Simulation Experiment Recipe

Objectives

The objective of this simulation experiment is to provide a basic example on how to use simChef and showcase the automated R Markdown-generated documentation. For the sake of illustration, this toy simulation experiment studies the performance of linear regression versus random forests under a linear data-generating process across varying noise levels.

[Typically, the objective of the simulation experiment (and this blurb) will be more scientific than instructive and will warrant additional context/background and domain knowledge.]

Data Generation

Linear DGP

We simulate the (test and training) covariate/design matrix \(\mathbf{X} \in \mathbb{R}^{n \times p}\) from a standard normal distribution and the response vector \(\mathbf{y} \in \mathbb{R}^n\) from a linear model. Specifically,

\[\begin{align*}\mathbf{y} = \mathbf{X} \boldsymbol{\beta} + \boldsymbol{\epsilon},\\\end{align*}\]

where\[\begin{align*}& \mathbf{X}_{ij} \stackrel{iid}{\sim} N\left(0, 1\right) \text{ for all } i = 1, \ldots, n \text{ and } j = 1, \ldots, p, \\& \boldsymbol{\epsilon}_i \stackrel{iid}{\sim} N(0, \sigma^2) \text{ for all } i = 1, \ldots, n.\end{align*}\]

Default Parameters in DGP

  • Number of training samples: \(n_{\text{train}} = 200\)
  • Number of test samples: \(n_{\text{test}} = 200\)
  • Number of features: \(p = 2\)
  • Amount of noise: \(\sigma = 1\)
  • Coefficients: \(\boldsymbol{\beta} = (1, 0)^\top\)

[In practice, documentation of DGPs should answer the questions “what” and “why”. That is, “what” is the DGP, and “why” are we using/studying it? As this simulation experiment is a contrived example, we omit the “why” here.]

Function

#> function(n_train, n_test, p, beta, noise_sd) {
#>   n <- n_train + n_test
#>   X <- matrix(rnorm(n * p), nrow = n, ncol = p)
#>   y <- X %*% beta + rnorm(n, sd = noise_sd)
#>   data_list <- list(
#>     X_train = X[1:n_train, , drop = FALSE],
#>     y_train = y[1:n_train],
#>     X_test = X[(n_train + 1):n, , drop = FALSE],
#>     y_test = y[(n_train + 1):n]
#>   )
#>   return(data_list)
#> }
#> <bytecode: 0x5645ddacb0d8>

Input Parameters

#> $n_train
#> [1] 200
#> 
#> $n_test
#> [1] 200
#> 
#> $p
#> [1] 2
#> 
#> $beta
#> [1] 1 0
#> 
#> $noise_sd
#> [1] 1

Methods and Evaluation

Methods

Linear Regression

Given some training covariate data \(\mathbf{X}\) and response vector \(\mathbf{y}\), we fit a linear regression model (i.e., ordinary least squares) on \(\mathbf{X}\) to predict \(\mathbf{y}\) by minimizing the following objective:

\[\begin{align*}\boldsymbol{\hat{\beta}} = \text{argmin}_{\boldsymbol{\beta}} || \mathbf{y} - \mathbf{X} \boldsymbol{\beta} ||_2^2.\end{align*}\]

Then, to make prediction given some test data \(\mathbf{X}^{\text{test}}\), we compute:

\[\begin{align*}\boldsymbol{\hat{y}}^{\text{test}} = \mathbf{X}^{\text{test}} \boldsymbol{\hat{\beta}}.\end{align*}\]

[In practice, documentation of methods should answer the questions “what” and “why”. That is, “what” is the method, and “why” are we using/studying it? As this simulation experiment is a contrived example, we omit the “why” here.]

Function

#> function(X_train, y_train, X_test, y_test) {
#>   train_df <- dplyr::bind_cols(data.frame(X_train), y = y_train)
#>   fit <- lm(y ~ ., data = train_df)
#>   predictions <- predict(fit, data.frame(X_test))
#>   return(list(predictions = predictions, y_test = y_test))
#> }
#> <bytecode: 0x5645dffc7630>

Input Parameters

#> list()

Random Forest

Given some training covariate data \(\mathbf{X}\) and response vector \(\mathbf{y}\), we fit a random forest on \(\mathbf{X}\) to predict \(\mathbf{y}\). At a high-level, a random forest is an ensemble of classification or regression trees (CART) that are fitted independently of one another on bootstrapped samples of the training data. Further, each CART model is fitted by performing recursive axis-aligned binary splits, where the optimal split at each node is chosen from a random subsample of features to minimize an impurity decrease criterion.

