Simulation Experiment Recipe
Objectives
The objective of this simulation experiment is to provide a toy 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 at the surface-level with the sole purpose of facilitating an easy-to-understand walkthrough.
[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 Gaussian DGP
In the Linear Gaussian DGP, we simulate the feature/design matrix \(\mathbf{X} \in \mathbb{R}^{n \times p}\) from a normal distribution and the response vector \(\mathbf{y} \in \mathbb{R}^n\) from a linear model. Specifically,
\[\begin{gather*}\mathbf{X} \sim N\left(\mathbf{0}, \begin{pmatrix} 1 & \rho \\ \rho & 1 \end{pmatrix}\right), \\\mathbf{y} = \mathbf{X} \boldsymbol{\beta} + \boldsymbol{\epsilon},\\\boldsymbol{\epsilon} \sim N(\mathbf{0}, \sigma^2 \mathbf{I}_n)\end{gather*}\]
Default Parameters in DGP
- Number of samples: \(n = 200\)
- Number of features: \(p = 2\)
- Correlation among features: \(\rho = 0\)
- 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, beta, rho, sigma) {
#> cov_mat <- matrix(c(1, rho, rho, 1), byrow = T, nrow = 2, ncol = 2)
#> X <- MASS::mvrnorm(n = n, mu = rep(0, 2), Sigma = cov_mat)
#> y <- X %*% beta + rnorm(n, sd = sigma)
#> return(list(X = X, y = y))
#> }
#> <bytecode: 0x564b4ddbb488>Input Parameters
#> $n
#> [1] 200
#>
#> $beta
#> [1] 1 0
#>
#> $rho
#> [1] 0
#>
#> $sigma
#> [1] 1Methods and Evaluation
Methods
OLS
Given some data \(\mathbf{X}\) and \(\mathbf{y}\), we fit ordinary least squares (OLS) and examine the p-values for each coefficient in the model. The p-values are computed using a two-sided t-test (see summary.lm()).
Elaborating further on the testing, we are interested in testing:
\[\begin{align*}H_0: \beta_i = 0 \quad vs. \quad H_1: \beta_i \neq 0.\end{align*}\]
To test this, we compute the observed T-statistic, defined as
\[\begin{align*}T = \frac{\hat{\beta}_i}{\hat{SE}(\hat{\beta_i})},\end{align*}\]
and then compute the two-sided p-value under the t distribution with \(n - p - 1\) degrees of freedom. If the p-value is lower than some significance value \(\alpha\), then there is sufficient evidence to reject the null hypothesis \(H_0\). Otherwise, there is not sufficient evidence, and we fail to reject the null hypothesis.
[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, y, cols = c("X1", "X2")) {
#> lm_fit <- lm(y ~ X)
#> pvals <- summary(lm_fit)$coefficients[cols, "Pr(>|t|)"] %>%
#> setNames(paste(names(.), "p-value"))
#> return(pvals)
#> }
#> <bytecode: 0x564b4d193390>Input Parameters
#> list()Evaluation
Rejection Prob. (alpha = 0.1)
We define the rejection probability as the proportion of repetitions in the simulation experiment that result in a p-value \(\leq \alpha\). Here, we choose to set the significance level \(\alpha = 0.1\).
[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? As this simulation experiment is a contrived example, we omit the “why” here.]
