From 86ed6697a166e09f696cb2d431b98369e5c7e02e Mon Sep 17 00:00:00 2001
From: "Luke W. Johnston" <lwjohnst@gmail.com>
Date: Mon, 28 Oct 2024 09:54:08 -0400
Subject: [PATCH] style: :lipstick: show where code should be written in

Closes #149
---
 preamble/pre-course.qmd              | 12 +++--
 sessions/pipelines.qmd               | 70 +++++++++++++++++-----------
 sessions/smoother-collaboration.qmd  |  5 ++
 sessions/stats-analyses-basic.qmd    | 29 ++++++++++--
 sessions/stats-analyses-multiple.qmd | 20 +++++++-
 5 files changed, 99 insertions(+), 37 deletions(-)

diff --git a/preamble/pre-course.qmd b/preamble/pre-course.qmd
index ec06516..5388191 100644
--- a/preamble/pre-course.qmd
+++ b/preamble/pre-course.qmd
@@ -113,7 +113,7 @@ If you *have not* used it, please do these tasks:
     from the introduction course. Specifically, type in the RStudio
     Console:
 
-    ``` r
+    ```{.r filename="Console"}
     # There will be a pop-up to type in your name (first and 
     # last), as well as your email
     r3::setup_git_config()
@@ -134,7 +134,7 @@ If you *have not* used it, please do these tasks:
 Regardless of whether you've done the steps above or not, *everyone*
 needs to run:
 
-``` r
+```{.r filename="Console"}
 r3::check_setup()
 ```
 
@@ -207,6 +207,7 @@ When the RStudio Project opens up again, run these commands in the R
 Console to finish the setup:
 
 ```{r more-project-setup}
+#| filename: Console
 #| purl: true
 # Add Git to the project
 prodigenr::setup_with_git()
@@ -258,6 +259,7 @@ Please do these two tasks:
     `doc/` folder.
 
     ```{r add-rmarkdown-doc}
+#| filename: Console
 #| purl: true
 r3::create_qmd_doc()
     ```
@@ -295,6 +297,7 @@ your `AdvancedR3` R Project, go to the Console pane in RStudio and type
 out:
 
 ```{r create-data-raw}
+#| filename: Console
 #| purl: true
 usethis::use_data_raw("nmr-omics")
 ```
@@ -313,7 +316,7 @@ The R script should have opened up for you, otherwise, go into the
 thing to do is delete all the code in the script. Than, copy and paste
 the code below into the script.
 
-```{r insert-data-raw-script, file=here::here('data-raw/nmr-omics.R')}
+```{r insert-data-raw-script, file=here::here('data-raw/nmr-omics.R'), filename="data-raw/nmr-omics.R"}
 ```
 
 ```{r purl-only-paste-processing-code}
@@ -367,6 +370,7 @@ ignore the files created in the `data-raw/nmr-omics/` folder. In the
 Console, type out the code below. You only need to do this once.
 
 ```{r ignore-xlsx-files}
+#| filename: Console
 #| purl: true
 usethis::use_git_ignore("data-raw/nmr-omics/")
 ```
@@ -380,7 +384,7 @@ git_ci(
 )
 ```
 
-``` r
+```{.r filename="Console"}
 r3::check_project_setup_advanced()
 ```
 
diff --git a/sessions/pipelines.qmd b/sessions/pipelines.qmd
index 649b2b8..7bf8610 100644
--- a/sessions/pipelines.qmd
+++ b/sessions/pipelines.qmd
@@ -202,6 +202,7 @@ file with:
 
 ```{r add-targets-as-dep}
 #| purl: true
+#| filename: Console
 use_package("targets")
 ```
 
@@ -210,6 +211,7 @@ to start using it!
 
 ```{r use-targets}
 #| purl: true
+#| filename: Console
 targets::use_targets()
 ```
 
@@ -354,6 +356,7 @@ project library. `{tidyverse}` is a special "meta"-package so we need to
 add it to the `"depends"` section of the `DESCRIPTION` file.
 
 ```{r add-tidyverse-deps}
+#| filename: Console
 #| purl: true
 use_package("tidyverse", "depends")
 ```
@@ -365,7 +368,7 @@ Now, let's start doing some data analysis so that we can add to our
 pipeline later on. First, open up the `doc/learning.qmd` file and create
 a new header and code chunk at the bottom of the file.
 
