R/incRenv_old.R

In PabloCapilla/incR: Analysis of Incubation Data

#' @title Matching environmental and nest temperatures - Old version
#' @description
#' This function takes a data frame with recordings of environmental temperature and another
#' with nest temperatures and merges both per unit of time. The user can do this work manually, however, 
#' \code{\link{incRenv}} is thought to
#' automate data preparation (in combination with \code{\link{incRprep}}) to use 
#' \code{\link{incRscan}} after.
#' @param data.nest data frame containing nest temperature recordings. It must have two compulsory
#' columns 'date' and 'hour' displaying dates and the hour of each observation. These two columns
#' are provided if the user uses \code{\link{incRprep}} before. 
#' @param data.env data frame containing environmental temperatures to be merged with nest temperature
#' records. Please, provide date and time of each observation in one unique column as requested 
#' for \code{\link{incRprep}}.
#' @param env.temperature.name name of the column containing temperature recordings in the 
#' \emph{data.env} data frame.
#' @param env.date.name name of the column containing date and time in the 
#' \emph{data.env} data frame.
#' @param env.date.format format of \emph{env.date.name}. Similar to \code{\link{incRprep}}.
#' @param env.timezone time zone of the environmental recordings. Similar to \code{\link{incRprep}}.
#' @return The original \emph{data.nest} with an additional column for hour-averaged environmental
#' temperature. This new variable is thought to serve as \emph{env_temp} in
#' \code{\link{incRscan}}.
#' @details This function is thought to be used after \code{\link{incRprep}} as it uses some of the
#' additional variables created by \code{\link{incRprep}}.
#' @author Pablo Capilla-Lasheras
#' @examples
#' data(incR_envdata)  # environmental data
#' head (incR_envdata)
#' 
#' data(incR_rawdata)  # loading nest data
#' head (incR_rawdata)
#' 
#' # the first step in to format the raw data using incRprep
#' new.data <- incRprep (data=incR_rawdata,
#'                       date.name= "DATE",
#'                       date.format= "%d/%m/%Y %H:%M",
#'                       timezone="GMT",
#'                       temperature.name="temperature")
#'                       
#' # then use incRenv to merge environmental data
#' new.data2 <- incRenv_old (data.nest = new.data,
#'                       data.env = incR_envdata, 
#'                       env.temperature.name = "env_temperature", 
#'                       env.date.name = "DATE", 
#'                       env.date.format = "%d/%m/%Y %H:%M", 
#'                       env.timezone = "GMT")
#'                      
#' head (new.data2, 3)
#' @seealso \code{\link{incRprep}} \code{\link{incRscan}}
#' @export 
incRenv_old <- function (data.nest,
                         data.env, 
                         env.temperature.name, 
                         env.date.name,
                         env.date.format, 
                         env.timezone) {
  
  # data reading and creating variables
  data.env$t <- base::strptime(data.env[[env.date.name]], 
                               format=env.date.format, 
                               tz=env.timezone)
  data.env$time <-  base::strftime (data.env$t, format= "%H:%M")
  data.env$date <-  base::as.Date( base::format(data.env$t,"%Y-%m-%d"))
  data.env$index <-  base::seq(1, to= base::nrow(data.env), by=1)
  data.env$hour <-  base::as.numeric(base::format (data.env$t, "%H"))
  # necessary list
  time.temp.list <- list(NA)
  
  # average temperatures per hour
  for (d in 1: base::length(base::split(data.env, data.env$date))) {
    df00 <-  base::split(data.env, data.env$date)[[d]]
    temp <-  base::as.numeric( base::tapply(df00[[env.temperature.name]], 
                                            df00$hour, 
                                            base::mean, 
                                            rm.na=TRUE))
    hour <-  base::as.numeric( base::unique(df00$hour))
    date <-  base::rep(base::unique(base::as.Date(df00$date)), 
                       length= base::length(hour))
    time.temp.list[[d]] <-  base::data.frame(date=date, hour=hour, temp=temp)
  }
  
  
  time.temp <-  base::do.call("rbind",time.temp.list)
  
  ######
  ###### matching env.temp with the inside-nest data
  ##
  # stop if there is no data and no hour in data.nest
  if ( base::is.null(data.nest[["date"]])){
    stop("Provide a column in data.nest under the name 'date' containing
         the date of each observation")
  }
  
  if ( base::is.null(data.nest[["hour"]])){
    stop("Provide a column in data.nest under the name 'hour' containing
         the hour of each observation")
  }
  
  # actual calculation
  data.nest[["env_temp"]] <- NA
  for (p in 1: base::length(base::split(data.nest, data.nest[["date"]]))){
    df.nest <-  base::split(data.nest, data.nest[["date"]])[[p]]
    df.env <- time.temp[time.temp$date== base::unique(df.nest$date),]
    
    for (h in 1: base::length(data.nest[["hour"]])){
      hour.match <- data.nest[["hour"]][h]
      index <- data.nest$index[data.nest$date== base::unique(df.nest$date) & 
                                 data.nest$hour==hour.match]      
      data.nest$env_temp[data.nest$date== base::unique(df.nest$date) & 
                           data.nest$hour==hour.match] <- 
        base::rep(df.env$temp[df.env$hour==hour.match], length= base::length(index))
      
    } 
  }
  return(data.nest)
}