如何将分钟数据转换为每小时平均数据

I have one week of data with a reading every 5 seconds. An example of data is below.

我有一周的数据,每5秒读一次。下面是一个数据示例。

9/1/2012 00:00:00    1
9/1/2012 00:00:05    2
9/1/2012 00:00:10    3

I want to calculate the hourly average for each day. Then make a multi-line plot of "average hourly reading vs. hour" with lines representing different dates.

我想计算每一天的每小时平均值。然后使用代表不同日期的线条制作“平均每小时读数与小时数”的多线图。

The one I have here is for weekly average

我在这里的是每周平均值

data$date = as.POSIXct(strptime(data$date, 
                  format = "%d/%m/%Y %H:%M","GMT")) 
means <- aggregate(data["nox"], format(data["date"],"%Y-%U"),
                 mean, na.rm = TRUE)

For daily average, it is

对于每日平均值,它是

data$date = as.POSIXct(strptime(data$date, 
                 format = "%d/%m/%Y %H:%M","GMT"))
means <- aggregate(data["nox"], format(data["date"],"%Y-%j"),
                 mean, na.rm = TRUE)

Any one knows how to calculate the hourly average for each day.

任何人都知道如何计算每天的每小时平均值。

3 个解决方案

#1

I like @DWin's answer, but I had also remembered seeing once a help file for ?cut.Date which can also be used in this case. I've made up some data so you can see the results over a few hours:

我喜欢@DWin的答案,但我还记得曾经看过一个帮助文件?cut.Date,它也适用于这种情况。我已经编制了一些数据,因此您可以在几个小时内看到结果:

set.seed(1)
data <- data.frame(date = seq(from = ISOdatetime(2012, 01, 01, 00, 00, 00),
                              length.out = 4320, by=5),
                   nox = sample(1:20, 4320, replace=TRUE))

hr.means <- aggregate(data["nox"], 
                      list(hour = cut(data$date, breaks="hour")), 
                      mean, na.rm = TRUE)
hr.means
#                  hour      nox
# 1 2012-01-01 00:00:00 10.60694
# 2 2012-01-01 01:00:00 10.13194
# 3 2012-01-01 02:00:00 10.33333
# 4 2012-01-01 03:00:00 10.38194
# 5 2012-01-01 04:00:00 10.51111
# 6 2012-01-01 05:00:00 10.26944

#2

It would only require changing your format specification in the by-vector:

它只需要在向量中更改格式规范:

hr.means <- aggregate(dat["V1"], format(dat["date"],"%Y-%m-%d %H"),
             mean, na.rm = TRUE) 
hr.means
#---------
           date V2
1 2012-01-09 00  2

#3

I got here from here so I have the data in a slightly different form, but using lubridate you can easy parse your data format as well.

我从这里到这里,所以我的数据略有不同,但使用lubridate你也可以轻松解析数据格式。

library(tibble)
library(dplyr)
library(lubridate)

tbl <- tribble(
    ~TIME,                 ~MEASURE,
    "2018-01-01 06:58:50",    05,
    "2018-01-01 07:00:00",    10,
    "2018-01-01 07:04:45",    20,
    "2018-01-01 07:04:55",    25,
    "2018-01-01 07:21:00",    20,
    "2018-01-01 07:58:04",    18,
    "2018-01-01 07:59:59",    12,
    "2018-01-01 08:00:00",    17,
    "2018-01-01 08:01:04",    30
) %>% mutate(TIME = ymd_hms(TIME))

With the data in a form where you can manipulate the date/time, you can summarise it per date+hour or just per hour over all dates as this:

使用可以操作日期/时间的表格中的数据,您可以按日期+小时或每小时在所有日期汇总,如下所示:

# if you want per date
tbl %>% 
    mutate(date = date(TIME), hour = hour(TIME)) %>% 
    group_by(date, hour) %>% summarise(m = mean(MEASURE))

# if you want per hour over all dates
tbl %>% 
    mutate(hour = hour(TIME)) %>% 
    group_by(hour) %>% summarise(m = mean(MEASURE))

To plot it using points and lines with ggplot2, you can do

要使用ggplot2使用点和线来绘制它,你可以这样做

library(ggplot2)
tbl %>% 
    mutate(hour = hour(TIME)) %>% 
    group_by(hour) %>% summarise(m = mean(MEASURE)) %>%
    ggplot(aes(x = hour, y = m)) + geom_point() + geom_line()

#1