在 R 中创建一个 24 小时向量，时间间隔为 60 分钟和 1 分钟答案

【问题标题】：Create a 24 hour vector with 60 and 1 minutes time interval in R在 R 中创建一个 24 小时向量，时间间隔为 60 分钟和 1 分钟
【发布时间】：2018-05-06 15:30:47
【问题描述】：

我有一个防火墙日志文件，其中包括日期、小时、src_address、dest_address 和 Date.time。我想为每次（例如；从 2018/01/01 到 2018/05/06）创建一个 24 小时向量，时间间隔为 60 分钟和 1 分钟。然后在这些时间间隔中，我想找到一对 src_address 和 dest_address 的出现。最后，每对 src_address 和 dest_address 的这些外观最多。这是我的文件；

                date     hour    src_address  dest_address           Date.Time

1996  2018-04-14 08:24:01    1.11.201.19 172.16.16.100 2018-04-14 08:24:01
3702  2018-04-15 12:10:27    1.119.43.90 172.16.16.100 2018-04-15 12:10:27
1154  2018-04-14 00:59:27    1.119.43.90 172.16.16.153 2018-04-14 00:59:27
2414  2018-04-14 12:33:29    1.119.43.90 192.168.1.112 2018-04-14 12:33:29
18013 2018-04-28 18:49:05   1.171.43.133   172.16.16.5 2018-04-28 18:49:05
18015 2018-04-28 18:49:05   1.171.43.133   172.16.16.5 2018-04-28 18:49:05
6903  2018-04-25 21:31:52   1.179.191.82   172.16.16.5 2018-04-25 21:31:52
11741 2018-04-27 01:08:43   1.179.191.82 192.168.1.111 2018-04-27 01:08:43
11933 2018-04-27 02:00:10   1.179.191.82 192.168.1.111 2018-04-27 02:00:10
11023 2018-04-26 21:39:39   1.179.191.82 192.168.1.112 2018-04-26 21:39:39
11175 2018-04-26 22:31:01   1.179.191.82 192.168.1.112 2018-04-26 22:31:01
13073 2018-04-27 08:24:58   1.180.72.186 172.16.16.153 2018-04-27 08:24:58
13735 2018-04-27 12:07:34   1.180.72.186 172.16.16.153 2018-04-27 12:07:34
2752  2018-04-14 19:34:53   1.202.165.40 172.16.16.153 2018-04-14 19:34:53
4046  2018-04-15 18:16:40    1.203.84.52   172.16.16.5 2018-04-15 18:16:40
4048  2018-04-15 18:18:43    1.203.84.52 192.168.1.112 2018-04-15 18:18:43
3020  2018-04-15 01:35:40    1.209.171.4 192.168.1.111 2018-04-15 01:35:40
4870  2018-04-16 05:33:42   1.214.34.114 172.16.16.100 2018-04-16 05:33:42
7025  2018-04-25 22:28:06   1.214.34.114 172.16.16.100 2018-04-25 22:28:06
4262  2018-04-15 23:31:56   1.214.34.114 172.16.16.153 2018-04-15 23:31:56
9369  2018-04-26 10:32:50   1.214.34.114 172.16.16.153 2018-04-26 10:32:50
2716  2018-04-14 18:49:30   1.214.34.114   172.16.16.5 2018-04-14 18:49:30
9563  2018-04-26 12:34:58   1.214.34.114   172.16.16.5 2018-04-26 12:34:58
1110  2018-04-14 00:27:02   1.214.34.114 192.168.1.111 2018-04-14 00:27:02
4470  2018-04-16 01:27:32   1.214.34.114 192.168.1.112 2018-04-16 01:27:32
9581  2018-04-26 12:55:39    1.55.249.92 172.16.16.153 2018-04-26 12:55:39
2970  2018-04-15 00:01:18    1.55.249.92   172.16.16.5 2018-04-15 00:01:18
15329 2018-04-27 21:53:16    1.55.249.92   172.16.16.5 2018-04-27 21:53:16
15537 2018-04-28 00:02:30    1.55.249.92   172.16.16.5 2018-04-28 00:02:30
19249 2018-04-29 06:28:04   1.71.188.254 172.16.16.100 2018-04-29 06:28:04
19243 2018-04-29 06:28:04   1.71.188.254 172.16.16.153 2018-04-29 06:28:04
19241 2018-04-29 06:28:04   1.71.188.254 172.16.16.159 2018-04-29 06:28:04
19239 2018-04-29 06:28:04   1.71.188.254   172.16.16.5 2018-04-29 06:28:04
19247 2018-04-29 06:28:04   1.71.188.254 192.168.1.111 2018-04-29 06:28:04
19245 2018-04-29 06:28:04   1.71.188.254 192.168.1.112 2018-04-29 06:28:04
6315  2018-04-25 18:56:08     1.85.18.88 172.16.16.153 2018-04-25 18:56:08
14623 2018-04-27 16:41:00     1.85.18.88 172.16.16.153 2018-04-27 16:41:00

