使用tidyquant R遍歷參數列表

Question

我有一個數據集，我想使用tq_mutate處理並使用不同的參數值進行rollapply處理。

當前，我正在使用for循環遍歷所有參數值，但是我確定這不是執行此任務的最有效或最快的方法（尤其是當我要查看大量參數值時）。 如何改進或刪除for循環？ 我懷疑這意味着使用purrr :: map或其他某種方法（多線程/多核等），但是我無法在線找到有用的示例。

下面是一些示例代碼。 請忽略數據集和比例函數輸出的簡單性，僅出於說明目的。 我想做的是遍歷許多不同的V0值。

library(dplyr)
library(tidyverse)
library(broom)
library(tidyquant)

my_bogus_function <- function(df, V0=1925) { 
  # WILL HAVE SOMETHING MORE SOPHISTICATED IN HERE BUT KEEPING IT SIMPLE
  # FOR THE PURPOSES OF THE QUESTION
  c(V0, V0*2)
}

window_size <- 7 * 24
cnames = c("foo", "bar")
df <- c("FB") %>%
    tq_get(get = "stock.prices", from = "2016-01-01", to = "2017-01-01") %>% 
    dplyr::select("date", "open")

# CAN THIS LOOP BE DONE IN A MORE EFFICIENT MANNER? 
for (i in (1825:1830)){
  df <- df %>% 
        tq_mutate(mutate_fun = rollapply,
                  width      = window_size,
                  by.column  = FALSE,
                  FUN        = my_bogus_function,
                  col_rename = gsub("$", sprintf(".%d", i), cnames), 
                  V0 = i
    )
}
# END OF THE FOR LOOP I WANT FASTER

Answer 1

鑒於R使用一個內核，我發現通過使用並行，doSNOW和foreach軟件包允許使用多個內核（請注意，我在Windows計算機上，因此某些其他軟件包不可用）而有所改進。

我確信對於多線程/並行化/向量化代碼還有其他答案。

這是有興趣的人的代碼。

library(dplyr)
library(tidyverse)
library(tidyquant)
library(parallel)
library(doSNOW)  
library(foreach)

window_size <- 7 * 24
cnames = c("foo", "bar")
df <- c("FB") %>%
  tq_get(get = "stock.prices", from = "2016-01-01", to = "2017-01-01") %>% 
  dplyr::select("date", "open")

my_bogus_function <- function(df, V0=1925) { 
  # WILL HAVE SOMETHING MORE SOPHISTICATED IN HERE BUT KEEPING IT SIMPLE
  # FOR THE PURPOSES OF THE QUESTION
  c(V0, V0*2)
}

# CAN THIS LOOP BE DONE IN A MORE EFFICIENT/FASTER MANNER? YES 
numCores <- detectCores() # get the number of cores available
cl <- makeCluster(numCores, type = "SOCK")
registerDoSNOW(cl) 

# Function to combine the outputs 
mycombinefunc <-  function(a,b){merge(a, b, by = c("date","open"))}

# Run the loop over multiple cores
meh <- foreach(i = 1825:1830, .combine = "mycombinefunc") %dopar% {
  message(i)
  df %>% 
    # Adjust everything
    tq_mutate(mutate_fun = rollapply,
              width      = window_size,
              by.column  = FALSE,
              FUN        = my_bogus_function,
              col_rename = gsub("$", sprintf(".%d", i), cnames), 
              V0 = i
    )
}
stopCluster(cl)
# END OF THE FOR LOOP I WANTED FASTER