Question

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

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 tibbles。最后一步，使用map函数，需要16分钟才能完成。有没有办法让这更快？

Answer 1

您可以使用future_map功能代替map。

此功能来自包furrr，是map系列的并行选项。以下是该软件包README的链接。

由于您的代码问题无法重现，因此我无法在map和future_map函数之间准备基准。

具有future_map功能的代码如下：

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))

如何使purrr map功能运行得更快？

1 个答案: