# Examples and Recipes

This vignette demonstrates some typical example usage and recipes when learning about ipaddress.

## Addresses from integers

Although IPv4 addresses are usually displayed in a human-readable format (e.g. `192.168.0.1`), they are often saved to disk as integers to reduce storage requirements. R is unable to store the entire range of IPv4 addresses in its integer data type, but they can instead be stored in its numeric data type. More details are found under `help("ip_to_integer")`.

Given this, it’s quite possible that you’ll receive IPv4 addresses represented as integers, and we’ll want to convert them to the `ip_address()` vector type. Here’s an example of how to do that:

    integer_to_ip(c(0, 3232235521, 4294967295))
    #> <ip_address[3]>
    #> [1] 0.0.0.0         192.168.0.1     255.255.255.255

## Networks from address ranges

There are multiple equivalent ways to represent an IP network:

1. start address + prefix length (e.g. CIDR notation)
2. start address + netmask
3. start address + hostmask
4. start address + end address

Although the `ip_network()` function can handle the first 3 options, we use the `common_network()` function for the final option.

    tibble(
      start = ip_address(c("192.168.0.0", "2001:db8::")),
      end = ip_address(c("192.168.0.15", "2001:db8::ffff:ffff:ffff"))
    ) %>%
      mutate(network = common_network(start, end))
    #> # A tibble: 2 × 3
    #>         start                      end        network
    #>     <ip_addr>                <ip_addr>     <ip_netwk>
    #> 1 192.168.0.0             192.168.0.15 192.168.0.0/28
    #> 2  2001:db8:: 2001:db8::ffff:ffff:ffff  2001:db8::/80

Note that this approach assumes the two addresses do actually correspond to the first and last addresses of the network, otherwise an expanded network will be returned (see `help("common_network")` for details).

## Is an address in a network?

A very common task is to check if an address is within a network, so the ipaddress package provides a couple of different functions to help with this workflow: `is_within()` and `is_within_any()`. We also provide `is_subnet()` and `is_supernet()` to test if a network is within another network.

To see how these functions can be used in practice, let’s consider a couple of IP networks:

    my_networks <- tibble(
      network = ip_network(c("192.168.0.0/16", "2001:db8::/32")),
      label = c("Private", "Documentation")
    )

    my_networks
    #> # A tibble: 2 × 2
    #>          network label
    #>       <ip_netwk> <chr>
    #> 1 192.168.0.0/16 Private
    #> 2  2001:db8::/32 Documentation

and a handful of addresses:

    my_addresses <- tibble(
      address = ip_address(c("192.168.100.1", "1.2.3.4", "2001:db8::8a2e:370:7334", "::1"))
    )

First, we’ll check if each address is in any of our networks.

    my_addresses %>%
      mutate(in_network = is_within_any(address, my_networks$network))
    #> # A tibble: 4 × 2
    #>                   address in_network
    #>                 <ip_addr> <lgl>
    #> 1           192.168.100.1 TRUE
    #> 2                 1.2.3.4 FALSE
    #> 3 2001:db8::8a2e:370:7334 TRUE
    #> 4                     ::1 FALSE

But what if we need to know which of our networks the address was found in? We can do that using the excellent fuzzyjoin package together with the `is_within()` function.

    my_addresses %>%
      fuzzyjoin::fuzzy_left_join(my_networks, c("address" = "network"), is_within)
    #> # A tibble: 4 × 3
    #>                   address        network label
    #>                 <ip_addr>     <ip_netwk> <chr>
    #> 1           192.168.100.1 192.168.0.0/16 Private
    #> 2                 1.2.3.4             NA <NA>
    #> 3 2001:db8::8a2e:370:7334  2001:db8::/32 Documentation
    #> 4                     ::1             NA <NA>

## Randomly generate public addresses

ipaddress provides functions to sample from a specific network (`sample_network()`) or the entire address space (`sample_ipv4()` and `sample_ipv6()`). However, it can be more difficult to sample from the majority of address space, while excluding certain networks.

A good example is sampling public IPv4 addresses. The simplest solution is to use an accept-reject algorithm – sampling the entire IPv4 address space and rejecting addresses that are reserved.

    sample_public <- function(size) {
      result <- sample_ipv4(size)

      all_public <- FALSE
      while (!all_public) {
        public <- is_global(result)
        n_replace <- sum(!public)

        if (n_replace == 0) {
          all_public <- TRUE
        } else {
          result[!public] <- sample_ipv4(n_replace)
        }
      }

      result
    }

We now sample 10 addresses and make sure they are all public addresses.

    tibble(address = sample_public(10)) %>%
      mutate(is_public = is_global(address))
    #> # A tibble: 10 × 2
    #>           address is_public
    #>         <ip_addr> <lgl>
    #>  1 163.19.241.101 TRUE
    #>  2    77.44.11.33 TRUE
    #>  3      9.76.8.16 TRUE
    #>  4  58.181.212.25 TRUE
    #>  5  25.101.160.50 TRUE
    #>  6   93.168.72.94 TRUE
    #>  7  99.31.191.237 TRUE
    #>  8 218.109.41.173 TRUE
    #>  9 207.72.150.212 TRUE
    #> 10  197.229.46.12 TRUE