# Introduction to jagstargets

The `jagstargets` package makes it easy to run a single jags model and keep track of the results. `R2jags` fits the models, and `targets` manages the workflow and helps avoid unnecessary computation.

Consider the simple regression model below with response variable `y` and covariate `x`.

\[ \begin{aligned} y_i &\stackrel{\text{iid}}{\sim} \text{Normal}(x_i \beta, 1) \\ \beta &\sim \text{Normal}(0, 1) \end{aligned} \]

We write this model in the JAGS model file below.

    lines <- "model {
      for (i in 1:n) {
        y[i] ~ dnorm(x[i] * beta, 1)
      }
      beta ~ dnorm(0, 1)
    }"
    writeLines(lines, "x.jags")

A typical workflow proceeds as follows:

1. Prepare a list of input data to JAGS, including vector elements `x` and `y`.
2. Fit the JAGS model using the list of input data.
3. Use the fitted model object to compute posterior summaries and convergence diagnostics.
4. Use the fitted model object to extract posterior draws of parameters and store them in a tidy data frame.
5. If there are other models to compare, use the fitted model object to compute the deviance information criterion (DIC).

`jagstargets` encapsulates this workflow with the `tar_jags()` function. To use it in a `targets` pipeline, invoke it from the `_targets.R` script of the project.

    # _targets.R
    library(targets)
    library(jagstargets)

    generate_data <- function(n = 10) {
      true_beta <- stats::rnorm(n = 1, mean = 0, sd = 1)
      x <- seq(from = -1, to = 1, length.out = n)
      y <- stats::rnorm(n, x * true_beta, 1)
      out <- list(n = n, x = x, y = y)
    }

    # The _targets.R file ends with a list of target objects
    # produced by jagstargets::tar_jags(), targets::tar_target(), or similar.
    list(
      tar_jags(
        example,
        jags_files = "x.jags",
        parameters.to.save = "beta",
        data = generate_data()
      )
    )

`tar_jags()` only defines the pipeline. It does not actually run JAGS, it declares the targets that will eventually run JAGS. The specific targets are as follows. Run `tar_manifest()` to show specific details about the targets declared.

    tar_manifest()
    #> # A tibble: 7 × 3
    #>   name              command                                          description
    #>   <chr>             <chr>                                            <chr>
    #> 1 example_data      "tar_jags_example_data()"                        <NA>
    #> 2 example_file_x    "\"x.jags\""                                     x.jags
    #> 3 example_lines_x   "readLines(con = example_file_x)"                x.jags
    #> 4 example_mcmc_x    "jagstargets::tar_jags_run(jags_lines = example… x.jags
    #> 5 example_summary_x "jagstargets::tar_jags_df(example_mcmc_x, data … x.jags
    #> 6 example_dic_x     "jagstargets::tar_jags_df(fit = example_mcmc_x,… x.jags
    #> 7 example_draws_x   "jagstargets::tar_jags_df(fit = example_mcmc_x,… x.jags

Each target is responsible for a piece of the workflow.

- `example_file_x`: Reproducibly track changes to the jags model file.
- `example_data`: Run the code you supplied to the `data` argument of `tar_jags()` and return a dataset compatible with JAGS.
- `example_mcmc_x`: Run the MCMC and return an object of class `rjags` from `R2jags`.
- `example_draws_x`: Return a friendly `tibble` of the posterior draws from `example`.
- `example_summaries_x`: Return a friendly `tibble` of the posterior summaries from `example`. Uses `posterior::summarize_draws()`
- `example_dic_x`: Return a friendly `tibble` with each model’s DIC and penalty.

The suffix `_x` comes from the base name of the model file, in this case `x.jags`. If you supply multiple model files to the `jags_files` argument, all the models share the same dataset, and the suffixes distinguish among the various targets.

The targets depend on one another: for example, `example_mcmc_x` takes `example_data` as input. `targets` can visualize the dependency relationships in a dependency graph, which is helpful for understanding the pipeline and troubleshooting issues.

    tar_visnetwork(targets_only = TRUE)

Run the computation with `tar_make()`.

    tar_make()
    #> ▶ dispatched target example_data
    #> ● completed target example_data [0 seconds, 317 bytes]
    #> ▶ dispatched target example_file_x
    #> ● completed target example_file_x [0 seconds, 87 bytes]
    #> ▶ dispatched target example_lines_x
    #> ● completed target example_lines_x [0 seconds, 127 bytes]
    #> ▶ dispatched target example_mcmc_x
    #> ● completed target example_mcmc_x [0.028 seconds, 95.824 kilobytes]
    #> ▶ dispatched target example_summary_x
    #> ● completed target example_summary_x [0.02 seconds, 1.08 kilobytes]
    #> ▶ dispatched target example_dic_x
    #> ● completed target example_dic_x [0.001 seconds, 411 bytes]
    #> ▶ dispatched target example_draws_x
    #> ● completed target example_draws_x [0 seconds, 51.627 kilobytes]
    #> ▶ ended pipeline [0.13 seconds]

The output lives in a special folder called `_targets/` and you can retrieve it with functions `tar_load()` and `tar_read()` (from `targets`).

    tar_read(example_summary_x)
    #> # A tibble: 2 × 11
    #>   variable   mean median    sd   mad    q5    q95  rhat ess_bulk ess_tail
    #>   <chr>     <dbl>  <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl>    <dbl>    <dbl>
    #> 1 beta     -0.889 -0.886 0.447 0.447 -1.63 -0.149  1.00    2932.    2869.
    #> 2 deviance 25.8   25.3   1.38  0.601 24.8  28.6    1.00    3014.    2867.
    #> # ℹ 1 more variable: .join_data <dbl>

