Feedback regarding column superscripts

[DavisVaughan]

I have some feedback about the column superscripts / abbreviations. I feel like it actually adds more burden onto the programmer when working interactively.

Say you are exploring the flights data for the first time.

library(nycflights13)

flights
#> # A tibble: 336,776 × 19
#>     year month   day dep_time sched_de…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
#>    <int> <int> <int>    <int>      <int>   <dbl>   <int>   <int>   <dbl> <chr>  
#>  1  2013     1     1      517        515       2     830     819      11 UA     
#>  2  2013     1     1      533        529       4     850     830      20 UA     
#>  3  2013     1     1      542        540       2     923     850      33 AA     
#>  4  2013     1     1      544        545      -1    1004    1022     -18 B6     
#>  5  2013     1     1      554        600      -6     812     837     -25 DL     
#>  6  2013     1     1      554        558      -4     740     728      12 UA     
#>  7  2013     1     1      555        600      -5     913     854      19 B6     
#>  8  2013     1     1      557        600      -3     709     723     -14 EV     
#>  9  2013     1     1      557        600      -3     838     846      -8 B6     
#> 10  2013     1     1      558        600      -2     753     745       8 AA     
#> # … with 336,766 more rows, 9 more variables: flight <int>, tailnum <chr>,
#> #   origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
#> #   minute <dbl>, time_hour <dttm>, and abbreviated variable names
#> #   ¹​sched_dep_time, ²​dep_delay, ³​arr_time, ⁴​sched_arr_time, ⁵​arr_delay

If this is the first time you've seen this data, then you are probably going to take a look at the column names. But the abbreviations make it very hard to figure out what each column is named.

Take sched...4 for example. I have no idea what this column means, and there are multiple sched_* columns, so I have to spend quite a long time mapping the 4 in the superscript down to the 4sched_arr_time in the footer.

To me it feels like this is actively getting in the way of my analysis.

---

Another example:

I've worked with this data quite a bit, and was attempting to write mutate(flights, speed = air_time / distance, .before = sched_dep_time) but couldn't quite remember the full name of the sched_dep_time column. I had to:

• Find it in the column header
• Figure out the superscript number
• Map that superscript number to a superscript in the footer

That is a lot of work just to figure out a column name, where previously it was one step.

---

My personal preference is to have no superscripts / abbreviations at all

[hadley]

One place to start would be to massively bump up pillar.min_title_chars. I'd suggest 20 would be more appropriate.

Another principle might be to only abbreviate to a unique prefix, but I suspect would be a quite a bit of work to implement, so probably not worth it.

[krlmlr]

We had pillar.min_title_chars at 15, I don't mind changing the default.

There is a tension between "seeing more columns" and "being able to copy-paste column names". Showing the columns in the footer allows both.

@DavisVaughan: I hear you, but isn't this mostly a matter of setting pillar.min_title_chars to a convenient value? Are you still experiencing the same difficulties with options(pillar.min_title_chars = 15) or even options(pillar.min_title_chars = Inf) ?

[DavisVaughan]

I didn't know about that global option until now, but I agree that it is too low. 25 feels good to me personally.

Beyond just reducing the number of abbreviated columns, I think upping this option would also have the benefit of greatly reducing the chances for ambiguity that I mentioned earlier. i.e. if it printed out at least the first 25 characters then you'd be way less likely to run into situations where you have arr_t…³ and arr_d…⁵ or even two columns that say arr_

If we met in the middle at Hadley's suggestion of 20, then that seems good enough for me 😄

(I will probably still set this to Inf personally)

[topepo]

I found the superscripts jarring and not really necessary when people can use names(), str(), View() etc to get more information. I didn't see the point of this change and it was substantial enough to warrant discussion for users of tibbles.

As I said in another issue, it would be good to not increase the vertical space used just to print a tibble. Less is more.

[krlmlr]

Thanks for your inputs.

@DavisVaughan: How wide is your terminal typically (getOption("width"))? If you can afford the horizontal space, of course you don't need to squeeze column names.

@topepo: By default the footer is now seven lines tall at most. There is an option that controls this. (Also discussing off-band.)

[DavisVaughan]

@krlmlr it seems like I typically have it sit right at 80

[krlmlr]

One downside of options(pillar.min_title_chars = Inf) : if you have a column with a long name, other columns are omitted -- only shown as names in the footer. With a lot of white (or black) space that could otherwise be data.

In the poll at https://twitter.com/krlmlr/status/1518145876258082820, a vast majority was in favor of lowering the (then) default of 15 to 8 or even lower. I chose 5.

Users can customize to their liking. For tests, should testthat use other defaults perhaps?

[DavisVaughan]

if you have a column with a long name, other columns are omitted -- only shown as names in the footer

that does not and has never bothered me. But I can see how some people might want to use that space, which is why I'd be ok with 20

a vast majority was in favor of lowering the (then) default of 15 to 8 or even lower. I chose 5.

After dealing with this for a little while I am convinced that twitter is wrong 😛

[DavisVaughan]

FYI it is documented as 3 even though the default is indeed 5! https://github.com/r-lib/pillar/blob/5a5619d5a738840e318ab0da2b11ac5d7b8e7bb0/R/options.R#L107

[krlmlr]

There's a PR that fixes this.

