A Guide to the AEC's Election Datasets

This guide explores the get_election_data function in the scgElectionsAU package, designed to retrieve raw election data from the Australian Electoral Commission (AEC). Hosted on GitHub (scgElectionsAU Repository), the package simplifies access to datasets from federal elections (2004–2022), by-elections (2008–2024), and the 2023 referendum, covering candidate lists, voting results, and more. The function is primarily used for raw data with optional processing; for analysis-ready data, see get_results. Below, we detail the function’s parameters, available datasets, usage examples, and the context of AEC data collection.

Introduction to AEC Election Data

The AEC provides comprehensive data on Australia’s electoral processes through its Tally Room (AEC Tally Room) and downloads pages (AEC Downloads). These datasets include national, state, divisional, and polling place results for federal elections, by-elections, and referendums, governed by the Commonwealth Electoral Act 1918. They enable analysis of voter behaviour, candidate performance, and electoral trends, supporting transparency in Australia’s democratic process. The get_election_data function streamlines access to these datasets, offering flexibility to retrieve raw or processed data for research and public interest.

The get_election_data Function

The get_election_data function retrieves raw AEC data with optional processing, using the internal aec_elections_index dataset to map parameters to specific files. It downloads data from the AEC’s website, typically in CSV format. Users can specify the dataset, election type, data category, and time period, with an option to disable processing for raw outputs. When retrieving data for multiple elections, the function combines data into a single data frame, with an event column distinguishing each election, facilitating longitudinal analysis.

Parameter	Description	Default	Valid Options
file_name	Name of the AEC dataset (e.g., “National list of candidates”). Must match aec_elections_index entries.	None	See dataset descriptions below
type	Type of electoral event.	“Federal Election”	“Federal Election”, “By-Election”, “Referendum”
category	Data subset (e.g., House, Senate).	“House”	House”, “Senate”, “Referendum”, “General”, “Statistics”
date_range	List with from and to dates (YYYY-MM-DD).	list(from = “2022-01-01”, to = “2025-01-01”)	Any valid date range
process	Logical; applies processing (e.g., standardising columns).	TRUE	TRUE, FALSE

Dataset Categories

The datasets are organised into categories:

• Candidates & Nominations: Candidate lists, nomination statistics by state, division, and gender.
• Seats: Data on electoral divisions, including changes in party control and decision methods.
• First Preferences: Vote counts for first preferences at party, candidate, and polling place levels.
• Two Party Preferred (TPP): Results for the two major parties (Coalition and Labor).
• Two Candidate Preferred (TCP): Results for the top two candidates in each division.
• Distribution of Preferences: Detailed preference flows in preferential voting.
• Flow of Preferences: How preferences are distributed among parties and candidates.
• Votes: Informal votes, turnout, and vote types (e.g., postal, pre-poll).
• General: Political parties, polling places, and enrolment statistics.
• Statistics: Postal and pre-poll voting, votes by SA1, and overseas voting.

Data Sources and Collection

The AEC collects election data through its electoral processes, publishing results via the Tally Room and downloads pages. Data is typically available in CSV or TAB format shortly after each election, with final results confirmed weeks later. The scgElectionsAU package standardises these datasets for consistency across years, handling variations in column names and formats. Users should verify dataset availability for specific elections, as some datasets (e.g., “Votes by SA1”) are only available from later years.

Available Datasets and Examples

The following sections detail the datasets available for each return type, including example usage of the get_election_data function and sample data output.

Candidates & Nominations

National list of candidates

The National list of candidates dataset contains a comprehensive list of all candidates who participated in Australian federal elections. It covers both Senate and House elections from 2004 to the present and type=By-Election. The 2004 dataset is processed in the following way:

• The SittingMemberFl column is renamed to Elected using the process_elected() function.
• Based on this column, the data are further standardised to match later years with “Y” and “N” values inputted. This replaces the original corresponding “#” and NA values.

This dataset enables you to:

• View candidate details (CandidateID, Surname, GivenNm) and their party affiliations (PartyAb, PartyNm).
• Map candidates to electoral divisions (DivisionID, DivisionNm) and states (StateAb).
• Identify elected candidates (Elected) and incumbents (HistoricElected).

NB. There is no consistent pattern in when candidates retain their candidate ID (CandidateID) or when they do not. Some candidates appear to retain theirs across elections, while others do not. It is therefore not a column to correctly identify candidates across elections. Instead it should only be used across datasets of a single election.

Example Usage:

df <- get_election_data(
  file_name = "National list of candidates",
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  type = c("Federal Election", "By-Election"), # Any or All for House only. Default option for Senate (Federal Election).
  category = "House", # OR "Senate"
)

Sample Data:

date	event	StateAb	DivisionID	DivisionNm	PartyAb	PartyNm	CandidateID	Surname	GivenNm	Elected	HistoricElected
2024-05-13	2024 Cook By-Election	NSW	112	Cook	AJP	Animal Justice Party	38292	BROWN	Natasha	N	N

Members/Senators elected

The Members elected and Senators elected datasets provide comprehensive lists of the elected Members of Parliament (MPs) and Senators at federal elections. These datasets cover elections from 2004 to the present and are available for the House and Senate, respectively.

• Members elected (House): Contains details of MPs who won their respective electoral divisions.
• Senators elected (Senate): Contains details of Senators elected for each state or territory, including the ElectedOrder column, which specifies the order in which they were elected.

There is no additional processing for these datasets, as both are already standardised across all election years.

This dataset enables you to:

• Identify elected representatives:
• For the House: MPs by electoral division (DivisionID, DivisionNm).
• For the Senate: Senators by state or territory (StateAb), including their election order (ElectedOrder).
• View candidate details (CandidateID, Surname, GivenNm) and party affiliations (PartyNm, PartyAb).

Example Usage (Members Elected):

# Example usage
df <- get_election_data(
  file_name = "Members elected",
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  category = "House"
)

Sample Data (Members Elected):

date	event	DivisionID	DivisionNm	StateAb	CandidateID	GivenNm	Surname	PartyNm	PartyAb
2022-05-21	2022 Federal Election	179	Adelaide	SA	36973	Steve	GEORGANAS	Australian Labor Party	ALP

Example Usage (Senators Elected):

df <- get_election_data(
  file_name = "Senators elected",
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  category = "Senate"
)

Sample Data (Senators Elected):

date	event	StateAb	GivenNm	Surname	PartyNm	PartyAb	ElectedOrder
2022-05-21	2022 Federal Election	ACT	Katy	GALLAGHER	Australian Labor Party	ALP	1

Nominations

The Nominations by state dataset provides a summary of nominations for federal elections, detailing:

• The number of divisions (Divisions) and nominations (Nominations) per state (StateAb, StateNm) for the House.
• The number of seats (Seats), candidates (Candidates), groups (Groups), and ungrouped candidates (UngroupedCandidates) per state (StateAb, StateNm) for the Senate.

Covering elections from 2004 to the present, these datasets are available for both the House and Senate categories. No additional processing is required, as the data are already standardised across all election years.

Example Usage ‘by state’ (House):

df <- get_election_data(
  file_name = "Nominations by state",
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  category = "House"
)

Sample Data ‘by state’ (House):

date	event	StateAb	StateNm	Divisions	Nominations
2022-05-21	2022 Federal Election	NSW	New South Wales	47	350

Example Usage ‘by state’ (Senate):

df <- get_election_data(
  file_name = "Nominations by state",
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  category = "Senate"
)

Sample Data ‘by state’ (Senate):

date	event	StateAb	StateNm	Seats	Candidates	Groups	UngroupedCandidates
2022-05-21	2022 Federal Election	NSW	New South Wales	6	75	23	5

The Nominations by division dataset provides a summary of nominations for federal elections, detailing:

• The number of nominations (Nominations) per division (DivisionID, DivisionNm, DivisionAb) for the House.
• Enrolment figures (Enrolment) and demographic classifications (Demographics) for each electorate.

