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decimal

Overview

decimal provides exact, arbitrary-precision decimal vectors for R. If you’ve ever been surprised that 0.1 + 0.2 == 0.3 is FALSE, this package is for you:

library(decimal)

0.1 + 0.2 == 0.3
#> [1] FALSE
decimal("0.1") + decimal("0.2") == decimal("0.3")
#> [1] TRUE

Doubles are binary fractions, so they can’t represent most decimal numbers exactly, and tiny errors accumulate as you compute. That’s usually fine — but not when you’re working with money, invoices, exchange rates, or anything else where cents have to add up. decimal uses a decimal representation and performs arithmetic under an explicit decimal context, so any rounding is controlled and observable.

Under the hood, decimal is built on:

• mpdecimal, the battle-tested C library behind Python’s decimal module, implementing the General Decimal Arithmetic standard.

• vctrs, so decimal vectors work naturally in data frames, tibbles, dplyr::mutate(), joins, sorting, and everything else you already do with vectors.

Highlights:

• Exact values. Strings and integers are parsed exactly; promotion to a finer shared scale only adds trailing zeros. Values round-trip through as.character() without loss — nothing changes on the way to a CSV file or database column and back.

• Full arithmetic. +, -, *, /, ^, %%, %/%, comparisons, and math functions like abs(), sqrt(), exp(), and log(), plus reductions sum(), prod(), min(), max(), and mean().

• Decimal-aware tools. quantize() to round to a fixed number of digits (say, cents), normalize(), fma(), same_quantum(), adjusted(), and number_class().

• You control the rules. A decimal context sets the precision, rounding mode, and which conditions (overflow, division by zero, …) are errors — see vignette("contexts-and-signals").

• Special values. NA, signed zeros, infinities, and quiet and signaling NaNs are supported throughout.

Installation

Install the released version from CRAN:

install.packages("decimal")

Usage

Create decimal vectors from strings (exact, and the recommended way) or integers, and use them like any other numeric vector:

library(decimal)

x <- decimal(c("1.20", "2.30", "3.40"))
x
#> <decimal[3]>
#> [1] 1.20 2.30 3.40

sum(x)
#> <decimal[1]>
#> [1] 6.90
mean(x)
#> <decimal[1]>
#> [1] 2.30

Decimal vectors are first-class citizens in tibbles and dplyr pipelines:

library(dplyr)

sales <- tibble::tibble(
  item  = c("coffee", "bagel", "juice"),
  price = decimal(c("2.50", "1.25", "3.95")),
  qty   = c(3L, 2L, 1L)
)

sales |>
  mutate(total = price * qty) |>
  summarise(revenue = sum(total))
#> # A tibble: 1 × 1
#>   revenue
#>     <dec>
#> 1   13.95

Exactness matters most when small errors compound — literally, in the case of interest:

principal <- decimal(c("1000.00", "2500.00", "500.00"))
rate <- decimal("0.05")

balance <- principal * (1L + rate)^4L
balance
#> <decimal[3]>
#> [1] 1215.5062500000 3038.7656250000 607.7531250000

# round to cents for reporting
quantize(balance, decimal("0.01"))
#> <decimal[3]>
#> [1] 1215.51 3038.77 607.75

Learning more

• vignette("decimal-values") introduces decimal vectors: how to create them, how scale works, and the everyday operations.

• vignette("contexts-and-signals") covers the arithmetic context: precision, rounding modes, traps, and flags.

• The General Decimal Arithmetic specification is the standard that mpdecimal implements and this package follows.

License

decimal is MIT licensed. The vendored mpdecimal library retains its own BSD-2-Clause terms; see inst/COPYRIGHTS.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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