Hello,
And another solution, taking advantage of Rasmus' one:
simplify2array(parallel::mclapply(c(
? "%Y",
? "%m",
? "%d",
? "%H"), function(fmt, x) {
??? as.integer(format(as.POSIXct(x), format = fmt))
}, x = dta$forecast.date))
#???? [,1] [,2] [,3] [,4]
#[1,] 2020??? 8??? 1?? 12
#[2,] 2020??? 8??? 1?? 12
#[3,] 2020??? 8??? 1?? 12
#[4,] 2020??? 8??? 1?? 12
#[5,] 2020??? 8??? 1?? 12
The data set dta is Jeff's, it's in dput format.
Hope this helps,
Rui Barradas
?s 18:26 de 02/08/2020, Rasmus Liland escreveu:
On 2020-08-02 09:24 -0700, Philip wrote:
| Below is some Weather Service data. I
| would like to parse the forecast date
| field into four different columns:
| Year, Month, Day, Hour
Dear Philip,
I'm largely re-iterating Eric and Jeff's
excellent solutions:
> dat <- structure(list(forecast.date =
+ c("2020-08-01 12:00:00",
+ "2020-08-01 12:00:00",
+ "2020-08-01 12:00:00",
+ "2020-08-01 12:00:00",
+ "2020-08-01 12:00:00"
+ ), TMP = c("305.495", "305.245",
+ "305.057", "305.745", "305.495"
+ )), row.names = c(NA, 5L),
+ class = "data.frame")
> t(apply(simplify2array(
+ strsplit(dat$forecast.date, "-| |:")),
+ 2, as.numeric))
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 2020 8 1 12 0 0
[2,] 2020 8 1 12 0 0
[3,] 2020 8 1 12 0 0
[4,] 2020 8 1 12 0 0
[5,] 2020 8 1 12 0 0
> simplify2array(parallel::mclapply(c(
+ lubridate::year,
+ lubridate::month,
+ lubridate::day,
+ lubridate::hour), function(FUN, x) {
+ FUN(x)
+ }, x=dat$forecast.date))
[,1] [,2] [,3] [,4]
[1,] 2020 8 1 12
[2,] 2020 8 1 12
[3,] 2020 8 1 12
[4,] 2020 8 1 12
[5,] 2020 8 1 12
V
r