Getting started with publicdataau
In publicdataau: Australian Government Open Data from 'publicdata.au'

publicdata.au republishes Australian government open data that the publishers already release under open licences. It keeps every version at a URL that never changes, serves each one in twelve formats, and answers queries over the whole table. This package reads all of it from R. Every function takes a dataset's slug, the part of its page's URL after /d/.

library(publicdataau)

Find a dataset

pd_datasets() searches what the site serves, and filters by publisher, topic and government.

pd_datasets("road crashes")[, c("slug", "title")]
#> # A tibble: 12 × 2
#>    slug                                title                                              
#>    <chr>                               <chr>                                              
#>  1 act-road-crashes                    Road crashes, ACT                                  
#>  2 qld-road-casualties                 Road casualties, Queensland                        
#>  3 qld-road-crash-restraint-helmet-use Seatbelt and helmet use in road crash casualties, …
#>  4 qld-road-crash-locations            Road crash locations, Queensland                   
#>  5 qld-road-crash-vehicle-types        Vehicle types in road crashes, Queensland          
#>  6 sa-road-crash-locations             Road crash locations, South Australia              
#>  7 tas-road-crashes                    Road crashes, Tasmania                             
#>  8 qld-road-crash-factors              Factors in road crashes, Queensland                
#>  9 nsw-road-crashes                    Road crashes, New South Wales                      
#> 10 vic-road-crashes                    Road crashes, Victoria                             
#> 11 qld-road-crash-driver-demographics  Driver demographics in road crashes, Queensland    
#> 12 au-road-deaths                      Australian Road Deaths Database, fatalities
pd_datasets(topic = "roads", jurisdiction = "Qld")$slug
#> [1] "qld-road-crash-driver-demographics"  "qld-road-crash-factors"             
#> [3] "qld-road-casualties"                 "qld-road-crash-locations"           
#> [5] "qld-road-crash-restraint-helmet-use" "qld-road-crash-vehicle-types"

pd_catalogue() searches every dataset on the government portals, including the many the site does not serve yet. Each record says whether it is served; one that is not can be voted for on its page.

pd_catalogue("water quality", jurisdiction = "Queensland", limit = 3)[, c("title", "publisher", "status")]
#> # A tibble: 3 × 3
#>   title                                                          publisher          status
#>   <chr>                                                          <chr>              <chr> 
#> 1 Pumicestone Passage Bulcock Beach - Water quality monitoring   Environment, Tour… votab…
#> 2 Brisbane River - Colmslie Site - Water Quality Monitoring Buoy Environment, Tour… votab…
#> 3 Sentinel Water Quality                                         City of Gold Coast votab…

See what a dataset holds

pd_fields() lists each field with its type, the publisher's description, its range, and its values when it holds few. A query names fields and values exactly, so this is the first call to make.

pd_fields("act-road-crashes")[1:8, c("name", "type", "min", "max")]
#> # A tibble: 8 × 4
#>   name            type    min          max         
#>   <chr>           <chr>   <chr>        <chr>       
#> 1 crash_id        integer 1            354433      
#> 2 crash_date      date    2015-01-01   2026-09-07  
#> 3 crash_time      string  <NA>         <NA>        
#> 4 suburb_location string  <NA>         <NA>        
#> 5 longitude       number  148.7876728  149.39631165
#> 6 latitude        number  -35.89116025 -35.14329768
#> 7 intersection    boolean <NA>         <NA>        
#> 8 midblock        boolean <NA>         <NA>
pd_fields("act-road-crashes")$values[[11]]
#> [1] "Fatal"                "Injury"               "Property damage only"

Query rows and totals

pd_rows() and pd_aggregate() ask the query API, so a filter or a total runs over the whole table without a download. Each column comes back typed as its field says and labelled with its description.