To make predictions, CART identifies the unique leaf node containing the test data point and predicts the mean response (of training data) in that node. For a random forest, the predictions are averaged across all CARTs in the forest.

For further details, we refer to Breiman (2001).

[In practice, documentation of methods should answer the questions “what” and “why”. That is, “what” is the method, and “why” are we using/studying it? As this simulation experiment is a contrived example, we omit the “why” here.]

Function

#> function(X_train, y_train, X_test, y_test, ...) {
#>   train_df <- dplyr::bind_cols(data.frame(X_train), y = y_train)
#>   fit <- ranger::ranger(y ~ ., data = train_df, ...)
#>   predictions <- predict(fit, data.frame(X_test))$predictions
#>   return(list(predictions = predictions, y_test = y_test))
#> }
#> <bytecode: 0x5645e128a460>

Input Parameters

#> $num.threads
#> [1] 1

Evaluation

Prediction Accuracy

Given the true responses \(\mathbf{y} \in \mathbb{R}^n\) and predicted responses \(\mathbf{\hat{y}} \in \mathbb{R}^n\) from various methods, we evaluate several prediction accuracy metrics, namely:

  • Root Mean Squared Error (RMSE): \(\sqrt{\frac{1}{n} || \mathbf{y} - \mathbf{\hat{y}} ||_2^2}\)
  • Mean Absolute Error (MAE): \(\frac{1}{n} || \mathbf{y} - \mathbf{\hat{y}} ||_1\)
  • R-squared (\(R^2\)): \(1 - \frac{|| \mathbf{y} - \mathbf{\hat{y}} ||_2^2}{|| \mathbf{y} - \mathbf{\bar{y}} ||_2^2}\)

We choose to evaluate both RMSE and MAE as these can convey different messages in the presence of outliers. Further, \(R^2\) provides a convenient normalization of RMSE that can often be more easily interpreted.

[In practice, documentation of evaluation metrics should answer the questions “what” and “why”. That is, “what” is the metric, and “why” are we using/studying it?]

Function

#> function (fit_results, vary_params = NULL, nested_cols = NULL, 
#>     truth_col, estimate_col, prob_cols = NULL, group_cols = NULL, 
#>     metrics = NULL, na_rm = FALSE, summary_funs = c("mean", "median", 
#>         "min", "max", "sd", "raw"), custom_summary_funs = NULL, 
#>     eval_id = "pred_err") 
#> {
#>     group_vars <- c(".dgp_name", ".method_name", vary_params, 
#>         group_cols, ".metric")
#>     eval_tib <- eval_pred_err(fit_results = fit_results, vary_params = vary_params, 
#>         nested_cols = nested_cols, truth_col = truth_col, estimate_col = estimate_col, 
#>         prob_cols = prob_cols, group_cols = group_cols, metrics = metrics, 
#>         na_rm = na_rm) %>% dplyr::group_by(dplyr::across(tidyselect::any_of(group_vars)))
#>     eval_summary <- eval_summarizer(eval_data = eval_tib, eval_id = eval_id, 
#>         value_col = ".estimate", summary_funs = summary_funs, 
#>         custom_summary_funs = custom_summary_funs, na_rm = na_rm)
#>     return(eval_summary)
#> }
#> <bytecode: 0x5645dd2cdc50>
#> <environment: namespace:simChef>

Input Parameters

#> $truth_col
#> [1] "y_test"
#> 
#> $estimate_col
#> [1] "predictions"

Visualizations

Prediction Accuracy Plot

We plot the prediction accuracy between the true and predicted responses, as measured via RMSE, MAE, and \(R^2\), to understand how characteristics of the DGP affect various prediction methods.

[In practice, documentation of the plotters should answer the questions “what” and “why”. That is, “what” is the plot, and “why” are we using/studying it?]

