汇总缺乏明确分组变量的每日数据（月份）

Question

我有数据框，有6000个位置。对于每个地点，我有36年的每日降雨量数据。

样本数据：

      set.seed(123)

      mat <- matrix(round(rnorm(6000*36*365), digits = 2),nrow = 6000*36, ncol = 365)
      dat <- data.table(mat)
      names(dat) <- rep(paste0("d_",1:365))

      dat$loc.id <- rep(1:6000, each = 36)
      dat$year <- rep(1980:2015, times = 6000)

我想做的是每个地点，每个月产生长期平均降雨量。对于例如for loc.id = 1，平均降雨量在1月，2月，3月和12月。

让我们说这个数据叫做df，这是一个数据表

    library(dplyr)

这是我做的：

    loc.list <- unique(dat$loc.id)
      my.list <- list() # a list to store results 

      ptm <- proc.time()

      for(i in seq_along(loc.list)){

          n <- loc.list[i]
          df1 <- dat[dat$loc.id == n,]
          df2 <- gather(df1, day, rain, -year)   # this melts the data in long format

          df3 <- df2 %>% mutate(day = gsub("d_","", day)) %>% # since the day column was in "d_1" format, I converted into integer (1,2,3..365)
                         mutate(day = as.numeric(as.character(day))) %>%  # ensure that day column is numeric. For some reasonson, some NA.s appear.
                         arrange(year,day) %>% # ensure that they are arranged in order
                         mutate(month = strptime(paste(year, day), format = "%Y %j")$mon + 1) %>% # assing each day to a month
                         group_by(year,month) %>%  # group by year and month
                         summarise(month.rain = sum(rain)) %>% # calculate for each location, year and month, total rainfall
                         group_by(month) %>% # group by month
                         summarise(month.mean = round(mean(month.rain), digits = 2)) #  calculate for each month, the long term mean

          my.list[[i]] <- df3
          }
      proc.time() - ptm

      user  system elapsed 
      1036.17    0.20 1040.68

我想询问是否有更有效，更快捷的方法来完成这项任务

Answer 1

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Answer 2

另一答案