---
title: "Collecting output"
output:
  rmarkdown::html_vignette:
    toc: true
    toc_depth: 4
description: >
  Shows how to collect and group pipeline output.
vignette: >
  %\VignetteIndexEntry{Collecting output}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r knitr-setup, include = FALSE}
require(pipeflow)

knitr::opts_chunk$set(
  comment = "#",
  prompt = FALSE,
  tidy = FALSE,
  cache = FALSE,
  collapse = TRUE
)

old <- options(width = 100L)
library(ggplot2)
```

Generally, one should keep pipeline steps as simple as possible, basically following the
principle *"one step, one task"*. This means that usually a lot of pipeline steps are
used to calculate intermediate results and only a few steps contain the final results
that we are interested in. This vignette shows how to conveniently collect and possibly
group the output of those final steps.

### Flag output steps

Output steps are flagged by settting the `keepOut` argument to `TRUE` when adding a step to the pipeline. In the following example, we will want to keep the output of the steps
`data_summary`, `model_summary`, and `model_plot`.

```{r pipeline-with-output}
library(pipeflow)
library(ggplot2)

pip <- pipe_new(
        "my-pipeline",
        data = airquality
    ) |>

    pipe_add(
        "data_prep",
        function(data = ~data) {
            replace(data, "Temp.Celsius", (data[, "Temp"] - 32) * 5/9)
        }
    ) |>

    pipe_add(
        "data_summary",
        function(
            data = ~data_prep,
            xVar = "Temp.Celsius",
            yVar = "Ozone"
        ) {
            data[, c(xVar, yVar)]
        },
        keepOut = TRUE              # <- keep this
    ) |>

    pipe_add(
        "model_fit",
        function(
            data = ~data_prep,
            xVar = "Temp.Celsius",
            yVar = "Ozone"
        ) {
            lm(paste(yVar, "~", xVar), data = data)
        }
    ) |>

    pipe_add(
        "model_summary",
        function(
            fit = ~model_fit
        ) {
            summary(fit)
        },
        keepOut = TRUE              # <- keep this
    ) |>

    pipe_add(
        "model_plot",
        function(
            model = ~model_fit,
            data = ~data_prep,
            xVar = "Temp.Celsius",
            yVar = "Ozone",
            title = "Linear model fit"
        ) {
            coeffs <- coefficients(model)
            ggplot(data) +
                geom_point(aes(.data[[xVar]], .data[["Ozone"]])) +
                geom_abline(intercept = coeffs[1], slope = coeffs[2]) +
                labs(title = title)
        },
        keepOut = TRUE              # <- keep this
    )
```

Looking at the pipeline, we see that the steps `data_summary`, `model-summary`, and
`model_plot` have been flagged accordingly (see column `keepOut`).

```{r}
pip
```

Graphically, steps flagged with `keepOut = TRUE` are displayed with a circle
shape while "normal" steps are shown as rectangle boxes.

```{r, echo = FALSE, eval = getOption("pipeflow.visNetwork", default = FALSE)}
library(visNetwork)
do.call(visNetwork, args = c(pip$get_graph(), list(height = 300))) |>
    visHierarchicalLayout(direction = "LR", sortMethod = "directed")
```


Now let's run and collect the output of the flagged steps using the `collect_out` method, which returns a list with the output of the flagged steps.

```{r}
pip$run()

out <- pip$collect_out()

names(out)
```

As expected, the output list contains the output of the flagged steps.

```{r}
str(out, max.level = 1)
```

### Grouping output steps

Often certain output steps are related and should be grouped together. This can be
achieved conveniently by setting the `group` argument when adding a step to the
pipeline. Let's illustrate this by slightly modifying the previous example.

```{r pipeline-with-grouped-output}
pip <- Pipeline$new("my-pipeline", data = airquality) |>

    pipe_add(
        "data_prep",
        function(data = ~data) {
            replace(data, "Temp.Celsius", (data[, "Temp"] - 32) * 5/9)
        }
    ) |>

    pipe_add(
        "used_data",
        function(
            data = ~data_prep,
            xVar = "Temp.Celsius",
            yVar = "Ozone"
        ) {
            data[, c(xVar, yVar)]
        },
        keepOut = TRUE,
        group = "Data"                 # <- define 'Data' group here
    ) |>

    pipe_add(
        "model_fit",
        function(
            data = ~data_prep,
            xVar = "Temp.Celsius",
            yVar = "Ozone"
        ) {
            lm(paste(yVar, "~", xVar), data = data)
        }
    ) |>

    pipe_add(
        "model_summary",
        function(
            fit = ~model_fit
        ) {
            summary(fit)
        },
        keepOut = TRUE,
        group = "Model"                # <- define 'Model' group here
    ) |>

    pipe_add(
        "model_plot",
        function(
            model = ~model_fit,
            data = ~data_prep,
            xVar = "Temp.Celsius",
            yVar = "Ozone",
            title = "Linear model fit"
        ) {
            coeffs <- coefficients(model)
            ggplot(data) +
                geom_point(aes(.data[[xVar]], .data[["Ozone"]])) +
                geom_abline(intercept = coeffs[1], slope = coeffs[2]) +
                labs(title = title)
        },
        keepOut = TRUE,
        group = "Model"                # <- define 'Model' group here
    )
```


Looking at the pipeline, the defined groups are shown in the `group` column.

```{r}
pip
```

As you see, by default, the group is identical to the step name, that is, each step
represents the trivial case of a one-sized group. Again, we run the pipeline and
collect the output.

```{r}
pip$run()

out <- pip$collect_out()

names(out)
```

As we can see, the output related to the modelling has been grouped into one sublist
named `Model`.

```{r}
str(out, max.level = 2)
```


```{r, include = FALSE}
options(old)
```