-````         
+````{.markdown filename="doc/learning.qmd"}
 
 ```{{r setup}}
 library(tidyverse)
@@ -407,6 +410,7 @@ column (`across(where(is.numeric))`) to `round()` to 1 digit. Let's
 write out the code!
 
 ```{r mean-sd-by-each-metabolite}
+#| filename: "doc/learning.qmd"
 #| eval: true
 lipidomics %>%
   group_by(metabolite) %>%
@@ -429,7 +433,14 @@ to take the code we wrote above and convert it into a function. Complete
 these tasks:
 
 1.  Wrap the code with `function() {...}` and name the new function
-    `descriptive_stats`.
+    `descriptive_stats`. Here is some scaffolding to help you get started:
+
+    ```{.r filename="doc/learning.qmd"}
+    descriptive_stats <- function(___) {
+      ___
+    }
+    ```
+
 2.  Replace `lipidomics` with `data` and put `data` as an argument
     inside the brackets of `function()`.
 3.  Add `dplyr::` to the start of each `{dplyr}` function used inside
@@ -458,14 +469,6 @@ be the output of the function. This is called an "implicit return" and
 we will be using this feature throughout the rest of this course.
 :::
 
-Here is some scaffolding to help you get started:
-
-``` r
-descriptive_stats <- function(___) {
-  ___
-}
-```
-
 ```{r solution-descriptive-stats}
 #| eval: true
 #| code-fold: true
@@ -495,7 +498,9 @@ target to load the lipidomic data. In the second, replace it with the
 what the target output is, we can add `df_` to remind us that it is a
 data frame. It should look like:
 
-``` r
+```{r}
+#| eval: false
+#| filename: "targets.R"
 list(
   tar_target(
     name = lipidomics,
@@ -511,7 +516,9 @@ list(
 Let's run `{targets}` to see what happens! You can either use
 {{< var keybind.targets-make >}} or run this code in the Console:
 
-``` r
+```{r}
+#| eval: false
+#| filename: "Console"
 targets::tar_make()
 ```
 
@@ -524,7 +531,9 @@ data file. We can accomplish this by using the argument
 `format = "file"` inside the `tar_target()` before loading the data
 using `{readr}`.
 
-``` r
+```{r}
+#| eval: false
+#| filename: "targets.R"
 list(
     tar_target(
         name = file,
@@ -586,7 +595,7 @@ explicitly called via `::` (e.g. `%>%`). For now, we only need to add
 We can now put this code in the `packages` argument of
 `tar_option_set()` in the `_targets.R` file:
 
-``` r
+```{.r filename="targets.R"}
 packages = c("tibble", "dplyr")
 ```
 
@@ -616,6 +625,7 @@ automatically):
 
 ```{r visualize-targets}
 #| purl: true
+#| filename: Console
 targets::tar_visnetwork()
 ```
 
@@ -623,6 +633,7 @@ Or to see what pipeline targets are outdated:
 
 ```{r outdated-targets}
 #| purl: true
+#| filename: Console
 targets::tar_outdated()
 ```
 
@@ -646,10 +657,11 @@ write this code, let's add it to our `DESCRIPTION` file.
 