Function

#> function (fit_results = NULL, eval_results = NULL, eval_name = NULL, 
#>     eval_fun = "summarize_pred_err", eval_fun_options = NULL, 
#>     vary_params = NULL, metrics = NULL, show = c("point", "line"), 
#>     ...) 
#> {
#>     .metric <- NULL
#>     arg_list <- get_dot_args(user_args = rlang::list2(...), default_args = list(eval_id = "pred_err", 
#>         facet_formula = ~.metric, facet_type = "wrap", facet_args = list(scales = "free")))
#>     plot_data <- get_plot_data(fit_results = fit_results, eval_results = eval_results, 
#>         eval_name = eval_name, eval_fun = eval_fun, eval_fun_options = c(eval_fun_options, 
#>             list(metrics = metrics)))
#>     if (!is.null(metrics)) {
#>         if (!inherits(metrics, "metric_set")) {
#>             abort("Unknown metrics. metrics must be of class 'yardstick::metric_set' or NULL.")
#>         }
#>         metric_names <- names(attr(metrics, "metrics"))
#>         plot_data <- plot_data %>% dplyr::filter(.metric %in% 
#>             metric_names)
#>     }
#>     plt <- do.call(plot_eval_constructor, args = c(list(plot_data = plot_data, 
#>         vary_params = vary_params, show = show), arg_list))
#>     return(plt)
#> }
#> <bytecode: 0x5645dc992648>
#> <environment: namespace:simChef>

Input Parameters

#> $eval_name
#> [1] "Prediction Accuracy"

Linear DGP

Varying noise_sd

Prediction Accuracy

Prediction Accuracy Plot

Parameter Values

#> $dgp
#> $dgp$`Linear DGP`
#> $dgp$`Linear DGP`$noise_sd
#> [1] 0.1 0.5 1.0 2.0
#> 
#> 
#> 
#> $method
#> list()
---
title: "`r params$sim_name`"
author: "`r params$author`"
date: "`r format(Sys.time(), '%B %d, %Y')`"
output: rmarkdown::html_document
params:
  author: 
    label: "Author:"
    value: ""
  sim_name:
    label: "Simulation Experiment Name:"
    value: ""
  sim_path:
    label: "Path to Simulation Experiment Folder:"
    value: ""
  write_filename:
    label: "Output File:"
    value: ""
  show_code:
    label: "Show Code:"
    value: TRUE
  show_eval:
    label: "Show Evaluators:"
    value: TRUE
  show_viz:
    label: "Show Visualizers:"
    value: TRUE
  eval_order:
    label: "Order of Evaluators:"
    value: NULL
  viz_order:
    label: "Order of Visualizers:"
    value: NULL
  use_icons:
    label: "Use Icons:"
    value: TRUE
  use_vmodern:
    label: "Use vthemes::vmodern:"
    value: TRUE
  write:
    label: "Write File:"
    value: FALSE
  verbose:
    label: "Verbose Level:"
    value: 2
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(
  echo = FALSE,
  warning = FALSE,
  message = FALSE,
  cache = FALSE,
  fig.align = "center",
  fig.pos = "H",
  fig.height = 12,
  fig.width = 10
)

options(
  width = 10000,
  knitr.kable.NA = "NA"
)

# scrollable text output
local({
  hook_output <- knitr::knit_hooks$get("output")
  knitr::knit_hooks$set(output = function(x, options) {
    if (!is.null(options$max.height)) {
      options$attr.output <- c(
        options$attr.output,
        sprintf('style="max-height: %s;"', options$max.height)
      )
    }
    hook_output(x, options)
  })
})

chunk_idx <- 1
doc_dir <- file.path(params$sim_path, "docs")
write_filename <- params$write_filename
```

```{r helper-funs}

#' Wrap text/code in knitr code chunk string
#'
#' @param code String of code to wrap in knitr code chunk
#' @param chunk_args String of arguments to place in the knitr code chunk header
#' @return String of code, wrapped inside the knitr code chunk ``` markers
write_code_chunk <- function(code = "", chunk_args = "") {
  sprintf("\n```{r, %s}\n%s\n```\n", chunk_args, code)
}

#' Write text to vector (write_flag = TRUE) or to console (write_flag = FALSE)
#'
#' @param ... Text to write to vector or to console
#' @param old_text Previous text to append to when writing to a vector
#' @param write_flag Boolean indicating whether to write text to a vector
#'   (write_flag = TRUE) or to console (write_flag = FALSE)
#' @return If write_flag = TRUE, returns vector of text. Otherwise, text is
#'   written to console via `cat()`.
write <- function(..., old_text = NULL, write_flag) {
  if (write_flag) {
    return(c(old_text, ...))
  } else {
    dots_list <- list(...) %>%
      purrr::map(
        function(x) {
          if (stringr::str_detect(x, "`r .*`")) {
            # run r code before printing results in cat()
            out <- stringr::str_replace(
              x, "`r .*`",
              eval(parse(text = stringr::str_extract(x, "(?<=`r )(.*?)(?=`)")))
            )
          } else {
            out <- x
          }
          return(out)
        }
      )
    do.call(cat, args = c(dots_list, list(sep = "")))
  }
}

