1 P-4, 1 P-3, 1 FS, 1 NS 可以用动物群进行:

L <- "Date,DBT
01/01/2000 01:00,30
01/01/2000 02:00,31
01/01/2000 03:00,33
12/31/2000 23:00,25"

library(zoo)
stat <- function(x) c(min = min(x), max = max(x), mean = mean(x))
z <- read.zoo(text = L, header = TRUE, sep = ",", format = "%m/%d/%Y", aggregate = stat)

因此:

> z
           min max     mean
2000-01-01  30  33 31.33333
2000-12-31  25  25 25.00000

2) 这里的解决办法只是使用核心R:

DF <- read.csv(text = L)
DF$Date <- as.Date(DF$Date, "%m/%d/%Y")
ag <- aggregate(DBT ~ Date, DF, stat) # same stat as in zoo solution 

最后一行是:

> ag
        Date  DBT.min  DBT.max DBT.mean
1 2000-01-01 30.00000 33.00000 31.33333
2 2000-12-31 25.00000 25.00000 25.00000

EDIT: (1) Since this first appeared the text= argument to read.zoo was added in the zoo package. (2) minor improvements.

利用strptime(),trunc(和dply(>,从纸浆包中取:

#Make the data
ZZ <- textConnection("Date,DBT
01/01/2000 01:00,30
01/01/2000 02:00,31
01/01/2000 03:00,33
12/31/2000 23:00,25")
dataframe <- read.csv(ZZ,header=T)
close(ZZ)

# Do the calculations
dataframe$Date <- strptime(dataframe$Date,format="%m/%d/%Y %H:%M")
dataframe$day <- trunc(dataframe$Date,"day")

require(plyr)

ddply(dataframe,.(day),
      summarize,
      aveDBT=mean(DBT),
      maxDBT=max(DBT),
      minDBT=min(DBT)
)

gives

         day   aveDBT maxDBT minDBT
1 2000-01-01 31.33333     33     30
2 2000-12-31 25.00000     25     25

澄清:

<代码>strptime根据格式将特性改为日期。 如欲了解你如何具体说明格式,见?strptime.trunc,然后将这些日期时间限定在具体单位,即该单位为时日。

<编码>dp将在数据框架内根据<编码>日对<> summarize进行分类后评价该功能。

There is also a nice package called hydroTSM. It uses zoo objects and can convert to other aggregates in time

The function in your case is subdaily2daily. You can choose if the aggregation should be based on min / max / mean...

A couple of options:

1. Timetk

如果您有数据框架(或易碎),则<代码>即按时间(<>条码>功能>从<条码>改为 可使用:

library(tidyverse)
library(timetk)

# Collect Data
text <- "Date,DBT
01/01/2000 01:00,30
01/01/2000 02:00,31
01/01/2000 03:00,33
12/31/2000 23:00,25"

df <- read_csv(text, col_types = cols(Date = col_datetime("%m/%d/%Y %H:%M")))
df
#> # A tibble: 4 x 2
#>   Date                  DBT
#>   <dttm>              <dbl>
#> 1 2000-01-01 01:00:00    30
#> 2 2000-01-01 02:00:00    31
#> 3 2000-01-01 03:00:00    33
#> 4 2000-12-31 23:00:00    25

# Summarize
df %>%
  summarise_by_time(
    .date_var = Date, 
    .by       = "day",
    min       = min(DBT),
    max       = max(DBT),
    mean      = mean(DBT)
  )
#> # A tibble: 2 x 4
#>   Date                  min   max  mean
#>   <dttm>              <dbl> <dbl> <dbl>
#> 1 2000-01-01 00:00:00    30    33  31.3
#> 2 2000-12-31 00:00:00    25    25  25

^{Created on 2021-05-21 by reprex Pack (v2.0.0)}

library(tidyquant)

df
#> # A tibble: 4 x 2
#>                  Date   DBT
#>                <dttm> <dbl>
#> 1 2000-01-01 01:00:00    30
#> 2 2000-01-01 02:00:00    31
#> 3 2000-01-01 03:00:00    33
#> 4 2000-12-31 23:00:00    25

stat_fun <- function(x) c(min = min(x), max = max(x), mean = mean(x))

df %>%
    tq_transmute(select     = DBT,
                 mutate_fun = apply.daily,
                 FUN        = stat_fun)
# A tibble: 2 x 4
#>                 Date   min   max     mean
#>                <dttm> <dbl> <dbl>    <dbl>
#> 1 2000-01-01 03:00:00    30    33 31.33333
#> 2 2000-12-31 23:00:00    25    25 25.00000