如何使purrr map函数运行得更快？

本文介绍了如何使purrr map函数运行得更快？的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述

我正在使用 purrr 库中的 map 函数来应用 segmented 函数（来自 segmented 库），如下所示：

I am using map function from purrr library to apply segmented function (from segmented library) as follows:

library(purrr) library(dplyr) library(segmented) # Data frame is nested to create list column by_veh28_101 <- df101 %>% filter(LCType=="CFonly", Lane %in% c(1,2,3)) %>% group_by(Vehicle.ID2) %>% nest() %>% ungroup() # Functions: segf2 <- function(df){ try(segmented(lm(svel ~ Time, data=df), seg.Z = ~Time, psi = list(Time = df$Time[which(df$dssvel != 0)]), control = seg.control(seed=2)), silent=TRUE) } segf2p <- function(df){ try(segmented(lm(PrecVehVel ~ Time, data=df), seg.Z = ~Time, psi = list(Time = df$Time[which(df$dspsvel != 0)]), control = seg.control(seed=2)), silent=TRUE) } # map function: models8_101 <- by_veh28_101 %>% mutate(segs = map(data, segf2), segsp = map(data, segf2p))

对象 by_veh28_101 包含2457个小标题。最后一步是使用 map 函数，需要16分钟才能完成。有什么方法可以使它更快？

The object by_veh28_101 contains 2457 tibbles. And the last step, where map function is used, takes 16 minutes to complete. Is there any way to make this faster?

推荐答案

您可以使用函数 future_map 而不是地图。

You may use the function future_map instead of map.

此函数来自软件包 furrr ，它是 map 家庭。这是该软件包的自述的链接。

This function comes from the package furrr and is a parallel option for the map family. Here is the link for the README of the package.

由于您的代码问题不可复制，因此我无法在 map 和 future_map之间准备基准测试函数。

Because your code question it is not reproducible, I cant prepare a benchmark between the map and future_map functions.

具有 future_map 函数的代码如下：

library(tidyverse) library(segmented) library(furrr) # Data frame stuff.... # Your functions.... # future_map function # this distribute over the different cores of your computer # You set a "plan" for how the code should run. The easiest is `multiprocess` # On Mac this picks plan(multicore) and on Windows this picks plan(multisession) plan(strategy = multiprocess) models8_101 <- by_veh28_101 %>% mutate(segs = future_map(data, segf2), segsp = future_map(data, segf2p))