这是我的期望；

   src_address  dest_address max(per hour) max(per minute)
2  1.11.201.19 172.16.16.100           1       1   
3  1.119.43.90 172.16.16.100           1       1   
4  1.119.43.90 172.16.16.153           1       1   
5  1.119.43.90 192.168.1.112           1       1   
6 1.171.43.133   172.16.16.5           2       2

【问题讨论】：

标签： r

【解决方案1】：

要获取摘要数据，需要做很多事情。可以使用dplyr、tidyr 和lubridate 包来转换数据。

方法：

通过合并日期和小时并转换为来创建DateTime 列 ymd_hms

src_addres、dest_address 和 Year-Month-Day Hour 上的群组计算每小时发生次数

在src_addres、dest_address 和Year-Month-Day Hour:Min 上分组以计算 > 每分钟出现次数

在src_addres、dest_address 上进行分组并汇总以获得每小时和每分钟的最大发生次数

library(dplyr)
library(tidyr)
library(lubridate)

df %>% unite("DateTime", c("date","hour"), sep=" ") %>% 
  mutate(DateTime = ymd_hms(DateTime)) %>%
  group_by(src_addres, dest_address, YMD_H = format(DateTime, "%Y-%m-%d %H")) %>%
  mutate(HourlyAppearance = n()) %>%
  group_by(src_addres, dest_address, YMD_HM = format(DateTime, "%Y-%m-%d %H:%M")) %>%
  mutate(PerMinAppearance = n()) %>%
  group_by(src_addres, dest_address) %>%
  summarise( 'max(per hour)' = max(HourlyAppearance), 
           'max(per min)' = max(PerMinAppearance)) %>%
  as.data.frame()

#      src_addres  dest_address max(per hour) max(per min)
# 1   1.11.201.19 172.16.16.100             1            1
# 2   1.119.43.90 172.16.16.100             1            1
# 3   1.119.43.90 172.16.16.153             1            1
# 4   1.119.43.90 192.168.1.112             1            1
# 5  1.171.43.133   172.16.16.5             2            2
# 6  1.179.191.82   172.16.16.5             1            1
# 7  1.179.191.82 192.168.1.111             1            1
# 8  1.179.191.82 192.168.1.112             1            1
# 9  1.180.72.186 172.16.16.153             1            1
# 10 1.202.165.40 172.16.16.153             1            1
# 11  1.203.84.52   172.16.16.5             1            1
# 12  1.203.84.52 192.168.1.112             1            1
# 13  1.209.171.4 192.168.1.111             1            1
# 14 1.214.34.114 172.16.16.100             1            1
# 15 1.214.34.114 172.16.16.153             1            1
# 16 1.214.34.114   172.16.16.5             1            1
# 17 1.214.34.114 192.168.1.111             1            1
# 18 1.214.34.114 192.168.1.112             1            1
# 19  1.55.249.92 172.16.16.153             1            1
# 20  1.55.249.92   172.16.16.5             1            1
# 21 1.71.188.254 172.16.16.100             1            1
# 22 1.71.188.254 172.16.16.153             1            1
# 23 1.71.188.254 172.16.16.159             1            1
# 24 1.71.188.254   172.16.16.5             1            1
# 25 1.71.188.254 192.168.1.111             1            1
# 26 1.71.188.254 192.168.1.112             1            1
# 27   1.85.18.88 172.16.16.153             1            1

