If you want to track the rise and fall of Australian girl names over time, the ozbabynames dataset is the main open source to use. It contains popular Australian baby names by sex, state and year, according to the package documentation at https://pkg.robjhyndman.com/ozbabynames/.
This article explains what the dataset covers, where the gaps are, and how to read year-by-year trends for female names without overstating what the data can show.
What the ozbabynames dataset actually contains
The ozbabynames package provides the dataset ozbabynames. This contains popular Australian baby names by sex, state and year, as stated by the package documentation (https://pkg.robjhyndman.com/ozbabynames/).
Each record includes:
- year
- state
- sex
- name
- count
A sample from the documentation shows 2024 New South Wales female names such as Charlotte, Amelia, Olivia, Mia, Isla and Chloe with registered counts.
The dataset is built from state and territory sources. It is not a single national register managed by one Australian government department.
Where the data is strong and where it is not
Coverage is very uneven. Some states only provide very recent data, and some states only provide the top 50 or 100 names, according to the Known Issues section of the package site (https://pkg.robjhyndman.com/ozbabynames/).
Key limits to check before drawing trends:
| State / Territory | Documented coverage note |
|---|---|
| South Australia | Full coverage of all names back to 1944 |
| ACT | Not included; does not provide counts |
| Tasmania | Only top 10 by rank available after 2016 |
| Other states | Varies; some recent only or top 50/100 only |
South Australia has by far the best data, with full coverage of all names back to 1944, per the package documentation.
Practical interpretation: if you are tracking a girl name from the 1950s through to today, South Australia gives the most complete view. For a national rise-and-fall chart, you need to check whether each state contributed counts for that year and name.
How to follow girl name trends by year
You can install the package from CRAN or the development version from GitHub, as described at https://pkg.robjhyndman.com/ozbabynames/.
Basic steps a reader can verify:
1. Install ozbabynames in R using install.packages("ozbabynames") or install_github("robjhyndman/ozbabynames").
2. Filter the data for sex == "Female" and the name of interest.
3. Group by year and sum count if you want an Australia-wide view, or keep state to compare states.
4. Plot year against count to see the rise and fall.
The package documentation also shows an example using 1952 top ten names by sex, filtered and summarised with dplyr, then plotted with ggplot2.
Questions to verify yourself: - Does your name appear in every state for the years you want? - Are missing years due to no births, or no data released? - Are you comparing counts, or rank within a top-N list?
Reading rise and fall without overstating it
A name can appear to rise because more states reported counts, not because more babies were named that. This is a data-structure issue, not a naming trend.
Because some states only provide top 50 or top 100 names, a name outside that cut-off will show as absent. That does not prove the name was unused.
Practical interpretation: compare like with like. If compare South Australia across decades, the picture is clearer than mixing states with different reporting rules.
The package also links related datasets for other countries, including US, UK, France, Ireland, Norway, New Zealand and Chile, but those are separate packages and not part of ozbabynames.
What you should check before publishing or sharing a trend
Use this checklist: - [ ] Source year and state are recorded for every point. - [ ] ACT is excluded and not silently treated as zero. - [ ] Tasmania post-2016 uses top 10 by rank only. - [ ] Names below state top-N cuts are not assumed to be zero. - [ ] National totals are summed only from states with counts.
If you are writing about a specific girl name, confirm the state coverage before claiming a Australia-wide peak or decline.
Next step
Start with South Australia in ozbabyn you want the longest continuous female name series back to 1944. Filter forsex == "Female", pick your name, and plotyearagainstcount`.
If you are browsing name lists by year rather than running R code, see related name resources at /money/home-loans/ for site navigation help, or use /match/ if you are looking for a specific name tool.
This article is general information about an open dataset. It is not personal advice, and it does not name any provider as best or guarantee any trend result. Always verify current coverage and definitions with the source at https://pkg.robjhyndman.com/ozbabynames/ before relying on a chart.