extra-exercises.qmd

# Extra exercises

## Creating automatic analysis pipelines

### Exercise: Update function to also calculate median and IQR

> Time: \~8 minutes.

Let's make a change to our function and test out how the
`tar_outdated()` and `tar_visnetwork()` work.

1.  Open up the `R/functions.R` file.
2.  Add median and interquartile range (IQR) to the `summarise()`
    function, by adding it to the end of `list(mean = mean, sd = sd)`,
    after the second `sd`. Note, IQR should look like `iqr = IQR` since
    we want the output columns to have a lowercase for the column names.
3.  Run `tar_outdated()` and `tar_visnetwork()` in the Console (or by
    using the Command Palette {{< var keybind.palette >}}, then "targets
    outdated" or "targets visual"). What does it show?
4.  Style using {{< var keybind.styler >}}. You might need to force a
    reformat if the code is too long by highlighting the line and using
    {{< var keybind.palette >}}, then "reformat".
5.  Run `tar_make()` in the Console or with
    {{< var keybind.targets_make >}}. Re-check for outdated targets and
    visualize the network again.
6.  Open up the Git interface and commit the changes to the Git history
    with {{< var keybind.git >}}.

```{r solution-new-function-descriptive-stats}
#| code-fold: true
#| code-summary: "**Click for a potential solution**. Only click if you are struggling or are out of time."
#' Calculate descriptive statistics of each metabolite.
#'
#' @param data Lipidomics dataset.
#'
#' @return A data.frame/tibble.
#'
descriptive_stats <- function(data) {
  data %>%
    dplyr::group_by(metabolite) %>%
    dplyr::summarise(dplyr::across(value, list(
      mean = mean,
      sd = sd,
      median = median,
      iqr = IQR
    ))) %>%
    dplyr::mutate(dplyr::across(tidyselect::where(is.numeric), ~round(.x, digits = 1)))
}
```

## A general approach to doing statistical analyses

### Exercise: How would you define a linear regression with parsnip?

> Time: \~10 minutes.

Using [parsnip's "Examples"
vignette](https://parsnip.tidymodels.org/articles/Examples.html) as well
as the code we wrote for the logistic regression above as a template,
write `{parsnip}` code that would define a simple (an engine of`"lm"`)
linear regression model. Begin by making a new Markdown header and code
chunk at the bottom of the `doc/learning.qmd` file, like listed below:

````         
## Exercises
### Linear regression model definition

```{{r}}

```
````

After writing the code, style the file using {{< var keybind.styler >}}.
We will eventually delete these exercise text in the Quarto file, but
for now, commit the changes to the Git history with
{{< var keybind.git >}}.

```{r solution-define-linear-reg-model}
#| eval: false
#| code-fold: true
#| code-summary: "**Click for the solution**. Only click if you are struggling or are out of time."
linear_reg_specs <- linear_reg() %>%
  set_engine("lm")
```

## Building a website with Quarto

### Exercise: Look through the new website folder

> Time: \~5 minutes.

In your main project folder, there should now be a folder called
`_site/`. Look through the folder. What files are now created? Open up
the `index.html` file in your browser while in RStudio by clicking the
`index.html` file and then selecting the "Open in web browser".

<!-- TODO: Confirm the name of the website folder. -->

Recall how we described what a website is. Can you see how these files
and folders are simply HTML files that the browser shows as website? In
the browser URL, change the `index.html` ending link to
`doc/report.html`. See how it changes to the file based on how it is
structured in the folder?

### Exercise: Make figures prettier

> Time: \~25 minutes

Looking through the [Cell
Reference](https://quarto.org/docs/reference/cells/cells-knitr.html)
documentation, complete these tasks.

-   Add a `fig-cap` option to each of the figures.

-   For the distribution plot, set the code chunk option `column` to
    `page` to make it bigger.

Look through the `{ggplot2}` documentation on `theme_set()` and
`theme()` to update the look of all your figures. In the `setup` code
chunk of the `doc/learning.qmd` file, make a new theme object and use
`theme_set()` to apply that theme to all plots. Use this as a scaffold:

```{{r setup}}
# ... previous code
updated_theme <- theme(
  # ... make changes
)

# Or start with a template like theme_classic()

updated_theme <- theme_TEMPLATE() +
  theme(
    # ...
  )

theme_set(updated_theme)
```