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我正在尝试使用drakeR 包来处理跨多个计划的多个文件输入，因此我可以迭代地构建我的目标，测试每个阶段的工作原理。下面是一个简单的代表，显示了我想要完成的事情。官方文档展示了如何在一个计划中做这种事情，但我的困难是我想跨过多个计划执行此操作。

我无法弄清楚转换应该是什么才能将正确的输入名称（来自read_in_plan的目标）放入munge_plan 

library(drake)
dplyr_version_dep = pkg_description = utils::packageDescription('dplyr')

munge_data = function(input){
  message("I did something!")
}

file_inputs = c("file1.csv", "file2.csv")

# get my data in
read_in_plan = drake_plan(
  # make the plan dependent on changes to dplyr
  pkg = utils::packageDescription('dplyr'),
  data = target(
    read.csv(input),
    transform = map(input = !!file_inputs)
  )
)

read_in_plan
#> # A tibble: 3 x 2
#>   target         command                           
#>   <chr>          <expr>                            
#> 1 pkg            utils::packageDescription("dplyr")
#> 2 data_file1.csv read.csv("file1.csv")             
#> 3 data_file2.csv read.csv("file2.csv")

# now do something to each of those targets
munge_plan = drake_plan(
  munged = munge_data(data_file1.csv)
)

munge_plan
#> # A tibble: 1 x 2
#>   target command                   
#>   <chr>  <expr>                    
#> 1 munged munge_data(data_file1.csv)

# but really I want to do munge data on all of the
# data_file1.csv
# data_file2.csv

# munge_data_proper = drake_plan(
#   munged = target(
#     # some kind of transform here
#   )
# )

full_plan = bind_plans(read_in_plan,
                       munge_plan)
# make(full_plan)

由reprex 包（v0.2.1）于 2019 年 5 月 23 日创建

4

1 回答 1

2

转换被设计为在一次调用中发生drake_plan()，因此很难将data_*目标和munged_*下面的目标分成不同的计划。

library(drake)
dplyr_version_dep = pkg_description = utils::packageDescription('dplyr')

munge_data = function(input){
    message("I did something!")
}

file_inputs <- c("file1.csv", "file2.csv")

plan <- drake_plan(
    pkg = target(
        dplyr_version_dep,
        # Triggers are always checked even though commands do not always run:
        trigger = trigger(change = utils::packageDescription("dplyr"))
    ),
    data = target(
        read.csv(input),
        transform = map(input = !!file_inputs, .id = FALSE)
    ),
    # Borrow from the previous transform:
    munged = target(
        munge_data(data),
        transform = map(data)
    )
)

drake_plan_source(plan)
#> drake_plan(
#>   pkg = target(
#>     command = dplyr_version_dep,
#>     trigger = trigger(
#>       change = utils::packageDescription("dplyr")
#>     )
#>   ),
#>   data = read.csv("file1.csv"),
#>   data_2 = read.csv("file2.csv"),
#>   munged_data = munge_data(data),
#>   munged_data_2 = munge_data(data_2)
#> )

由reprex 包（v0.3.0）于 2019 年 5 月 23 日创建

对您来说，一种技巧是从drake_plan(trace = TRUE). 脆弱，但在这个小例子中很有用。

library(drake)
dplyr_version_dep = pkg_description = utils::packageDescription('dplyr')

munge_data = function(input){
    message("I did something!")
}

file_inputs <- c("file1.csv", "file2.csv")

plan1 <- drake_plan(
    pkg = target(
        dplyr_version_dep,
        # Triggers are always checked even though commands do not always run:
        trigger = trigger(change = utils::packageDescription("dplyr"))
    ),
    data = target(
        read.csv(input),
        transform = map(input = !!file_inputs, .id = FALSE)
    ),
    trace = TRUE
)

plan1
#> # A tibble: 3 x 5
#>   target command         trigger                           input      data 
#>   <chr>  <expr>          <expr>                            <chr>      <chr>
#> 1 pkg    dplyr_version_… trigger(change = utils::packageD… <NA>       <NA> 
#> 2 data   read.csv("file… NA                              … "\"file1.… data 
#> 3 data_2 read.csv("file… NA                              … "\"file2.… data…

plan1$input
#> [1] NA              "\"file1.csv\"" "\"file2.csv\""

plan1$data
#> [1] NA       "data"   "data_2"

# Put together the data manually for the next transformation.
library(magrittr)
data <- plan1$data %>%
  na.omit() %>%
    unique() %>%
    rlang::syms()

str(data)
#> List of 2
#>  $ : symbol data
#>  $ : symbol data_2

plan2 <- drake_plan(
    munged = target(
        munge_data(d),
        transform = map(d = !!data) # !! is key
    )
)

plan2
#> # A tibble: 2 x 2
#>   target        command           
#>   <chr>         <expr>            
#> 1 munged_data   munge_data(data)  
#> 2 munged_data_2 munge_data(data_2)

full_plan <- bind_plans(dplyr::select(plan1, target, command), plan2)

full_plan
#> # A tibble: 5 x 2
#>   target        command              
#>   <chr>         <expr>               
#> 1 pkg           dplyr_version_dep    
#> 2 data          read.csv("file1.csv")
#> 3 data_2        read.csv("file2.csv")
#> 4 munged_data   munge_data(data)     
#> 5 munged_data_2 munge_data(data_2)

由reprex 包（v0.3.0）于 2019 年 5 月 23 日创建

于 2019-05-23T16:09:59.057 回答