每日，每月和每年的平均值

时间：2017-10-29 06:06:00

标签： r

我每小时都有数据。我需要转换成每日，每月，然后在年度。

此外，还缺少一些日期，所以我也希望将其包括在内。

#Date
24/02/2000/05:25:00 NaN NaN NaN

26/02/2000/05：10：00 0.227 0.2002496 0.2009378 26/02/2000/06：50：00 NaN NaN NaN 27/02/2000/05：55：00 0.21 0.1687891 0.1630572 28/02/2000/05：00：00 NaN NaN 0.1265696 28/02/2000/06：35：00 0.136 0.1446176 0.1479067 29/02/2000/05：40：00 0.293 0.2279881 0.1900514 01/03/2000/04：45：00 NaN NaN NaN 01/03/2000/06：25：00 0.322 0.3068518 0.2880579 02/03/2000/05：30：00 0.332 0.2793714 0.2391622 02/03/2000/07：05：00 NaN NaN NaN 03/03/2000/06：10：00 0.335 0.2151302 0.2218139 04/03/2000/05：15：00 0.1 0.1138773 0.1168912 04/03/2000/06：55：00 NaN NaN NaN 05/03/2000/06：00：00 0.117 0.1333082 0.147145 06/03/2000/05：05：00 NaN 0.2426362 0.2401871 06/03/2000/06：40：00 NaN 0.32587 0.2845067 07/03/2000/05：45：00 0.323 0.3143821 0.3096662 08/03/2000/04：50：00 NaN NaN NaN 08/03/2000/06：30：00 0.236 0.23232 0.2300107 10/03/2000/06：20：00 0.113 0.1429935 0.1453774 11/03/2000/05：25：00 0.276 0.3238274 0.3150585 11/03/2000/07：00：00 NaN NaN NaN 12/03/2000/06：05：00 0.215 0.2537585 0.2512374 13/03/2000/05：10：00 0.163 0.2273455 0.2679352 13/03/2000/06：50：00 NaN NaN NaN 14/03/2000/05：55：00 0.09 0.1311507 0.1761056 15/03/2000/05：00：00 NaN NaN 0.1447348 15/03/2000/06：35：00 0.125 0.1232291 0.1387782 16/03/2000/05：40：00 0.019 0.06970426 0.11602 17/03/2000/04：45：00 NaN NaN NaN 17/03/2000/06：25：00 0.194 0.1964414 0.1874403 18/03/2000/05：30：00 0.263 0.2749394 0.242199 18/03/2000/07：05：00 NaN NaN NaN 19/03/2000/06：10：00 0.217 0.217737 0.2183706 20/03/2000/05：15：00 0.253 0.2307511 0.2089891 20/03/2000/06：55：00 NaN NaN NaN 21/03/2000/06：00：00 0.282 0.2413632 0.2511235 22/03/2000/05：05：00 NaN 0.382685 0.3944636 22/03/2000/06：45：00 NaN 0.2734097 0.241442 23/03/2000/05：50：00 0.347 0.3289219 0.3003848 24/03/2000/04：50：00 NaN NaN NaN 24/03/2000/06：30：00 0.18 0.1892378 0.2021516 25/03/2000/05：35：00 0.216 0.1871835 0.206762 26/03/2000/06：20：00 0.189 0.1836237 0.2116453 27/03/2000/05：25：00 0.195 0.1817446 0.1804464 27/03/2000/07：00：00 NaN NaN NaN 28/03/2000/06：05：00 0.208 0.168515 0.1819115 29/03/2000/05：10：00 0.162 0.1598227 0.1689523 29/03/2000/06：50：00 NaN NaN NaN 30/03/2000/05：55：00 0.145 0.1472181 0.1723774 31/03/2000/05：00：00 NaN NaN 0.157723 31/03/2000/06：35：00 0.226 0.2108984 0.2339231

2 个答案:

答案 0 :(得分：0)