 ```{r add-ggplot2-deps}
 #| purl: true
+#| filename: Console
 use_package("ggplot2")
 ```
 
-Next, we'll switch back to `doc/lesson.qmd` and write the code to this
+Next, we'll switch back to `doc/learning.qmd` and write the code to this
 plot of the distribution of each metabolite. We'll use
 `geom_histogram()`, nothing too fancy. And since the data is already in
 long format, we can easily use `facet_wrap()` to create a plot for each
@@ -659,6 +671,7 @@ have the same range of values (some are small, others are quite large).
 ```{r histogram-metabolites}
 #| fig-cap: "Histograms showing the distribution of all metabolites in the lipidomics dataset."
 #| eval: true
+#| filename: "doc/learning.qmd"
 metabolite_distribution_plot <- ggplot(lipidomics, aes(x = value)) +
   geom_histogram() +
   facet_wrap(vars(metabolite), scales = "free")
@@ -676,8 +689,16 @@ convert it into a function. Just like you did with the
 `descriptive_stats()` function in the exercise above, complete these
 tasks:
 
-1.  Wrap the plot code inside `doc/lesson.qmd` with `function() {...}`
+1.  Wrap the plot code inside `doc/learning.qmd` with `function() {...}`
     and name the new function `plot_distributions`.
+    Use this scaffolding code to help guide you to write the code into a
+    function.
+
+    ```{.r filename="doc/learning.qmd"}
+    plot_distributions <- function(___) {
+      ___
+    }
+    ```
 2.  Replace `lipidomics` with `data` and put `data` as an argument
     inside the brackets of `function()`.
 3.  Add `ggplot2::` to the start of each `{ggplot2}` function used
@@ -697,15 +718,6 @@ tasks:
 8.  Save both files and then open the Git interface and commit the
     changes you made to them with {{< var keybind.git >}}.
 
-Use this scaffolding code to help guide you to write the code into a
-function.
-
-``` r
-plot_distributions <- function(___) {
-  ___
-}
-```
-
 ```{r solution-new-function-descriptive-plots}
 #| eval: false
 #| code-fold: true
@@ -732,7 +744,7 @@ this `tar_target()` item within the `list()` inside `_targets.R`. To
 make it easier to track things, add `fig_` to the start of the `name`
 given.
 
-``` r
+```{.r filename="targets.R"}
 list(
   ...,
   tar_target(
@@ -788,6 +800,7 @@ install the helper package `{tarchetypes}` first, as well as the
 
 ```{r tarchetypes-deps}
 #| purl: true
+#| filename: Console
 use_package("tarchetypes")
 use_package("quarto")
 ```
@@ -801,7 +814,7 @@ needs, and the file path to the Quarto file. Again, like the other
 using the `doc/learning.qmd` as a sandbox, we won't include it as a
 pipeline target. Instead we will use the `doc/learning.qmd` file:
 
-``` r
+```{.r filename="targets.R"}
 list(
   ...,
   tar_quarto(
@@ -828,7 +841,7 @@ use of the `targets::tar_read()`.
 
 <!-- Because the Quarto file is in another folder, -->
 
-````         
+````{.markdown filename="doc/learning.qmd"}
 ---
 # YAML header
 ---
@@ -892,6 +905,7 @@ string, you can use columns from a data frame, like `value_mean`. So we
 can use it to format the final table text to be `mean value (SD value)`:
 
 ```{r stats-to-table}
+#| filename: "doc/learning.qmd"
 targets::tar_read(df_stats_by_metabolite) %>% 
   mutate(MeanSD = glue::glue("{value_mean} ({value_sd})")) %>%
   select(Metabolite = metabolite, `Mean SD` = MeanSD) %>%
diff --git a/sessions/smoother-collaboration.qmd b/sessions/smoother-collaboration.qmd
index af08b95..9592f67 100644
--- a/sessions/smoother-collaboration.qmd
+++ b/sessions/smoother-collaboration.qmd
@@ -249,6 +249,7 @@ these tasks:
 #| code-fold: true
 #| code-summary: "**Click for the solution**. Only click if you are really struggling or are out of time for the exercise."
 #| purl: true
+#| filename: Console
 usethis::use_package("stringr")
 usethis::use_package("readxl")
 usethis::use_package("dplyr")
@@ -319,6 +320,7 @@ and add some code into it.
 
 ```{r add-usethis-rprofile}
 #| eval: false
+#| filename: Console
 usethis::use_usethis()
 ```
 