数据：

OP 没有以非常简单的格式提供数据。包含日期和时间列使其变得更加困难。也许这就是对这个问题的低响应的原因。我更喜欢分别阅读date 和time 部分，然后阅读unite 获得Date/Time。

strtext <- "Sl  date hour  src_addres  dest_address  Date_t   Time_t
1996  2018-04-14 08:24:01    1.11.201.19 172.16.16.100 2018-04-14 08:24:01
3702  2018-04-15 12:10:27    1.119.43.90 172.16.16.100 2018-04-15 12:10:27
1154  2018-04-14 00:59:27    1.119.43.90 172.16.16.153 2018-04-14 00:59:27
2414  2018-04-14 12:33:29    1.119.43.90 192.168.1.112 2018-04-14 12:33:29
18013 2018-04-28 18:49:05   1.171.43.133   172.16.16.5 2018-04-28 18:49:05
18015 2018-04-28 18:49:05   1.171.43.133   172.16.16.5 2018-04-28 18:49:05
6903  2018-04-25 21:31:52   1.179.191.82   172.16.16.5 2018-04-25 21:31:52
11741 2018-04-27 01:08:43   1.179.191.82 192.168.1.111 2018-04-27 01:08:43
11933 2018-04-27 02:00:10   1.179.191.82 192.168.1.111 2018-04-27 02:00:10
11023 2018-04-26 21:39:39   1.179.191.82 192.168.1.112 2018-04-26 21:39:39
11175 2018-04-26 22:31:01   1.179.191.82 192.168.1.112 2018-04-26 22:31:01
13073 2018-04-27 08:24:58   1.180.72.186 172.16.16.153 2018-04-27 08:24:58
13735 2018-04-27 12:07:34   1.180.72.186 172.16.16.153 2018-04-27 12:07:34
2752  2018-04-14 19:34:53   1.202.165.40 172.16.16.153 2018-04-14 19:34:53
4046  2018-04-15 18:16:40    1.203.84.52   172.16.16.5 2018-04-15 18:16:40
4048  2018-04-15 18:18:43    1.203.84.52 192.168.1.112 2018-04-15 18:18:43
3020  2018-04-15 01:35:40    1.209.171.4 192.168.1.111 2018-04-15 01:35:40
4870  2018-04-16 05:33:42   1.214.34.114 172.16.16.100 2018-04-16 05:33:42
7025  2018-04-25 22:28:06   1.214.34.114 172.16.16.100 2018-04-25 22:28:06
4262  2018-04-15 23:31:56   1.214.34.114 172.16.16.153 2018-04-15 23:31:56
9369  2018-04-26 10:32:50   1.214.34.114 172.16.16.153 2018-04-26 10:32:50
2716  2018-04-14 18:49:30   1.214.34.114   172.16.16.5 2018-04-14 18:49:30
9563  2018-04-26 12:34:58   1.214.34.114   172.16.16.5 2018-04-26 12:34:58
1110  2018-04-14 00:27:02   1.214.34.114 192.168.1.111 2018-04-14 00:27:02
4470  2018-04-16 01:27:32   1.214.34.114 192.168.1.112 2018-04-16 01:27:32
9581  2018-04-26 12:55:39    1.55.249.92 172.16.16.153 2018-04-26 12:55:39
2970  2018-04-15 00:01:18    1.55.249.92   172.16.16.5 2018-04-15 00:01:18
15329 2018-04-27 21:53:16    1.55.249.92   172.16.16.5 2018-04-27 21:53:16
15537 2018-04-28 00:02:30    1.55.249.92   172.16.16.5 2018-04-28 00:02:30
19249 2018-04-29 06:28:04   1.71.188.254 172.16.16.100 2018-04-29 06:28:04
19243 2018-04-29 06:28:04   1.71.188.254 172.16.16.153 2018-04-29 06:28:04
19241 2018-04-29 06:28:04   1.71.188.254 172.16.16.159 2018-04-29 06:28:04
19239 2018-04-29 06:28:04   1.71.188.254   172.16.16.5 2018-04-29 06:28:04
19247 2018-04-29 06:28:04   1.71.188.254 192.168.1.111 2018-04-29 06:28:04
19245 2018-04-29 06:28:04   1.71.188.254 192.168.1.112 2018-04-29 06:28:04
6315  2018-04-25 18:56:08     1.85.18.88 172.16.16.153 2018-04-25 18:56:08
14623 2018-04-27 16:41:00     1.85.18.88 172.16.16.153 2018-04-27 16:41:00"

df <- read.table(text = strtext,header = TRUE, stringsAsFactors = FALSE)

【讨论】：

@Hüseyin 让我们做一件事。您只需要dplyr、tidyr 和lubridate。用这 3 个包试试我的答案。