Covering elections from 2011 to the present, these datasets are available for the House only. Minor amendments are made to the original dataset, changing TransactionId to TransactionID and DivisionId to DivisionID. This ensures consistency in column names across all datasets.

Example Usage ‘by division’ (House):

df <- get_election_data(
  file_name = "Nominations by division",
  date_range = list(from = "2011-01-01", to = "2025-01-01"),
  category = "House"
)

Sample Data ‘by division’ (House):

date	event	TransactionID	DivisionID	StateAb	DivisionNm	DivisionAb	Enrolment	Demographic	Nominations
2022-05-21	2022 Federal Election	27966	179	SA	Adelaide	ADEL	129960	Inner Metropolitan	7

The Nominations by gender dataset provides a summary of nominations for federal elections, detailing the number of nominations (Nominations) per party (PartyAb, PartyNm) for the House and Senate. Each state and territory have their own columns for the two genders (MalesNSW, FemalesNSW) and an unspecified column (UnspecifiedNSW). There is also a grand total (GrandTotal) of nominations by party which is useful for turning data into percentages.

Covering elections from 2004 to the present, these datasets are available for both the House and Senate categories. No additional processing is required, as the data are already standardised across all election years.

Example Usage ‘by gender’ (House):

df <- get_election_data(
  file_name = "Nominations by gender",
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  category = "House" # OR "Senate"
)

Sample Data ‘by gender’ (House):

date	event	PartyAb	PartyNm	MalesNSW	FemalesNSW	UnspecifiedNSW	MalesVIC	FemalesVIC	UnspecifiedVIC	MalesQLD	FemalesQLD	UnspecifiedQLD	MalesWA	FemalesWA	UnspecifiedWA	MalesSA	FemalesSA	UnspecifiedSA	MalesTAS	FemalesTAS	UnspecifiedTAS	MalesACT	FemalesACT	UnspecifiedACT	MalesNT	FemalesNT	UnspecifiedNT	TotalMales	TotalFemales	TotalUnspecified	GrandTotal
2022-05-21	2022 Federal Election	AJP	Animal Justice Party	4	4	0	6	10	0	1	11	0	1	1	1	2	2	0	1	3	1	0	0	0	0	0	0	15	31	2	48

Party representation

The Party representation dataset provides a summary of elected representatives for federal elections, detailing:

• The number of MPs per party (Party) per state and territory (NSW,VIC,QLD,WA,SA,TAS,ACT,NT) and nationally (National) for the House. It also includes the number of MPs previously representing each party at the previous election (LastElection).
• The number of Senators per party (PartyAb, PartyNm) per state and territory (NSW,VIC,QLD,WA,SA,TAS,ACT,NT) and nationally (TOTAL) for the Senate.

Covering elections from 2004 to the present, these datasets are available for both the House and Senate categories. In order to standardise datasets across election years, additional processing is conducted for election years prior to 2010 in the House. Using the process_reps() function:

• Pre-2010 columns are renamed from Total to National and from LastElectionTotal to LastElection.
• The PartyAb column is removed.

No additional processing is required for the Senate, as the data are already standardised across all election years.

Example Usage (House):

df <- get_election_data(
  file_name = "Party representation",
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  category = "House",
  process = TRUE # OR FALSE
)

Sample Data (House):

date	event	PartyNm	NSW	VIC	QLD	WA	SA	TAS	ACT	NT	National	LastElection
2022-05-21	2022 Federal Election	Liberal	9	8	0	5	3	2	0	0	27	44

Example Usage (Senate):

df <- get_election_data(
  file_name = "Party representation",
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  category = "Senate"
)

Sample Data (Senate):

date	event	PartyAb	PartyNm	NSW	VIC	QLD	WA	SA	TAS	ACT	NT	TOTAL
2022-05-21	2022 Federal Election	LP	Liberal	0	0	0	2	3	2	0	0	7

Seats

Seats that changed hands

The Seats that changed hands dataset provides a summary of the seats that have changed from one party to another, detailing:

• The division details (StateAb, DivisionID, DivisionNm).
• The previous party that held the seat (PreviousPartyAb, PreviousPartyNm).
• The details of the newly elected MP who won that seat at the election (CadidateID, Surname, GivenNm, PartyAb, PartyNm).

Covering elections from 2004 to the present, these datasets are available for the House only. No additional processing is required, as the data are already standardised across all election years.

Example Usage:

df <- get_election_data(
  file_name = "Seats that changed hands",
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  category = "House"
)

Sample Data:

date	event	StateAb	DivisionID	DivisionNm	PreviousPartyAb	PreviousPartyNm	CandidateID	Surname	GivenNm	PartyAb	PartyNm
2022-05-21	2022 Federal Election	NSW	105	Bennelong	LP	Liberal	36827	LAXALE	Jerome	ALP	Australian Labor Party

Seats decided on FPTP or preferences

The Seats decided on first preferences dataset provides a summary of the seats that were decided on first preferences (i.e., the winning candidate in a division achieved equal to or more than 50% of the primary vote - similar to First-Past-The-Post), detailing:

• The division details (StateAb, DivisionID, DivisionNm).
• The winning party (PartyAb, PartyNm).

Covering elections from 2004 to the present, these datasets are available for the House only. No additional processing is required, as the data are already standardised across all election years.

Example Usage ‘first’:

df <- get_election_data(
  file_name = "Seats decided on first preferences",
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  category = "House"
)

Sample Data ‘first’:

date	event	DivisionID	DivisionNm	StateAb	PartyAb	PartyNm
2022-05-21	2022 Federal Election	180	Barker	SA	LP	Liberal

The Seats decided on preferences dataset provides a summary of the seats that were decided on preferences (i.e., no candidate in a division achieved more than 50% of the primary vote), detailing:

• The division details (StateAb, DivisionID, DivisionNm).
• The winning party (PartyAb, PartyNm).

Covering elections from 2004 to the present, these datasets are available for the House only. No additional processing is required, as the data are already standardised across all election years.

Example Usage ‘preference’:

df <- get_election_data(
  file_name = "Seats decided on preferences",
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  category = "House"
)

Sample Data ‘preference’:

date	event	DivisionID	DivisionNm	StateAb	PartyAb	PartyNm
2022-05-21	2022 Federal Election	179	Adelaide	SA	ALP	Australian Labor Party

Non-classic divisions

The Non-classic divisions dataset provides a summary of the seats in which the two-party contest included a non-major party candidate, detailing:

• The division details (StateAb, DivisionID, DivisionNm).
• The latest time and date that the data were updated (Updated).
• The first candidate details (PartyAb1, PartyNm1, CandidateID1, Surname1, GivenNm1, Elected1, HistoricElected1).
• The second candidate details (PartyAb2, PartyNm2, CandidateID2, Surname2, GivenNm2, Elected2, HistoricElected2).

Covering elections from 2010 to the present, these datasets are available for the House only. Minor amendments are made to the original dataset, changing CandidateId to CandidateID and DivisionId to DivisionID. This ensures consistency in column names across all datasets.

Example Usage:

df <- get_election_data(
  file_name = "Non-classic divisions",
  date_range = list(from = "2010-01-01", to = "2025-01-01"),
  category = "House"
)

Sample Data:

date	event	StateAb	DivisionID	DivisionNm	Updated	PartyAb1	PartyNm1	CandidateID1	Surname1	GivenNm1	Elected1	HistoricElected1	PartyAb2	PartyNm2	CandidateID2	Surname2	GivenNm2	Elected2	HistoricElected2
2022-05-21	2022 Federal Election	ACT	101	Canberra	Wed 15 Jun 2022 4:36:53 PM AEST	GRN	The Greens	36241	HOLLO	Tim	N	N	ALP	Australian Labor Party	36228	PAYNE	Alicia	Y	Y

First Preferences

First preferences by party

The First preferences by party dataset provides a summary of the first preferences received by each party, detailing:

• The party details (PartyAb, PartyNm).
• The number of divisions that the party stood a candidate (Contested), the number of candidate nominations submitted by the party (Nominations), and the number of candidates who were elected/won their division (Elected).
• The number of votes that the party received by vote type (OrdinaryVotes, AbsentVotes, ProvisionalVotes, DeclarationPrePollVotes, PostalVotes, TotalVotes).
• The percentage of votes received by each party by vote type (OrdinaryPercentage, AbsentPercentage, ProvisionalPercentage, PrePollPercentage, PostalPercentage, TotalPercentage).
• The overall swing in total votes received by the party compared to the previous election (TotalSwing).

