Beiträge von EmSiMa

mean_diff_pal <- Inf
while(mean_diff_pal > limit) {
  data_grouped <- list(data.frame(), data.frame(), data.frame(), data.frame())
  
  # Images are both sorted for WHO categories and palatability, and selection
  # within each four-image-subset can be either permutation-based or random.
  for(i in 1:(nrow(data_selected_without_remainder)/4)) {
    
    lower_border <- i*4-3
    upper_border <- i*4
    
    data_subset <- data_selected_without_remainder[lower_border:upper_border,]
    
    if(i <= length(permutations) & usePermutations == TRUE) {
      order <- unlist(permutations[i])
    } else {
      order <- sample(1:4)
    }
    
    for(j in 1:length(order)) {
      data_grouped[[order[j]]] <- rbind(data_grouped[[order[j]]], data_subset[j, ])
    }
  }
  
  # Images are sorted for palatability, group selection within each subset is random.
  for(i in 1:(nrow(data_selected_remainder)/4)) {
    
    lower_border <- i*4-3
    upper_border <- i*4
    
    data_subset <- data_selected_remainder[lower_border:upper_border, ]
    order <- sample(1:4)
    
    for(j in 1:length(order)) {
      data_grouped[[order[j]]] <- rbind(data_grouped[[order[j]]], data_subset[j, ])
    }
  }
  
  # function for mean calculations
  calc_mean <- function(list, list_length, column_name) {
    temp <- vector()
    
    for (i in 1:list_length) {
      result <- mean(list[[i]][[column_name]])
      temp <- c(temp, result)
    }
    
    return(temp)
  }
  
  # print means of palatability and kcal per 100g
  m_pal <- calc_mean(data_grouped, 4, "Mean_palatability")
  print(m_pal)
  m_kcal <- calc_mean(data_grouped, 4, "kcal_100g")
  print(m_kcal)
  
  # update difference
  mean_diff_pal <- max(calc_mean(data_grouped, 4, "Mean_palatability")) - min(calc_mean(data_grouped, 4, "Mean_palatability"))
}