This topic has been brought up before by @grayskripko in #2047 (comment). @moodymudskipper has also shared an implementation in #2047 (comment), born from a Stack Overflow question. It's my hope that separating this from #2047 into an issue of its own could reinvigorate a more focused discussion about a specialized verb for moving columns, outside the specific context of mutate().
It's often useful to reorder columns within a table: for example to group related columns together, or to bring identifier/grouping variables to the front to emphasize the structure of the data.
Crafting a call to select() to accomplish the job often takes your focus off the analysis and into thinking about the mechanics of moving columns to the right place while making sure not to accidentally drop any of them. The resulting calls are often long, unintuitive, and can contain duplication of column names or references to columns that aren't directly related to the operation. Later, when reading the code, it can take a while to decipher what's happening. Consider, for example:
select(iris, 1:(Petal.Width - 1), -Sepal.Width, Sepal.Width, everything())
It does make sense after you think about it, given that you are familiar with how tidyselect works; it describes the mechanics, but it doesn't do a very good job of communicating intent. Contrast with:
move(iris, Sepal.Width, .before = Petal.Width)
It's concise, the intent is instantly clear, matches how you likely thought about the operation in the first place, and leaves the mechanics for the function implementation to handle.
Having a dedicated verb to move columns would be a big quality of life improvement that would make analysis and reporting flow more naturally.
PS. If this is something deemed a good fit for dplyr, I also have a branch from a little while back with an initial implementation that I could turn into a PR. Here's some examples:
# remotes::install_github("mikmart/dplyr@move")
library(dplyr, warn.conflicts = FALSE, lib.loc = "~/tmp")
(iris <- as_tibble(head(iris))) # so it prints a little nicer
#> # A tibble: 6 x 5
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosa
# Basic usage
move(iris, Sepal.Width, .before = Petal.Width)
#> # A tibble: 6 x 5
#> Sepal.Length Petal.Length Sepal.Width Petal.Width Species
#> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 5.1 1.4 3.5 0.2 setosa
#> 2 4.9 1.4 3 0.2 setosa
#> 3 4.7 1.3 3.2 0.2 setosa
#> 4 4.6 1.5 3.1 0.2 setosa
#> 5 5 1.4 3.6 0.2 setosa
#> 6 5.4 1.7 3.9 0.4 setosa
# Several columns selected and as reference
move(iris, starts_with("Sepal"), .after = starts_with("Petal"))
#> # A tibble: 6 x 5
#> Petal.Length Petal.Width Sepal.Length Sepal.Width Species
#> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 1.4 0.2 5.1 3.5 setosa
#> 2 1.4 0.2 4.9 3 setosa
#> 3 1.3 0.2 4.7 3.2 setosa
#> 4 1.5 0.2 4.6 3.1 setosa
#> 5 1.4 0.2 5 3.6 setosa
#> 6 1.7 0.4 5.4 3.9 setosa
# Move a variable to first
move(iris, Species, .before = everything())
#> # A tibble: 6 x 5
#> Species Sepal.Length Sepal.Width Petal.Length Petal.Width
#> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 setosa 5.1 3.5 1.4 0.2
#> 2 setosa 4.9 3 1.4 0.2
#> 3 setosa 4.7 3.2 1.3 0.2
#> 4 setosa 4.6 3.1 1.5 0.2
#> 5 setosa 5 3.6 1.4 0.2
#> 6 setosa 5.4 3.9 1.7 0.4
# Move a variable to last
move(iris, Sepal.Length, Sepal.Width, .after = last_col())
#> # A tibble: 6 x 5
#> Petal.Length Petal.Width Species Sepal.Length Sepal.Width
#> <dbl> <dbl> <fct> <dbl> <dbl>
#> 1 1.4 0.2 setosa 5.1 3.5
#> 2 1.4 0.2 setosa 4.9 3
#> 3 1.3 0.2 setosa 4.7 3.2
#> 4 1.5 0.2 setosa 4.6 3.1
#> 5 1.4 0.2 setosa 5 3.6
#> 6 1.7 0.4 setosa 5.4 3.9
Created on 2019-10-19 by the reprex package (v0.3.0)
cderv, pasipasi123, mattwarkentin and IndrajeetPatil
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