Covering elections from 2004 to the present, these datasets are available for the House only. PrePollVotes is renamed DeclarationPrePollVotes for consistency and clarification.

Example Usage ‘by party’ (House):

df <- get_election_data(
  file_name = "First preferences by party",
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  category = "House"
)

Sample Data ‘by party’ (House):

date	event	PartyAb	PartyNm	OrderOfAppearance	Contested	Nominations	Elected	OrdinaryVotes	OrdinaryPercentage	AbsentVotes	AbsentPercentage	ProvisionalVotes	ProvisionalPercentage	DeclarationPrePollVotes	DeclarationPrePollPercentage	PostalVotes	PostalPercentage	TotalVotes	TotalPercentage	TotalSwing
2022-05-21	2022 Federal Election	LP	Liberal	1	108	108	27	2712423	23.63	90795	19.58	9209	19.98	117364	22.11	572922	26.76	3502713	23.89	-4.28

The First preferences by state by party dataset further breaks down the above figures by state (StateAb).

Example Usage of ‘by state by party’ (House):

df <- get_election_data(
  file_name = "First preferences by state by party",
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  category = "House"
)

Sample Data of ‘by state by party’ (House):

date	event	StateAb	PartyAb	PartyNm	OrderOfAppearance	Contested	Nominations	Elected	OrdinaryVotes	OrdinaryPercentage	AbsentVotes	AbsentPercentage	ProvisionalVotes	ProvisionalPercentage	DeclarationPrePollVotes	DeclarationPrePollPercentage	PostalVotes	PostalPercentage	TotalVotes	TotalPercentage	TotalSwing
2022-05-21	2022 Federal Election	NSW	LP	Liberal	1	38	38	9	1069603	27.78	29333	23.51	3093	23.73	38955	26.44	175150	33.94	1316134	28.3	-4.49

The First preferences by group by vote type dataset is the Senate-equivalent and provides first preference vote totals by party groupings, detailing:

• The party details (PartyAb, PartyNm).
• The number of votes that the party received by vote type (OrdinaryVotes, AbsentVotes, ProvisionalVotes, DeclarationPrePollVotes, PostalVotes, TotalVotes).
• The percentage of votes received by each party by vote type (OrdinaryPercentage, AbsentPercentage, ProvisionalPercentage, DeclarationPrePollPercentage, PostalPercentage, TotalPercentage).

Covering elections from 2004 to the present, these datasets are available for the Senate only. The 2004 dataset is processed in the following way:

• The SittingMemberFl column is renamed to Elected using the process_elected() function.
• Based on this column, the data are further standardised to match later years with “Y” and “N” values inputted. This replaces the original corresponding “#” and NA values.

Example Usage ‘by group’ (Senate):

df <- get_election_data(
  file_name = "First preferences by group by vote type",
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  category = "Senate",
  process = TRUE # OR FALSE
)

Sample Data ‘by group’ (Senate):

date	event	PartyGroupAb	PartyGroupNm	OrdinaryVotes	OrdinaryPercentage	AbsentVotes	AbsentPercentage	ProvisionalVotes	ProvisionalPercentage	DeclarationPrePollVotes	DeclarationPrePollPercentage	PostalVotes	PostalPercentage	TotalVotes	TotalPercentage
2022-05-21	2022 Federal Election	LP	Liberal	791650	6.77	32237	6.17	4899	4.66	36399	6.69	187386	8.6	1052571	7

The First preferences by state by group by vote type dataset further breaks down the above figures by state (StateAb).

Example Usage ‘by state by group’ (Senate):

df <- get_election_data(
  file_name = "First preferences by state by group by vote type",
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  category = "Senate",
  process = TRUE # OR FALSE
)

Sample Data ‘by state by group’ (Senate):

date	event	StateAb	PartyGroupAb	PartyGroupNm	OrdinaryVotes	OrdinaryPercentage	AbsentVotes	AbsentPercentage	ProvisionalVotes	ProvisionalPercentage	DeclarationPrePollVotes	DeclarationPrePollPercentage	PostalVotes	PostalPercentage	TotalVotes	TotalPercentage
2022-05-21	2022 Federal Election	NSW	LPNP	Liberal Party of AustraliaNSW Division-National Party of Australia NSW	1446601	36.69	38943	27.33	8304	25.14	50846	33.52	218380	41.14	1763074	36.73

First preferences by candidate by vote type

The First preferences by candidate by vote type dataset provides a summary of the first preferences received by each individual candidate, detailing:

• The division details (StateAb, DivisionID, DivisionNm).
• The candidate details (CandidateID, Surname, GivenNm), including their position on the ballot position (BallotPosition), and whether they were elected (Elected) or an incumbent (HistoricElected), and the party (PartyAb, PartyNm) that they represent.
• The number of votes that the candidate received by vote type (OrdinaryVotes, AbsentVotes, ProvisionalVotes, DeclarationPrePollVotes, PostalVotes, TotalVotes).
• The overall swing in total votes received by the candidate compared to the previous election (TotalSwing).

Covering elections from 2004 to the present, this dataset is available for the House only. The 2004 dataset is processed in the following way:

• The SittingMemberFl column is renamed to Elected using the process_elected() function.
• Based on this column, the data are further standardised to match later years with “Y” and “N” values inputted. This replaces the original corresponding “#” and NA values.
• PrePollVotes is renamed DeclarationPrePollVotes for consistency and clarification.

Example Usage (House):

df <- get_election_data(
  file_name = "First preferences by candidate by vote type",
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  category = "House",
  process = TRUE # OR FALSE
)

Sample Data (House):

date	event	StateAb	DivisionID	DivisionNm	CandidateID	Surname	GivenNm	BallotPosition	Elected	HistoricElected	PartyAb	PartyNm	OrdinaryVotes	AbsentVotes	ProvisionalVotes	DeclarationPrePollVotes	PostalVotes	TotalVotes	Swing
2022-05-21	2022 Federal Election	ACT	318	Bean	36239	CONWAY	Sean	1	N	N	UAPP	United Australia Party	2479	72	7	110	163	2831	0.48

The First preferences by state by vote type dataset is the equivalent for the Senate, albeit it differs in some columns. The dataset details:

• The division details (StateAb, DivisionID, DivisionNm).
• The candidate details (CandidateID), including their ballot details (BallotPosition,CandidateDetails, Group), and whether they were elected (Elected) or an incumbent (HistoricElected), and the party (PartyAb, PartyNm) that they represent.
• The number of votes that the candidate received by vote type (OrdinaryVotes, AbsentVotes, ProvisionalVotes, DeclarationPrePollVotes, PostalVotes, TotalVotes).

Covering elections from 2004 to the present, this dataset is available for the Senate only. Minor amendments are made to the original dataset, changing PartyName to PartyNm and PrePollVotes to DeclarationPrePollVotes. This ensures consistency in column names across all datasets.

Example Usage (Senate Division-level):

df <- get_election_data(
  file_name = "First preferences by division by vote type",
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  category = "Senate"
)

Sample Data (Senate Division-level):

date	event	StateAb	DivisionID	DivisionNm	Group	BallotPosition	CandidateID	CandidateDetails	PartyNm	PartyAb	Elected	HistoricElected	OrdinaryVotes	AbsentVotes	ProvisionalVotes	DeclarationPrePollVotes	PostalVotes	TotalVotes
2022-05-21	2022 Federal Election	ACT	318	Bean	A	0	37878	A Above-the-line Votes	Australian Labor Party	ALP	N	N	22966	391	156	695	2510	26718

The state-level version of this Senate dataset is also available by calling First preferences by state by vote type.

