R/load_dataframe.r

In roqua/autovar: Automated Vector Autoregression Model Creation

#' Returns an av_state for data loaded from a data.frame
#'
#' This function constructs an object of class \code{av_state} based on a given \code{data.frame}.
#' @param df a \code{data.frame} with features in columns and measurements in rows.
#' @param net_cfg a net_cfg object providing metadata about the networks
#' @param log_level sets the minimum level of output that should be shown (a number between 0 and 3). A lower level means more verbosity. Specify a log_level of 3 to hide messages about the data set that was loaded.
#' @return This function returns the newly created \code{av_state} object.
#' @examples
#' av_state <- load_dataframe(generate_test_data(),new_net_cfg())
#' print(av_state)
#' @export
load_dataframe <- function(df,net_cfg,log_level=0) {
  av_state <- new_av_state()
  av_state$file_name <- 'fake_filename.sav'
  av_state$real_file_name <- 'fake_filename.sav'
  av_state$file_type <- 'SPSS'
  name_labels <- to_var_labels(names(df),net_cfg)
  if (any(name_labels != names(df)))
    attr(df,'variable.labels') <- name_labels
  av_state$raw_data <- df
  av_state$data <- list('multiple'=av_state$raw_data)
  scat(log_level,2,"load_dataframe loaded data.frame with",length(av_state$data[[1]]),"columns:\n")
  sprint(log_level,2,column_names_output(av_state))
  av_state
}
to_var_labels <- function(rnames,net_cfg) {
  r <- NULL
  for (rname in rnames)
    r <- c(r,format_property_name(rname,net_cfg))
  names(r) <- rnames
  r
}