# Using custom shuffle schedule

    library(designit)
    library(tidyverse)
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    #> ✔ purrr     1.0.2     ✔ tibble    3.2.1
    #> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
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    #> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

In this example we would like to distribute animals among cages with constraints:

- There should be not more than one male per cage.
- Number of treatment/control animals should be comparable per cage
- Average weight per cage should be comparable between cages

    set.seed(43)
    samples <- tibble(
      id = 1:params$n_samples,
      sex = sample(c("F", "M"), params$n_samples, replace = TRUE, prob = c(0.8, 0.2)),
      group = sample(c("treatment", "control"), params$n_samples, replace = TRUE),
      weight = runif(params$n_samples, 20, 30)
    )
    samples |>
      head()
    #> # A tibble: 6 × 4
    #>      id sex   group     weight
    #>   <int> <chr> <chr>      <dbl>
    #> 1     1 F     treatment   26.2
    #> 2     2 M     treatment   26.1
    #> 3     3 F     control     26.6
    #> 4     4 F     control     25.4
    #> 5     5 F     treatment   24.3
    #> 6     6 F     treatment   21.5

    samples |>
      count(sex)
    #> # A tibble: 2 × 2
    #>   sex       n
    #>   <chr> <int>
    #> 1 F        42
    #> 2 M         8

We create a `BatchContainer` with 11 cages and 5 positions per cage. Note that positions do not actually matter; this is just to limit the number of animals per cage.

We start by assigning samples randomly.

    set.seed(42)
    bc <- BatchContainer$new(
      dimensions = c("cage" = 11, "position" = 5)
    ) |>
      assign_random(samples)
    bc
    #> Batch container with 55 locations and 50 samples (assigned).
    #>   Dimensions: cage, position

Functions to plot number of males per cage, weights per cage and treatment/control ratios.

    males_per_cage <- function(bc) {
      bc$get_samples() |>
        filter(sex == "M") |>
        count(cage) |>
        ggplot(aes(cage, n)) +
        geom_col()
    }

    weight_d <- function(bc) {
      bc$get_samples() |>
        ggplot(aes(factor(cage), weight)) +
        geom_violin() +
        geom_point() +
        stat_summary(fun = mean, geom = "point", size = 2, shape = 23, color = "red")
    }

    group_d <- function(bc) {
      bc$get_samples(remove_empty_locations = TRUE) |>
        ggplot(aes(factor(cage), fill = group)) +
        geom_bar(position = "fill")
    }

    males_per_cage(bc)

    weight_d(bc)
    #> Warning: Removed 5 rows containing non-finite outside the scale range
    #> (`stat_ydensity()`).
    #> Warning: Removed 5 rows containing non-finite outside the scale range
    #> (`stat_summary()`).
    #> Warning: Removed 5 rows containing missing values or values outside the scale range
    #> (`geom_point()`).

    group_d(bc)

First, we use OSAT scoring function to ensure even distribution of males among cages. Only `cage` and `sex` interactions are considered in the scoring function. We only use 10 iterations, since shuffling is limited to locations with males and enforces change of cage on every iteration.

    set.seed(10)

    bc <- optimize_design(
      bc,
      scoring = osat_score_generator(
        "cage",
        "sex"
      ),
      shuffle_proposal_func = shuffle_with_constraints(
        sex == "M",
        cage != .src$cage
      ),
      max_iter = 10
    )
    #> Warning in osat_score(bc, batch_vars = batch_vars, feature_vars = feature_vars,
    #> : NAs in features / batch columns; they will be excluded from scoring
    #> Checking variances of 1-dim. score vector.
    #> ... (21.451) - OK
    #> Initial score: 15.818
    #> Achieved score: 11.818 at iteration 1
    #> Achieved score: 9.818 at iteration 10

    bc$plot_trace()

We expect the distribution of males become even, while other variables are not significantly affected.

    males_per_cage(bc)

    weight_d(bc)
    #> Warning: Removed 5 rows containing non-finite outside the scale range
    #> (`stat_ydensity()`).
    #> Warning: Removed 5 rows containing non-finite outside the scale range
    #> (`stat_summary()`).
    #> Warning: Removed 5 rows containing missing values or values outside the scale range
    #> (`geom_point()`).

    group_d(bc)

Here we only define our custom scoring function which ensures even distribution of weights and treatment/control groups. Only female samples are shuffled and male samples are kept in their locations. We also ensure that on every iteration the cage number is changed; we do this because `position` dimension does affect actual animal allocation.

    scoring_f <- function(bc) {
      samples <- bc$get_samples(include_id = TRUE, as_tibble = FALSE)
      avg_w <- samples[, mean(weight, na.rm = TRUE)]
      avg_w_per_cage <- samples[!is.na(weight), mean(weight), by = cage]$V1
      trt_per_cage <- samples[!is.na(group), sum(group == "treatment") / .N, by = cage]$V1

      w_score <- mean((avg_w - avg_w_per_cage)**2)
      trt_score <- mean((trt_per_cage - 0.5)**2)
      w_score + 10 * trt_score
    }

    set.seed(12)
    bc <- optimize_design(bc,
      scoring = scoring_f,
      shuffle_proposal = shuffle_with_constraints(
        sex == "F",
        cage != .src$cage & (is.na(sex) | sex != "M")
      ),
      n_shuffle = c(rep(10, 20), rep(5, 20), rep(3, 20), rep(1, 140)),
      max_iter = 200
    )
    #> Checking variances of 1-dim. score vector.
    #> ... (0.457) - OK
    #> Initial score: 2.657
    #> Achieved score: 1.916 at iteration 2
    #> Achieved score: 1.835 at iteration 5
    #> Achieved score: 1.289 at iteration 9
    #> Achieved score: 1.225 at iteration 10
    #> Achieved score: 1.089 at iteration 11
    #> Achieved score: 1.074 at iteration 16
    #> Achieved score: 1.033 at iteration 18
    #> Achieved score: 0.875 at iteration 19
    #> Achieved score: 0.574 at iteration 22
    #> Achieved score: 0.523 at iteration 23
    #> Achieved score: 0.522 at iteration 32
    #> Achieved score: 0.483 at iteration 41
    #> Achieved score: 0.394 at iteration 49
    #> Achieved score: 0.379 at iteration 67
    #> Achieved score: 0.372 at iteration 73
    #> Achieved score: 0.365 at iteration 84
    #> Achieved score: 0.361 at iteration 90
    #> Achieved score: 0.348 at iteration 96
    #> Achieved score: 0.322 at iteration 102
    #> Achieved score: 0.302 at iteration 131
    #> Achieved score: 0.302 at iteration 141
    #> Achieved score: 0.246 at iteration 165
    #> Achieved score: 0.245 at iteration 166
    #> Achieved score: 0.241 at iteration 184
    #> Achieved score: 0.241 at iteration 191
    #> Achieved score: 0.237 at iteration 196
    bc$plot_trace()

    scoring_f(bc)
    #> [1] 0.2370109

Now we have a much more even distribution of weights and treatment/control balance.

    males_per_cage(bc)

    weight_d(bc)
    #> Warning: Removed 5 rows containing non-finite outside the scale range
    #> (`stat_ydensity()`).
    #> Warning: Removed 5 rows containing non-finite outside the scale range
    #> (`stat_summary()`).
    #> Warning: Removed 5 rows containing missing values or values outside the scale range
    #> (`geom_point()`).

    group_d(bc)