根据R中第二个数据框中的日期范围对数据框中的数据进行分组

我有以下两个数据帧：

Date <- seq(as.Date("2013/1/1"), by = "day", length.out = 46)
x <-data.frame(Date)
x$discharge <- c("1000","1100","1200","1300","1400","1200","1300","1300","1200","1100","1200","1200","1100","1400","1200","1100","1400","1000","1100","1200","1300","1400","1200","1300","1300","1200","1100","1200","1200","1100","1400","1200","1100","1400","1000","1100","1200","1300","1400","1200","1300","1300","1200","1100","1200","1200")
x$discharge <- as.numeric(x$discharge)

---

但是，如果您在数据框中检查范围，2013-01-01到2013-01-07的范围将扩展到2013-01-14，但我只需要它到2013-01-07，然后在下一个范围从2013-01-15开始之前暂停。

您可以尝试

tidyverse

library(tidyverse)
y %>% 
  split(seq_along(1:nrow(.))) %>% 
  map(~filter(x, between(Date, .$Date_from, .$Date_to)) %>% 
        summarise(Mean=mean(discharge))) %>% 
  bind_rows() %>% 
  bind_cols(y,.)
   Date_from    Date_to concentration     Mean
1 2013-01-01 2013-01-07           1.5 1214.286
2 2013-01-15 2013-01-20           2.5 1166.667
3 2013-01-21 2013-01-25           1.5 1300.000
4 2013-02-10 2013-02-15           3.5 1216.667

仅使用此代码可以查看值和组

y %>% 
  split(seq_along(1:nrow(.))) %>% 
  map(~filter(x, between(Date, .$Date_from, .$Date_to))) 

---

下面是一个

base

答案：

helper <- merge(x, y)
helper <- helper[helper$Date >= helper$Date_from & helper$Date <= helper$Date_to, ]
aggregate(helper$discharge,
          list(Date_from = helper$Date_from,
               Date_to = helper$Date_to),
          FUN = 'mean')

   Date_from    Date_to        x
1 2013-01-01 2013-01-07 1214.286
2 2013-01-15 2013-01-20 1166.667
3 2013-01-21 2013-01-25 1300.000
4 2013-02-10 2013-02-15 1216.667

---

helper抱歉，我无法安装tidyverse。R似乎在依赖项方面存在一些问题。我建议使用RStudio和最新的R版本3.4.1。
y %>% 
  split(seq_along(1:nrow(.))) %>% 
  map(~filter(x, between(Date, .$Date_from, .$Date_to))) 

helper <- merge(x, y)
helper <- helper[helper$Date >= helper$Date_from & helper$Date <= helper$Date_to, ]
aggregate(helper$discharge,
          list(Date_from = helper$Date_from,
               Date_to = helper$Date_to),
          FUN = 'mean')

   Date_from    Date_to        x
1 2013-01-01 2013-01-07 1214.286
2 2013-01-15 2013-01-20 1166.667
3 2013-01-21 2013-01-25 1300.000
4 2013-02-10 2013-02-15 1216.667