#' Write text to file
#'
#' @param path Path to output file
#' @param ... Text to output to file
write_to_file <- function(path, ...) {
  storelines <- readLines(path)
  storelines <- c(storelines, ...)
  writeLines(storelines, path)
}

#' Get order of objects to display
#'
#' @param obj_names Vector of all object names that need to be displayed.
#' @param obj_order Vector of object names in the desired appearance order.
#' @return Vector of object names in the order in which they will be displayed.
get_object_order <- function(obj_names, obj_order = NULL) {
  if (is.null(obj_order)) {
    return(obj_names)
  } else {
    return(intersect(obj_order, obj_names))
  }
}

#' Get all experiments under a given directory name
#'
#' @param dir_name name of directory
#' @return list of named experiments
get_descendants <- function(dir_name) {
  experiments <- list()
  for (d in list.dirs(dir_name)) {
    if (file.exists(file.path(d, "experiment.rds"))) {
      if (identical(d, params$sim_path)) {
        exp_name <- "Base"
      } else {
        exp_name <- stringr::str_replace_all(
          stringr::str_remove(d, paste0(params$sim_path, "/")),
          "/", " - "
        )
      }
      experiments[[exp_name]] <- readRDS(file.path(d, "experiment.rds"))
    }
  }
  return(experiments)
}

#' Check if experiment exists
#'
#' @param dir_name name of directory or vector thereof
#' @param recursive logical; if TRUE, checks if experiment exists under the
#'   given directory(s); if FALSE, checks if any experiment exists under the
#'   directory(s) and its descendants
#' @return TRUE if experiment exists and FALSE otherwise
experiment_exists <- function(dir_name, recursive = FALSE) {
  res <- purrr::map_lgl(
    dir_name,
    function(d) {
      if (!recursive) {
        exp_fname <- file.path(d, "experiment.rds")
        return(file.exists(exp_fname))
      } else {
        descendants <- get_descendants(d)
        return(length(descendants) > 0)
      }
    }
  )
  return(any(res))
}

#' Displays content for specified part of recipe
#'
#' @param field_name part of recipe to show; must be one of "dgp", "method",
#'   "evaluator", or "visualizer"
#' @param write_flag Boolean indicating whether to write text to a vector
#'   (write_flag = TRUE) or to console (write_flag = FALSE)
#' @return content for recipe
show_recipe <- function(field_name = c(
                          "dgp", "method", "evaluator", "visualizer"
                        ),
                        write_flag = FALSE) {
  field_name <- match.arg(field_name)
  func_name <- dplyr::case_when(
    field_name == "evaluator" ~ "eval",
    field_name == "visualizer" ~ "viz",
    TRUE ~ field_name
  )
  descendants <- get_descendants(dir_name = params$sim_path)
  objs <- purrr::map(descendants, ~ .x[[paste0("get_", field_name, "s")]]())
  obj_names <- unique(purrr::reduce(sapply(objs, names), c))

  if (field_name %in% c("method", "evaluator")) {
    obj_header <- "\n\n#### %s {.tabset .tabset-pills .tabset-circle .tabset-recipe}\n\n"
    showtype_header <- "\n\n##### %s {.tabset .tabset-pills}\n\n"
    exp_header <- "\n\n###### %s \n\n"
  } else {
    obj_header <- "\n\n### %s {.tabset .tabset-pills .tabset-circle .tabset-recipe}\n\n"
    showtype_header <- "\n\n#### %s {.tabset .tabset-pills}\n\n"
    exp_header <- "\n\n##### %s \n\n"
  }

  if (params$use_icons) {
    if (params$use_vmodern) {
      description_label <- "`r fontawesome::fa('readme', fill = 'white')`"
      code_label <- "`r fontawesome::fa('code', fill = 'white')`"
    } else {
      description_label <- "`r fontawesome::fa('readme')`"
      code_label <- "`r fontawesome::fa('code')`"
    }
  } else {
    description_label <- "Description"
    code_label <- "Code"
  }

  if (all(sapply(objs, length) == 0)) {
    if (write_flag) {
      return("N/A")
    } else {
      return(cat("N/A"))
    }
  }

