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#' Centering for multilevel analyses
#'
#' This function will group mean centered the scores at the level 1 and create a mean score for each group at L2.
#'
#' @param data A data.frame or a data.frame extension (e.g. a tibble).
#' @param cols Columns that need to be centered. See `dplyr::dplyr_tidy_select` for available options.
#' @param group the grouping variable. Must be character.
#' @param keep_original default is `TRUE`. Set to `FALSE` to remove original columns
#' @return
#' An object of the same type as .data. The output has the following properties:
#' 1. Columns from .data will be preserved
#' 2. Columns with L1 scores that are group-mean centered.
#' 3. Columns with L2 aggregated means.
#'
#' @export
#'
#' @examples
#' center_mlm(iris,dplyr::ends_with('Length'),group = 'Species')
#'
center_mlm = function(data,cols,group,keep_original = TRUE){
data_names = names(data)
cols = data %>% dplyr::select(!!enquo(cols)) %>% names()
group = enquo(group)
group_name = data %>% dplyr::select(!!enquo(group)) %>% names()
# aggregate mean
mean_data = data %>%
dplyr::group_by(dplyr::across(!!group)) %>%
dplyr::summarise(dplyr::across(!!cols,~mean(.,na.rm = TRUE))) %>%
#dplyr::mutate(dplyr::across(!!cols, function(x) { (x - mean(x,na.rm = TRUE))})) %>%
dplyr::ungroup() %>%
dplyr::rename_with(.fn = ~paste0(.,'_mean',recycle0 = TRUE),.cols = !!cols)
# group-mean centering
original_df = data %>% dplyr::select(!!cols)
centered_data = data %>%
dplyr::group_by(dplyr::across(!!group)) %>%
dplyr::mutate(dplyr::across(!!cols, function(x) { (x - mean(x,na.rm = TRUE))})) %>%
dplyr::rename_with(~ paste(.,'_group_c',sep = ''),!!cols) %>%
dplyr::ungroup()
if (keep_original == TRUE) {
centered_data = dplyr::bind_cols(centered_data,original_df)
}
return_data = dplyr::full_join(centered_data,mean_data,by = group_name) %>%
dplyr::select(c(dplyr::all_of(data_names),dplyr::ends_with('group_c'),dplyr::ends_with('_mean'),dplyr::everything()))
return(return_data)
}
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