All the ways I know to get things in the right date/time format

timestimestimes.R

# All of my times are in mm/dd/yyyy hh:mm:ss

# From a character string (read in from read.csv(stringsAsFactors = FALSE))

# By far the easiest way is with lubridate::mdy_hms()

#Ex:
options(stringsAsFactors = FALSE) 
Sys.timezone(location = FALSE)
Sys.setenv(TZ = "Pacific/Pitcairn")
Sys.timezone() # make sure it took
files <- list.files(pattern = "*.csv") #create list of files by character name; change working directory first
d <- plyr::adply(files, 1, read.csv) #combine into one big dataframe
colnames(d) <- c("DateTimeUTC", "Monitor" , "TagID")
d$DateTimeUTC <-mdy_hms(f$DateTimeUTC) # Convert to POSIXct

# Another example of converting from yearday (or Julian day) back to a full POSIXct date
#---# Assuming you have a year, month and yday column, you can back calculate a POSIXct date/datetime column

# OPTION 1: If you have separate year ("yr"), month ("mon"), year day ("yday") columns:
date<-ymd(strptime(paste0(yr,"-", mon,"-", yday),format = "%Y-%m-%j")

# OPTION 2: If you only have year ("yr") and year ("yday") day columns
date<-as.Date(x= (yday - 1), origin=paste0(yr, "-01-01"))

# this is with times
datetime<-ymd_hms(strptime(paste0(yr,"-", mon,"-", yday, " ", hour,":00"),format = "%Y-%m-%j %H:%M")

# Add a water year column (water year starts on Oct 01 and ends Sep 30)

# Make Water Year column from date column (assumes your column is already POSIXct or as.Date)
wtr_yr <- function(dates, start_month=10) {
  # Convert dates into POSIXlt
  dates.posix = as.POSIXlt(dates)
  # Year offset
  offset = ifelse(dates.posix$mon >= start_month - 1, 1, 0)
  # Water year
  adj.year = dates.posix$year + 1900 + offset
  # Return the water year
  adj.year
}

## AN EXAMPLE
library(lubridate)
library(dplyr)

sometime<-seq(ymd('2011-10-01'),ymd('2015-09-30'), by = '1 day') # create a seq of dates
jday<-yday(sometime) # add julian day
WY<-wtr_yr(sometime) # use water year function to add water year (Oct 1 through Sep 30)
df<-data.frame("sometime"=sometime, "jday"=jday, "WY"=WY) # bind dataframe

# this method takes leap years into account
df$wyd<-ave(df$WY, df$WY, FUN=seq_along) # break by water year, df must be ordered by date

# use dplyr to double check data...
df %>% group_by(WY) %>% summarize("days"=n())


# Rough way to do this, need a POSIXct formatted date column "date"
wyday<-ifelse(month(df$date)<10, (yday(df$date)+92), 
                                 (yday(df$date)-273))