Merge pull request #9 from robitalec/fix/headings-raw

Fix/headings raw

Author: icrichmond

exercises/functions/02-functions-development.qmd

@@ -45,7 +45,7 @@ This process of editing two files might seem like extra work, but we recommend s
 Create a new function called `prepare_csv()` that reads in raw data and cleans it.
 :::
 
-### `prepare_csv()`
+### prepare_csv()
 
 Instruction: setup your function script and test script for your new function `prepare_csv()`. 
 

exercises/functions/03-functions-checks.qmd

@@ -95,7 +95,7 @@ is.numeric(DF$numbers)
 
 ## Filters 
 
-### Exercise: `filter_islands()`
+### Exercise: filter_islands()
 
 First, write a function to filter a data.frame, providing the column name for the data to be filtered and the values to filter on. Use the example data that we prepared with our `prepare_csv` function. 
 
@@ -131,7 +131,7 @@ Pass the data.frame's column that you want to filter on to the plot function's f
 </details>
 
 <details>
-<summary>Hints: `dplyr`</summary>
+<summary>Hints: dplyr</summary>
 <hr>
 This function has one step: taking an input data.frame and filtering across a single column. 
 
@@ -148,7 +148,7 @@ details here: <https://dplyr.tidyverse.org/articles/in-packages.html>
 
 ## Writing Checks 
 
-### Exercise: writing checks for `filter_islands()`
+### Exercise: writing checks for filter_islands()
 ::: callout-note
 ## Objective
 Write and test a complete function.
@@ -183,15 +183,15 @@ of metaprogramming. The Advanced R book has a detailed section on this concept.
 
 ### Bonus
 
-#### Writing checks for `prepare_csv()`
+#### Writing checks for prepare_csv()
 
 Instruction: add the following checks to your `prepare_csv()` function and
 test them in your test script (`tests/test_prepare_csv.R`). 
 
 - check if the path points to a file that exists
 - before returning the object, check that it is a data.frame
 
-#### More informative errors with `stopifnot()`
+#### More informative errors with stopifnot()
 
 Instruction: Write more informative errors for your `stopifnot()` checks using the following syntax: `stopifnot("error message" = logical_statement)`. Think about your user (either someone else or future you) - what would help them understand and resolve this error?
 

exercises/targets/01-targets-setup.qmd

@@ -1,4 +1,4 @@
-# Setup
+# Setting up targets
 
 ::: callout-tip
 ## Learning Goals
@@ -11,7 +11,7 @@ This set of exercises are centered around an approach to setting up your project
 
 A {targets} pipeline is declared in the `_targets.R` file in the root of your project's directory. We'll also set up a central place to put all our of required packages that helps us keep track of them, and will later make it easier to set up {renv}. 
 
-## `_targets.R`
+## _targets.R
 
 The `_targets.R` is where we declare our {targets} pipeline. It is an R file saved in the root directory of your project. The generalized structure of a `_targets.R` file is:
 
@@ -37,7 +37,7 @@ c(
 )
 ```
 
-### Exercise: `_targets.R`
+### Exercise: _targets.R
 
 ::: callout-note
 ## Objective
@@ -58,7 +58,7 @@ To declare which packages we need across all our functions, we have two main opt
 We prefer the second approach because it keeps our `_targets.R` tidier, and helps us setup {renv} package versioning and {conflicted} later. You might find your list of packages gets long, and this gets them out of our way. You can also use this script when you are exploring your results later - source the `packages.R` script and you will have all your required packages. 
 
 
-### Exercise: `packages.R`
+### Exercise: packages.R
 
 ::: callout-note
 ## Objective

exercises/targets/02-targets-usage.qmd

@@ -1,4 +1,4 @@
-# Application
+# Application of targets
 
 :::callout-tip
 ## Learning Goals
@@ -35,7 +35,7 @@ Related functions to `tar_visnetwork()` include:
 - `tar_network()` returns a network of edges and nodes representing the pipeline
 
 
-### Exercise: `tar_visnetwork()`
+### Exercise: tar_visnetwork()
 
 ::: callout-note
 ## Objective
@@ -63,12 +63,12 @@ tar_visnetwork(starts_with('plot'))
 </details>
 
 
-## `tar_make()`
+## tar_make()
 
 Run the {targets} pipeline with `tar_make()`. The pipeline is run in a new external R process, which means that `tar_make()` doesn't impact your current workspace and, more importantly, is not influenced by your current workspace. This totally isolated environment is how {targets} offers us a truly reproducible approach - only things defined in the `_targets.R` script (or sourced within it) are considered. 
 
 
-### Exercise: `tar_make()`
+### Exercise: tar_make()
 
 ::: callout-note
 ## Objective
@@ -91,11 +91,11 @@ tar_make()
 </details>
 
 
-## `tar_read()`, `tar_load()`
+## tar_read(), tar_load()
 
 To look at results from the {targets} pipeline, use `tar_read()` and `tar_load()`. `tar_read()` reads targets without saving them as an object in your environment and `tar_load()` loads the object directly to your environment using the name of the target as the object. 
 
-### Exercise: `tar_read()`, `tar_load()`
+### Exercise: tar_read(), tar_load()
 
 ::: callout-note
 ## Objective
@@ -122,11 +122,11 @@ tar_read(plotted)
 </details>
 
 
-## `tar_meta()`
+## tar_meta()
 
 The metadata related to the {targets} pipeline is accessible using the function `tar_meta()`. 
 
-### Exercise: `tar_meta()`
+### Exercise: tar_meta()
 
 ::: callout-note
 ## Objective

exercises/targets/03-targets-extensions.qmd

@@ -1,4 +1,4 @@
-# Extensions
+# Targets extensions
 
 Let's look at a {targets} pipeline that more closely resembles what a full project would be including figures, model, output, tables and a manuscript object. We'll start with a demo, so we can show off some of the power and extended features of {targets}, then we can work through it piece by piece to explain the details.