Data Analysis for IP Addresses and Networks

Classes and functions for working with IP (Internet Protocol) addresses and networks, inspired by the Python 'ipaddress' module. Offers full support for both IPv4 and IPv6 (Internet Protocol versions 4 and 6) address spaces. It is specifically designed to work well with the 'tidyverse'.

ipaddress

ipaddress provides data classes and functions for working with IP addresses and networks. Its interface is inspired by the Python ipaddress module.

Here are some key features:

• Functions to generate and analyze IP data
• Full support for both IPv4 and IPv6 address spaces
• Data stored in native bit format for reduced memory footprint
• Calculations written in C++ for fast performance
• Compatible with the tidyverse

For data visualization of IP addresses and networks, check out the ggip package.

Installation

Install the released version from CRAN with:

install.packages("ipaddress")

Install the development version from GitHub with:

# install.packages("remotes")
remotes::install_github("davidchall/ipaddress")

Usage

Use ip_address() and ip_network() to create standalone vectors or data frame columns.

library(tibble)
library(ipaddress)

address <- ip_address(c("192.168.0.1", "2001:db8::8a2e:370:7334"))
network <- ip_network(c("192.168.100.0/22", "2001:db8::/80"))

tibble(address, network)
#> # A tibble: 2 × 2
#>                   address          network
#>                 <ip_addr>       <ip_netwk>
#> 1             192.168.0.1 192.168.100.0/22
#> 2 2001:db8::8a2e:370:7334    2001:db8::/80

It looks like we’ve simply stored the character vector, but we’ve actually validated each input and stored its native bit representation. When the vector is displayed, the print() method formats each value back to the human-readable character representation. There are two main advantages to storing IP data in their native bit representation:

• The data occupy less space in memory (up to 80% reduction),
• Subsequent use is much faster, since we don’t repeatedly parse the character vector.

Read vignette("ip-data") to learn more about these vector classes. For a demonstration of common recipes using ipaddress vectors and functions, see vignette("recipes").

Related work

• iptools – A well established R package for working with IP addresses and networks. Unfortunately IPv6 support is severely limited. Also, addresses and networks are stored as character vectors, so they must be parsed to their native bit representation for every operation. It served as an excellent guide and motivation for ipaddress.
• cyberpandas – A Python package for using IP addresses in a pandas DataFrame. This offers full support for IPv6 and stores addresses in the native bit representation. However, most “interesting” operations must deserialize each address to a Python ipaddress object, which is slow. It also doesn’t support IP networks.

Please note that the ipaddress project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.