You are doing a great job, @amsnyder! :star2: :collision: :tropical_fish:
But you may be asking why we asked you to go through all of the hard work of connecting functions, files, and targets together using a makefile. We don't blame you for wondering...
The real power of depedency management is when something changes - that's the EUREKA! moment, but we haven't put you in a situation where it would show up. That will come further down the road on later training activities and also in the project work you will be exposed to.
In the meantime, here are a few nice tricks given you have a functional pipeline.
[x] run tar_make() again. What happens? Hopefully not much. I see this:
Which means everthing is up to date so all targets are :OK:
[x] now try making a change to one of your functions in your code. What happens after running tar_make() then?
[x] access the eval_data target by using tar_load(eval_data). (You may or may not have an R-object target named eval_data in your own repo at this point, so go ahead and try it with some target that you do have.) In this example, we have passed in the unquoted target name eval_data to tar_load() which creates a data.frame object in our environment called eval_data because that's what our example function process_data() creates. If you load a file target, like tar_load(model_RMSEs_csv), the resulting object in your environment is a character vector with the path to the target's file.
[x] now try making a change to the template_1 variable in your function that creates the .txt file. What happens after running tar_make() then? Which targets get rebuilt and which do not?
Lastly, imagine the following comment appeared on your pull request.
Oh shoot @amsnyder, I am using your results for FANCY BIG PROJECT and I have coded everything to assume your outputs use a character for the experiment number (the exper_n column), of the form "01", "02", etc. It looks like you are using numbers. Can you update your code accordingly?
Would your code be easy to adjust to satisfy this request? Would you need to re-run any steps that aren't associated with this naming choice? Did the use of a dependency management solution allow you to both make the change efficiently (i.e., by avoiding rebuilding any unnecessary parts of the pipeline) and increase your confidence in delivering the results?
You have completed introductions to pipelines I. Great work!
Below you will find some quick links that can help you review the content covered here
You are doing a great job, @amsnyder! :star2: :collision: :tropical_fish:
But you may be asking why we asked you to go through all of the hard work of connecting functions, files, and targets together using a makefile. We don't blame you for wondering...
The real power of depedency management is when something changes - that's the EUREKA! moment, but we haven't put you in a situation where it would show up. That will come further down the road on later training activities and also in the project work you will be exposed to.
In the meantime, here are a few nice tricks given you have a functional pipeline.
[x] run
Which means everthing is up to date so all targets are :OK:
tar_make()again. What happens? Hopefully not much. I see this:[x] now try making a change to one of your functions in your code. What happens after running
tar_make()then?[x] access the
eval_datatarget by usingtar_load(eval_data). (You may or may not have an R-object target namedeval_datain your own repo at this point, so go ahead and try it with some target that you do have.) In this example, we have passed in the unquoted target nameeval_datatotar_load()which creates a data.frame object in our environment calledeval_databecause that's what our example functionprocess_data()creates. If you load a file target, liketar_load(model_RMSEs_csv), the resulting object in your environment is a character vector with the path to the target's file.[x] now try making a change to the
template_1variable in your function that creates the .txt file. What happens after runningtar_make()then? Which targets get rebuilt and which do not?Lastly, imagine the following comment appeared on your pull request.
Would your code be easy to adjust to satisfy this request? Would you need to re-run any steps that aren't associated with this naming choice? Did the use of a dependency management solution allow you to both make the change efficiently (i.e., by avoiding rebuilding any unnecessary parts of the pipeline) and increase your confidence in delivering the results?
You have completed introductions to pipelines I. Great work!
Below you will find some quick links that can help you review the content covered here