Base
Function
#> function(fit_results, alpha = 0.05) {
#> group_vars <- c(".dgp_name", ".method_name")
#> eval_out <- fit_results %>%
#> dplyr::group_by(across({{group_vars}})) %>%
#> dplyr::summarise(
#> `X1 Reject Prob.` = mean(`X1 p-value` < alpha),
#> `X2 Reject Prob.` = mean(`X2 p-value` < alpha)
#> )
#> return(eval_out)
#> }Input Parameters
#> $alpha
#> [1] 0.1Linear Gaussian DGP - Varying beta
Function
#> function(fit_results, vary_params = NULL, alpha = 0.05) {
#> group_vars <- c(".dgp_name", ".method_name", vary_params)
#> eval_out <- fit_results %>%
#> dplyr::group_by(across({{group_vars}})) %>%
#> dplyr::summarise(
#> `X1 Reject Prob.` = mean(`X1 p-value` < alpha),
#> `X2 Reject Prob.` = mean(`X2 p-value` < alpha)
#> )
#> return(eval_out)
#> }
#> <bytecode: 0x564b4e919d18>Input Parameters
#> $alpha
#> [1] 0.1Linear Gaussian DGP - Varying rho
Function
#> function(fit_results, vary_params = NULL, alpha = 0.05) {
#> group_vars <- c(".dgp_name", ".method_name", vary_params)
#> eval_out <- fit_results %>%
#> dplyr::group_by(across({{group_vars}})) %>%
#> dplyr::summarise(
#> `X1 Reject Prob.` = mean(`X1 p-value` < alpha),
#> `X2 Reject Prob.` = mean(`X2 p-value` < alpha)
#> )
#> return(eval_out)
#> }
#> <bytecode: 0x564b4ec36538>Input Parameters
#> $alpha
#> [1] 0.1Linear Gaussian DGP - Varying sigma
Function
#> function(fit_results, vary_params = NULL, alpha = 0.05) {
#> group_vars <- c(".dgp_name", ".method_name", vary_params)
#> eval_out <- fit_results %>%
#> dplyr::group_by(across({{group_vars}})) %>%
#> dplyr::summarise(
#> `X1 Reject Prob.` = mean(`X1 p-value` < alpha),
#> `X2 Reject Prob.` = mean(`X2 p-value` < alpha)
#> )
#> return(eval_out)
#> }Input Parameters
#> $alpha
#> [1] 0.1Visualizations
Power
To examine the power of the test, we plot the rejection probability as a function of \(\alpha\), that is, \(\mathbb{P}(\text{p-value} \leq \alpha)\) vs. \(\alpha\). If the coefficient is non-zero in the underlying DGP, then a larger AUC would indicate better performance in terms of the power. We will primarily focus on plotting the power of the first coefficient \(\beta_1\).
[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? As this simulation experiment is a contrived example, we omit the “why” here.]
Base
Function
#> function(fit_results, col = "X1") {
#> plt <- ggplot2::ggplot(fit_results) +
#> ggplot2::aes(x = .data[[paste(col, "p-value")]],
#> color = as.factor(.method_name)) +
#> ggplot2::geom_abline(slope = 1, intercept = 0,
#> color = "darkgray", linetype = "solid", size = 1) +
#> ggplot2::stat_ecdf(size = 1) +
#> ggplot2::scale_x_continuous(limits = c(0, 1)) +
#> ggplot2::labs(x = "t", y = "P( p-value \u2264 t )",
#> linetype = "", color = "Method")
#> return(plt)
#> }Input Parameters
#> list()Linear Gaussian DGP - Varying beta
Function
#> function(fit_results, vary_params = NULL, col = "X1") {
#>
#> if (!is.null(vary_params)) {
#> # deal with the case when we vary across a parameter that is vector-valued
#> if (is.list(fit_results[[vary_params]])) {
#> fit_results[[vary_params]] <- list_col_to_chr(fit_results[[vary_params]],
#> name = vary_params,
