计算多个数据集的每日，每月和每年平均值

Question

我有一个数据框：

MS_NR SS_NR      DATE       HOUR     VALUE
1 13095010    68 1/01/2014 0:00:00    9,8
2 13095010    68 1/01/2014 1:00:00    8,0
3 13095010    68 1/01/2014 2:00:00    NA
4 13095010    68 1/01/2014 3:00:00    7,5
5 13095010    68 1/01/2014 4:00:00    7,0
6 13095010    68 1/01/2014 5:00:00    8,5

是每小时采集的气象站的温度观测值，我想计算不同气象站的几个数据帧的每日，每周，每月和每年平均值。如何在循环中执行此操作，以使该过程不重复？

Answer 1

在处理水文气象数据时，我通常使用xts和hydroTSM包，因为它们具有许多用于数据聚合的功能。

您没有提供任何数据，因此我创建了一个用于演示目的的数据

library(xts)
library(hydroTSM)

# Generate random data
set.seed(2018)
date = seq(from = as.Date("2016-01-01"), to = as.Date("2018-12-31"),
           by = "days")
temperature = runif(length(date), -15, 35)
dat <- data.frame(date, temperature)

# Convert to xts object for xts & hydroTSM functions
dat_xts <- xts(dat[, -1], order.by = dat$date)

# All daily, monthly & annual series in one plot
hydroplot(dat_xts, pfreq = "dma", var.type = "Temperature")

# Weekly average
dat_weekly <- apply.weekly(dat_xts, FUN = mean)
plot(dat_weekly)

# Monthly average
dat_monthly <- daily2monthly(dat_xts, FUN = mean, na.rm = TRUE)
plot.zoo(dat_monthly, xaxt = "n", xlab = "")
axis.Date(1, at = pretty(index(dat_monthly)),
          labels = format(pretty(index(dat_monthly)), format = "%b-%Y"),
          las = 1, cex.axis = 1.1)

# Seasonal average: need to specify the months
dat_seasonal <- dm2seasonal(dat_xts, season = "DJF", FUN = mean, na.rm = TRUE)
plot(dat_seasonal)

# Annual average
dat_annual <- daily2annual(dat_xts, FUN = mean, na.rm = TRUE)
plot(dat_annual)

编辑：使用OP的数据

df <- readr::read_csv2("Temp_2014_Hour.csv")
str(df)

# Convert DATE to Date object & put in a new column
df$date <- as.Date(df$DATE, format = "%d/%m/%Y")
dat <- df[, c("date", "VALUE")]
str(dat)

dat_xts <- xts(dat[, -1], order.by = dat$date)

由reprex package（v0.2.0）创建于2018-02-28。

Answer 2

我试试这个

首先使用read.table加载文件

library(openair)

Temp <- read.table (file, header=TRUE, sep=";",stringsAsFactors = FALSE, dec = ",", na.strings = "NA")

tiempos <- Temp$HOUR
timestamps <- as.POSIXlt(as.POSIXct('1900-1-1', tz='UTC') 
                         + as.difftime(as.character(tiempos))
time <- format(timestamps, format='%H:%M:%S')
date<-paste(Temp[,3], time, sep=" ")
date

Temp_met <- cbind(date, CovTemp[-c(3,4)])
Temp_met$date <- as.POSIXct(strptime(Met_CovTemp$date,
                                                  format = "%d/%m/%Y %H:%M", "GMT"))

## daily mean
Temp_daily <- timeAverage(Met_CovTemp, avg.time = "day")
## weekly mean
Temp_week <- timeAverage(Met_CovTemp, avg.time = "week")
## monthly mean
Temp_month <- timeAverage(Met_CovTemp, avg.time = "month")
## annual mean
Temp_annual <- timeAverage(Met_CovTemp, avg.time = "year")