ТИРОВАТЬ: Уменьшен размер набора данных

Пример данных:

df <- data.frame(loc.id = rep(1:10, each = 80*36), 
             year = rep(rep(1980:2015, each = 80), times = 10),
             day = rep(rep(1:80, times = 36),times = 10),
             rain = runif(10*36*80, min = 0 , max = 5),
             swc = runif(10*36*80,min = 0, max = 50),
             SW.max = rep(runif(10, min = 100, max = 200), each = 80*36),
             SW.ini = runif(10*36*80),
             PETc = runif(10*36*80, min = 0 , max = 1.3),
             SW = NA,
             PAW = NA, 
             aetc = NA)

df содержит ежедневные данные (80 дней) за 1980-2015 гг. для 10 населенных пунктов. Для каждого местоположения X комбинации года я хочу сделать следующий расчет

list.result <- list() # create a list to store all results
ptm <- proc.time()
n <- 0

for(i in seq_along(unique(df$loc.id))){

location <- unique(df$loc.id)[i]
print(location)

for(j in seq_along(unique(df$year))){

yr <- unique(df$year)[j]
print(yr)

df_year <- df[df$loc.id == location & df$year == yr,] # subset data for location i and year y

# for the first row of data frame, i need to calculate some values 
SW.ini <- df_year$SW.ini[1] 
SW.max <- df_year$SW.max[1]

df_year$PAW[1] <- SW.ini + df_year$rain[1]
df_year$aetc[1] <- ifelse(df_year$PAW[1] >= df_year$swc[1], 
df_year$PETc[1],(df_year$PAW[1]/df_year$swc[1])*df_year$PETc[1])
df_year$aetc[1] <- ifelse(df_year$aetc[1] > df_year$PAW[1], df_year$PAW[1], df_year$aetc[1])
df_year$SW[1] <- SW.ini + df_year$rain[1] -  df_year$aetc[1]
df_year$SW[1] <- ifelse(df_year$SW[1] > SW.max, SW.max, ifelse(df_year$SW[1] < 0, 0,df_year$SW[1]))

# for row 2 till row n of df_year, I need to do this:
for (day in 2:nrow(df_year)){
df_year$PAW[day] <- df_year$SW[day - 1] + df_year$rain[day]

df_year$aetc[day] <- ifelse(df_year$PAW[day] >= df_year$swc[day], df_year$PETc[day], (df_year$PAW[day]/df_year$swc[day]) * df_year$PETc[day])

df_year$aetc[day] <- ifelse(df_year$aetc[day] > df_year$PAW[day], df_year$PAW[day],df_year$aetc[day])

df_year$SW[day] <- df_year$SW[day - 1] + df_year$rain[day] -  df_year$aetc[day]

df_year$SW[day] <- ifelse(df_year$SW[day] > SW.max,SW.max, ifelse(df_year$SW[day] < 0, 0,df_year$SW[day]))

   }
n <- n + 1
list.result[[n]] <- df_year
}}
proc.time() - ptm
user  system elapsed 
8.64    0.00    8.75

final.dat <- rbindlist(list.result)

Этот цикл является последовательным, и я подумал, что это хороший кандидат на foreach в R. Я действительно не работал с foreach, поэтому некоторые онлайн-исследования привели меня к следующему:

  library(doParallel)
  cl <- makeCluster(4) # if I understood this correctly, it assings number of cores to be used 
  registerDoParallel(cl)

  foreach(i = seq_along(unique(df$loc.id)) %dopar% {
    list.result <- list()
    for(j in seq_along(1980:2015)){

      df_year <- df[df$loc.id == unique(df$loc.id)[i] & df$year == unique(df$year)[j],] # subset data for location i and year y

      # for the first row of data frame, i need to calculate some values 
      SW.ini <- df_year$SW.ini[1] 
      SW.max <- df_year$SW.max[1]

      df_year$PAW[1] <- SW.ini + df_year$rain[1]
      df_year$aetc[1] <- ifelse(df_year$PAW[1] >= df_year$swc[1], df_year$PETc[1],(df_year$PAW[1]/df_year$swc[1])*df_year$PETc[1])
      df_year$aetc[1] <- ifelse(df_year$aetc[1] > df_year$PAW[1], df_year$PAW[1], df_year$aetc[1])
      df_year$SW[1] <- SW.ini + df_year$rain[1] -  df_year$aetc[1]
      df_year$SW[1] <- ifelse(df_year$SW[1] > SW.max, SW.max, ifelse(df_year$SW[1] < 0, 0,df_year$SW[1]))

      # for row 2 till row n of df_year, I need to do this:
      for (day in 2:nrow(df_year)){
        df_year$PAW[day] <- df_year$SW[day - 1] + df_year$rain[day]
        df_year$aetc[day] <- ifelse(df_year$PAW[day] >= df_year$swc[day], df_year$PETc[day], (df_year$PAW[day]/df_year$swc[day]) * df_year$PETc[day])
        df_year$aetc[day] <- ifelse(df_year$aetc[day] > df_year$PAW[day], df_year$PAW[day],df_year$aetc[day])
        df_year$SW[day] <- df_year$SW[day - 1] + df_year$rain[day] -  df_year$aetc[day]
        df_year$SW[day] <- ifelse(df_year$SW[day] > SW.max,SW.max, ifelse(df_year$SW[day] < 0, 0,df_year$SW[day]))

      }
      list.result[[j]] <- df_year
    }
    dat <- rbindlist(list.result)
    fwrite(dat,paste0(i,"dat.csv"))
 }

Мои вопросы:

1) Являются ли приведенные выше данные хорошим кандидатом на foreach?

2) Внутри foreach есть цикл for. Имеет ли это смысл?

3) Как мне сделать вышеупомянутый прогон foreach и вернуть все результаты