#> verbatim = TRUE)
#> }
#> }
#>
#> plt <- ggplot2::ggplot(fit_results) +
#> ggplot2::aes(x = .data[[paste(col, "p-value")]],
#> color = as.factor(.method_name)) +
#> ggplot2::geom_abline(slope = 1, intercept = 0,
#> color = "darkgray", linetype = "solid", linewidth = 1) +
#> ggplot2::stat_ecdf(size = 1) +
#> ggplot2::scale_x_continuous(limits = c(0, 1)) +
#> ggplot2::labs(x = "t", y = "P( p-value \u2264 t )",
#> linetype = "", color = "Method")
#> if (!is.null(vary_params)) {
#> plt <- plt + ggplot2::facet_wrap(~ .data[[vary_params]])
#> }
#> return(plt)
#> }
#> <bytecode: 0x564b49065620>Input Parameters
#> list()Linear Gaussian DGP - Varying rho
Function
#> function(fit_results, vary_params = NULL, col = "X1") {
#>
#> if (!is.null(vary_params)) {
#> # deal with the case when we vary across a parameter that is vector-valued
#> if (is.list(fit_results[[vary_params]])) {
#> fit_results[[vary_params]] <- list_col_to_chr(fit_results[[vary_params]],
#> name = vary_params,
#> verbatim = TRUE)
#> }
#> }
#>
#> plt <- ggplot2::ggplot(fit_results) +
#> ggplot2::aes(x = .data[[paste(col, "p-value")]],
#> color = as.factor(.method_name)) +
#> ggplot2::geom_abline(slope = 1, intercept = 0,
#> color = "darkgray", linetype = "solid", linewidth = 1) +
#> ggplot2::stat_ecdf(size = 1) +
#> ggplot2::scale_x_continuous(limits = c(0, 1)) +
#> ggplot2::labs(x = "t", y = "P( p-value \u2264 t )",
#> linetype = "", color = "Method")
#> if (!is.null(vary_params)) {
#> plt <- plt + ggplot2::facet_wrap(~ .data[[vary_params]])
#> }
#> return(plt)
#> }
#> <bytecode: 0x564b48bc18f8>Input Parameters
#> list()Linear Gaussian DGP - Varying sigma
Function
#> function(fit_results, vary_params = NULL, col = "X1") {
#>
#> if (!is.null(vary_params)) {
#> # deal with the case when we vary across a parameter that is vector-valued
#> if (is.list(fit_results[[vary_params]])) {
#> fit_results[[vary_params]] <- list_col_to_chr(fit_results[[vary_params]],
#> name = vary_params,
#> verbatim = TRUE)
#> }
#> }
#>
#> plt <- ggplot2::ggplot(fit_results) +
#> ggplot2::aes(x = .data[[paste(col, "p-value")]],
#> color = as.factor(.method_name)) +
#> ggplot2::geom_abline(slope = 1, intercept = 0,
#> color = "darkgray", linetype = "solid", linewidth = 1) +
#> ggplot2::stat_ecdf(size = 1) +
#> ggplot2::scale_x_continuous(limits = c(0, 1)) +
#> ggplot2::labs(x = "t", y = "P( p-value \u2264 t )",
#> linetype = "", color = "Method")
#> if (!is.null(vary_params)) {
#> plt <- plt + ggplot2::facet_wrap(~ .data[[vary_params]])
#> }
#> return(plt)
#> }
#> <bytecode: 0x564b47c45170>Input Parameters
#> list()Rejection Prob. (alpha = 0.1) Plot
We plot the rejection probability for \(\beta_1\) across varying parameters of the DGP to understand how characteristics of the DGP affect the test.We define the rejection probability as the proportion of repetitions in the simulation experiment that result in a p-value \(\leq \alpha\). Here, we choose to set the significance level \(\alpha = 0.1\).
[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? As this simulation experiment is a contrived example, we omit the “why” here.]