  recipe <- c()
  for (idx in 1:length(obj_names)) {
    obj_name <- obj_names[idx]
    description_fpath <- file.path(
      doc_dir, paste0(field_name, "s"), paste0(obj_name, ".md")
    )
    
    if (params$use_vmodern) {
      recipe <- write(
        "\n\n<div class='panel panel-default padded-panel'>\n\n",
        old_text = recipe, write_flag = write_flag
      )
    }

    recipe <- write(
      sprintf(obj_header, obj_name),
      sprintf(showtype_header, description_label),
      pasteMd(description_fpath),
      old_text = recipe, write_flag = write_flag
    )

    if (params$show_code) {
      recipe <- write(
        sprintf(showtype_header, code_label),
        old_text = recipe, write_flag = write_flag
      )

      keep_objs <- purrr::compact(purrr::map(objs, obj_name))
      is_identical <- all(
        purrr::map_lgl(keep_objs, ~ isTRUE(check_equal(.x, keep_objs[[1]])))
      )
      for (exp in names(keep_objs)) {
        obj <- keep_objs[[exp]]
        if (!is_identical) {
          recipe <- write(
            sprintf(exp_header, exp),
            old_text = recipe, write_flag = write_flag
          )
        }

        recipe <- write(
          "\n\n**Function**\n\n",
          old_text = recipe, write_flag = write_flag
        )
        if (write_flag) {
          recipe <- sprintf(
            "show_recipe(%s_objs, '%s', '%s', what = 'function')",
            func_name, obj_name, exp
          ) %>%
            write_code_chunk(chunk_args = "max.height='200px'") %>%
            write(old_text = recipe, write_flag = write_flag)
        } else {
          vthemes::subchunkify(
            obj[[paste0(func_name, "_fun")]], chunk_idx,
            other_args = "max.height='200px'"
          )
          chunk_idx <<- chunk_idx + 1
        }

        recipe <- write(
          "\n\n**Input Parameters**\n\n",
          old_text = recipe, write_flag = write_flag
        )
        if (write_flag) {
          recipe <- sprintf(
            "show_recipe(%s_objs, '%s', '%s', what = 'parameters')",
            func_name, obj_name, exp
          ) %>%
            write_code_chunk(chunk_args = "max.height='200px'") %>%
            write(old_text = recipe, write_flag = write_flag)
        } else {
          vthemes::subchunkify(
            obj[[paste0(func_name, "_params")]], chunk_idx,
            other_args = "max.height='200px'"
          )
          chunk_idx <<- chunk_idx + 1
        }

        if (is_identical) {
          break
        }
      }
    }

    if (params$use_vmodern) {
      recipe <- write(
        "\n\n</div>\n\n", old_text = recipe, write_flag = write_flag
      )
    }
  }

  return(recipe)
}

#' Reads in file if it exists and returns NULL if the file does not exist
#'
#' @param filename name of .rds file to try reading in
#' @return output of filename.rds if the file exists and NULL otherwise
get_results <- function(filename) {
  if (file.exists(filename)) {
    results <- readRDS(filename)
  } else {
    results <- NULL
  }
  return(results)
}

#' Displays output (both from evaluate() and visualize()) from saved results under
#' a specified directory
#'
#' @param dir_name name of directory
#' @param depth integer; depth of directory from parent/base experiment's folder
#' @param base logical; whether or not this is a base experiment
#' @param show_header logical; whether or not to show section header
#' @param verbose integer; 0 = no messages; 1 = print out directory name only;
#'   2 = print out directory name and name of evaluators/visualizers
#' @param write_flag Boolean indicating whether to write text to a vector
#'   (write_flag = TRUE) or to console (write_flag = FALSE)
#' @return content results from evaluate() and visualize() from the experiment
show_results <- function(dir_name, depth, base = FALSE, show_header = TRUE,
                         verbose = 1, write_flag = FALSE) {
  if (verbose >= 1) {
    inform(paste0(paste(rep("*", depth), collapse = ""), basename(dir_name)))
  }

  if (depth == 1) {
    header_template <- "\n\n%s %s {.tabset .tabset-pills .tabset-vmodern}\n\n"
  } else {
    if (base || !experiment_exists(dir_name)) {
      header_template <- "\n\n%s %s {.tabset .tabset-pills}\n\n"
    } else {
      header_template <- "\n\n%s %s {.tabset .tabset-pills .tabset-circle}\n\n"
    }
  }