@@ -335,6 +337,7 @@ Let's restart R with {{< var keybind.restart-r >}} before using
 `use_package()` to add `{usethis}` as a workflow dependency.
 
 ```{r suggests-dep}
+#| filename: Console
 #| eval: false
 #| purl: true
 use_package("usethis", "suggests")
@@ -429,6 +432,7 @@ it to the `DESCRIPTION` file.
 ```{r add-styler-dep}
 #| eval: false
 #| purl: true
+#| filename: Console
 use_package("styler", "suggests")
 ```
 
@@ -462,6 +466,7 @@ If you wanted to run `{styler}` on all the files, we can use:
 
 ```{r style-dir}
 #| eval: false
+#| filename: Console
 styler::style_dir()
 ```
 
diff --git a/sessions/stats-analyses-basic.qmd b/sessions/stats-analyses-basic.qmd
index 813808c..b7eba4f 100644
--- a/sessions/stats-analyses-basic.qmd
+++ b/sessions/stats-analyses-basic.qmd
@@ -366,6 +366,7 @@ find this "store" of outputs, and import the data using `tar_read()`. We
 also need to add `library(tidymodels)` to the `setup` code chunk. Copy
 and paste this code chunk below into the Quarto file.
 
+```` {.markdown filename="doc/learning.qmd"}
 ```{{r setup}}
 targets::tar_config_set(store = here::here("_targets"))
 library(tidyverse)
@@ -374,6 +375,7 @@ library(tidymodels)
 source(here::here("R/functions.R"))
 lipidomics <- tar_read(lipidomics)
 ```
+````
 
 Since we will be using `{tidymodels}`, we need to install it, as well as
 explicitly add the `{parsnip}`, `{recipes}`, and `{workflows}` packages.
@@ -382,6 +384,7 @@ is a "meta-package". We might need to force installing it with
 `pak::pak("tidymodels")`.
 
 ```{r tidymodels-to-deps}
+#| filename: Console
 #| purl: true
 #| eval: false
 use_package("tidymodels", "depends")
@@ -395,7 +398,7 @@ Before continuing, let's **commit** the changes to the Git history with
 {{< var keybind.git >}}. Next, in the `doc/learning.qmd` file, on the
 bottom of the document create a new header and code chunk:
 
-````         
+````{.markdown filename="doc/learning.qmd"}
 ## Building the model
 
 ```{{r}}
@@ -406,6 +409,7 @@ bottom of the document create a new header and code chunk:
 In the new code chunk, we will set up the model specs:
 
 ```{r logistic-reg-specs}
+#| filename: "doc/learning.qmd"
 log_reg_specs <- logistic_reg() %>%
   set_engine("glm")
 log_reg_specs
@@ -431,6 +435,7 @@ can be fixed with `{recipes}`. Can you spot them?
 
 ```{r print-lipidomics-data}
 #| column: page-inset-right
+#| filename: Console
 lipidomics
 ```
 
@@ -449,6 +454,7 @@ that there seems to be a data input error, since there are three
 `Cholesterol` values, while all other metabolites only have one:
 
 ```{r too-many-cholesterols}
+#| filename: "doc/learning.qmd"
 lipidomics %>%
   count(code, metabolite) %>%
   filter(n > 1)
@@ -477,6 +483,7 @@ by setting the `values_fn` with `mean`.
 