Linear Gaussian DGP - Varying beta
Function
#> function(eval_results, vary_params = NULL,
#> alpha = 0.05) {
#> eval_results <- eval_results$`Rejection Prob. (alpha = 0.1)`
#> if (is.list(eval_results[[vary_params]])) {
#> # deal with the case when we vary across a parameter that is vector-valued
#> eval_results[[vary_params]] <- list_col_to_chr(eval_results[[vary_params]],
#> name = vary_params,
#> verbatim = TRUE)
#> }
#> plt <- ggplot2::ggplot(eval_results) +
#> ggplot2::aes(x = .data[[vary_params]], y = `X1 Reject Prob.`,
#> color = as.factor(.method_name),
#> fill = as.factor(.method_name)) +
#> ggplot2::labs(x = vary_params,
#> y = sprintf("Rejection Probability (alpha = %s)", alpha),
#> color = "Method", fill = "Method") +
#> ggplot2::scale_y_continuous(limits = c(0, 1))
#> if (is.numeric(eval_results[[vary_params]])) {
#> plt <- plt +
#> ggplot2::geom_line() +
#> ggplot2::geom_point(size = 2)
#> } else {
#> plt <- plt +
#> ggplot2::geom_bar(stat = "identity")
#> }
#> return(plotly::ggplotly(plt))
#> }
#> <bytecode: 0x564b4900dc40>Input Parameters
#> $alpha
#> [1] 0.1Linear Gaussian DGP - Varying rho
Function
#> function(eval_results, vary_params = NULL,
#> alpha = 0.05) {
#> eval_results <- eval_results$`Rejection Prob. (alpha = 0.1)`
#> if (is.list(eval_results[[vary_params]])) {
#> # deal with the case when we vary across a parameter that is vector-valued
#> eval_results[[vary_params]] <- list_col_to_chr(eval_results[[vary_params]],
#> name = vary_params,
#> verbatim = TRUE)
#> }
#> plt <- ggplot2::ggplot(eval_results) +
#> ggplot2::aes(x = .data[[vary_params]], y = `X1 Reject Prob.`,
#> color = as.factor(.method_name),
#> fill = as.factor(.method_name)) +
#> ggplot2::labs(x = vary_params,
#> y = sprintf("Rejection Probability (alpha = %s)", alpha),
#> color = "Method", fill = "Method") +
#> ggplot2::scale_y_continuous(limits = c(0, 1))
#> if (is.numeric(eval_results[[vary_params]])) {
#> plt <- plt +
#> ggplot2::geom_line() +
#> ggplot2::geom_point(size = 2)
#> } else {
#> plt <- plt +
#> ggplot2::geom_bar(stat = "identity")
#> }
#> return(plotly::ggplotly(plt))
#> }
#> <bytecode: 0x564b48b95098>Input Parameters
#> $alpha
#> [1] 0.1Base Linear Regression Experiment
Rejection Prob. (alpha = 0.1)
Power
Linear Gaussian DGP
Varying beta
Rejection Prob. (alpha = 0.1)
Power
Rejection Prob. (alpha = 0.1) Plot
Parameter Values
#> $dgp
#> $dgp$`Linear Gaussian DGP`
#> $dgp$`Linear Gaussian DGP`$beta
#> $dgp$`Linear Gaussian DGP`$beta[[1]]
#> [1] 1 0
#>
#> $dgp$`Linear Gaussian DGP`$beta[[2]]
#> [1] 1.0 0.5
#>
#> $dgp$`Linear Gaussian DGP`$beta[[3]]
#> [1] 1 1
#>
#> $dgp$`Linear Gaussian DGP`$beta[[4]]
#> [1] 1.0 1.5
#>
#> $dgp$`Linear Gaussian DGP`$beta[[5]]
#> [1] 1 2
#>
#>
#>
#>
#> $method
#> list()Varying rho
Rejection Prob. (alpha = 0.1)
Power
Rejection Prob. (alpha = 0.1) Plot
Parameter Values
#> $dgp
#> $dgp$`Linear Gaussian DGP`
#> $dgp$`Linear Gaussian DGP`$rho
#> [1] 0.0 0.2 0.5 0.9
#>
#>
#>
#> $method
#> list()Varying sigma
Rejection Prob. (alpha = 0.1)
Power
Parameter Values
#> $dgp
#> $dgp$`Linear Gaussian DGP`
#> $dgp$`Linear Gaussian DGP`$sigma
#> [1] 1 2 4 8
#>
#>
#>
#> $method
#> list()---
title: "`r params$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$doc_show) {
        if (verbose >= 1) {
          inform(paste0(paste(rep(" ", depth + 1), collapse = ""), eval_name))
        }
        results <- write(
          sprintf(figname_template, eval_name),
          old_text = results, write_flag = write_flag
        )
        if (write_flag) {
          results <- sprintf(
            "show_results('%s', '%s', 'evaluator')", dir_name, eval_name
          ) %>%
            write_code_chunk(chunk_args = "results = 'asis'") %>%
            write(old_text = results, write_flag = write_flag)
        } else {
          do.call(
            vthemes::pretty_DT,
            c(list(eval_results[[eval_name]]), evaluator$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)
}
```