  results <- c()
  if (show_header) {
    results <- sprintf(
      header_template,
      paste(rep("#", depth), collapse = ""),
      basename(dir_name)
    ) %>%
      write(old_text = results, write_flag = write_flag)
  }

  if (base) {
    results <- sprintf(
      "\n\n%s Base - %s {.tabset .tabset-pills .tabset-circle}\n\n",
      paste(rep("#", depth + 1), collapse = ""),
      basename(dir_name)
    ) %>%
      write(old_text = results, write_flag = write_flag)
    depth <- depth + 1
  }

  showtype_template <- paste0(
    "\n\n", paste(rep("#", depth + 1), collapse = ""), " %s\n\n"
  )
  figname_template <- paste0(
    "\n\n", paste(rep("#", depth + 2), collapse = ""), " %s\n\n"
  )
  invisible_header <- paste0(
    "\n\n", paste(rep("#", depth + 3), collapse = ""),
    " {.tabset .tabset-pills}\n\n"
  )
  plt_template <- paste0(
    "\n\n", paste(rep("#", depth + 4), collapse = ""), " %s\n\n"
  )

  if (params$use_icons) {
    if (params$use_vmodern) {
      evaluator_label <- "`r fontawesome::fa('table', fill = 'white')`"
      visualizer_label <- "`r fontawesome::fa('chart-bar', fill = 'white')`"
      code_label <- "`r fontawesome::fa('code', fill = 'white')`"
    } else {
      evaluator_label <- "`r fontawesome::fa('table')`"
      visualizer_label <- "`r fontawesome::fa('chart-bar')`"
      code_label <- "`r fontawesome::fa('code')`"
    }
  } else {
    evaluator_label <- "Evaluators"
    visualizer_label <- "Visualizers"
    code_label <- "Varying Parameters"
  }

  exp_fname <- file.path(dir_name, "experiment.rds")
  eval_fname <- file.path(dir_name, "eval_results.rds")
  viz_fname <- file.path(dir_name, "viz_results.rds")

  exp <- get_results(exp_fname)
  eval_results <- get_results(eval_fname)
  viz_results <- get_results(viz_fname)

  if (!is.null(eval_results) && params$show_eval) {
    results <- write(
      sprintf(showtype_template, evaluator_label),
      old_text = results, write_flag = write_flag
    )

    eval_names <- get_object_order(names(eval_results), params$eval_order)
    for (eval_name in eval_names) {
      evaluator <- exp$get_evaluators()[[eval_name]]
      if (evaluator$doc_show) {
        if (verbose >= 1) {
          inform(paste0(paste(rep(" ", depth + 1), collapse = ""), eval_name))
        }
        results <- write(
          sprintf(figname_template, eval_name),
          old_text = results, write_flag = write_flag
        )
        if (is.null(evaluator$doc_nrows)) {
          eval_results_show <- eval_results[[eval_name]]
        } else {
          keep_rows <- 1:min(evaluator$doc_nrows, nrow(eval_results[[eval_name]]))
          eval_results_show <- eval_results[[eval_name]][keep_rows, ]
          if (nrow(eval_results[[eval_name]]) > evaluator$doc_nrows) {
            omitted_nrows <- nrow(eval_results[[eval_name]]) - evaluator$doc_nrows
            results <- write(
              sprintf(
                "Showing preview of %s results. %s rows have been omitted.\n\n",
                eval_name, omitted_nrows
              ),
              old_text = results, write_flag = write_flag
            )
          }
        }
        if (write_flag) {
          results <- sprintf(
            "show_results('%s', '%s', 'evaluator')", dir_name, eval_name
          ) %>%
            write_code_chunk(chunk_args = "results = 'asis'") %>%
            write(old_text = results, write_flag = write_flag)
        } else {
          do.call(
            vthemes::pretty_DT,
            c(list(eval_results_show), evaluator$doc_options)
          ) %>%
            vthemes::subchunkify(i = chunk_idx)
          chunk_idx <<- chunk_idx + 1
        }
      }
    }
  }

  if (!is.null(viz_results) && params$show_viz) {
    results <- write(
      sprintf(showtype_template, visualizer_label),
      old_text = results, write_flag = write_flag
    )

