Meet the example problem about ds-pipelines-3 HOT 7 CLOSED

⌨️ Activity: Switch to a new branch

Before you edit any code, create a local branch called "three-states" and push that branch up to the remote location "origin" (which is the github host of your repository).

git checkout master
git pull origin master
git checkout -b three-states
git push -u origin three-states

The first two lines aren't strictly necessary when you don't have any new branches, but it's a good habit to head back to master and sync with "origin" whenever you're transitioning between branches and/or PRs.

Comment on this issue once you've created and pushed the "three-states" branch.

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⌨️ Activity: Explore the starter pipeline

Without modifying any code, start by inspecting and running the existing data pipeline.

• Open up remake.yml and read through - can you guess what will happen when you build the pipeline?
• Build all targets in the pipeline.
• Check out the contents of oldest_active_sites.

💡 Refresher hints:

• To build a pipeline, run library(scipiper) and then scmake().
• To assign an R-object pipeline target to your local environment, run mytarget <- scmake('mytarget'). This function will check/build the object to make sure it's up-to-date before passing it to you.
• If you don't want scipiper to check for currentness or rebuild first, run mytarget <- remake::fetch('mytarget'). This function is faster to run and can be handy for getting the old value of an object before you rebuild it.
• You'll pretty much always want to call library(scipiper) in your R session while developing pipeline code - otherwise, you need to call scipiper::scmake() in place of scmake() anytime you run that command, and all that extra typing can add up.

When you're satisfied that you understand the current pipeline, include the value of oldest_active_sites$site_no and the image from site_map.png in a comment on this issue.

Add a comment to this issue to proceed.

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[1] "04073500" "05211000" "04063522"

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⌨️ Activity: Spot the split-apply-combine

Hey, did you notice that there's a split-apply-combine action happening in this repo already?

Check out the find_oldest_sites() function:

find_oldest_sites <- function(states, parameter) {
  purrr::map_df(states, find_oldest_site, parameter)
}

This function:

• splits states into each individual state
• applies find_oldest_site to each state
• combines the results back into a single tibble

and it all happened in just one line! The split-apply-combine operations we'll be exploring in this course require more code and are more useful for slow or fault-prone activities, but they follow the same general pattern.

Check out the documentation for map_df at ?purrr::map_df or online here if this function is new to you.

When you're ready, comment again on this issue.

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⌨️ Activity: Apply a downloading function to each state

Awesome, time for your first code changes ✏️.

• Write three scipiper targets in remake.yml to apply get_site_data() to each state in states. The targets should be named wi_data, mn_data, and mi_data.

• Modify the sources section of remake.yml as needed to make your pipeline executable.

• Modify the main target so that your new targets will be built by default.

• Test it: You should be able to run scmake() with no arguments to get everything built.

💡 Hint: the get_site_data() function already exists and shouldn't need modification. You can find it by browsing the repo or by hitting Ctrl-. in RStudio and then searching for "get_site_data".

When you're satisfied with your code, open a PR to merge the "three-states" branch into "master". Make sure to add .remake, 3_visualize/out, and any .DS_Store files to your .gitignore file before committing anything. In the description box for your PR, include a screenshot or transcript of your console session where the targets get built.

I'll respond in your new PR. You may need to refresh the PR page to see my response.

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