 ```{r lipidomic-to-wider}
 #| column: page-inset-right
+#| filename: "doc/learning.qmd"
 lipidomics_wide <- lipidomics %>%
   mutate(metabolite = snakecase::to_snake_case(metabolite)) %>%
   pivot_wider(
@@ -495,6 +502,7 @@ moving them over into the `R/functions.R` file.
 
 ```{r first-snakecase-fn}
 #| column: page-inset-right
+#| filename: "doc/learning.qmd"
 column_values_to_snake_case <- function(data) {
   data %>%
     dplyr::mutate(metabolite = snakecase::to_snake_case(metabolite))
@@ -571,6 +579,7 @@ We can use curly-curly (combined with `across()`) to apply
 
 ```{r second-snakecase-fn}
 #| column: page-inset-right
+#| filename: "doc/learning.qmd"
 column_values_to_snake_case <- function(data, columns) {
   data %>%
     dplyr::mutate(dplyr::across({{ columns }}, snakecase::to_snake_case))
@@ -583,10 +592,10 @@ lipidomics %>%
 Move this new function over into the `R/functions.R` file, add Roxygen
 documentation with {{< var keybind.roxygen >}}, style using
 {{< var keybind.styler >}}, `source()` the modified `R/functions.R` file
-with {{< var keybind.source >}}, and add the new function above the
-`pivot_wider()` code in the `doc/learning.qmd` file.
+with {{< var keybind.source >}}
 
 ```{r new-function-column-values-to-snakecase}
+#| filename: "R/functions.R"
 #' Convert a column's character values to snakecase format.
 #'
 #' @param data The lipidomics dataset.
@@ -663,6 +672,7 @@ predictors, we can explicitly select which variables are which. This has
 some nice features that we will use later on.
 
 ```{r recipes-without-formula}
+#| filename: "doc/learning.qmd"
 recipe(lipidomics_wide) %>%
   update_role(metabolite_cholesterol, age, gender, new_role = "predictor") %>%
   update_role(class, new_role = "outcome")
@@ -708,6 +718,7 @@ distribution. This means we can more easily compare values between
 variables. We can add this to the end of the recipe:
 
 ```{r recipes-with-step-normalize}
+#| filename: "doc/learning.qmd"
 recipe(lipidomics_wide) %>%
   update_role(metabolite_cholesterol, age, gender, new_role = "predictor") %>%
   update_role(class, new_role = "outcome") %>%
@@ -722,6 +733,7 @@ might use a different metabolite later. Note, when adding all the
 from the `{tidyselect}` package.
 
 ```{r new-function-create-recipe-spec}
+#| filename: "R/functions.R"
 #' A transformation recipe to pre-process the data.
 #'
 #' @param data The lipidomics dataset.
@@ -740,6 +752,7 @@ create_recipe_spec <- function(data, metabolite_variable) {
 And test it out:
 
 ```{r use-create-recipe-specs-fn}
+#| filename: "doc/learning.qmd"
 #| column: page-inset-right
 recipe_specs <- lipidomics_wide %>%
   create_recipe_spec(metabolite_cholesterol)
@@ -777,6 +790,7 @@ slightly different types). All model workflows need to start with
 
 ```{r use-workflow-for-model}
 #| column: page-inset-right
+#| filename: "doc/learning.qmd"
 workflow() %>%
   add_model(log_reg_specs) %>%
   add_recipe(recipe_specs)
@@ -788,6 +802,7 @@ workflow that we've used before, where the function should ultimately be
 inside the `R/functions.R` file.
 
 ```{r new-function-create-model-workflow}
+#| filename: "R/functions.R"
 #' Create a workflow object of the model and transformations.
 #'
 #' @param model_specs The model specs
@@ -809,6 +824,7 @@ creation from scratch:
 
 ```{r full-model-workflow-from-almost-scratch}
 #| column: page-inset-right
+#| filename: "doc/learning.qmd"
 model_workflow <- create_model_workflow(
   logistic_reg() %>%
     set_engine("glm"),
@@ -823,6 +839,7 @@ Now, we can do the final thing: Fitting the data to the model with
 
 ```{r fit-model-workflow-to-data}
 #| column: page-inset-right
+#| filename: "doc/learning.qmd"
 fitted_model <- model_workflow %>%
   fit(lipidomics_wide)
 fitted_model
@@ -836,6 +853,7 @@ the `extract_fit_parsnip()` function.
 