At this point, all our results are up to date because their dependencies did not change.

    tar_make()
    #> ✔ skipped target example_data
    #> ✔ skipped target example_file_x
    #> ✔ skipped target example_lines_x
    #> ✔ skipped target example_mcmc_x
    #> ✔ skipped target example_summary_x
    #> ✔ skipped target example_dic_x
    #> ✔ skipped target example_draws_x
    #> ✔ skipped pipeline [0.07 seconds]

But if we change the underlying code or data, some of the targets will no longer be valid, and they will rerun during the next `tar_make()`. Below, we change the jags model file, so the MCMC reruns while the data is skipped. This behavior saves time and enhances reproducibility.

    write(" ", file = "x.jags", append = TRUE)

    tar_outdated()
    #> [1] "example_summary_x" "example_dic_x"     "example_file_x"
    #> [4] "example_draws_x"   "example_mcmc_x"    "example_lines_x"

    tar_visnetwork(targets_only = TRUE)

    tar_make()
    #> ✔ skipped target example_data
    #> ▶ dispatched target example_file_x
    #> ● completed target example_file_x [0.001 seconds, 89 bytes]
    #> ▶ dispatched target example_lines_x
    #> ● completed target example_lines_x [0 seconds, 129 bytes]
    #> ▶ dispatched target example_mcmc_x
    #> ● completed target example_mcmc_x [0.025 seconds, 95.829 kilobytes]
    #> ▶ dispatched target example_summary_x
    #> ● completed target example_summary_x [0.018 seconds, 1.08 kilobytes]
    #> ▶ dispatched target example_dic_x
    #> ● completed target example_dic_x [0.001 seconds, 411 bytes]
    #> ▶ dispatched target example_draws_x
    #> ● completed target example_draws_x [0 seconds, 51.627 kilobytes]
    #> ▶ ended pipeline [0.14 seconds]

At this point, we can add more targets and custom functions for additional post-processing. See below for a custom summary target (which is equivalent to customizing the `summaries` argument of `tar_jags()`.)

    # _targets.R
    library(targets)
    library(jagstargets)

    generate_data <- function(n = 10) {
      true_beta <- stats::rnorm(n = 1, mean = 0, sd = 1)
      x <- seq(from = -1, to = 1, length.out = n)
      y <- stats::rnorm(n, x * true_beta, 1)
      out <- list(n = n, x = x, y = y)
    }

    list(
      tar_jags(
        example,
        jags_files = "x.jags",
        parameters.to.save = "beta",
        data = generate_data()
      ),
      tar_target(
        custom_summary,
        posterior::summarize_draws(
          dplyr::select(example_draws_x, -starts_with(".")),
          ~posterior::quantile2(.x, probs = c(0.25, 0.75))
        )
      )
    )

In the graph, our new `custom_summary` target should be connected to the upstream `example` target, and only `custom_summary` should be out of date.

    tar_visnetwork(targets_only = TRUE)

In the next `tar_make()`, we skip the expensive MCMC and just run the custom summary.

    tar_make()
    #> ✔ skipped target example_data
    #> ✔ skipped target example_file_x
    #> ✔ skipped target example_lines_x
    #> ✔ skipped target example_mcmc_x
    #> ✔ skipped target example_summary_x
    #> ✔ skipped target example_draws_x
    #> ✔ skipped target example_dic_x
    #> ▶ dispatched target custom_summary
    #> ● completed target custom_summary [0.006 seconds, 235 bytes]
    #> ▶ ended pipeline [0.154 seconds]

    tar_read(custom_summary)
    #> # A tibble: 2 × 3
    #>   variable   q25    q75
    #>   <chr>    <dbl>  <dbl>
    #> 1 beta     -1.18 -0.582
    #> 2 deviance 24.9  26.1

## Multiple models

`tar_jags()` and related functions allow you to supply multiple models to `jags_files`. If you do, each model will run on the same dataset. Consider a new model, `y.jags`.

    lines <- "model {
      for (i in 1:n) {
        y[i] ~ dnorm(x[i] * x[i] * beta, 1) # Regress on x^2 instead of x.
      }
      beta ~ dnorm(0, 1)
    }"
    writeLines(lines, "y.jags")

Below, we add `y.jags` to the `jags_files` argument of `tar_jags()`.

    # _targets.R
    library(targets)
    library(jagstargets)

    generate_data <- function(n = 10) {
      true_beta <- stats::rnorm(n = 1, mean = 0, sd = 1)
      x <- seq(from = -1, to = 1, length.out = n)
      y <- stats::rnorm(n, x * true_beta, 1)
      out <- list(n = n, x = x, y = y)
    }

    list(
      tar_jags(
        example,
        jags_files = c("x.jags", "y.jags"),
        parameters.to.save = "beta",
        data = generate_data()
      ),
      tar_target(
        custom_summary,
        posterior::summarize_draws(
          dplyr::select(example_draws_x, -starts_with(".")),
          ~posterior::quantile2(.x, probs = c(0.25, 0.75))
        )
      )
    )

In the graph below, notice how the `*_x` targets and `*_y` targets are both connected to `example_data` upstream.

    tar_visnetwork(targets_only = TRUE)

## More information

For more on `targets`, please visit the reference website https://docs.ropensci.org/targets/ or the user manual https://books.ropensci.org/targets/. The manual walks though advanced features of `targets` such as high-performance computing and cloud storage support.