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.
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.
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 ZThe 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.
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
#> # ℹ 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
#> # ℹ 1,850 more rowsnumFormatting 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, …).
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] -2Similarly, for mathematical operations, the formatting is inherited.
In some cases, the ideal formatting changes after a transformation.
num() can be applied repeatedly, the last setting wins.
The var() function is one of the examples where the
formatting is lost:
The median() function is worse, it breaks for
num() objects:
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] 2e0For 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] 2.333333e0This 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: