#' Extractor incubatortemp data
#'
#' Convert all \code{.cvs} files in \code{incubatortemp} folder to \code{data.frame} and save as \code{.rds} file.
#'
#' This function is extracting data to be added to the database (and therefore make accessible for further analysis and forecasting)
#' from \code{.csv} files.
#'
#' @param input directory from which to read the data
#' @param output directory to which to write the data
#'
#' @return invisibly \code{TRUE} when completed successful
#'
#' @importFrom dplyr bind_rows
#' @export
#'
extractor_incubatortemp <- function(
input,
output )
{
message("\n########################################################\n")
message("\nExtracting Incubatortemp\n")
# Get csv file names ------------------------------------------------------
incubatortemp_path <- file.path( input, "incubatortemp" )
incubatortemp_files <- list.files(
path = incubatortemp_path,
pattern = "*.txt",
full.names = TRUE,
recursive = TRUE
)
if (length(incubatortemp_files) == 0) {
message("nothing to extract\n")
message("\n########################################################\n")
return(invisible(FALSE))
}
# Read file ---------------------------------------------------------------
itmp <- lapply(
incubatortemp_files,
function(fn) {
data <- read.table(
file = fn,
fileEncoding = "UTF-16LE",
header = TRUE
)
return(data)
}
)
# combine intu one large tibble
itmp <- dplyr::bind_rows(itmp)
# SAVE --------------------------------------------------------------------
add_path <- file.path( output, "incubatortemp" )
dir.create( add_path, recursive = TRUE, showWarnings = FALSE )
saveRDS(
object = itmp,
file = file.path(add_path, "incubatortemp.rds")
)
# Finalize ----------------------------------------------------------------
message("done\n")
message("\n########################################################\n")
invisible(TRUE)
}
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