nataliamd11

# > head(m_mm_ac)
#    month.order year accumulated.value
# 1          10 2007               0.6
# 2          11 2007              26.7
# 3          12 2007              68.3
# > head(m_t_mean)
#   month.order year mean.value
# 1          10 2007   22.48750
# 2          11 2007   21.04500
# 3          12 2007   23.29516

nataliamd11

calera_organized <- OrganizeDateColumns(data.x = calera_raw, ncol.date = 1, orders = "mdyHMS", ncol.day = NULL, ncol.month = NULL, ncol.year = NULL)
head(calera_organized)
#
# > head(calera_organized)
#        fecha mm temp humedad day month year
# 1 2018-04-27  0 22.8    49.8  27     4 2018
# 2 2018-04-27  0 24.0    47.9  27     4 2018
# 3 2018-04-27  0 26.4    45.5  27     4 2018
# 4 2018-04-27  0 25.9    49.7  27     4 2018
# 5 2018-04-27  0 26.0    56.7  27     4 2018

nataliamd11

HourlyToDaily <- function(data.x, ncol.obs, ncol.date, na.presence = TRUE, value = "mean") {
  #
  if (!any(value == c("mean", "accumulated"))) {
    stop(" value must to be 'mean' or 'accumulated'")
  }
  # Calculate the mean of the variable of interest
  first.year <- min(data.x$year, na.rm = na.presence)
  last.year  <- max(data.x$year, na.rm = na.presence)
  x.daily.per.years <- list()
  date.tot <- list()

nataliamd11

# First we read the .csv with the data from La Calera
#
calera_raw <- read.csv(".../meteorologicos_la_calera.csv")
head(calera_raw)
#
#> head(calera)
#                 fecha mm temp humedad
# 1 04/27/2018 17:00:00  0 22.8    49.8
# 2 04/27/2018 16:00:00  0 24.0    47.9
# 3 04/27/2018 15:00:00  0 26.4    45.5

nataliamd11

# Function that creates day, month and year columns.
# 
OrganizeDateColumns <- function(data.x, ncol.date = 1, orders = "mdyHMS", ncol.day = NULL, ncol.month = NULL, ncol.year = NULL) {
  #
  require(lubridate)
  #
  # Organizes dates's columns using lubridate
  # If columns with days, months or years are NULL, creates these columns with a 
  # function inside the function
  #