2

我正在使用库中的函数来应用函数（来自map库），如下所示： purrrsegmentedsegmented

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))

该对象by_veh28_101包含 2457 tibbles。最后一步，map使用函数，需要 16 分钟才能完成。有什么办法可以让这更快吗？

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1 回答 1

5

您可以使用该功能future_map而不是map.

此功能来自软件包furrr，是map家庭的并行选项。这是软件包自述文件的链接。

因为您的代码问题不可重现，所以我无法在mapandfuture_map函数之间准备基准。

您的future_map函数代码如下：

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))
于 2018-06-09T21:00:23.330 回答