Example Usage (Senate State-level):

df <- get_election_data(
  file_name = "First preferences by state by vote type",
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  category = "Senate",
  process = TRUE # OR FALSE
)

Sample Data (Senate State-level):

date	event	StateAb	Group	CandidateID	BallotPosition	CandidateDetails	PartyNm	OrdinaryVotes	AbsentVotes	ProvisionalVotes	DeclarationPrePollVotes	PostalVotes	TotalVotes
2022-05-21	2022 Federal Election	ACT	A	37878	0	A Above-the-line Votes	Australian Labor Party	65283	1249	475	2381	7521	76909

First preferences by candidate by polling place

The First preferences by candidate by polling place dataset provides first preference vote totals of each candidate by polling place, detailing:

• The polling place details (StateAb, DivisionID, DivisionNm, PollingPlaceID, PollingPlaceNm).
• The candiate details (CandidateID, Surname, GivenNm), including their position on the ballot position (BallotPosition), and whether they were elected (Elected) or an incumbent (HistoricElected), and the party (PartyAb, PartyNm) they represent.
• The number of votes that the party received (OrdinaryVotes).
• The overall swing in ordinary votes received by the candidate compared to the previous election (Swing).

Covering elections from 2004 to the present, these datasets are available for the House only. The 2004 dataset is processed in the following way:

• The SittingMemberFl column is renamed to Elected using the process_elected() function.
• Based on this column, the data are further standardised to match later years with “Y” and “N” values inputted. This replaces the original corresponding “#” and NA values.

*Caution should be taken if retrieving multiple election years as this dataset is large. Sizes include:

• 2022: 17 columns by 76,452 rows (1,299,684 cells)
• 2019: 17 columns by 71,805 rows (1,220,685 cells)
• 2016: 17 columns by 64,112 rows (1,089,904 cells)
• 2013: 17 columns by 82,398 rows (1,400,766 cells)
• 2010: 17 columns by 60,663 rows (1,031,271 cells)
• 2007: 17 columns by 66,269 rows (1,126,573 cells)
• 2004: 16 columns by 69,098 rows (1,105,568 cells)

Example Usage:

df <- get_election_data(
  file_name = "First preferences by candidate by polling place",
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  type = c("Federal Election", "By-Election") # Any or All
)

Sample Data:

date	event	StateAb	DivisionID	DivisionNm	PollingPlaceID	PollingPlaceNm	CandidateID	Surname	GivenNm	BallotPosition	Elected	HistoricElected	PartyAb	PartyNm	OrdinaryVotes	Swing
2022-05-21	2022 Federal Election	NSW	103	Banks	31	Allawah	37018	COLEMAN	David	1	Y	Y	LP	Liberal	531	-3.46

Two Party Preferred

Two party preferred by state

The Two party preferred by state dataset provides the two party preferred (TPP) vote by state (StateAb) detailing:

• The Coalition votes and percentage (Liberal/National Coalition Votes, Liberal/National Coalition Percentage)
• The ALP votes and percentage (Australian Labor Party Votes, Australian Labor Party Percentage.
• The total votes (TotalVotes) and swing (Swing).

Covering elections from 2004 to the present, this dataset is available for the House only. No changes are made.

Example Usage ‘by state’:

df <- get_election_data(
  file_name = "Two party preferred by state",
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  category = "House"
)

Sample Data ‘by state’:

date	event	StateAb	StateNm	Liberal/National Coalition Votes	Liberal/National Coalition Percentage	Australian Labor Party Votes	Australian Labor Party Percentage	TotalVotes	Swing
2022-05-21	2022 Federal Election	NSW	New South Wales	2259639	48.58	2391301	51.42	4650940	-3.2

The Two party preferred by division dataset further breaks down the above figures by division (DivisionID, DivisionNm) and includes the winning party of the seat (PartyAb).

Example Usage ‘by division’:

df <- get_election_data(
  file_name = "Two party preferred by division",
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  category = "House"
)

Sample Data ‘by division’:

date	event	DivisionNm	DivisionID	StateAb	PartyAb	Liberal/National Coalition Votes	Liberal/National Coalition Percentage	Australian Labor Party Votes	Australian Labor Party Percentage	TotalVotes	Swing
2022-05-21	2022 Federal Election	Adelaide	179	SA	ALP	42948	38.09	69816	61.91	112764	-3.73

Two party preferred by division by vote type

The Two party preferred by division by vote type dataset provides the two party preferred (TPP) vote by vote type detailing:

• The division details (StateAb, DivisionNm, DivisionID).
• The winning party of the seat (PartyAb).
• The Coalition votes by vote type (Liberal/National Coalition OrdinaryVotes,Liberal/National Coalition AbsentVotes,Liberal/National Coalition ProvisionalVotes,Liberal/National Coalition PostalVotes,Liberal/National Coalition DeclarationPrePollVotes).
• The Coalition percentage by vote type (Liberal/National Coalition OrdinaryPercentage,Liberal/National Coalition AbsentPercentage,Liberal/National Coalition ProvisionalPercentage,Liberal/National Coalition PostalPercentage,Liberal/National Coalition DeclarationPrePollPercentage).
• The ALP votes by vote type (Australian Labor Party OrdinaryVotes,Australian Labor Party AbsentVotes,Australian Labor Party ProvisionalVotes,Australian Labor Party PostalVotes,Australian Labor Party DeclarationPrePollVotes).
• The ALP percentage by vote type (Australian Labor Party OrdinaryPercentage,Australian Labor Party AbsentPercentage,Australian Labor Party ProvisionalPercentage,Australian Labor Party PostalPercentage,Australian Labor Party DeclarationPrePollPercentage).
• The total number of votes (TotalVotes).

Covering elections from 2004 to the present, this dataset is available for the House only. No changes are made.

Example Usage ‘by division’:

df <- get_election_data(
  file_name = "Two party preferred by division by vote type",
  date_range = list(from = "2016-01-01", to = "2025-01-01"),
  category = "House"
)

Sample Data ‘by division’:

date	event	DivisionNm	DivisionID	StateAb	PartyAb	Liberal/National Coalition OrdinaryVotes	Liberal/National Coalition OrdinaryPercentage	Australian Labor Party OrdinaryVotes	Australian Labor Party OrdinaryPercentage	Liberal/National Coalition AbsentVotes	Liberal/National Coalition AbsentPercentage	Australian Labor Party AbsentVotes	Australian Labor Party AbsentPercentage	Liberal/National Coalition ProvisionalVotes	Liberal/National Coalition ProvisionalPercentage	Australian Labor Party ProvisionalVotes	Australian Labor Party ProvisionalPercentage	Liberal/National Coalition PostalVotes	Liberal/National Coalition PostalPercentage	Australian Labor Party PostalVotes	Australian Labor Party PostalPercentage	TotalVotes	Liberal/National Coalition DeclarationPrePollVotes	Liberal/National Coalition DeclarationPrePollPercentage	Australian Labor Party DeclarationPrePollVotes	Australian Labor Party DeclarationPrePollPercentage
2022-05-21	2022 Federal Election	Adelaide	179	SA	ALP	31414	37.27	52878	62.73	2349	38.33	3780	61.67	217	32.34	454	67.66	7825	41.91	10846	58.09	112764	1143	38.09	1858	61.91

The Two party preferred by polling place dataset further breaks down the above figures by polling place (PollingPlaceID, PollingPlaceNm).

