from <- 2018
to <- 2020
list(
tar_target(dat, mydat()),
tar_target(dat_of_interest, myfun(dat, from = from, to = to))
)
tar_make()
# ▶ start target dat
# ● built target dat [0.02 seconds]
# ▶ start target dat_of_interest
# ● built target dat_of_interest [0 seconds]
# ▶ end pipeline [4.39 seconds]
tar_make()
# ✔ skip target dat
# ✔ skip target dat_of_interest
# ✔ skip pipeline [2.62 seconds]
from <- 2017
tar_make()
# ✔ skip target dat
# ▶ start target dat_of_interest
# ● built target dat_of_interest [0 seconds]
# ▶ end pipeline [3.5 seconds]
但是，您也可以更具体地将其作为自己的目标，通过在目标中硬编码常量，或者通过创建包装器函数:
tar_target(from, 2019)
## or
get_from <- function() {
2019
}
tar_target(from, get_from())
在第一种情况下，要更改from，在后一种情况下更改tar_target中的值，更改函数的返回值.我会 Select 全局方法或包装器函数.