R：使用foreach读取csv数据，并在数据上应用函数，然后导出回csv

我有3个csv文件，即

file1.csv

，

file2.csv

和

file3.csv

现在，对于文件的每个，我想导入csv并对其执行一些功能，然后导出转换后的csv。因此，3个csv输入和3个转换csv输出。只有3个独立的任务。所以我想我可以尝试使用

foreach

%dopar%

。请不要告诉我我正在使用窗口机器

然而，我无法让它工作

library(foreach)
library(doParallel)
library(xts)
library(zoo)
numCores <- detectCores()
cl <- parallel::makeCluster(numCores)
doParallel::registerDoParallel(cl)

filenames <- c("file1.csv","file2.csv","file3.csv")
foreach(i = 1:3, .packages = c("xts","zoo")) %dopar%{
  df_xts          <- data_processing_IMPORT(filenames[i])
  ddates                <- unique(date(df_xts))
}

它仍然不起作用。我想了解我的逻辑出了什么问题，我应该如何解决这个问题？我只是尝试在数据上应用简单的函数，我仍然没有转换数据并将它们单独导出到csv。但我已经被卡住了

有趣的是，我已经编写了下面的简单代码，效果很好。在

foreach

中，

a

就像上面的

df_xts

一样，存储在变量中并传递到

Fun2

进行处理。下面的代码运行良好。但上面没有。我不明白为什么

numCores <- detectCores()
cl <- parallel::makeCluster(numCores)
doParallel::registerDoParallel(cl)


# Define the function
Fun1=function(x){
  a=2*x
  b=3*x
  c=a+b
  return(c)
}

Fun2=function(x){
  a=2*x
  b=3*x
  c=a+b
  return(c)
}

foreach(i = 1:10)%dopar%{
  x <- rnorm(5)
  a <- Fun1(x)
  tst <- Fun2(a)
  return(tst)
  }
### Output: No error

parallel::stopCluster(cl)

---

numCores使用
foreach（）
是正确的。您在
ddates中使用了
date（）
，我遇到了关于读取、修改和写入几个CSV文件的相同问题。我试图找到一个
tidyverse
解决方案，虽然它并没有真正解决上面的
date
问题，但这里是--如何使用
purr
中的
map
读取、修改和写入几个csv文件

library(tidyverse)


# There are some sample csv file in the "sample" dir.
# First get the paths of those.

datapath <- fs::dir_ls("./sample", regexp = ("csv"))

datapath

# Then read in the data, such as it is a list of data frames
# It seems simpler to write them back to disk as separate files.
# Another way to read them would be:
# newsampledata <- vroom::vroom(datapath, ";", id = "path")
# but this will return a DF and separating it to different files
# may be more complicated.

sampledata <- map(datapath, ~ read_delim(.x, ";"))

# Do some transformation of the data.
# Here I just alter the column names.

transformeddata <- sampledata %>% 
  map(rename_all, tolower)

# Then prepare to write new files
names(transformeddata) <- paste0("new-", basename(names(transformeddata)))

# Write the csv files and check if they are there
map2(transformeddata, names(transformeddata), ~ write.csv(.x, file = .y))
dir(pattern = "new-")

库（tidyverse）
#“示例”目录中有一些示例csv文件。
#首先要知道这些的路径。
数据路径功能
data\u processing\u IMPORT
做什么？我在您给定的包中找不到它，它所做的只是读取.csv并将其转换为xts对象。仅此而已。您使用的
date（）
错误，您想将
用作.date（）
date（）
返回当前系统时间。
ddates <- unique(as.Date(df_xts))

library(tidyverse)


# There are some sample csv file in the "sample" dir.
# First get the paths of those.

datapath <- fs::dir_ls("./sample", regexp = ("csv"))

datapath

# Then read in the data, such as it is a list of data frames
# It seems simpler to write them back to disk as separate files.
# Another way to read them would be:
# newsampledata <- vroom::vroom(datapath, ";", id = "path")
# but this will return a DF and separating it to different files
# may be more complicated.

sampledata <- map(datapath, ~ read_delim(.x, ";"))

# Do some transformation of the data.
# Here I just alter the column names.

transformeddata <- sampledata %>% 
  map(rename_all, tolower)

# Then prepare to write new files
names(transformeddata) <- paste0("new-", basename(names(transformeddata)))

# Write the csv files and check if they are there
map2(transformeddata, names(transformeddata), ~ write.csv(.x, file = .y))
dir(pattern = "new-")