我猜您在谈论在年，月和日中拆分日期变量，然后您想要计算一些您未在示例中包含的另一个varibale的分组统计信息。如果是这种情况，您可以执行以下操作：

# load package 
library(dplyr)

#Date
 Date <- data.frame( Date =strptime(c("24/02/2000/05:25:00", 
"26/02/2000/05:10:00",
"26/02/2000/06:50:00",
"27/02/2000/05:56:00",
"28/02/2000/05:00:00",
"28/02/2000/06:35:00",
"29/02/2000/05:40:00",
"01/03/2000/04:45:00",
"01/03/2000/06:25:00",
"02/03/2000/05:30:00",
"02/03/2000/07:05:00",
"03/03/2000/06:10:00",
"04/03/2000/05:15:00",
"04/03/2000/06:55:00",
"05/03/2000/06:00:00",
"06/03/2000/05:05:00",
"06/03/2000/06:40:00",
"07/03/2000/05:45:00",
"08/03/2000/04:50:00",
"08/03/2000/06:30:00",
"10/03/2000/06:20:00",
"11/03/2000/05:25:00",
"11/03/2000/07:00:00",
"12/03/2000/06:05:00",
"13/03/2000/05:10:00",
"13/03/2000/06:50:00",
"14/03/2000/05:55:00",
"15/03/2000/05:00:00",
"15/03/2000/06:35:00",
"16/03/2000/05:40:00",
"17/03/2000/04:45:00",
"17/03/2000/06:25:00",
"18/03/2000/05:30:00",
"18/03/2000/07:05:00",
"19/03/2000/06:10:00",
"20/03/2000/05:15:00",
"20/03/2000/06:55:00",
"21/03/2000/06:00:00",
"22/03/2000/05:05:00",
"22/03/2000/06:45:00",
"23/03/2000/05:50:00",
"24/03/2000/04:50:00",
"24/03/2000/06:30:00",
"25/03/2000/05:35:00",
"26/03/2000/06:20:00",
"27/03/2000/05:25:00",
"27/03/2000/07:00:00",
"28/03/2000/06:05:00",
"29/03/2000/05:10:00",
"29/03/2000/06:50:00",
"30/03/2000/05:55:00",
"31/03/2000/05:00:00",
"31/03/2000/06:35:00"), format = "%d/%m/%Y/%H:%M:%S"))


# Split your Date variable in days, months, and years

Date[,"Year"] <- format(Date$Date, format = "%Y")
Date[,"Month"] <- format(Date$Date, format = "%m")
Date[,"Day"] <- format(Date$Date, format = "%d")

# Make up some random variable to calculate summary statistics on

Date[,"Random"] <- sample(seq(1,7,1),size=dim(Date)[1], replace = TRUE)

# Now you can calculate grouped statistics by day, month, or year

MonthMean <- Date %>%
                group_by(Month) %>%
                select(Month, Random) %>%
                summarise(Mean = mean(Random))

# Output 
# A tibble: 2 × 2
  Month     Mean
  <chr>    <dbl>
1    02 3.142857
2    03 4.217391

答案 1 :(得分：0)

我在日，月和年分割了数据然后计算了Daymean，Monthlymean和Annualmean

使用代码：

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<appSettings>

<add key="ChartImageHandler" value="storage=memory;deleteAfterServicing=true;" />
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         //here is my database connection only

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<compilation debug="true" targetFramework="4.5">


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<authentication mode="Forms">
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<membership defaultProvider="DefaultMembershipProvider">
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    <add name="DefaultMembershipProvider" type="System.Web.Providers.DefaultMembershipProvider, System.Web.Providers, Version=1.0.0.0, Culture=neutral, PublicKeyToken=31bf3856ad364e35" connectionStringName="DefaultConnection" enablePasswordRetrieval="false" enablePasswordReset="true" requiresQuestionAndAnswer="false" requiresUniqueEmail="false" maxInvalidPasswordAttempts="5" minRequiredPasswordLength="6" minRequiredNonalphanumericCharacters="0" passwordAttemptWindow="10" applicationName="/"/>
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</system.web> 
<entityFramework>
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