bignum provides numeric vectors with greater precision than R atomic numeric vectors.
biginteger() stores any integer (i.e. arbitrary precision).bigfloat() stores 50 decimal digits of precision.They prioritize precision over performance, so computations are slower than those usinginteger() ordouble().
Under the hood, bignum uses thecpp_int andcpp_bin_float_50 data types from the Boost.Multiprecision C++ library.
You can install the released version of bignum fromCRAN with:
install.packages("bignum")Or you can install the development version from GitHub:
# install.packages("remotes")remotes::install_github("davidchall/bignum")Before starting, we’ll increase the displayed precision so we can see the benefits of bignum.
The limited precision of atomic vectors introduces errors when working with very large integers. As an example, let’s calculate the factorial of 23. In base R, we’d calculate:
factorial(23)#> [1] 25852016738884978212864The factorial of 23 includes a factor of 10, and so the final digitmust be zero. Usingbiginteger() yields the correct result:
prod(biginteger(1:23))#> <biginteger[1]>#> [1] 25852016738884976640000bigfloat() vectors support much higher precision thandouble() vectors:
1/3#> [1] 0.33333333333333331483bigfloat(1)/3#> <bigfloat[1]>#> [1] 0.33333333333333333333333333333333333333333333333333However, you need to be careful not to limit the precision accidentally:
bigfloat(1/3)#> <bigfloat[1]>#> [1] 0.333333333333333Please note that the bignum project is released with aContributor Code of Conduct. By contributing to this project, you agree to abide by its terms.