Refactor data_color() so that it executes faster

[rich-iannone]

The data_color() function was seen to be extremely slow. Reason for this was a combination of missed opportunities for vectorization, overuse of loops, and longer than necessary routes to an otherwise simple addition of _styles rows.

Below is reproducible code that would otherwise take 5-8 s before the changes in this PR (it now takes about 0.15 seconds):

library(gt)
library(tidyverse)
library(scales)
library(paletteer)

sza %>%
  dplyr::filter(latitude == 20) %>%
  dplyr::select(-latitude) %>%
  dplyr::filter(!is.na(sza)) %>%
  tidyr::spread(key = "tst", value = sza) %>%
  gt(rowname_col = "month") %>%
  fmt_missing(
    columns = TRUE,
    missing_text = ""
  ) %>%
  data_color(
    columns = TRUE,
    colors = scales::col_numeric(
      palette = paletteer::paletteer_d(
        palette = "ggsci::orange_material"
      ) %>% as.character(),
      domain = c(0, 90),
      na.color = "#FFFFFF"
    )
  )

Fixes: #543

[jcheng5]

Let's take a dependency on htmltools >= 1.1.0 and remove the workaround for the scales bug.

[jcheng5]

see comments

[rich-iannone]

Turns out, we already have a min version requirement of 1.1.0 on scales, so, no change required in DESCRIPTION.

[rich-iannone]

Just confirmed that removing this code:

        if (length(colors) > 1) {
          nlvl <-
            if (is.factor(data_vals)) {
              nlevels(data_vals)
            } else {
              nlevels(factor(data_vals))
            }
          if (length(colors) > nlvl) {
            colors <- colors[seq_len(nlvl)]
          }
        }

does NOT result in non-interpolated colors with scales 1.1.0 (unit test that checks the colors correction code fails).

Code for reproducing:

tbl <-
  countrypops %>%
  dplyr::filter(country_name == "Mongolia") %>%
  dplyr::select(-contains("code")) %>%
  tail(10)

tbl[1, ] <- NA

tbl %>%
  gt() %>%
  data_color(
    columns = vars(country_name),
    colors = c("red", "orange", "green", "blue")
  )

With correction code retained, we get the correct colors (subsetting):

With that correction code removed, we get an interpolation of colors: