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rstatisticsxtshydrotsm

Compute daily, month and annual average of several data sets

I have a data frame:

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

are temperature observations of a weather station taken every hour, I want to calculate the daily, weekly, monthly and annual averages of several data frames of different weather stations. How can I do this within a loop, so that the process is not repetitive?

Solution

  • When working with hydro-meteorological data, I usually use xts and hydroTSM packages as they have many functions for data aggregation.

    You didn't provide any data so I created one for demonstration purpose 

    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)

    Edit: using OP's data

    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)

    Created on 2018-02-28 by the reprex package (v0.2.0).