crashes <- pd_rows("act-road-crashes", crash_severity = "Fatal", crash_date = pd_gte("2025-01-01"),
                   .select = c("crash_id", "crash_date", "crash_type", "sa2_2021_name"))
crashes
#> # A tibble: 14 × 4
#>    crash_id crash_date crash_type                        sa2_2021_name   
#>       <int> <date>     <chr>                             <chr>           
#>  1   340074 2025-10-01 Struck pedestrian (Off Road)      Canberra East   
#>  2   347129 2026-02-16 Struck object (Off Road)          Hume            
#>  3   336058 2025-03-11 Right angle collision             Nicholls        
#>  4   351537 2025-11-02 Collision with parked vehicle     Mitchell        
#>  5   342444 2025-12-05 Head on collision                 Kaleen          
#>  6   343635 2026-01-09 Right angle collision             Tuggeranong     
#>  7   330178 2025-03-11 Other - vehicle to vehicle        Lyneham         
#>  8   340260 2025-09-27 Struck pedestrian (Off Road)      Lyneham         
#>  9   347489 2025-03-23 Struck object (Off Road)          Watson          
#> 10   343981 2025-12-03 Rear end collision                ACT - South West
#> 11   340806 2025-10-06 Struck object (Off Road)          ACT - South West
#> 12   345222 2026-01-24 Struck object (Off Road)          O'Malley        
#> 13   347390 2026-04-04 Other - Single vehicle (Off Road) ACT - South West
#> 14   345211 2026-02-17 Struck pedestrian (On Road)       Lyneham
class(crashes$crash_date)
#> [1] "Date"

pd_aggregate("act-road-crashes", group = "crash_severity")
#> # A tibble: 3 × 2
#>   crash_severity       count
#>   <chr>                <int>
#> 1 Fatal                  105
#> 2 Injury                6576
#> 3 Property damage only 69976

Every answer carries the attribution the publisher's licence requires.

pd_attribution(crashes)
#> [1] "Roads ACT, City and Environment Directorate, ACT Government, ACT Road Crash Data, https://www.data.act.gov.au/d/6jn4-m8rx, accessed 30 September 2026, licensed under CC BY 4.0."

Whole tables

pd_read() downloads a version's Parquet file and reads it with 'arrow'. columns reads only the fields asked for.

rate <- pd_read("rba-cash-rate")
tail(rate, 3)
#> # A tibble: 3 × 7
#>   date       cash_rate_target_change cash_rate_target es_rate_change es_rate
#>   <date>                       <dbl>            <dbl>          <dbl>   <dbl>
#> 1 2026-03-18                    0.25             4.1            0.25    4   
#> 2 2026-05-06                    0.25             4.35           0.25    4.25
#> 3 2026-09-30                    0.25             4.6            0.25    4.5 
#> # ℹ 2 more variables: overnight_repo_rate_change <dbl>, overnight_repo_rate <dbl>

A version never changes once published, so a file can be kept and reused. Nothing is kept unless you ask, with cache = TRUE on a call or options(publicdataau.cache = TRUE) for every call; pd_cache_list() shows what is kept and pd_cache_clear() deletes it.

Query a version in place with dplyr

Every version has a DuckDB file. pd_tbl() attaches it over HTTPS and returns a table for 'dplyr'; verbs become SQL that runs against the file, reading only the blocks they touch.

library(dplyr, warn.conflicts = FALSE)
pd_tbl("act-road-crashes") |>
  filter(crash_severity == "Fatal") |>
  count(lighting_condition, sort = TRUE) |>
  collect()
#> # A tibble: 5 × 2
#>   lighting_condition              n
#>   <chr>                       <dbl>
#> 1 Daylight                       63
#> 2 Dark - good street lighting    21
#> 3 Dark - no street lights        11
#> 4 Dark - poor street lighting     6
#> 5 Semi-darkness                   4

G-NAF, the national address file, is a database of 37 tables served as one DuckDB file. Its licence sets a condition beyond attribution, on compiling addresses for mail, and the package states it once per session when G-NAF is first read; this vignette turns that notice off with options(publicdataau.quiet = TRUE).

pd_tables("gnaf")[1:5, c("name", "rows")]
#> # A tibble: 5 × 2
#>   name                        rows
#>   <chr>                      <dbl>
#> 1 address_detail          16970406
#> 2 address_default_geocode 16970406
#> 3 address_site            16970406
#> 4 address_site_geocode    20754564
#> 5 address_alias             878895

Maps

Rows of a point dataset carry the codes of the ABS areas their point falls in. Total by one of them and pd_join_boundaries() attaches the boundaries, in GDA2020.

