Identifying names used for both girls and boys in Australian birth data

To identify names used for both girls and boys in Australian birth data, start with the ozbabynames dataset. The package documentation states it contains popular Australian baby names by sex, state and year (ozbabynames, https://pkg.robjhyndman.com/ozbabynames/). A name qualifies as recorded for both genders when the same name string appears under the Female and Male categories within that data, either for a single state-year or across the combined series.

This guide explains what the dataset covers, how to isolate dual-gender entries, where the coverage is uneven, and what you should verify before treating any shortlist as complete.

What the ozbabynames dataset provides

The ozbabynames package supplies a table of Australian baby names with the columns: year, state, sex, name, and count (ozbabynames). The sex field separates records so you can directly compare which name strings appear in each category.

According to the package site, the underlying figures are drawn from state and territory registries, with source links for South Australia, New South Wales, Tasmania, Victoria, Western Australia, Queensland and the Northern Territory via data.gov.au (ozbabynames). The documented version is 0.2.0.9000, with a CRAN release also available. The tool is maintained by named authors including Rob Hyndman, Mitchell O’Hara-Wild, Jessie Roberts and Nick Tierney (ozbabynames).

How to locate names recorded for both sexes

The practical step is to group the data by name and sex, then keep only names that have both a Female and a Male record. In the R environment used by the package authors, this involves filtering the ozbabynames table, summarising counts by sex and name, and checking for names where both sex levels are present (ozbabynames example usage). You can replicate the logic in any software that reads the published data:

1. Obtain the full dataset from the CRAN package or the development GitHub repository.
2. Group records by name and sex.
3. For each name, count distinct sex values.
4. Retain names where the distinct sex count is greater than one.

The documentation also demonstrates filtering for a specific set of names—such as “Robin”, “Robert”, “Mitchell”, “Nicholas”, “Jessie” and “Jessica”—and plotting their counts by year (ozbabynames). That mechanics is the same you would use to test whether a candidate name appears under both genders, though the example does not itself confirm any name as dual-gender.

This method returns names that the source data recorded for both girls and boys. It does not tell you whether a name is culturally perceived as neutral, nor does it predict future popularity. Those are separate questions outside the dataset’s scope.

Known coverage gaps and exclusions

Before you trust a dual-gender list, weigh the documented limitations. The ozbabynames site explicitly warns that “coverage is very uneven” (ozbabynames, Known Issues). Verified points from the source:

• The Australian Capital Territory does not provide counts, so no ACT data are included (ozbabynames).
• South Australia has by far the best data, with full coverage of all names back to 1944 (ozbabynames).
• Some states supply only very recent years.
• Some states supply only the top 50 or 100 names for a given period.
• For some jurisdictions, only the top 10 by rank are available after 2016 (ozbabynames).

The effect on your search: if a state reports only its top 100 names, a less common dual-gender name may be invisible there. Pooling states improves coverage but still cannot recover missing ACT entries or names suppressed by rank limits.

A name absent from the dataset is therefore not proof it was never used for both genders. It may sit below a state’s publication threshold or fall in a year a jurisdiction did not supply.

Questions to verify for your own analysis

Because the publisher of this article is not the data owner, you should confirm details that affect your use:

• Which states are included in the package version you downloaded, and have any added post-2016 top-10 restrictions changed?
• Does the release you use predate recent annual updates? The publication date of this guide is 2026-10-19, but the dataset may have newer pulls.
• Are you analysing a single year (where top-10 caps bite) or a multi-year sum (which may still miss truncated states)?
• If you need ACT comparisons, can you source separate territory data elsewhere, since ozbabynames omits it?
• Have you cross-checked any shortlist against the original data.gov.au links for the relevant states?

These are verification steps, not warnings about any particular name.

Reader checklist

Before relying on a dual-gender name list:

• [ ] Confirm the ozbabynames version and release date you are using.
• [ ] Note the ACT exclusion from the dataset.
• [ ] Recognise South Australia’s full history back to 1944.
• [ ] Identify which states impose top 50/100 or post-2016 top 10 limits.
• [ ] Run the group-by-name-and-sex query described above.
• [ ] Validate findings against the source data.gov.au links.

Next steps to access and query the data

To run your own identification:

1. Install the CRAN version with install.packages("ozbabynames") or the development version from GitHub as documented (ozbabynames).
2. Load the library and inspect the ozbabynames object’s structure to confirm columns.
3. Apply the group-and-filter method to retain names with both sex records.
4. If you prefer not to code, open the underlying data.gov.au datasets linked on the package site in compatible software and perform the same grouping manually.

This hands-on approach lets you reproduce the dual-gender extraction under the coverage conditions that matter to your question.

General information boundary

This article describes the structure and limitations of a public dataset for informational purposes. It is not a name advisory, nor does it provide legal, tax, migration, credit or financial advice. No outcome—such as approval of a name or a measure of neutrality—is guaranteed. Always consult the original source for the latest figures and verify coverage before making decisions based on the data.