R/select_cases.R

In lcsm: Univariate and Bivariate Latent Change Score Modelling

#' Select cases based on minimum number of available session scores on one longitudinal measure
#' @param data  Dataset in wide format.
#' @param id_var String, specifying id variable.
#' @param var_list  Vector, specifying variable names in sequential order.
#' @param min_count  Numeric, specifying minimum number of available scores
#' @param return_id_only Logical, if TRUE only return ID. This is needed for select_bi_cases
#'
#' @return tibble
#' @export
#' @examples
#' select_uni_cases(data_uni_lcsm,
#'   id_var = "id",
#'   var_list = names(data_uni_lcsm)[-1],
#'   min_count = 7
#' )
#'
select_uni_cases <- function(data, id_var, var_list, min_count, return_id_only = FALSE) {
  var_count <- base::length(var_list)

  # Move id variable to beginning of dataset
  data_select <- dplyr::select(data, dplyr::all_of(id_var), dplyr::all_of(var_list))

  # Count available datapoints
  data_select$count <- base::apply(data_select[, 2:(var_count + 1)], 1, function(x) sum(!is.na(x)))

  # Select cases with greater or equal number specified in function
  data_export <- dplyr::filter(data_select, count >= min_count)

  # Delete count from dataframe
  if (return_id_only == FALSE) {
    data_export <- base::subset(data_export, select = -count)
  }

  if (return_id_only == TRUE) {
    data_export <- base::subset(data_export, select = id)
  }
  return(data_export)
}

#' Select cases based on minimum number of available session scores on two longitudinal measures
#' @param data A data frame in "wide" format, i.e. one column for each measurement point and one row for each observation.
#' @param id_var String, specifying id variable.
#' @param var_list_x  Vector, specifying variable names of construct X in sequential order.
#' @param var_list_y  Vector, specifying variable names of construct Y in sequential order.
#' @param min_count_x  Numeric, specifying minimum number of available scores for construct X.
#' @param min_count_y  Numeric, specifying minimum number of available scores for construct Y.
#'
#' @return tibble
#' @export
#' @examples
#' select_bi_cases(data_bi_lcsm,
#'   id_var = "id",
#'   var_list_x = names(data_bi_lcsm)[2:11],
#'   var_list_y = names(data_bi_lcsm)[12:21],
#'   min_count_x = 7,
#'   min_count_y = 7
#' )
#'
select_bi_cases <- function(data, id_var, var_list_x, var_list_y, min_count_x, min_count_y) {
  # Select cases from x and return id only
  id_select_x <- select_uni_cases(
    data = data,
    id_var = id_var,
    var_list = var_list_x,
    min_count = min_count_x,
    return_id_only = TRUE
  )

  # Select cases from y and return id only
  id_select_y <- select_uni_cases(
    data = data,
    id_var = id_var,
    var_list = var_list_y,
    min_count = min_count_y,
    return_id_only = TRUE
  )

  # Select ids where criterion of min count is met for x and y
  id_select_xy <- dplyr::inner_join(id_select_x, id_select_y, by = id_var)

  # Return data for cases where both criterions are met
  dplyr::left_join(id_select_xy, data, by = id_var)
}