by_sa2 <- pd_aggregate("act-road-crashes", group = "sa2_2021_code")
map <- pd_join_boundaries(by_sa2)
map
#> Simple feature collection with 100 features and 3 fields
#> Geometry type: MULTIPOLYGON
#> Dimension:     XY
#> Bounding box:  xmin: 148.9614 ymin: -35.48032 xmax: 149.3993 ymax: -35.1244
#> Geodetic CRS:  GDA2020
#> # A tibble: 100 × 4
#>    sa2_2021_code count sa2_2021_name                                              geometry
#>    <chr>         <int> <chr>                                            <MULTIPOLYGON [°]>
#>  1 101021012         2 Queanbeyan West - Jerrabomberra (((149.1957 -35.36126, 149.1997 -3…
#>  2 801011001       107 Aranda                          (((149.0741 -35.25529, 149.0742 -3…
#>  3 801011002      2712 Belconnen                       (((149.057 -35.23908, 149.0574 -35…
#>  4 801011003      1836 Bruce                           (((149.0738 -35.24299, 149.0734 -3…
#>  5 801011004       301 Charnwood                       (((149.0285 -35.19852, 149.0284 -3…
#>  6 801011005       191 Cook                            (((149.0572 -35.25635, 149.058 -35…
#>  7 801011006       200 Dunlop                          (((149.0077 -35.19727, 149.0082 -3…
#>  8 801011007       431 Evatt                           (((149.0673 -35.20722, 149.0673 -3…
#>  9 801011008       671 Florey                          (((149.0409 -35.22017, 149.0411 -3…
#> 10 801011009       338 Flynn (ACT)                     (((149.0495 -35.2059, 149.0492 -35…
#> # ℹ 90 more rows
plot(map["count"], main = "Road crashes by SA2, ACT", border = NA, logz = TRUE)

plot of chunk map

pd_boundary_layers() lists the layers, and pd_sf() reads any dataset's own map layer.

Versions, changes and provenance

Each release is compared with the one before it on the dataset's key.

pd_changes("rba-money-market-daily")[, c("from", "to", "added", "removed", "changed")]
#> # A tibble: 1 × 5
#>   from       to         added removed changed
#>   <chr>      <chr>      <dbl>   <dbl>   <dbl>
#> 1 2026-09-30 2026-10-01     1       0       1

pd_provenance() gives the record kept with a version: the publisher's file it was read from, its checksum and when it was fetched.

pd_provenance("act-road-crashes")
#> <pd_provenance> act-road-crashes 2026-10-01
#> Source       https://www.data.act.gov.au/api/views/6jn4-m8rx/rows.csv?accessType=DOWNLOAD
#> File         6jn4-m8rx.csv
#> Bytes        14,256,520
#> SHA-256      45dbb9445b51fdbd831d2c89efa8965f5a3071d50ccf04e0e63374a49ed39a00
#> Fetched      2026-09-30T19:25:31+00:00
#> Licence      Creative Commons Attribution 4.0 International (read 2026-09-30T19:25:24+00:00)
#> Rows         76,657
#> Version      https://publicdata.au/d/act-road-crashes/v/2026-10-01/

Reproducible work

An answer without a version comes from the newest one and changes when the publisher releases again. Pass a date from pd_versions() for one that never changes. pd_url() gives a fixed file address for another tool, such as a 'targets' pipeline with format = "url".

pd_url("act-road-crashes", "parquet", pd_latest("act-road-crashes"))
#> [1] "https://publicdata.au/d/act-road-crashes/v/2026-10-01/data.parquet"

Citing

pd_cite() gives a version's citation, with the attribution its licence requires; utils::toBibtex() turns it into a BibTeX entry.

utils::toBibtex(pd_cite("rba-cash-rate"))
#> @Misc{rba-cash-rate-2026-09-29,
#>   title = {Cash rate target changes, Reserve Bank of Australia},
#>   author = {{Reserve Bank of Australia}},
#>   year = {2026},
#>   howpublished = {Version 2026-09-29, serialised and versioned by National Digital at publicdata.au},
#>   url = {https://publicdata.au/d/rba-cash-rate/v/2026-09-29/},
#>   note = {Source: Reserve Bank of Australia, statistical table A2 Changes in Monetary Policy and Administered Rates, sourced 30 September 2026 from https://www.rba.gov.au/statistics/tables/, republished under sections 4 and 5 of the RBA copyright notice, https://www.rba.gov.au/copyright/. The RBA does not endorse this site or any use of this data. The RBA publishes this data free of charge at rba.gov.au},
#> }