---
title: "Controlling display of numbers"
output: html_vignette
vignette: >
  %\VignetteIndexEntry{Controlling display of numbers}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---



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.


```r
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.


```r
tibble(x = 123.4567)
#> # A tibble: 1 x 1
#>       x
#>   <dbl>
#> 1  123.
old <- options(pillar.sigfig = 7)
tibble(x = 123.4567)
#> # A tibble: 1 x 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.


```r
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 x 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   10.0    100  12.3   A B C D E F G ~ T U V W X Y Z
#> 4 1100.5000   1100.5 1100.50   11.0   1000 123     A B C D E F G ~ T U V W X Y Z
#> 5 1200.5000   1200.5 1200.50   12.0  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.


```r
library(dplyr, warn.conflicts = FALSE)

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

markets
#> # A tibble: 1,860 x 5
#>     time   DAX   SMI   CAC  FTSE
#>    <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1 1991. 1629. 1678. 1773. 2444.
#>  2 1992. 1614. 1688. 1750. 2460.
#>  3 1992. 1607. 1679. 1718  2448.
#>  4 1992. 1621. 1684. 1708. 2470.
#>  5 1992. 1618. 1687. 1723. 2485.
#>  6 1992. 1611. 1672. 1714. 2467.
#>  7 1992. 1631. 1683. 1734. 2488.
#>  8 1992. 1640. 1704. 1757. 2508.
#>  9 1992. 1635. 1698. 1754  2510.
#> 10 1992. 1646. 1716. 1754. 2497.
#> # i 1,850 more rows
markets %>%
  mutate(across(-time, ~ num(.x, digits = 3)))
#> # A tibble: 1,860 x 5
#>     time       DAX       SMI       CAC      FTSE
#>    <dbl> <num:.3!> <num:.3!> <num:.3!> <num:.3!>
#>  1 1991.  1628.750  1678.100  1772.800  2443.600
#>  2 1992.  1613.630  1688.500  1750.500  2460.200
#>  3 1992.  1606.510  1678.600  1718.000  2448.200
#>  4 1992.  1621.040  1684.100  1708.100  2470.400
#>  5 1992.  1618.160  1686.600  1723.100  2484.700
#>  6 1992.  1610.610  1671.600  1714.300  2466.800
#>  7 1992.  1630.750  1682.900  1734.500  2487.900
#>  8 1992.  1640.170  1703.600  1757.400  2508.400
#>  9 1992.  1635.470  1697.500  1754.000  2510.500
#> 10 1992.  1645.890  1716.300  1754.300  2497.400
#> # i 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.


```r
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.


```r
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] 84.1 90.9 14.1
```


### Override

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


```r
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:


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

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


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

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


```r
num(var(x), notation = "eng")
#> <pillar_num(eng)[1]>
#> [1] 2.33e0
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:


```r
var_ <- function(x, ...) {
  out <- var(vctrs::vec_proxy(x), ...)
  vctrs::vec_restore(out, x)
}
var_(x)
#> <pillar_num(eng)[1]>
#> [1] 2.33e0
```

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:


```r
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] 2.33e0
sd_(x)
#> <pillar_num(eng)[1]>
#> [1] 1.53e0
```
