Formulating experimental feeds

1 Introduction

The purpose of this markdown is to:

library(arrow)
library(sf)
library(dplyr)
library(tidyr)
library(terra)
library(magrittr)
library(furrr)
library(future)
library(tictoc)
library(ggplot2)
library(fs)
library(conflicted)
library(stringr)
library(readxl)
library(units)
library(qs)
library(here)
conflicts_prefer(dplyr::filter(), dplyr::select(), .quiet = T)

here("src") %>% list.files(pattern = "\\.R$", full.names = TRUE) %>% purrr::walk(source)

ingred_inputs_file <- file.path(input_feed_profile_path, "all_ingredients.csv")
feed_input_file <- file.path(input_feed_profile_path, "all_feeds.xlsx")

2 Import ingredients

There is no check or correction here to ensure that protein+lipid+ash+carb = 1 in the incoming data. This was checked manually prior to import.

ingreds <- ingred_inputs_file %>% 
  read.csv() %>% 
  mutate(ingredient = as.factor(ingredient))
ingred_nms <- levels(ingreds$ingredient)

3 Import feeds

feed_inputs <- feed_input_file %>% 
  read_excel(sheet = "all_feeds") %>% 
  pivot_longer(names_to = "feed", values_to = "proportion", cols = !contains(c("ingredient", "category"))) %>% 
  mutate(feed = as.factor(feed),
         ingredient = as.factor(ingredient)) %>% 
  merge(ingreds, by = "ingredient")

feed_types <- levels(feed_inputs$feed)

Check that ingredient proportions sum to 1.

feed_inputs <- feed_inputs %>% 
  filter(!is.na(proportion) & proportion != 0) %>% 
  group_by(feed) %>% 
  mutate(
    sum = sum(proportion),
    proportion = proportion/sum
  ) %>% 
    ungroup()

The following chunk allows as many ingredients and feeds to be added to the incoming data as needed, it will still be formatted into a form that the targets pipelines can use.

feed_params <- purrr::map(feed_types, function(ft) {
  df <- feed_inputs %>% 
    filter(feed == ft) 
  list(
    Proteins = df %>% 
      select(ingredient, proportion, contains("protein"), -contains("feed")) %>%
      rename(macro = protein, digest = protein_digestibility),
    Carbohydrates = df %>% 
      select(ingredient, proportion, contains("carb"), -contains("feed")) %>%
      rename(macro = carb, digest = carb_digestibility),
    Lipids = df %>% 
      select(ingredient, proportion, contains("lipid"), -contains("feed")) %>%
      rename(macro = lipid, digest = lipid_digestibility)
  )
}) %>% 
  setNames(feed_types)

qsave(feed_params, file.path(output_species_data_path, "feed_params.qs"))

4 Similarity

For later - how similar are the diets?

feed_simil <- feed_inputs %>% 
  select(ingredient, feed, proportion) %>% 
  as.data.frame() %>% 
  pivot_wider(
    names_from = feed,
    values_from = proportion,
    id_cols = ingredient,
    values_fn = as.numeric
  ) %>% 
  mutate(
    diff_PD_MD = abs(marine_dominant - plant_dominant),
    diff_PD_NI = abs(novel_inclusive - plant_dominant)
  )

1 - sum(feed_simil$diff_PD_MD, na.rm = T)
1 - sum(feed_simil$diff_PD_NI, na.rm = T)