Example Usage ‘by polling place’:

df <- get_election_data(
  file_name = "Two party preferred by polling place",
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  type = c("Federal Election", "By-Election"), # Any or All
  category = "House"
)

Sample Data ‘by polling place’:

date	event	StateAb	DivisionID	DivisionNm	PollingPlaceID	PollingPlaceNm	Liberal/National Coalition Votes	Liberal/National Coalition Percentage	Australian Labor Party Votes	Australian Labor Party Percentage	TotalVotes	Swing
2022-05-21	2022 Federal Election	ACT	318	Bean	93925	Belconnen BEAN PPVC	342	37.87	561	62.13	903	-2.92

Two Candidate Preferred

Two candidate preferred by candidate by vote type

The Two candidate preferred by candidate by vote type dataset provides details of the two candidate preferred (TCP) votes by vote type for each candidate, detailing:

• The division details (StateAb, DivisionID, DivisionNm).
• The candidate details (CandidateID, Surname, GivenNm), including their position on the ballot position (BallotPosition), and whether they were elected (Elected) or an incumbent (HistoricElected), and the party (PartyAb, PartyNm) that they represent.
• The number of votes that the candidate received by vote type (OrdinaryVotes, AbsentVotes, ProvisionalVotes, DeclarationPrePollVotes, PostalVotes, TotalVotes).
• The overall swing in total votes received by the candidate compared to the previous election (Swing).

Covering elections from 2004 to the present, this dataset is available for the House only. The 2004 dataset is processed in the following way:

• The SittingMemberFl column is renamed to Elected using the process_elected() function.
• Based on this column, the data are further standardised to match later years with “Y” and “N” values inputted. This replaces the original corresponding “#” and NA values.
• PrePollVotes is renamed DeclarationPrePollVotes for consistency and clarification.

Example Usage:

df <- get_election_data(
  file_name = "Two candidate preferred by candidate by vote type",
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  category = "House",
  process = TRUE # OR FALSE
)

Sample Data:

date	event	StateAb	DivisionID	DivisionNm	CandidateID	Surname	GivenNm	BallotPosition	Elected	HistoricElected	PartyAb	PartyNm	OrdinaryVotes	AbsentVotes	ProvisionalVotes	DeclarationPrePollVotes	PostalVotes	TotalVotes	Swing
2022-05-21	2022 Federal Election	ACT	318	Bean	36231	SMITH	David	3	Y	Y	ALP	Australian Labor Party	53845	1067	154	1554	5315	61935	5.43

Two candidate preferred by candidate by polling place

The Two candidate preferred by candidate by polling place dataset provides details of the two candidate preferred (TCP) votes for each candidate and the polling place-level, detailing:

• The polling place details (StateAb, DivisionID, DivisionNm, PollingPlaceID, PollingPlaceNm).
• The candidate details (CandidateID, Surname, GivenNm), including their position on the ballot position (BallotPosition), and whether they were elected (Elected) or an incumbent (HistoricElected), and the party (PartyAb, PartyNm) that they represent.
• The number of ordinary votes that the candidate received (OrdinaryVotes).
• The overall swing in total votes received by the candidate compared to the previous election (Swing).

Covering elections from 2004 to the present, this dataset is available for the House only. The 2004 dataset is processed in the following way:

• The SittingMemberFl column is renamed to Elected using the process_elected() function.
• Based on this column, the data are further standardised to match later years with “Y” and “N” values inputted. This replaces the original corresponding “#” and NA values.

Example Usage:

df <- get_election_data(
  file_name = "Two candidate preferred by candidate by polling place",
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  type = c("Federal Election", "By-Election"), # Any or All
  category = "House",
  process = TRUE # OR FALSE
)

Sample Data:

date	event	StateAb	DivisionID	DivisionNm	PollingPlaceID	PollingPlaceNm	CandidateID	Surname	GivenNm	BallotPosition	Elected	HistoricElected	PartyAb	PartyNm	OrdinaryVotes	Swing
2022-05-21	2022 Federal Election	ACT	318	Bean	93925	Belconnen BEAN PPVC	36231	SMITH	David	3	Y	Y	ALP	Australian Labor Party	561	2.92

Distribution of Preferences

Distribution of preferences by candidate

The Distribution of preferences by candidate by division dataset provides details of the distribution of preferences by division, detailing:

• The division details (StateAb, DivisionID, DivisionNm).
• The candidate details (CandidateID, Surname, GivenNm), including their position on the ballot position (BallotPosition), and whether they were elected (Elected) or an incumbent (HistoricElected), and the party (PartyAb, PartyNm) that they represent.
• The distrbution details (CountNumber, CalculationType, CalculationValue).

Covering elections from 2004 to the present, this dataset is available for the House only. The 2004 dataset is processed in the following way:

• The SittingMemberFl column is renamed to Elected using the process_elected() function.
• Based on this column, the data are further standardised to match later years with “Y” and “N” values inputted. This replaces the original corresponding “#” and NA values.

Example Usage ‘by division’:

df <- get_election_data(
  file_name = "Distribution of preferences by candidate by division",
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  category = "House",
  process = TRUE # OR FALSE
)

Sample Data ‘by division’:

date	event	StateAb	DivisionID	DivisionNm	CountNumber	BallotPosition	CandidateID	Surname	GivenNm	PartyAb	PartyNm	Elected	HistoricElected	CalculationType	CalculationValue
2022-05-21	2022 Federal Election	ACT	318	Bean	0	1	36239	CONWAY	Sean	UAPP	United Australia Party	N	N	Preference Count	2831

The Distribution of preferences by polling place dataset further breaks down the above figures by polling place (PollingPlaceID, PollingPlaceNm). Additional amendments are made to the original dataset, changing CountNum to CountNumber, PPId to PollingPlaceID, PPNm to PollingPlaceNm, CandidateId to CandidateID, and SittingMemberFl to Elected. This ensures consistency in column names across all datasets.

*Caution should be taken if retrieving multiple election years as this dataset is large. Sizes include:

• 2022: 17 columns by 2,132,816 rows (36,257,872 cells)
• 2019: 17 columns by 1,719,736 rows (29,235,512 cells)
• 2016: 17 columns by 1,475,704 rows (25,086,968 cells)
• 2013: 17 columns by 2,335,760 rows (39,707,920 cells)
• 2010: 17 columns by 1,155,304 rows (19,640,168 cells)
• 2007: 17 columns by 1,597,048 rows (27,149,816 cells)
• 2004: 17 columns by 1,770,184 rows (30,093,128 cells)
• Max (All Elections + By-Elections): 17 columns by 12,835,444 rows (218,202,548 cells)

Example Usage ‘by polling place’:

df <- get_election_data(
  file_name = "Distribution of preferences by polling place",
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  type = c("Federal Election", "By-Election"), # Any or All
  category = "House",
  process = TRUE # OR FALSE
)

Sample Data ‘by polling place’:

date	event	StateAb	DivisionID	DivisionNm	PollingPlaceID	PollingPlaceNm	CountNumber	BallotPosition	CandidateID	Surname	GivenNm	PartyAb	PartyNm	Elected	CalculationType	CalculationValue
2022-05-21	2022 Federal Election	NSW	103	Banks	0	ABSENT	0	1	37018	COLEMAN	David Bernard	LP	Liberal Party of Australia NSW Division	Y	Preference Count	921

Flow of Preferences

Two party preferred flow of preferences by party

The Two party preferred flow of preferences by party dataset provides the TPP flow of preferences, detailing:

• the party details from whom the votes are transferred (PartyAb,PartyNm).
• The votes and percentage of votes transferred to the Coalition (Liberal/National Coalition Transfer Votes, Liberal/National Coalition Tansfer Percentage)
• The votes and percentage of votes transferred to the ALP (Australian Labor Party Transfer Votes, Australian Labor Party Transfer Percentage.

Covering elections from 2004 to the present, this dataset is available for the House only. No changes are made.

