![]() Tidyr::nest() the new argument new_col makes the former. ![]() Arguments to functions can also be deprecated, e.g., in Tibble::data_frame(), with the preferred alternative Very important functions that become deprecated might next be defunct, which means that function continues to exist but the deprecation warning turns into an error. Generally functions will first be soft deprecated and then deprecated. If a function is noted as deprecated, this means a better alternative is available and this function is scheduled for removal. Teaching tip: feel free to teach any stable functions, they’re here to stay for the long run! This is the default state for most functions in the tidyverse and hence the badge is generally not shown. If breaking changes are needed, they will occur gradually. Stable indicates that breaking changes will be avoided where possible, and they’re only made if the long term benefit of such a change exceeds the short term pain of changing existing code. Let’s discuss each of these stages in detail, along with recommendations on how you might consider them in the context of teaching: The diagram below depicts the lifecycle stages of functions and packages in the tidyverse. Being aware of the lifecycle stages (and their associated badges) can be helpful as you review and revise your teaching materials or as you consider incorporating new tooling into your teaching. The lifecycle stages are a useful guide for teaching because they help you see what the tidyverse is moving toward and what it’s moving away from. These are experimental, stable, deprecated, and superseded. But instead of focusing on the package that implements this concept, when teaching, I recommend focusing on the stages of the lifecycle instead. Lifecycle package is used to manage the lifecycle of functions and features within the tidyverse, with clear messaging about what is still experimental and what the tidyverse team is moving away from in the future. Each question is accompanied with a short answer as well as an expanded example. These were compiled based on popular questions on StackOverflow and RStudio Community.
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