Convert dataframe with datetime column to time series in R - r

Question

I have a csv-file with a datetime column and a column with hourly consumption of energy.
Datetime AEP_MW
2004-12-31 01:00:00 13478
2004-12-31 02:00:00 12865
2004-12-31 03:00:00 12577
2004-12-31 04:00:00 12517
2004-12-31 05:00:00 12670
2004-12-31 06:00:00 13038
2004-12-31 07:00:00 13692
2004-12-31 08:00:00 14297
2004-12-31 09:00:00 14719
2004-12-31 10:00:00 14941
2004-12-31 11:00:00 15184
2004-12-31 12:00:00 15009
2004-12-31 13:00:00 14808
...
2018-08-03 00:00:00 14809
I want to convert the above hourly energy consumption data into time series format in order to decompose it in the next step.
I have tried to convert the datetime from character to POSIXlt
Datetime <- as.POSIXlt(Datetime, '%Y-%m-%d %H:%M:%S')
Warnings:
1: In strptime(xx, f, tz = tz) : unknown timezone '%Y-%m-%d %H:%M:%S'
2: In as.POSIXct.POSIXlt(x) : unknown timezone '%Y-%m-%d %H:%M:%S'
3: In strptime(x, f, tz = tz) : unknown timezone '%Y-%m-%d %H:%M:%S'
data_ts <- ts(AEP_MW, Datetime)
data_ts
Time Series:
Start = 2208913199
End = 2209034471
Frequency = 1
[1] 13478 12865 12577 12517 12670 13038 13692 14297 14719 14941 15184 15009 14808 14522 14349 14107 14410
[18] 15174 15261 14774 14363 14045 13478 12892 14097 13667 13451 13379 13506 14121 15066 15771 16047 16245
...
Unfortunately these are not the outputs I have expected to receive. How can I convert the data to receive an output as the nottem-data in R with the following format?
> nottem
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1920 40.6 40.8 44.4 46.7 54.1 58.5 57.7 56.4 54.3 50.5 42.9 39.8
1921 44.2 39.8 45.1 47.0 54.1 58.7 66.3 59.9 57.0 54.2 39.7 42.8
1922 37.5 38.7 39.5 42.1 55.7 57.8 56.8 54.3 54.3 47.1 41.8 41.7
...
How can I let R know that the frequency of my dataset is not 1 and decompose the time series?

Answer 1

Use the following code
df$Datetime <- with(df, as.POSIXct(paste(Date, time), format="%Y-%m-%d %H:%M:%OS"))
data_ts <- ts(df)
If you want to have the output as you have shown in your question you can use the following code
library(lubridate)
library(tidyverse)
df %>%
group_by(Date) %>%
summarise(daily = mean(AEP_MW)) %>%
mutate(Day = day(ymd(Date)),
Month = month(ymd(Date)),
Year = year(ymd(Date))) %>%
group_by(Month, Year) %>%
summarise(monthly = mean(daily)) %>%
pivot_wider(names_from = Month, values_from = monthly)
Data
df = structure(list(Date = c("2004-12-31", "2004-12-31", "2004-12-31",
"2004-12-31", "2004-12-31", "2004-12-31", "2004-12-31", "2004-12-31",
"2004-12-31", "2004-12-31", "2004-12-31", "2004-12-31", "2004-12-31"
), time = c("01:00:00", "02:00:00", "03:00:00", "04:00:00", "05:00:00",
"06:00:00", "07:00:00", "08:00:00", "09:00:00", "10:00:00", "11:00:00",
"12:00:00", "13:00:00"), AEP_MW = c(13478L, 12865L, 12577L, 12517L,
12670L, 13038L, 13692L, 14297L, 14719L, 14941L, 15184L, 15009L,
14808L)), class = "data.frame", row.names = c(NA, -13L))