    viz_names <- get_object_order(names(viz_results), params$viz_order)
    for (viz_name in viz_names) {
      visualizer <- exp$get_visualizers()[[viz_name]]
      if (visualizer$doc_show) {
        if (verbose >= 1) {
          inform(paste0(paste(rep(" ", depth + 1), collapse = ""), viz_name))
        }
        results <- write(
          sprintf(figname_template, viz_name),
          invisible_header,
          old_text = results, write_flag = write_flag
        )
        plts <- viz_results[[viz_name]]
        if (!inherits(plts, "list")) {
          plts <- list(plt = plts)
        }
        if (is.null(names(plts))) {
          names(plts) <- 1:length(plts)
        }
        for (plt_name in names(plts)) {
          if (length(plts) != 1) {
            results <- write(
              sprintf(plt_template, plt_name),
              old_text = results, write_flag = write_flag
            )
          }
          plt <- plts[[plt_name]]
          is_plot <- inherits(plt, "plotly") || 
            inherits(plt, "gg") || 
            inherits(plt, "ggplot")
          
          if (params$use_vmodern && is_plot) {
            chunk_args <- "fig.height = %s, fig.width = %s, out.width = '100%%', add.panel = TRUE"
            add_class <- "panel panel-default padded-panel"
          } else {
            chunk_args <- "fig.height = %s, fig.width = %s, out.width = '100%%'"
            add_class <- NULL
          }
          
          if (write_flag) {
            results <- sprintf(
              "show_results('%s', '%s', 'visualizer')", dir_name, viz_name
            ) %>%
              write_code_chunk(
                chunk_args = sprintf(
                  chunk_args,
                  visualizer$doc_options$height, visualizer$doc_options$width
                )
              ) %>%
              write(old_text = results, write_flag = write_flag)
          } else {
            vthemes::subchunkify(plt,
              i = chunk_idx,
              fig_height = visualizer$doc_options$height,
              fig_width = visualizer$doc_options$width,
              other_args = "out.width = '100%'", 
              add_class = add_class
            )
            chunk_idx <<- chunk_idx + 1
          }
        }
      }
    }
  }

  if (!is.null(exp) && params$show_code) {
    if ((length(exp$get_vary_across()$dgp) != 0) ||
        (length(exp$get_vary_across()$method) != 0)) {
      results <- write(
        sprintf(showtype_template, code_label),
        "\n\n**Parameter Values**\n\n",
        old_text = results, write_flag = write_flag
      )
      if (write_flag) {
        results <- sprintf(
          "show_results('%s', NULL, 'vary_params')", dir_name
        ) %>%
          write_code_chunk(chunk_args = "max.height='200px'") %>%
          write(old_text = results, write_flag = write_flag)
      } else {
        vthemes::subchunkify(exp$get_vary_across(),
          chunk_idx,
          other_args = "max.height='200px'"
        )
        chunk_idx <<- chunk_idx + 1
      }
    }
  }

  return(results)
}

#' Displays output of experiment for all of its (saved) descendants
#'
#' @param dir_name name of parent experiment directory
#' @param depth placeholder for recursion; should not be messed with
#' @param write_flag Boolean indicating whether to write text to a file
#'   (write_flag = TRUE) or to console (write_flag = FALSE)
#' @param write_filename Name of file to write to if write_flag = TRUE
#' @param ... other arguments to pass into show_results()
show_descendant_results <- function(dir_name, depth = 1, write_flag = FALSE,
                                    write_filename = NULL, ...) {
  children <- list.dirs(dir_name, recursive = FALSE)
  if (length(children) == 0) {
    return()
  }
  for (child_idx in 1:length(children)) {
    child <- children[child_idx]
    if (!experiment_exists(child, recursive = TRUE)) {
      next
    }
    if (experiment_exists(child, recursive = FALSE) &&
      (experiment_exists(list.dirs(child, recursive = TRUE)[-1]) ||
        (depth == 1))) {
      base <- TRUE
    } else {
      base <- FALSE
    }
    results <- show_results(child, depth, base, write_flag = write_flag, ...)
    if (write_flag) {
      write_to_file(path = write_filename, results)
    }
    show_descendant_results(child, depth + 1, write_flag, write_filename, ...)
  }
}

#' Clean output file (e.g., remove excessive blank lines)
#'
#' @param path Path to output file
clean_file <- function(path) {
  storelines <- readLines(path)
  rle_out <- rle(storelines == "")
  line_ids <- which((rle_out$lengths > 2) & rle_out$values)
  keep_lines <- rep(TRUE, length(storelines))
  for (line_id in line_ids) {
    num_blank <- rle_out$lengths[line_id]
    line_ptr <- sum(rle_out$lengths[1:line_id])
    # only allow for max of two consecutive blank lines
    keep_lines[(line_ptr - num_blank + 3):line_ptr] <- FALSE
  }
  writeLines(storelines[keep_lines], path)
}

