การใช้ foreach

การเขียนโปรแกรมแบบขนานใน R

Nabeel Imam

Data Scientist

loop รูปแบบใหม่

for loop แบบดั้งเดิมใน R

numbers <- 1:1e6

sqroots <- rep(0, length(numbers))

for (i in 1:length(numbers)) {
  sqroots[i] <- sqrt(numbers[i])
}

foreach loop

numbers <- 1:1e6

library(foreach)


sqroots <- foreach(i = numbers) %do% {
  sqrt(i)
}

loop แบบขนาน

numbers <- 1:1e6

sqroots <- foreach(i = numbers) %do% {
  sqrt(i)
}

cl <- makeCluster(4)


library(doParallel)
registerDoParallel(cl)


sqroots <- foreach(i = numbers
                   # The parallel operator
                  ) %dopar% {
  sqrt(i)
}


stopCluster(cl)

มหาวิทยาลัยวิศวกรรมชั้นนำ

print(uni_list)

 [1] "./uni_data/Argentina.csv"
 [2] "./uni_data/Australia.csv"
 [3] "./uni_data/Austria.csv"
 [4] "./uni_data/Azerbaijan.csv"
 [5] "./uni_data/Bahrain.csv"
 [6] "./uni_data/Bangladesh.csv"
 [7] "./uni_data/Belarus.csv"
 [8] "./uni_data/Belgium.csv"
 [9] "./uni_data/Bolivia.csv"
[10] "./uni_data/Bosnia and Herzegovina.csv"
...

cl <- makeCluster(4)

registerDoParallel(cl)


ls_df <- foreach(csv = uni_list) %dopar% {
  read.csv(csv)
}

stopCluster(cl)

การรวบรวมผลลัพธ์ด้วย foreach

cl <- makeCluster(4)
registerDoParallel(cl)
ls_df <- foreach(csv = uni_list) %dopar% {
  read.csv(csv)
}
stopCluster(cl)

[[1]]
 location                  institution score
Argentina  Universidad de Buenos Aires  68.9
...
[[2]]
 location                     institution score
Australia  Australian National University  82.1
...

cl <- makeCluster(4)
registerDoParallel(cl)

df_uni <- foreach(csv = uni_list,
                 .combine = "rbind") %dopar% {
  read.csv(csv)
}
stopCluster(cl)

   location                     institution score
1 Argentina     Universidad de Buenos Aires  68.9
2 Argentina  Universidad Católica Argentina  33.3
3 Argentina     Universidad de Palermo (UP)  29.1
...

อ่าน กรอง และรวมข้อมูล

library(dplyr)

n_unis <- 3


# Empty list
ls_df <- list()


for (i in 1:length(uni_list)) {
  # Read, filter, collect in empty list
  ls_df[[i]] <- read.csv(uni_list[[i]]) %>%
    top_n(n_unis, total_score)

}

# Combine the list into one
combined_df <- Reduce("rbind", ls_df)

foreach พิชิตทุกงาน

n_unis <- 3


cl <- makeCluster(4)
registerDoParallel(cl)


df_top3 <- foreach(csv = uni_list,

                   .packages = "dplyr",

                   .export = "n_unis",

                   .combine = "rbind") %dopar% {

  read.csv(csv) %>% 
    top_n(n_unis, score)
}

stopCluster(cl)

 location                     institution score
Argentina     Universidad de Buenos Aires  68.9
Argentina  Universidad Católica Argentina  33.3
Argentina     Universidad de Palermo (UP)  29.1
Australia  Australian National University  82.1
Australia     The University of Melbourne  81.6
Australia        The University of Sydney  79.6
  Austria            University of Vienna  50.6
  Austria     Technische Universität Wien  45.7
...

มาฝึกกันเถอะ!

การเขียนโปรแกรมแบบขนานใน R