Example Usage ‘by party’:

df <- get_election_data(
  file_name = "Two party preferred flow of preferences by party",
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  category = "House"
)

Sample Data ‘by party’:

date	event	PartyAb	PartyNm	Liberal/National Coalition Transfer Votes	Liberal/National Coalition Transfer Percentage	Australian Labor Party Transfer Votes	Australian Labor Party Transfer Percentage
2022-05-21	2022 Federal Election	LP	Liberal	15492	90.14	1695	9.86

The Two party preferred flow of preferences by state by party dataset further breaks down the above figures by state (StateAb).

Example Usage ‘by state’:

df <- get_election_data(
  file_name = "Two party preferred flow of preferences by state by party",
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  category = "House"
)

Sample Data ‘by state’:

date	event	StateAb	PartyAb	PartyNm	Liberal/National Coalition Transfer Votes	Liberal/National Coalition Transfer Percentage	Australian Labor Party Transfer Votes	Australian Labor Party Transfer Percentage
2022-05-21	2022 Federal Election	NSW	UAPP	United Australia Party	109813	59.95	73361	40.05

Two candidate preferred flow of preferences by party

The Two candidate preferred flow of preferences by party dataset provides the TCP flow of preferences, detailing:

• the party details from whom the votes are transferred (FromPartyGroupID,FromPartyGroupAb,FromPartyGroupNm,FromPartyOrderOfAppearance).
• the party details to whom the votes are transferred (ToPartyGroupID,ToPartyGroupAb,ToPartyGroupNm,ToPartyOrderOfAppearance).
• The number of votes and percentage of votes transferred (TransferVotes, TransferPercentage)

Covering elections from 2004 to the present, this dataset is available for the House only. No changes are made.

Example Usage ‘by party’:

df <- get_election_data(
  file_name = "Two candidate preferred flow of preferences by party",
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  category = "House"
)

Sample Data ‘by party’:

date	event	FromPartyGroupID	FromPartyGroupAb	FromPartyGroupNm	FromPartyOrderOfAppearance	ToPartyGroupID	ToPartyDisplayAb	ToPartyGroupNm	ToPartyOrderOfAppearance	TransferVotes	TransferPercentage
2022-05-21	2022 Federal Election	4	LP	Liberal	1	4	LP	Liberal	1	0	0

The Two candidate preferred flow of preferences by state by party dataset further breaks down the above figures by state (StateAb).

*Caution should be taken if retrieving multiple election years as this dataset is large. Sizes include:

• 2022: 21 columns by 127,404 rows (2,675,484 cells)
• 2019: 21 columns by 115,350 rows (2,422,350 cells)
• 2016: 21 columns by 101,664 rows (2,134,944 cells)
• 2013: 21 columns by 136,516 rows (2,866,836 cells)
• 2010: 21 columns by 91,136 rows (1,913,856 cells)
• 2007: 21 columns by 106,838 rows (2,243,598 cells)
• 2004: 21 columns by 112,788 rows (2,368,548 cells)

Example Usage ‘by state’:

df <- get_election_data(
  file_name = "Two candidate preferred flow of preferences by state by party",
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  category = "House"
)

Sample Data ‘by state’:

date	event	StateAb	FromPartyGroupID	FromPartyGroupAb	FromPartyGroupNm	FromPartyOrderOfAppearance	ToPartyGroupID	ToPartyDisplayAb	ToPartyGroupNm	ToPartyOrderOfAppearance	TransferVotes	TransferPercentage
2022-05-21	2022 Federal Election	NSW	4	LP	Liberal	1	4	LP	Liberal	1	0	0

Two candidate preferred flow of preferences by candidate

The Two candidate preferred flow of preferences by candidate by division dataset provides the TCP flow of preferences by candidate, detailing:

• the division details (StateAb, DivisionID, DivisionNm).
• the candidate details from whom the votes are transferred (FromCandidateID,FromCandidatePartyAb,FromCandidatePartyNm,FromCandidateSurname,FromCandidateGivenNm, FromCandidateBallotPosition).
• the candidate details to whom the votes are transferred (ToCandidateID,ToCandidatePartyAb,ToCandidatePartyNm,ToCandidateSurname,ToGivenNm, ToCandidateBallotPosition).
• The number of votes and percentage of votes transferred (TransferVotes, TransferPercentage)

Covering elections from 2004 to the present, this dataset is available for the House only. No changes are made.

Example Usage ‘by division’:

df <- get_election_data(
  file_name = "Two candidate preferred flow of preferences by candidate by division",
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  category = "House"
)

Sample Data ‘by division’:

date	event	StateAb	DivisionID	DivisionNm	FromCandidateID	FromCandidatePartyAb	FromCandidatePartyNm	FromCandidateSurname	FromCandidateGivenNm	FromCandidateBallotPosition	ToCandidateID	ToCandidatePartyAb	ToCandidatePartyNm	ToCandidateSurname	ToCandidateGivenNm	ToCandidateBallotPosition	TransferCount	TransferPercentage
2022-05-21	2022 Federal Election	ACT	318	Bean	0	NA	NA	First Preferences	NA	0	36231	ALP	Australian Labor Party	SMITH	David	3	41060	58.41

The Two candidate preferred flow of preferences by polling place dataset further breaks down the above figures by polling place (PollingPlaceID,PollingPlaceNm).

Example Usage ‘by polling place’:

df <- get_election_data(
  file_name = "Two candidate preferred flow of preferences by polling place",
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  type = c("Federal Election", "By-Election"),
  category = "House"
)

Sample Data ‘by polling place’:

date	event	StateAb	DivisionID	DivisionNm	PollingPlaceID	PollingPlaceNm	FromCandidateID	FromCandidatePartyAb	FromCandidatePartyNm	FromCandidateSurname	FromCandidateGivenNm	FromCandidateBallotPosition	ToCandidateID	ToCandidatePartyAb	ToCandidatePartyNm	ToCandidateSurname	ToCandidateGivenNm	ToCandidateBallotPosition	TransferCount	TransferPercentage
2022-05-21	2022 Federal Election	NSW	103	Banks	0	ABSENT	0	NA	NA	First Preferences	NA	0	37018	LP	Liberal Party of Australia NSW Division	COLEMAN	David	1	921	53.05

Votes

Informal votes

The Informal votes by state dataset provides a summary of formal and informal votes, detailing:

• the state details (StateAb, StateNm).
• the number of formal and informal votes (FormalVotes,InformalVotes).
• the total number of votes (formal + informal) (TotalVotes).
• The percentage of informal votes (InformalPercent).
• the overall swing in informal votes received compared to the previous election (InformalSwing).

Covering elections from 2004 to the present, this dataset is available for both the House, Senate, or Referendum. No changes are made.

Example Usage ‘by state’:

df <- get_election_data(
  file_name = "Informal votes by state",
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  # type = "Referendum", (for Referendum)
  category = "House" # OR "Senate" OR "Referendum"
)

Sample Data ‘by state’:

date	event	StateAb	StateNm	FormalVotes	InformalVotes	TotalVotes	InformalPercent	InformalSwing
2022-05-21	2022 Federal Election	NSW	New South Wales	4650940	308644	4959584	6.22	-0.79

The Informal votes by division dataset further breaks down the above figures by division (DivisionID,DivisionNm).

Example Usage ‘by division’:

df <- get_election_data(
  file_name = "Informal votes by division",
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  # type = "Referendum", (for Referendum)
  category = "House" # OR "Senate" OR "Referendum"
)

Sample Data ‘by division’:

date	event	DivisionID	DivisionNm	StateAb	FormalVotes	InformalVotes	TotalVotes	InformalPercent	InformalSwing
2022-05-21	2022 Federal Election	107	Blaxland	NSW	81677	9884	91561	10.79	-2.51

Turnout

The Turnout by state dataset provides a summary of voter turnout, detailing:

• the state details (StateAb, StateNm).
• the number of voters enrolled (Enrolment).
• the number and percentage of voters who voted (Turnout, TurnoutPercentage).
• the overall swing in turnout compared to the previous election (TurnoutSwing).