#' Insert lines to add extra resources (css/js) for simChef R Markdown theme
#' 
#' @param path Path to output file
insert_simChef_resources <- function(path) {
  storelines <- readLines(path)
  pattern <- "<Insert extra simChef resources here>"
  replace <- sprintf(
    '<script src="%s"></script>\n\n<link rel="stylesheet" href="%s">', 
    system.file("rmd", "js", "simchefNavClass.js",
                package = utils::packageName()),
    system.file("rmd", "css", "simchef.css",
                package = utils::packageName())
  ) 
  storelines[storelines == pattern] <- replace
  writeLines(storelines, path)
}

#' Remove lines with simChef R Markdown theme-specific code
#' 
#' @param path Path to output file
remove_simChef_resources <- function(path) {
  storelines <- readLines(path)
  
  pattern <- "add.panel = function"
  line_id <- which(stringr::str_detect(storelines, pattern))
  remove_lines <- (line_id - 2):(line_id + 5)
  storelines <- storelines[-remove_lines]
  
  pattern <- "<Insert extra simChef resources here>"
  remove_lines <- which(stringr::str_detect(storelines, pattern))
  storelines <- storelines[-remove_lines]
  
  writeLines(storelines, path)
}

```

```{r}
if (params$write) {
  if (params$use_vmodern) {
    insert_simChef_resources(write_filename)
  } else {
    remove_simChef_resources(write_filename)
  }
} else {
  if (params$use_vmodern) {
    htmltools::HTML('<script src="js/simchefNavClass.js"></script>\n\n<link rel="stylesheet" href="css/simchef.css">')
  }
}
```


# Simulation Experiment Recipe {.tabset .tabset-vmodern}

## Objectives

```{r objectives, results = "asis"}
if (params$use_vmodern) {
  objectives <- write(
    "\n\n<div class='panel panel-default padded-panel'>\n\n",
    pasteMd(file.path(doc_dir, "objectives.md")),
    "\n\n</div>\n\n",
    write_flag = params$write
  )
} else {
  objectives <- write(
    pasteMd(file.path(doc_dir, "objectives.md")),
    write_flag = params$write
  )
}
if (params$write) {
  write_to_file(path = write_filename, "\n\n## Objectives\n\n", objectives)
}
```

## Data Generation

```{r dgps, results = "asis"}
dgp_recipe <- show_recipe(field_name = "dgp", write_flag = params$write)
if (params$write) {
  write_to_file(path = write_filename, "\n\n## Data Generation\n\n", dgp_recipe)
}
```

## Methods and Evaluation

### Methods

```{r methods, results = "asis"}
method_recipe <- show_recipe(field_name = "method", write_flag = params$write)
if (params$write) {
  write_to_file(
    path = write_filename,
    "\n\n## Methods and Evaluation\n\n", "\n\n### Methods\n\n",
    method_recipe
  )
}
```

### Evaluation

```{r evaluators, results = "asis"}
eval_recipe <- show_recipe(field_name = "evaluator", write_flag = params$write)
if (params$write) {
  write_to_file(path = write_filename, "\n\n### Evaluation\n\n", eval_recipe)
}
```

## Visualizations

```{r visualizers, results = "asis"}
viz_recipe <- show_recipe(field_name = "visualizer", write_flag = params$write)
if (params$write) {
  write_to_file(path = write_filename, "\n\n## Visualizations\n\n", viz_recipe)
}
```



```{r res, results = "asis"}
if (params$verbose > 0) {
  inform(sprintf("Creating R Markdown report for %s...", params$sim_name))
}

# show results
if (experiment_exists(params$sim_path)) {
  base_header <- write(
    sprintf("\n\n# Base %s \n\n", params$sim_name),
    "\n\n## {.tabset .tabset-pills .tabset-circle}\n\n",
    write_flag = params$write
  )
  base_results <- show_results(
    params$sim_path,
    depth = 2, base = FALSE, show_header = FALSE,
    verbose = params$verbose, write_flag = params$write
  )

  if (params$write) {
    write_to_file(path = write_filename, base_header, base_results)
  }
}

show_descendant_results(
  params$sim_path,
  verbose = params$verbose,
  write_flag = params$write, 
  write_filename = write_filename
)
```



```{r}
if (params$write) {
  clean_file(path = write_filename)
}
```
