Hello,
I would suggest something like:
date <- seq(as.Date("2020-01-01"), as.Date("2020-12-31"), 1)
time <- sprintf("%02d:%02d", rep(0:23, each = 12), seq.int(0, 55, 5))
x <- data.frame(
date = rep(date, each = length(time)),
time = time
)
x$cfs <- stats::rnorm(nrow(x))
cols2aggregate <- "cfs" # add more as necessary
S <- split(x[cols2aggregate], x$date)
means <- do.call("rbind", lapply(S, colMeans, na.rm = TRUE))
sds <- do.call("rbind", lapply(S, function(xx) sapply(xx, sd, na.rm =
TRUE)))
On Sun, Aug 29, 2021 at 11:09 AM Rich Shepard <rshep...@appl-ecosys.com>
wrote:
> I have a year's hydraulic data (discharge, stage height, velocity, etc.)
> from a USGS monitoring gauge recording values every 5 minutes. The data
> files contain 90K-93K lines and plotting all these data would produce a
> solid block of color.
>
> What I want are the daily means and standard deviation from these data.
>
> As an occasional R user (depending on project needs) I've no idea what
> packages could be applied to these data frames. There likely are multiple
> paths to extracting these daily values so summary statistics can be
> calculated and plotted. I'd appreciate suggestions on where to start to
> learn how I can do this.
>
> TIA,
>
> Rich
>
> ______________________________________________
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