Timeline for If R were reprogrammed from scratch today, what changes would be most useful to the statistics community?
Current License: CC BY-SA 4.0
15 events
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| Jan 16, 2021 at 13:15 | comment | added | gung - Reinstate Monica |
@StephanKolassa, IMHO reshape() is a great function. But the arguments have names that are a little opaque & the documentation is really not up to the task.
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| Jan 12, 2021 at 8:06 | history | edited | Ben | CC BY-SA 4.0 |
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| Jan 12, 2021 at 8:03 | comment | added | Ben |
@StephenKolassa: reshape(ossified brain)
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| Jan 12, 2021 at 7:55 | comment | added | Stephan Kolassa |
Ben, I think your last comment is spot on. Descriptive labels and built-in data wrangling functions would be a step forward. Then again, there are already a number of such functions, like reshape(). I always wonder whether the fact that I can't get reshape() to work at the first try reflects a shortcoming in its design, or in my ossified brain. Probably the latter.
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| Jan 12, 2021 at 1:57 | history | edited | Ben | CC BY-SA 4.0 |
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| Jan 12, 2021 at 1:49 | comment | added | Ben |
IMHO, many of the criticisms of the "tidyverse" (e.g., concerns about complexity) are things that would actually be solved if the key functions were programmed at the base level of R using simple names. For example, there are a number of useful wrangling functions that change the shape/structure of a data-frame in useful ways, but are laborious to program in loops in base R. Having wrangling functions in the base program would seem to me to reduce the complexity, rather than increasing it.
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| Jan 11, 2021 at 21:53 | comment | added | Ben | In hindsight, my answer probably did overstate exactly what you should take from the "tidyverse", and I agree that some aspects can add to the learning curve. Nevertheless, what I had in mind was having base functions that do the major data-wrangling steps for data frames, which would be very useful. I have edited the answer to make a smaller claim with respect to these items. | |
| Jan 11, 2021 at 21:51 | history | edited | Ben | CC BY-SA 4.0 |
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| Jan 11, 2021 at 18:47 | comment | added | gung - Reinstate Monica | (Actually, I'm a little surprised to see here that others have a similar opinion on this that I do. You definitely have to be into the tidyverse if you want to be one of the cool kids these days.) | |
| Jan 11, 2021 at 18:19 | comment | added | Firebug |
As I mentioned, when regarding data.table, what's the advantage of incorporating tidy-stuff in base-R? Having something like a pipe operator built-in is cool, but the whole tidy thing could be left for packages.
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| Jan 11, 2021 at 16:59 | comment | added | COOLSerdash | @StephanKolassa It's rare to find someone that shares that sentiment about the tidyverse (at least this is my impression). Normal Matloff is a prominent tidyverse-critic who makes a lot of good points in my opinion. Personally, I'm a bit torn: On one side, some tasks are really conventient to achieve with the tidyverse but it's a bit of a chore to learn all the functions (which are not easy, imo). I also don't like that there is a cultural split among R users. | |
| Jan 11, 2021 at 16:58 | comment | added | gung - Reinstate Monica | @StephanKolassa, I agree. IMHO, Norman Matloff does a good job of discussing the issues here. | |
| Jan 11, 2021 at 16:34 | comment | added | Stephan Kolassa | IMO, the tidyverse is an active disvalue to R, so while I'm all in favor of descriptive labels, I'm not in favor of the additional baggage the tidyverse entails (and that adds to R's learning curve). | |
| Jan 11, 2021 at 14:27 | history | edited | COOLSerdash | CC BY-SA 4.0 |
Corrected spelling of Stata (software).
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| Jan 11, 2021 at 12:53 | history | answered | Ben | CC BY-SA 4.0 |