R/score_swls.R

In PennStateDEPENdLab/dependlab: Useful functions for the UNC Chapel Hill DEPENd Lab (Director: Michael Hallquist)

#' Score the Satisfaction with Life Scale (Diener, Emmons, Larsen, & Griffin, 1985)
#' 
#' @param df a data.frame containing the 5 SWLS items to be scored
#' @param item_prefix a character prefix of the items names in \code{df} to be scored. Default: "SWLS"
#' @param max_impute the proportion of missingness [0..1) or number [1..n] of missing values per scale.
#'           Below this threshold, the person subscale mean will be imputed for missing items.
#' @param drop_items whether to remove the item-level data from the \code{df}. Default: FALSE
#' @param min_value the minimum value for the item anchors. Default: 1
#' @param max_value the highest value for the item anchors. Default: 7
#' 
#' @details 
#' 
#' Adds one column, \code{SWLS_total}, to \code{df} containing
#' the SWLS scale items.
#'
#' Note: the default SWLS scoring uses the mean of the items for the scales.
#' 
#' Note: the code assumes that pasting together the \code{item_prefix} and the numbers 1:5
#' will yield the 5 items from the test.
#' 
#' See: \url{http://labs.psychology.illinois.edu/~ediener/SWLS.html}
#' 
#' @export
#' @author Michael Hallquist
#' 
#' @importFrom dplyr select mutate
#' 
score_swls <- function(df, item_prefix="SWLS", max_impute=0.2, 
                        drop_items=FALSE, min_value=1, max_value=7) {
  
  orig_items <- paste0(item_prefix, 1:5) #expect item names
  stopifnot(is.data.frame(df))
  stopifnot(all(orig_items %in% names(df)))
  
  #validate item responses
  responses_valid <- apply(df[,orig_items], 1, function(row) { all(row >= min_value & row <= max_value, na.rm=TRUE) })
  
  if (any(!responses_valid)) {
    warning("Some responses fall outside of the allowable range: ", min_value, " -- ", max_value, "\n  Returning data.frame unchanged")
    print(df[!responses_valid, orig_items])
    return(df)
  }
  
  
  #score items
  total_items <- paste0(item_prefix, c(1, 2, 3, 4, 5))
  
  #NB. There is no reverse scoring for the SWLS
  
  
  #mean impute, if requested (after reverse scoring to get item direction correct)
  if (max_impute > 0) {
    df <- mean_impute_items(df, total_items, thresh=max_impute)
  }
  
  #https://github.com/jennybc/row-oriented-workflows/blob/master/ex09_row-summaries.md
  df <- df %>% mutate(
    SWLS_total = rowSums(select(., total_items)))
  
  if (drop_items) { df <- df %>% select(-orig_items) }
  
  return(df)
}