Covering elections from 2004 to the present, this dataset is available for both the House, Senate, or Referendum. No changes are made.

Example Usage ‘by state’:

df <- get_election_data(
  file_name = "Turnout by state",
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  # type = "Referendum", (for Referendum)
  category = "House" # OR "Senate" OR "Referendum"
)

Sample Data ‘by state’:

date	event	StateAb	StateNm	Enrolment	Turnout	TurnoutPercentage	TurnoutSwing
2022-05-21	2022 Federal Election	NSW	New South Wales	5467993	4959584	90.7	-1.46

The Turnout by division dataset further breaks down the above figures by division (DivisionID,DivisionNm).

Example Usage ‘by division’:

df <- get_election_data(
  file_name = "Turnout by division",
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  # type = "Referendum", (for Referendum)
  category = "House" # OR "Senate" OR "Referendum"
)

Sample Data ‘by division’:

date	event	DivisionID	DivisionNm	StateAb	Enrolment	Turnout	TurnoutPercentage	TurnoutSwing
2022-05-21	2022 Federal Election	179	Adelaide	SA	129960	117202	90.18	-1.34

Votes Type

The Votes by state dataset provides the number of votes by vote type, detailing:

• the state details (StateAb, StateNm).
• the number of voters enrolled (Enrolment).
• the number votes by vote type (OrdinaryVotes, AbsentVotes, ProvisionalVotes, DeclarationPrePollVotes, PostalVotes).
• the total number of votes and percentage (TotalVotes, TotalPercentage).

Covering elections from 2004 to the present, this dataset is available for the House, Senate or Referendum. Minor amendments are made to the original dataset, changing PrePollVotes to DeclarationPrePollVotes. This ensures consistency in column names across all datasets.

Example Usage ‘by state’:

df <- get_election_data(
  file_name = "Votes by state",
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  # type = "Referendum", (for Referendum)
  category = "House" # OR "Senate" OR "Referendum"
)

Sample Data ‘by state’:

date	event	StateAb	StateNm	Enrolment	OrdinaryVotes	AbsentVotes	ProvisionalVotes	DeclarationPrePollVotes	PostalVotes	TotalVotes	TotalPercentage
2022-05-21	2022 Federal Election	NSW	New South Wales	5467993	4118664	135225	14223	154694	536778	4959584	90.7

The Votes by division dataset further breaks down the above figures by division (DivisionID,DivisionNm).

Example Usage ‘by division’:

df <- get_election_data(
  file_name = "Votes by division",
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  # type = "Referendum", (for Referendum)
  category = "House" # OR "Senate" OR "Referendum"
)

Sample Data ‘by division’:

date	event	DivisionID	DivisionNm	StateAb	Enrolment	OrdinaryVotes	AbsentVotes	ProvisionalVotes	DeclarationPrePollVotes	PostalVotes	TotalVotes	TotalPercentage
2022-05-21	2022 Federal Election	179	Adelaide	SA	129960	87699	6416	712	3079	19296	117202	90.18

The Votes by polling place dataset further breaks down the above figures by division (PollingPlaceID,PollingPlaceNm). It is only for Referendum.

Example Usage ‘by polling place’:

df <- get_election_data(
  file_name = "Votes by polling place",
  date_range = list(from = "2022-01-01", to = "2025-01-01"),
  type = "Referendum",
  category = "Referendum"
)

Sample Data ‘by polling place’:

date	event	QuestionNo	StateAb	DivisionID	DivisionNm	PollingPlaceID	PollingPlaceNm	YesVotes	YesPercentage	NoVotes	NoPercentage	FormalVotes	FormalPercentage	InformalVotes	InformalPercentage	TotalVotes
2023-10-14	2023 Referendum	1	NSW	119	Fowler	31643	Abbotsbury	339	24.93	1021	75.07	1360	98.27	24	1.73	1384

General

Political parties

The Political parties dataset provides a summary of the political parties being represented at each election, detailing:

• the state details (StateAb).
• the party details (PartyAb, PartyNm), including the registered party name (RegisteredPartyAb).

Covering elections from 2004 to the present, this dataset is available for the General category. No changes are made.

Example Usage:

df <- get_election_data(
  file_name = "Political parties",
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  category = "General"
)

Sample Data:

date	event	StateAb	PartyAb	RegisteredPartyAb	PartyNm
2022-05-21	2022 Federal Election	NSW	AJP	AJP	Animal Justice Party

Polling places

The Polling places dataset provides a summary of the polling places used at each election, detailing:

• the division details (StateAb, DivisionID, DivisionNm).
• the polling place details (PollingPlaceID, PollingPlaceTypeID, PollingPlaceNm).
• the address of the polling place (PremisesAddress1, PremisesAddress2, PremisesAddress3, PremisesSubrub, PremisesStateAb, PremisesPostCode).
• the coordinates of the polling place (Latitude, Longitude).

Covering elections from 2004 to the present, this dataset is available for the General category. The dataset is processed in the following way, using the process_coords() function:

• NA or 0 values in Latitude and Longitude are replaced if known, based on matching of the PollingPlaceID column within an internal dataset and the dataset retrieved from the AEC.

Example Usage:

df <- get_election_data(
  file_name = "Polling places",
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  type = c("Federal Election", "Referendum", "By-Election"), # Any or All
  category = "General",
  process = TRUE # OR FALSE
)

Sample Data:

date	event	StateAb	DivisionID	DivisionNm	PollingPlaceID	PollingPlaceTypeID	PollingPlaceNm	PremisesNm	PremisesAddress1	PremisesAddress2	PremisesAddress3	PremisesSuburb	PremisesStateAb	PremisesPostCode	Latitude	Longitude
2022-05-21	2022 Federal Election	ACT	318	Bean	93925	5	Belconnen BEAN PPVC	Belconnen Community Centre	Swanson Place	NA	NA	BELCONNEN	ACT	2617	-35.23895	149.0691

Enrolment

The Enrolment by state dataset provides a summary of the enrolment, detailing:

• the state details (StateAb, StateNm).
• the number of enrolments based on type (CloseOfRollsEnrolment,NotebookRollAdditions,NotebookRollDeletions,ReinstatementsPrePoll,ReinstatementsAbsent,ReinstatementsProvisional).
• the overall number of enrolled voters (Enrolment).

Covering elections from 2004 to the present, this dataset is available for the General category. No changes are made.

Example Usage ‘by state’:

df <- get_election_data(
  file_name = "Enrolment by state",
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  type = c("Federal Election", "Referendum"), # Any or All
  category = "General"
)

Sample Data ‘by state’:

date	event	StateAb	StateNm	CloseOfRollsEnrolment	NotebookRollAdditions	NotebookRollDeletions	ReinstatementsPostal	ReinstatementsPrePoll	ReinstatementsAbsent	ReinstatementsProvisional	Enrolment
2022-05-21	2022 Federal Election	NSW	New South Wales	5472469	92	5406	85	228	176	349	5467993

The Enrolment by division dataset further breaks down the above figures by division (DivisionID,DivisionNm).

Example Usage ‘by division’:

df <- get_election_data(
  file_name = "Enrolment by division",
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  type = c("Federal Election", "Referendum"), # Any or All
  category = "General"
)

Sample Data ‘by division’:

date	event	DivisionID	DivisionNm	StateAb	CloseOfRollsEnrolment	NotebookRollAdditions	NotebookRollDeletions	ReinstatementsPostal	ReinstatementsPrePoll	ReinstatementsAbsent	ReinstatementsProvisional	Enrolment
2022-05-21	2022 Federal Election	179	Adelaide	SA	130151	10	209	1	1	6	0	129960

Declaration votes

The Declaration votes issued by state and Declaration votes received by state datasets provide a summary of the number of declaration votes issued and received, respectively, detailing:

• the state details (StateAb, StateNm).
• the number of declaration votes by type (Provisional,Absent,PrePoll,PrePollOwnDivision,Postal,PostalOwnDivision).

