Remove the `data_*()` prefix from statistical transformation function names

[etiennebacher]

I also feel that we should remove the data_*() prefix from statistical transformation function names.

Originally posted by @IndrajeetPatil in #183 (comment)

Moved here, since it is not related to the vignette about equivalence with tidyverse

[IndrajeetPatil]

Specifically, here is what the function names look like right now:

Preparation

• data_filter()
• data_select()
• data_to_long()
• data_to_wide()
• data_rotate()
• data_rename()
• data_relocate()
• data_join()

Transformation:

• standardize()
• normalize()
• center()
• degroup()
• winsorize()
• data_cut()
• data_recode()
• data_shift()

And I am wondering if the following should lose their data_ prefix:

• data_cut()
• data_recode()
• data_shift()

[bwiernik]

The problem is cut() is in base and recode() is in dplyr. shift() is in data.table--that's probably less of a collision issue, but I imagine we probably have a fair number of data.table users

[bwiernik]

1. We could rename data_cut() to bin() (or cut() and ignore the masking considering it's a wrapper?)
2. Maybe rename data_recode() to remap() or change_code()? This one is tough to find a good alternative name--probably why recode() has stuck around as "questioning" in dplyr forever without the promised future replacement function.
3. We could rename data_shift() to translate() or baseline() (or shift() and ignore the masking with data.table--that one is probably fine?)

There are also:

• data_reverse() : change to just reverse() or reverse_scale()
• data_rescale() : change to just rescale()
• the various data_to_*() functions : change to just to_*()

All of these functions have methods for both vectors and data frames. Is it more confusing to be able to use a function without data_ with a data frame? Should we have separate versions of each function for vectors and data frames?

[etiennebacher]

How about:

1. data_cut -> categorize
2. ?
3. data_shift -> move

reverse_scale is already an alias for data_rescale.

I don't know which is better, removing data_ for these functions or not. I just want to say that I like having the prefix for the data prep functions, it's easy to see which functions are available with autocomplete. So if you want to remove data_ maybe it would be a good idea to replace it with another prefix for all data transformation functions. For example, having a prefix do_ would give the following:

• do_standardize()
• do_normalize()
• do_center()
• do_degroup()
• do_winsorize()
• do_recode()
• etc.

use_, make_ or apply_ could be other prefixes.

[mattansb]

cut > discretize?
recode > revalue?
shift > I like translate, or slide (like a slide ruler), or "add" 😅?

I think it's a good idea to have dedicated data_* functions for data frames. I've found that students don't often fully comprehend that difference classes have different methods for the same function. So having separate functions for dfs sounds good (or having aliases for the methods e.g. data_standardize <- standardize.data.frame).

[bwiernik]

I would prefer not to have prefixes for vector functions