style: 💄 show where code should be written in

Closes #149

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()
 ```
 

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")
 ```
 
@@ -616,13 +625,15 @@ automatically):
 
 ```{r visualize-targets}
 #| purl: true
+#| filename: Console
 targets::tar_visnetwork()
 ```
 
 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) %>%

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()
 ```