# Controlling display of numbers

Tibbles print numbers with three significant digits by default, switching to scientific notation if the available space is too small. Underlines are used to highlight groups of three digits. The display differs from the default display for data frames, see `vignette("digits")` for an overview over the differences. This display works for many, but not for all use cases.

    library(tibble)

## Options

The easiest way to customize the display of numbers and other data in a tibble is to define options. See `?pillar::pillar_options` for a comprehensive overview.

    tibble(x = 123.4567)
    #> # A tibble: 1 × 1
    #>          x
    #>      <dbl>
    #> 1 123.4567
    old <- options(pillar.sigfig = 7)
    tibble(x = 123.4567)
    #> # A tibble: 1 × 1
    #>          x
    #>      <dbl>
    #> 1 123.4567
    # Restore old options, see also rlang::local_options() for a more elegant way
    options(old)

This changes the display of all columns. Read on to see how too specify display options on a column-by-column basis.

## Per-column number formatting

The new `num()` constructor allows creating vectors that behave like numbers but allow customizing their display. Below a few examples are shown, see `?num` for a comprehensive overview. Similarly, `char()` allows customizing the display of character columns.

    num(-1:3, notation = "sci")
    #> <pillar_num(sci)[5]>
    #> [1] -1e0  0    1e0  2e0  3e0
    tibble(
      x4 = num(8:12 * 100 + 0.5, digits = 4),
      x1 = num(8:12 * 100 + 0.5, digits = -1),
      usd = num(8:12 * 100 + 0.5, digits = 2, label = "USD"),
      percent = num(8:12 / 100 + 0.0005, label = "%", scale = 100),
      eng = num(10^(-3:1), notation = "eng", fixed_exponent = -Inf),
      si = num(10^(-3:1) * 123, notation = "si"),
      char = char(paste(LETTERS, collapse = " "), shorten = "mid")
    )
    #> # A tibble: 5 × 7
    #>          x4       x1     usd percent   eng      si char
    #>   <num:.4!> <num:.1>     USD       % [e-3]    <si> <char>
    #> 1  800.5000    800.5  800.50    8.05     1 123   m A B C D E F G … T U V W X Y Z
    #> 2  900.5000    900.5  900.50    9.05    10   1.23  A B C D E F G … T U V W X Y Z
    #> 3 1000.5000   1000.5 1000.50   10.05   100  12.3   A B C D E F G … T U V W X Y Z
    #> 4 1100.5000   1100.5 1100.50   11.05  1000 123     A B C D E F G … T U V W X Y Z
    #> 5 1200.5000   1200.5 1200.50   12.05 10000   1.23k A B C D E F G … T U V W X Y Z

The pillar package that is responsible for the display of tibbles tries hard to get the number display right, however it is impossible to accommodate all use cases. Whenever the default formatting does not suit the application, `num()` or `char()` allow redefining the formatting for individual columns. The formatting survives most data transformations.

## Rule-based number formatting

Currently, formatting must be applied manually for each column. The following pattern may help doing this consistently for all columns in a tibble, or for some columns based on their name.

    library(dplyr, warn.conflicts = FALSE)

    markets <-
      as_tibble(EuStockMarkets) %>%
      mutate(time = time(EuStockMarkets), .before = 1)

    markets
    #> # A tibble: 1,860 × 5
    #>        time     DAX    SMI    CAC   FTSE
    #>       <dbl>   <dbl>  <dbl>  <dbl>  <dbl>
    #>  1 1991.496 1628.75 1678.1 1772.8 2443.6
    #>  2 1991.5   1613.63 1688.5 1750.5 2460.2
    #>  3 1991.504 1606.51 1678.6 1718   2448.2
    #>  4 1991.508 1621.04 1684.1 1708.1 2470.4
    #>  5 1991.512 1618.16 1686.6 1723.1 2484.7
    #>  6 1991.515 1610.61 1671.6 1714.3 2466.8
    #>  7 1991.519 1630.75 1682.9 1734.5 2487.9
    #>  8 1991.523 1640.17 1703.6 1757.4 2508.4
    #>  9 1991.527 1635.47 1697.5 1754   2510.5
    #> 10 1991.531 1645.89 1716.3 1754.3 2497.4
    #> # … with 1,850 more rows
    markets %>%
      mutate(across(-time, ~ num(.x, digits = 3)))
    #> # A tibble: 1,860 × 5
    #>        time       DAX       SMI       CAC      FTSE
    #>       <dbl> <num:.3!> <num:.3!> <num:.3!> <num:.3!>
    #>  1 1991.496  1628.750  1678.100  1772.800  2443.600
    #>  2 1991.5    1613.630  1688.500  1750.500  2460.200
    #>  3 1991.504  1606.510  1678.600  1718.000  2448.200
    #>  4 1991.508  1621.040  1684.100  1708.100  2470.400
    #>  5 1991.512  1618.160  1686.600  1723.100  2484.700
    #>  6 1991.515  1610.610  1671.600  1714.300  2466.800
    #>  7 1991.519  1630.750  1682.900  1734.500  2487.900
    #>  8 1991.523  1640.170  1703.600  1757.400  2508.400
    #>  9 1991.527  1635.470  1697.500  1754.000  2510.500
    #> 10 1991.531  1645.890  1716.300  1754.300  2497.400
    #> # … with 1,850 more rows