The worst that happens is if a dataset where you don't control the column names has columns of length > 15. You see a ton of white space, and still don't see the full column name. That's where the abbreviations came from. And with the abbreviations there's also the opportunity to show a bit more data.

20 means "3-4 columns" if you have expressive column names. Instead of 10-15 columns with the current default (on width 80).

If we change the default now, we should check how many packages would have to update their snapshot tests. (And perhaps help those packages with a GitHub action that opens a PR. I have one for my packages.)

[wurli]

A bit of extra feedback. Currently abbreviated columns are listed in the same paragraph as omitted column names - I think an extra linebreak here would improve readability. They also appear after omitted column names, not before. I'd suggest swapping the order here; this would more closely reflect the data structure and, IMO, would be more intuitive for end users.

E.g. we currently have this:

library(purrr)

words <- c("appropriate", "department", "difference", "environment", "experience")
cols <- do.call(paste, c(expand.grid(words, words), sep = "_"))
wide_data <- cols |> set_names() |> map_dfc(~ sample(letters, 25))

# Current behaviour:
wide_data 
#> # A tibble: 25 × 25
#>    approp…¹ depar…² diffe…³ envir…⁴ exper…⁵ appro…⁶ depar…⁷ diffe…⁸ envir…⁹ exper…˟ appro…˟ depar…˟ diffe…˟
#>    <chr>    <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>  
#>  1 q        x       n       c       h       y       l       o       m       w       v       z       j      
#>  2 a        j       c       m       t       q       k       z       r       v       y       s       n      
#>  3 e        h       j       r       j       s       p       i       p       t       b       n       w      
#>  4 x        m       y       i       a       k       y       q       w       o       h       a       s      
#>  5 s        r       u       p       v       w       a       p       q       l       s       m       f      
#>  6 i        k       q       q       f       n       d       r       h       x       j       d       x      
#>  7 d        s       e       h       e       m       w       t       o       b       p       x       b      
#>  8 z        i       g       t       x       g       o       d       i       r       l       b       z      
#>  9 p        v       v       e       d       h       z       n       y       p       g       y       e      
#> 10 c        o       b       g       r       o       r       a       s       h       x       e       o      
#> # … with 15 more rows, 12 more variables: environment_difference <chr>, experience_difference <chr>,
#> #   appropriate_environment <chr>, department_environment <chr>, difference_environment <chr>,
#> #   environment_environment <chr>, experience_environment <chr>, appropriate_experience <chr>,
#> #   department_experience <chr>, difference_experience <chr>, environment_experience <chr>,
#> #   experience_experience <chr>, and abbreviated variable names ¹​appropriate_appropriate,
#> #   ²​department_appropriate, ³​difference_appropriate, ⁴​environment_appropriate, ⁵​experience_appropriate,
#> #   ⁶​appropriate_department, ⁷​department_department, ⁸​difference_department, ⁹​environment_department, …
#> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names

# Proposed behaviour would be something like this:
wide_data
#> # A tibble: 25 × 25
#>    approp…¹ depar…² diffe…³ envir…⁴ exper…⁵ appro…⁶ depar…⁷ diffe…⁸ envir…⁹ exper…˟ appro…˟ depar…˟ diffe…˟
#>    <chr>    <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>  
#>  1 q        x       n       c       h       y       l       o       m       w       v       z       j      
#>  2 a        j       c       m       t       q       k       z       r       v       y       s       n      
#>  3 e        h       j       r       j       s       p       i       p       t       b       n       w      
#>  4 x        m       y       i       a       k       y       q       w       o       h       a       s      
#>  5 s        r       u       p       v       w       a       p       q       l       s       m       f      
#>  6 i        k       q       q       f       n       d       r       h       x       j       d       x      
#>  7 d        s       e       h       e       m       w       t       o       b       p       x       b      
#>  8 z        i       g       t       x       g       o       d       i       r       l       b       z      
#>  9 p        v       v       e       d       h       z       n       y       p       g       y       e      
#> 10 c        o       b       g       r       o       r       a       s       h       x       e       o      
#> # … with 15 more rows
#> # • abbreviated variables: ¹​appropriate_appropriate, ²​department_appropriate, ³​difference_appropriate, 
#> #   ⁴​environment_appropriate, ⁵​experience_appropriate, ⁶​appropriate_department, ⁷​department_department,
#> #   ⁸​difference_department, ⁹​environment_department, …
#> # • 12 more variables: environment_difference <chr>, experience_difference <chr>,
#> #   appropriate_environment <chr>, department_environment <chr>, difference_environment <chr>,
#> #   environment_environment <chr>, experience_environment <chr>, appropriate_experience <chr>,
#> #   department_experience <chr>, difference_experience <chr>, environment_experience <chr>,
#> #   experience_experience <chr>

[DavisVaughan]

Oh I really like that idea!

[hadley]

Me too

[krlmlr]

I like bullets (#542), we need to find a way to reduce the impact on the tidymodels team.

[DavisVaughan]

If it is just about updating snapshot tests, I think the tidymodels team is going to be more than willing to update their snapshots in favor of some improvements with the superscripts/footers here.