R/WG_centre.R

In LiamDBailey/MyFuncs:

#' Carry out within-group centring.
#'
#' Calculate within-group effects based on methods of van de Pol and Wright.
#'
#' @param data Dataset in which within-group centring will be carried out.
#' N.B. First argument makes this function compatible with dplyr piping.
#' @param Variable An unquoted column name. The name of variable column on which centring will occur.
#' @param Group An unquoted column name. The name of grouping column.
#'
#' @return A tibble with two new columns containing within group mean and deviance.
#' @export
#' @import dplyr
#' @import rlang
#'
#' @examples
#' library(dplyr)
#'
#' dummy_dat <- tibble(group = rep(LETTERS[1:4], 20), value = runif(80, 0, 10))
#'
#' #Stand alone function
#' WG_centre(dummy_dat, value, group)
#'
#' #Inside dplyr pipe
#' dummy_dat %>%
#'   #Merge two factor levels
#'   rowwise() %>%
#'   mutate(group = ifelse(group == "B", "A", group)) %>%
#'   WG_centre(value, group)
#'

WG_centre <- function(data, Variable, Group){

  #Define variables to avoid global variable warning
  WGmean <- WGdev <- NULL

  Variable <- enquo(Variable)
  Group    <- enquo(Group)

  mean_name <- sym(paste0(as_name(Variable), "_WGmean"))
  dev_name  <- sym(paste0(as_name(Variable), "_WGdev"))

  output <- data %>%
    group_by(!!Group) %>%
    mutate(WGmean = mean(!!Variable),
           WGdev  = !!Variable - WGmean) %>%
    rename(!!mean_name := WGmean,
           !!dev_name := WGdev)

  return(output)

}