## Computing on `num`

Formatting numbers is useful for presentation of results. If defined early on in the analysis, the formatting options survive most operations. It is worth defining output options that suit your data once early on in the process, to benefit from the formatting throughout the analysis. We are working on seamlessly applying this formatting to the final presentation (plots, tables, …).

### Arithmetics

When applying arithmetic operations on numbers created by `num()`, the result inherits the formatting of the first `num` object.

    num(1) + 2
    #> <pillar_num[1]>
    #> [1] 3
    1 + num(2)
    #> <pillar_num[1]>
    #> [1] 3
    1L + num(2)
    #> <pillar_num[1]>
    #> [1] 3
    num(3.23456, sigfig = 4) - num(2)
    #> <pillar_num:4[1]>
    #> [1] 1.235
    num(4, sigfig = 2) * num(3, digits = 2)
    #> <pillar_num:2[1]>
    #> [1] 12
    num(3, digits = 2) * num(4, sigfig = 2)
    #> <pillar_num:.2![1]>
    #> [1] 12.00
    -num(2)
    #> <pillar_num[1]>
    #> [1] -2

### Mathematics

Similarly, for mathematical operations, the formatting is inherited.

    min(num(1:3, label = "$"))
    #> <pillar_num{$}[1]>
    #> [1] 1
    mean(num(1:3, notation = "eng"))
    #> <pillar_num(eng)[1]>
    #> [1] 2e0
    sin(num(1:3, label = "%", scale = 100))
    #> <pillar_num{%}*100[3]>
    #> [1] 84.14710 90.92974 14.11200

### Override

In some cases, the ideal formatting changes after a transformation. `num()` can be applied repeatedly, the last setting wins.

    num(1:3 + 0.125, digits = 4)
    #> <pillar_num:.4![3]>
    #> [1] 1.1250 2.1250 3.1250
    transf <- 10^num(1:3 + 0.125, digits = 4)
    transf
    #> <pillar_num:.4![3]>
    #> [1]   13.3352  133.3521 1333.5214
    num(transf, sigfig = 3)
    #> <pillar_num:3[3]>
    #> [1]   13.3  133.  1334.

### Recovery

The `var()` function is one of the examples where the formatting is lost:

    x <- num(c(1, 2, 4), notation = "eng")
    var(x)
    #> [1] 2.333333

The `median()` function is worse, it breaks for `num()` objects:

    median(x)
    #> Error in `median()`:
    #> ! `median.pillar_num()` not implemented.

One way to recover is to apply `num()` to the result:

    num(var(x), notation = "eng")
    #> <pillar_num(eng)[1]>
    #> [1] 2.333333e0
    num(median(as.numeric(x)), notation = "eng")
    #> <pillar_num(eng)[1]>
    #> [1] 2e0

For automatic recovery, we can also define our version of `var()`, or even overwrite the base implementation. Note that this pattern is still experimental and may be subject to change:

    var_ <- function(x, ...) {
      out <- var(vctrs::vec_proxy(x), ...)
      vctrs::vec_restore(out, x)
    }
    var_(x)
    #> <pillar_num(eng)[1]>
    #> [1] 2.333333e0

This pattern can be applied to all functions that lose the formatting. The `make_restore()` function defined below is a function factory that consumes a function and returns a derived function:

    make_restore <- function(fun) {
      force(fun)
      function(x, ...) {
        out <- fun(vctrs::vec_proxy(x), ...)
        vctrs::vec_restore(out, x)
      }
    }

    var_ <- make_restore(var)
    sd_ <- make_restore(sd)

    var_(x)
    #> <pillar_num(eng)[1]>
    #> [1] 2.333333e0
    sd_(x)
    #> <pillar_num(eng)[1]>
    #> [1] 1.527525e0