Covering elections from 2004 to the present, this dataset is available for the General category. No changes are made.

Example Usage ‘by state’:

df <- get_election_data(
  file_name = "Declaration votes issued by state", # OR Declaration votes received by state
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  type = c("Federal Election", "Referendum"), # Any or All
  category = "General"
)

Sample Data ‘by state’:

date	event	StateAb	StateNm	Provisional	Absent	PrePoll	PrePollOwnDivision	Postal	PostalOwnDivision
2022-05-21	2022 Federal Election	NSW	New South Wales	53708	153717	147645	10409	676268	673398

The Declaration votes issued by division and Declaration votes received by division datasets further break down the above figures by division (DivisionID,DivisionNm).

Example Usage ‘by division’:

df <- get_election_data(
  file_name = "Declaration votes issued by division", # OR Declaration votes received by division
  date_range = list(from = "2004-01-01", to = "2025-01-01"),
  type = c("Federal Election", "Referendum"), # Any or All
  category = "General"
)

Sample Data ‘by division’:

date	event	DivisionID	DivisionNm	StateAb	Provisional	Absent	PrePoll	PrePollOwnDivision	Postal	PostalOwnDivision
2022-05-21	2022 Federal Election	179	Adelaide	SA	1606	7146	3074	70	26077	23045

<br

Statistics

Postal vote applications by total

The Postal vote applications by total dataset provides a summary of the number of postal vote applications received and postal votes returned, detailing:

• the division details (StateAb, DivisionNm).
• the number of valid applications received (Valid Applications Received).
• the number of postal votes returned (Postal Votes Returned).

Covering the2022 election only, this dataset is available for the Statistics category. No changes are made.

Example Usage:

df <- get_election_data(
  file_name = "Postal vote applications by total",
  date_range = list(from = "2022-01-01", to = "2025-01-01"),
  type = c("Federal Election", "Referendum", "By-Election"), # Any or All
  category = "Statistics"
)

Sample Data:

date	event	StateAb	DivisionNm	Valid Applications Received	Postal Votes Returned
2022-05-21	2022 Federal Election	ACT	Bean	10907	6828

Postal vote applications by date

The Postal vote applications by date dataset provides a summary of the number of postal votes returned each day, detailing:

• the division details (StateAb, DivisionNm).
• the date the postal vote was received (DateReceived).
• the total number of postal votes received (TotalPVAs).

Covering elections from 2010 to 2019, this dataset is available for the Statistics category. The dataset is processed in the following way, using the process_pva_date() function:

• column names are standardised, including Enrolment, Enrolment Division, PVA_Web_2_Date_Div and PVA_Web_2_V2_Div are renamed to DivisionNm and State_Cd to StateAb.
• Unnecessary columns are dropped, including <>, TOTAL to date(Inc GPV), and Date out of range.
• the date columns are pivoted from wide to long and converted into a date format for ease of comparison across years.

Example Usage:

df <- get_election_data(
  file_name = "Postal vote applications by date",
  date_range = list(from = "2010-01-01", to = "2022-01-01"),
  type = c("Federal Election", "By-Election"), # Any or All
  category = "Statistics",
  process = TRUE # OR FALSE
)

Sample Data:

date	event	StateAb	DivisionNm	DateReceived	TotalPVAs
2019-05-18	2019 Federal Election	ACT	Bean	2019-04-11	150

Postal vote applications by party

The Postal vote applications by party dataset provides a summary of the number of postal votes returned each day, detailing:

• the division details (StateAb, DivisionNm).
• the number of General Postal Voters (GPV) - these are voters who automatically receive ballot papers via post and generally include voters with a disability, silent electors, prisoners, those in remote areas, and those who are unable to attend a polling place on election day due to religious objections.
• the number of AEC-received postal vote applications and by the method in which they were received (AEC (Online), AEC (Paper), AEC (Total)).
• the number of party-received postal vote applications (ALP, CLP, DEM, LIB, LNP, NAT, OTH).
• the total number of AEC and party received postal vote applications (Total (AEC + Parties)).

Covering elections from 2010 to 2019, this dataset is available for the Statistics category. The dataset is processed in the following way, using the process_pva_party() function:

• column names are standardised, including abbreviating party names.

Example Usage:

df <- get_election_data(
  file_name = "Postal vote applications by party",
  date_range = list(from = "2010-01-01", to = "2022-01-01"),
  type = c("Federal Election", "By-Election"), # Any or All
  category = "Statistics",
  process = TRUE # OR FALSE
)

Sample Data:

date	event	StateAb	DivisionNm	GPV	AEC (Online)	AEC (Paper)	AEC (Total)	Total (AEC + Parties)	ALP	CLP	DEM	LIB	LNP	NAT	OTH
2019-05-18	2019 Federal Election	ACT	Bean	1642	3204	229	3433	5090	1	NA	NA	12	2	NA	NA

Pre-poll votes

The Pre-poll votes dataset provides a summary of the number of prepoll votes issued, detailing:

• the polling place details (StateAb, DivisionNm, PollingPlaceNm).
• the date the pre-poll vote was issued (IssueDate).
• the total number of prepoll votes issued (TotalPPVs).

Covering elections from 2010 to 2019, this dataset is available for the Statistics category. The dataset is processed in the following way, using the process_ppv() function:

• column names are standardised.
• the date columns are pivoted from wide to long and converted into a date format for ease of comparison across years.

Example Usage:

df <- get_election_data(
  file_name = "Pre-poll votes",
  date_range = list(from = "2010-01-01", to = "2022-01-01"),
  type = c("Federal Election", "Referendum", "By-Election"), # Any or All
  category = "Statistics",
  process = TRUE # OR FALSE
)

Sample Data:

date	event	StateAb	DivisionNm	PollingPlaceNm	IssueDate	TotalPPVs
2019-05-18	2019 Federal Election	ACT	Bean	Belconnen BEAN PPVC	2019-04-29	19

Votes by SA1

The Votes by SA1 dataset provides a summary of the number of votes by SA1, detailing:

• the SA1 details (StateAb, DivisionNm, PollingPlaceID, PollingPlaceNm, StatisticalAreaID).
• indicative count of otes cast per SA1 (Count).

Covering elections from 2013 to present, this dataset is available for the Statistics category. The dataset is processed in the following way, using the process_ccd() function:

• column names are standardised and the year column is removed.

Example Usage:

df <- get_election_data(
  file_name = "Votes by SA1",
  date_range = list(from = "2013-01-01", to = "2025-01-01"),
  type = c("Federal Election", "Referendum"), # Any or All
  category = "Statistics",
  process = TRUE # OR FALSE
)

Sample Data:

date	event	StateAb	DivisionNm	StatisticalAreaID	PollingPlaceID	PollingPlaceNm	Count
2019-05-18	2019 Federal Election	SA	Adelaide	4100101	0	Absent	20

Overseas

The Overseas dataset provides a summary of the breakdown of overseas voting, detailing:

• the the overseas post (OverseasPost).
• the division that the votes were cast (StateAb, DivisionNm).
• the total number of votes (TotalVotes), broken down by postal and prepoll voting types (PostalVotes,PrePollVotes).

Covering elections from 2013 to present, this dataset is available for the Statistics category. The dataset is processed in the following way, using the process_overseas() function:

• column names are standardised and the pp_sort_nm and Total columns are removed.
• the TotalVotes column is calculated based on the sum of PostalVotes and PrePollVotes.

Example Usage:

df <- get_election_data(
  file_name = "Overseas",
  date_range = list(from = "2013-01-01", to = "2025-01-01"),
  type = c("Federal Election", "Referendum"), # Any or All
  category = "Statistics",
  process = TRUE # OR FALSE
)

Sample Data:

date	event	StateAb	DivisionNm	OverseasPost	PostalVotes	PrePollVotes	TotalVotes
2019-05-18	2019 Federal Election	ACT	Bean	Amman	0	4	4