R functions for assigning and summarizing storm events based on interevent period and minimum event total precipitation

events.R

assign.events <- function(df, datetime.name="DATETIME", value.name="VALUE", interevent=8, threshold=0.1) {
  # assigns events to data frame such as storm events or discharge events
  require(plyr)
  if (!(datetime.name %in% names(df))) {
    stop(paste0('Could not find datetime column called ', datetime.name))
  }
  if (!(value.name %in% names(df))) {
    stop(paste0('Could not find value column called ', value.name))
  }
  if (!is.regular(df[, datetime.name])) {
    stop('Dataframe is not regular')
  }
  # make sure df is sorted by datetime
  df <- df[order(df[, datetime.name]),]
  
  # extract datetime and value
  x <- data.frame(DATETIME=df[, datetime.name],
                  VALUE=df[, value.name])
  
  # compute runs
  x.rle <- rle(x$VALUE > 0)
  
  # EVENT: logical column indicating if timestep is part of event
  # does not yet account for events that do not meet minimum threshold
  x$EVENT <- rep(x.rle$lengths*(x.rle$values | x.rle$lengths < interevent), x.rle$lengths)
  x$EVENT <- x$EVENT > 0
  
  # EVENT_ID: numeric column assigning unique IDs to each event
  x$EVENT_ID <- c(as.numeric(x$EVENT[1]), ifelse(diff(x$EVENT)>0, diff(x$EVENT), 0))
  x$EVENT_ID <- cumsum(x$EVENT_ID)*x$EVENT
  
  # compute sum of each event
  x.event <- ddply(x, .(EVENT_ID), summarise, SUM=sum(VALUE))
  
  # take subset of events that meet minimum threshold
  x.event <- subset(x.event, SUM >= threshold)
  
  # assign events an ID of 0 if they did not meet the threshold
  x$EVENT_ID[which(!(x$EVENT_ID %in% x.event$EVENT_ID))] <- 0
  
  # reassign EVENT based on current EVENT_ID
  x$EVENT <- x$EVENT_ID > 0
  
  # reassign EVENT_ID based on new EVENT
  x$EVENT_ID <- c(as.numeric(x$EVENT[1]), ifelse(diff(x$EVENT)>0, diff(x$EVENT), 0))
  x$EVENT_ID <- cumsum(x$EVENT_ID)*x$EVENT
  
  # assign DRY_ID for dry periods
  x$DRY_ID <- c(as.numeric(!x$EVENT[1]), ifelse(diff(!x$EVENT)>0, diff(!x$EVENT), 0))
  x$DRY_ID <- cumsum(x$DRY_ID)*(!x$EVENT)
  
  # get rle of EVENT_ID
  x.rle.event <- rle(x$EVENT_ID)
  
  # add counter for each dry/wet event
  x$COUNTER <- unlist(sapply(x.rle.event$lengths, seq))
  
  # set EVENT_ID during dry events and DRY_ID during wet events to NA 
  x$EVENT_ID[which(x$EVENT_ID==0)] <- NA
  x$DRY_ID[which(x$DRY_ID==0)] <- NA
  
  # assign counter for dry events (number of hours since end of last wet event)
  x$DRY_HOURS <- ifelse(!is.na(x$DRY_ID), x$COUNTER, 0)
  
  # assign counter for wet events (number of hours since end of last dry event)
  x$EVENT_HOURS <- ifelse(!is.na(x$EVENT_ID), x$COUNTER, 0)
  
  # add EVENT_ID, DRY_HOURS, WET_HOURS to df and return
  df$EVENT_ID <- x$EVENT_ID
  df$EVENT_HOURS <- x$EVENT_HOURS
  df$DRY_ID <- x$DRY_ID
  df$DRY_HOURS <- x$DRY_HOURS
  
  df
}

events.summary <- function(df, datetime.name="DATETIME", value.name="VALUE") {
  # summarizes each event event in dataframe df
  # assumes df contains columns EVENT_ID (from assign.events), DATETIME (POSIXct), and VALUE (numeric)
  if (!("EVENT_ID" %in% names(df))) {
    stop("Unable to find column EVENT_ID in dataframe")
  }
  if (!(datetime.name %in% names(df))) {
    stop("Unable to find datetime column in dataframe")
  }
  if (!(value.name %in% names(df))) {
    stop("Unable to find value column in dataframe")
  }
  df$DATETIME <- df[, datetime.name]
  df$VALUE <- df[, value.name]
  event <- ddply(subset(df, !is.na(EVENT_ID)), c("EVENT_ID"), summarise, 
                 DURATION=length(DATETIME),
                 START=min(DATETIME),
                 END=max(DATETIME),
                 TOTAL=sum(VALUE),
                 PEAK=max(VALUE))
  event
}

This is incredibly helpful;
I'm relatively new to R and have been working on trying to write my own version of rain event summary code.

One issue/request:
It looks as though the assumption is that each record is equivalent to a 1 hr timestep. Can you modify the function to incorporate a variable that allows users to define their record length? (For instance, much of my meteorological data is in 30 min steps).