label_number_si accuracy when labelling 'K' and 'M'

[samuelhuerga]

Hello!

I've been trying to scale my axis, that goes from 0 to 1.2M. Ideally, I would like to get numbers below 1M with K (and no decimals) but numbers above with one decimal (that is, 1.0M, 1.2M)

When I try default accuracy, I may get confused by numbers above 1M:
demo_continuous(c(1, 1.2e6), label = label_number_si())

But if I change accuracy, then I get decimals too for numbers below 1M:
demo_continuous(c(1, 1.2e6), label = label_number_si(0.1))

It would be awesome to have some parameters to control this kind of options.

Thanks!

[SimonDedman]

Similarly, were this level of control to exist, feasibly (elements of) it could be enabled by default. With the caveat that I've done these below plots in a loop and am able to exert more control if I do them individually and edit the accuracy parameter, I wonder if the existing background checks:

If NULL, the default, uses a heuristic that should ensure breaks have the minimum number of digits needed to show the difference between adjacent values

could be extended to implement higher accuracy when labels deviate too far from the real values? By way of examples:

Y scale 1500 Km labelled as 2K but the next item up is actually 2K so they both have the same label.

Here one can infer that the 2K is 2.5K but that's simply because a linear scale is more common than a log or similar.

In other instances it works out fine. Possible checks that could be implemented:

• If >1 ticks have the same label, increase accuracy by 1 and recheck
• If the percentage difference between label & tick value is too high, increase accuracy by 1 and recheck. E.g. mod(1500-2000) = 500, 500/1500 = 0.33 = label off by 33%.
  I'm not sure what the values should be, of course, and I strongly suspect this'll only be an issue at the start of small ranges, since long ranges should cause the problematic midpoints to disappear, and reduce the relative discrepancies.

Cheers, as ever.

[hadley]

With the current dev version:

library(scales)
x <- c(0, 250, 500, 750, 1250, 1500) * 1e3
label_number_si("")(x)
#> [1] "0.00"    "250.00k" "500.00k" "750.00k" "1.25M"   "1.50M"

^{Created on 2022-03-17 by the reprex package (v2.0.1)}

So it looks like we've solved one problem and created another (smaller) one 😄

[hadley]

I think probably we need to compute accuracy individually for each scale group (something like accuracy <- ave(x, rescale$scale, FUN = precision)) but it seems like that needs some downstream fixes to fully work.

...

Of course, because the ndigits argument to format() is not vectorised.

---

This need fixes everywhere that rescale_by_suffix() is used, i.e. also to label_bytes() and label_dollar().

So maybe need to move rescale_by_suffix() into number()?