R:如何获取时间序列数据中日期时间列的最大值

Question

我正在处理时间序列数据。我有 2 个日期时间列和 1 个财政周列。我举了一个例子，我遇到了如下情况，我需要获得 EditDate 的 MAX。

EditDate <- c("2015-04-01 11:40:13", "2015-04-03 02:54:45","2015-04-07 11:40:13")
ID <- c("DL1X8", "DL1X8","DL1X8")
Avg <- c(38.1517, 38.1517, 38.1517)
Sig <- c(11.45880000, 11.45880000, 11.45880000)
InsertDate <- c("2015-04-03 9:40:00", "2015-04-03 9:40:00",2015-04-10 9:40:00)
FW <- c("39","39","40")

df1 <- data.frame(EditDate , ID, Avg, Sig, InsertDate, FW)

这返回

+---------------------+-------+---------+-------------+--------------------+----+
|   EditDate          | ID    | Avg     |   Sig       |    InsertDate      | FW |
+---------------------+-------+---------+-------------+--------------------+----+
| 2015-04-01 11:40:13 | DL1X8 | 38.1517 | 11.45880000 | 2015-04-03 9:40:00 | 39 |
| 2015-04-03 02:54:45 | DL1X8 | 38.1517 | 11.45880000 | 2015-04-03 9:40:00 | 39 |
| 2015-04-07 11:40:13 | DL1X8 | 38.1517 | 11.45880000 | 2015-04-10 9:40:00 | 40 |
+---------------------+-------+---------+-------------+--------------------+----+

我想要的期望输出是

+---------------------+-------+---------+-------------+--------------------+----+
|   EditDate          | ID    | Avg     |   Sig       |    InsertDate      | FW |
+---------------------+-------+---------+-------------+--------------------+----+
| 2015-04-07 11:40:13 | DL1X8 | 38.1517 | 11.45880000 | 2015-04-10 9:40:00 | 40 |
+---------------------+-------+---------+-------------+--------------------+----+

我尝试使用库(RH2)使用 sqldf，但运行需要很多时间。

df2 <- sqldf("SELECT * FROM df1 
                        WHERE (EditDate = (SELECT MAX(EditDate) FROM df1))
                        ORDER BY EditDate ASC")

我不确定是否可以使用 dplyr 包来完成。有人可以提供有关如何使用 dplyr 或任何其他替代方法优化此功能的输入吗？

Answer 1

这是一个带有基础 R 的衬垫

df1[which.max(as.POSIXct(df1$InsertDate)), ]
#              EditDate    ID     Avg     Sig         InsertDate FW
# 3 2015-04-07 11:40:13 DL1X8 38.1517 11.4588 2015-04-10 9:40:00 40

或与 data.table

library(data.table)
setDT(df1)[which.max(as.POSIXct(InsertDate))]
#               EditDate    ID     Avg     Sig         InsertDate FW
# 1: 2015-04-07 11:40:13 DL1X8 38.1517 11.4588 2015-04-10 9:40:00 40