numbers.R
In pillar: Coloured Formatting for Columns

## 

## ## Integrating with other classes

## 

## ### gt

## 

## - Consumption via new `fmt_auto()`?

## 

## FIXME

## 

## ### units

## 

## xxx{r numbers-17}

## library(units)

## 

## set_units.pillar_num <- function(x, ...) {

##   unclassed <- x

##   class(unclassed) <- NULL

##   set_units(unclassed, ...)

## }

## 

## m <- set_units(1:3, m)

## km <- set_units(1:3, km)

## 

## tibble(

##   sci_int = set_num_opts(m + km, notation = "sci"),

##   digits_int = set_num_opts(km + m, digits = 4),

##   sci_ext = set_units(num(1:3 + 0.001, notation = "sci"), km)

## )

## 

## tibble(

##   sci_int = set_num_opts(m, notation = "sci") + km,

##   digits_int = set_num_opts(km, digits = 4) + m,

##   sci_ext = set_units(num(1:3, notation = "sci"), m) + km

## )

## xxx

## 

## ### formattable

## 

## xxx{r numbers-18, error = TRUE}

## library(formattable)

## 

## pillar_shaft.formattable <- function(x, ...) {

##   pillar::new_pillar_shaft_simple(format(x), align = "right")

## }

## 

## pillar_shaft.formattable_currency <- function(x, ...) {

##   formattable <- attr(x, "formattable", exact = TRUE)

## 

##   pillar_shaft(num(unclass(x), digits = formattable$digits))

## }

## 

## pillar_shaft.formattable_percent <- function(x, ...) {

##   formattable <- attr(x, "formattable", exact = TRUE)

## 

##   pillar_shaft(num(unclass(x), digits = formattable$digits, label = "%", scale = 100))

## }

## 

## pillar_shaft.formattable_scientific <- function(x, ...) {

##   pillar_shaft(num(unclass(x), notation = "sci"))

## }

## 

## type_sum.formattable <- function(x) {

##   formattable <- attr(x, "formattable", exact = TRUE)

## 

##   if (inherits(x, "formattable_currency")) {

##     I(sub("^formattable_", "", class(x)[[1]]))

##   } else if (inherits(x, "formattable_percent")) {

##     I("%")

##   } else {

##     abbreviate(sub("^formattable_", "", class(x)[[1]]), 4)

##   }

## }

## 

## num_currency(1:3 * 100 + 0.1)

## num_percent(1:3 * 0.1 + 0.001)

## num_scientific(1:3 * 0.1 + 0.001)

## 

## tibble(

##   currency = num_currency(1:3 * 100 + 0.1),

##   percent = num_percent(1:3 * 0.1 + 0.001),

##   scientific = num_scientific(1:3 * 0.1 + 0.001)

## )

## xxx

## 

## ### scales

## 

## xxx{r numbers-scales, error = TRUE}

## library(scales)

## 

## x <- num(1:10 / 100, label = "%", scale = 100)

## 

## scales::squish(x)

## 

## x < 0

## x < 0L

## 

## scales::cscale(x, scales::rescale_pal())

## xxx

## 

## ### ggplot2

## 

## xxx{r numbers-19}

## library(ggplot2)

## 

## scale_type.pillar_num <- function(x, ...) {

##   "continuous"

## }

## 

## data.frame(x = x, y = 1:10) %>%

##   ggplot(aes(x = x, y = y)) %>%

##   + geom_point()

## xxx

## 

## ## Rule-based decoration

## 

## 

## 

## xxx{r}

## library(dplyr)

## 

## data_units <-

##   palmerpenguins::penguins %>%

##   mutate(across(ends_with("_mm"), set_units, "mm")) %>%

##   mutate(across(ends_with("_g"), set_units, "g"))

## 

## data_units %>%

##   mutate(bill_area = bill_length_mm * bill_depth_mm, .after = island)

## xxx

## 

## xxx{r eval = FALSE}

## data_decor <-

##   data_units %>%

##   decorate(year, digits = 0) %>%

##   decorate(where(is.numeric), digits = 3)

## xxx

## 

## xxx{r eval = FALSE}

## data_decor %>%

##   mutate(bill_area = bill_length_mm * bill_depth_mm, .after = island)

## xxx

## 

## xxx{r echo = FALSE}

## data_units %>%

##   mutate(bill_area = bill_length_mm * bill_depth_mm, .after = island) %>%

##   mutate(across(year, set_num_opts, digits = 0)) %>%

##   mutate(across(where(is.numeric), set_num_opts, digits = 3))

## xxx

##