 ```{r extract-model-fit}
 #| column: page-inset-right
+#| filename: "doc/learning.qmd"
 fitted_model %>%
   extract_fit_parsnip()
 ```
@@ -848,6 +866,7 @@ dependencies:
 ```{r broom-to-deps}
 #| purl: true
 #| eval: false
+#| filename: Console
 use_package("broom")
 ```
 
@@ -861,6 +880,7 @@ coefficient. Here we choose `exponentiate = TRUE`:
 
 ```{r tidy-up-model-results}
 #| column: page-inset-right
+#| filename: "doc/learning.qmd"
 fitted_model %>%
   extract_fit_parsnip() %>%
   tidy(exponentiate = TRUE)
@@ -872,6 +892,7 @@ thing here: Make another function (and move it to `R/functions.R`)!
 :stuck_out_tongue:
 
 ```{r new-function-tidy-model-output}
+#| filename: "R/functions.R"
 #' Create a tidy output of the model results.
 #'
 #' @param workflow_fitted_model The model workflow object that has been fitted.
@@ -889,6 +910,7 @@ Replacing the code in the `doc/learning.qmd` file to use the function.
 
 ```{r use-tidy-model-output-fn}
 #| column: page-inset-right
+#| filename: "doc/learning.qmd"
 fitted_model %>%
   tidy_model_output()
 ```
@@ -897,6 +919,7 @@ If we revise the code so it is one pipe, it would look like:
 
 ```{r single-pipe-model-results}
 #| column: page-inset-right
+#| filename: "doc/learning.qmd"
 create_model_workflow(
   logistic_reg() %>%
     set_engine("glm"),
diff --git a/sessions/stats-analyses-multiple.qmd b/sessions/stats-analyses-multiple.qmd
index 349481e..aeaaa6c 100644
--- a/sessions/stats-analyses-multiple.qmd
+++ b/sessions/stats-analyses-multiple.qmd
@@ -89,7 +89,7 @@ the `lipidomics_wide` dataset. However, these types of
 long form. So we'll start with the original `lipidomics` dataset. Create
 a header and code chunk at the end of the `doc/learning.qmd` file:
 
-````         
+````{.markdown filename="doc/learning.qmd"}
 ## Running multiple models
 
 ```{{r}}
@@ -101,6 +101,7 @@ The first thing we want to do is convert the metabolite names into snake
 case:
 
 ```{r chain-col-to-snakecase}
+#| filename: "doc/learning.qmd"
 lipidomics %>%
   column_values_to_snake_case(metabolite)
 ```
@@ -122,6 +123,7 @@ dependency:
 ```{r purrr-to-deps}
 #| purl: true
 #| eval: false
+#| filename: Console
 use_package("purrr")
 ```
 
@@ -131,6 +133,7 @@ three.
 
 ```{r chain-split-by-metabolite}
 #| eval: false
+#| filename: "doc/learning.qmd"
 lipidomics %>%
   column_values_to_snake_case(metabolite) %>%
   group_split(metabolite)
@@ -151,6 +154,7 @@ the first three):
 
 ```{r chain-map-to-wider}
 #| eval: false 
+#| filename: "doc/learning.qmd"
 lipidomics %>%
   column_values_to_snake_case(metabolite) %>%
   group_split(metabolite) %>%
@@ -176,6 +180,7 @@ into the `R/functions.R` file, and then `source()` the file with
 {{< var keybind.source >}}.
 
 ```{r new-function-split-by-metabolite}
+#| filename: "R/functions.R"
 #' Convert the long form dataset into a list of wide form data frames.
 #'
 #' @param data The lipidomics dataset.
@@ -193,6 +198,7 @@ split_by_metabolite <- function(data) {
 In the `doc/learning.qmd`, use the new function in the code:
 
 ```{r split-by-metabolite}
+#| filename: "doc/learning.qmd"
 #| eval: false
 lipidomics %>%
   split_by_metabolite()
@@ -217,6 +223,7 @@ move into the `R/functions.R` file, and then `source()` the file with
 {{< var keybind.source >}}.
 
 ```{r new-function-generate-model-results}
+#| filename: "R/functions.R"
 #' Generate the results of a model
 #'
 #' @param data The lipidomics dataset.
@@ -239,6 +246,7 @@ Then we add it to the end of the pipe, but using `map()` and
 `list_rbind()` to convert to a data frame:
 
 ```{r chain-generate-model-results}
+#| filename: "doc/learning.qmd"
 lipidomics %>%
   split_by_metabolite() %>%
   map(generate_model_results) %>% 
@@ -250,6 +258,7 @@ let's keep only the `term` rows that are metabolites using `filter()`
 and `str_detect()`.
 
 ```{r chain-filter-terms}
+#| filename: "doc/learning.qmd"
 model_estimates <- lipidomics %>%
   split_by_metabolite() %>%
   map(generate_model_results) %>%
@@ -268,6 +277,7 @@ create a duplicate column of `metabolite` called `term` (to match the
 `model_estimates`) using `mutate()`.
 
 ```{r duplicate-original-vars}
+#| filename: "doc/learning.qmd"
 lipidomics %>%
   select(metabolite) %>%
   mutate(term = metabolite)
@@ -277,6 +287,7 @@ Right after that we will use our custom `column_values_to_snake_case()`
 function on the `term` column.
 
 ```{r dup-column-to-snakecase}
+#| filename: "doc/learning.qmd"
 lipidomics %>%
   select(metabolite) %>%
   mutate(term = metabolite) %>%
@@ -287,6 +298,7 @@ We can see that we are missing the `metabolite_` text before each snake
 case'd name, so we can add that with `mutate()` and `str_c()`:
 
 ```{r dup-column-append-metabolite}
+#| filename: "doc/learning.qmd"
 lipidomics %>%
   select(metabolite) %>%
   mutate(term = metabolite) %>%
@@ -300,6 +312,7 @@ There are 504 rows, but we only need the unique values of `term` and
 only the `metabolite` and `term` variables.
 
 ```{r dup-column-distinct}
+#| filename: "doc/learning.qmd"
 lipidomics %>%
   mutate(term = metabolite) %>%
   column_values_to_snake_case(term) %>%
@@ -310,6 +323,7 @@ lipidomics %>%
 The last step is to `right_join()` with the `model_estimates`:
 
 ```{r dup-column-full-join}
+#| filename: "doc/learning.qmd"
 lipidomics %>%
   mutate(term = metabolite) %>%
   column_values_to_snake_case(term) %>%
@@ -470,7 +484,7 @@ inside the `## Results` section. We'll want to use
 `tar_read(df_model_estimates)` so that `{targets}` is aware that the R
 Markdown file is dependent on this target.
 
-````         
+````{.markdown filename="doc/learning.qmd"}
 ### Figure of model estimates
 
 ```{{r}}
@@ -522,6 +536,7 @@ dot-whisker plots, the "geom" we would use is called
     but adding `std.error` instead.
 
 ```{r plot-estimates-pointrange-only}
+#| filename: "doc/learning.qmd"
 plot_estimates <- model_estimates %>% 
   ggplot(aes(
     x = estimate, 
@@ -541,6 +556,7 @@ eventually need to troubleshoot this issue, but for now, let's restrict
 the x axis to be between 0 and 5.
 
 ```{r plot-estimates-coord-fixed}
+#| filename: "doc/learning.qmd"
 plot_estimates +
   coord_fixed(xlim = c(0, 5))
 ```