To get started, it is good to know in advance the division to analyse, parties in the race, etc. The list_ family of functions will help you to get to bearings before getting and analysing the data. The functions include here are:
To figure out the election results included in the package, just run:
To retrieve the list of divisions for all the years, you can run list_divisions:
list_divisions()
#> # A tibble: 167 × 10
#> StateAb DivisionID DivisionNm `2022` `2019` `2016` `2013` `2010` `2007` `2004`
#> <chr> <int> <chr> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl>
#> 1 ACT 101 Canberra TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#> 2 ACT 102 Fenner TRUE TRUE TRUE NA NA NA NA
#> 3 ACT 102 Fraser NA NA NA TRUE TRUE TRUE TRUE
#> 4 ACT 318 Bean TRUE TRUE NA NA NA NA NA
#> 5 NSW 103 Banks TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#> 6 NSW 104 Barton TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#> 7 NSW 105 Bennelong TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#> 8 NSW 106 Berowra TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#> 9 NSW 107 Blaxland TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#> 10 NSW 108 Bradfield TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#> # … with 157 more rowsThis function allows for some filtering by using a list containing any of the columns included:
list_divisions(filter=list(DivisionNm=c("Batman","Cooper")))
#> # A tibble: 2 × 10
#> StateAb DivisionID DivisionNm `2022` `2019` `2016` `2013` `2010` `2007` `2004`
#> <chr> <int> <chr> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl>
#> 1 VIC 199 Batman NA NA TRUE TRUE TRUE TRUE TRUE
#> 2 VIC 320 Cooper TRUE TRUE NA NA NA NA NAIt is recommended to explore the list of parties that have participated in the elections in the data. For this **
list_parties()
#> # A tibble: 387 × 10
#> StateAb PartyAb PartyNm `2016` `2019` `2004` `2010` `2013` `2022` `2007`
#> <chr> <chr> <chr> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl>
#> 1 NSW AAPP Antipaedophile Party TRUE NA NA NA NA NA NA
#> 2 NSW ABFA Australian Better Families NA TRUE NA NA NA NA NA
#> 3 VIC ADP The Aged and Disability Pensioners Party NA NA TRUE NA NA NA NA
#> 4 NSW ADVP Veterans Party TRUE NA NA NA NA NA NA
#> 5 QLD ADVP Veterans Party TRUE NA NA NA NA NA NA
#> 6 VIC AEQ Marriage Equality TRUE NA NA NA NA NA NA
#> 7 NSW AFN Australia First Party TRUE TRUE NA TRUE TRUE NA NA
#> 8 NT AFN Australia First Party TRUE NA NA NA NA NA NA
#> 9 QLD AFN Australia First Party NA TRUE NA NA NA NA NA
#> 10 SA AFN Australia First Party NA NA NA NA TRUE NA NA
#> # … with 377 more rowsDue to changes in parties and the federal nature of the country, parties may have different names in different places and they may change over time. In addition to a filter argument (like in list_divisions), this function also allows filtering by party names using a regular expression, e.g.:
list_parties(party_regex = "Greens")
#> # A tibble: 17 × 10
#> StateAb PartyAb PartyNm `2004` `2007` `2010` `2013` `2016` `2019` `2022`
#> <chr> <chr> <chr> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl>
#> 1 ACT GRN The Greens TRUE TRUE TRUE NA TRUE TRUE TRUE
#> 2 ACT GRN Australian Greens NA NA NA TRUE NA NA NA
#> 3 NSW GRN The Greens TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#> 4 NT GRN The Greens TRUE TRUE TRUE NA TRUE TRUE TRUE
#> 5 NT GRN Australian Greens NA NA NA TRUE NA NA NA
#> 6 QLD GRN The Greens TRUE TRUE TRUE TRUE TRUE TRUE NA
#> 7 QLD GRN Queensland Greens NA NA NA NA NA NA TRUE
#> 8 SA GRN The Greens TRUE TRUE TRUE NA TRUE TRUE TRUE
#> 9 SA GRN Australian Greens NA NA NA TRUE NA NA NA
#> 10 TAS GRN Australian Greens TRUE NA TRUE TRUE NA NA NA
#> 11 TAS GRN The Greens NA TRUE NA NA TRUE TRUE TRUE
#> 12 VIC GRN Australian Greens TRUE TRUE TRUE NA NA NA NA
#> 13 VIC GRN The Greens NA NA NA TRUE TRUE NA TRUE
#> 14 VIC GRN The Greens (VIC) NA NA NA NA NA TRUE NA
#> 15 WA GRN The Greens TRUE TRUE TRUE NA NA NA NA
#> 16 WA GRN The Greens (WA) NA NA NA TRUE TRUE TRUE TRUE
#> 17 WA ODR Outdoor Recreation Party (Stop The Greens) NA NA NA NA TRUE NA NAFinally, it is possible to list all polling places, showing their active years. For example, for all the electorates in Bennelong
list_polling_places(filters=list(DivisionNm="Bennelong"))
#> # A tibble: 126 × 16
#> State DivisionID DivisionNm PollingPlaceID PollingPlaceTypeID PremisesNm Pollin…¹ Latit…² Longi…³ `2004` `2007` `2010` `2013` `2016` `2019` `2022`
#> <chr> <int> <chr> <int> <int> <chr> <chr> <dbl> <dbl> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl>
#> 1 NSW 105 Bennelong 75 1 Epping Boys High School Balacla… -33.8 151. TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#> 2 NSW 105 Bennelong 75 1 Epping Boys High School Marsfie… -33.8 151. TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#> 3 NSW 105 Bennelong 79 1 Australian Air League Building Eastview -33.8 151. TRUE TRUE TRUE TRUE TRUE TRUE NA
#> 4 NSW 105 Bennelong 79 1 Australian Air League Building Ryde -33.8 151. TRUE TRUE TRUE TRUE TRUE TRUE NA
#> 5 NSW 105 Bennelong 79 1 The Living Way Church Ryde -33.8 151. NA NA NA NA NA NA TRUE
#> 6 NSW 105 Bennelong 80 1 Eastwood Heights Public School Eastwoo… -33.8 151. TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#> 7 NSW 105 Bennelong 81 1 Epping Community Centre Epping … -33.8 151. TRUE TRUE TRUE NA NA NA NA
#> 8 NSW 105 Bennelong 81 1 Epping Public School Epping … -33.8 151. NA NA NA TRUE TRUE TRUE TRUE
#> 9 NSW 105 Bennelong 82 1 Epping North Public School Epping … -33.8 151. TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#> 10 NSW 105 Bennelong 84 1 Gladesville Public School Gladesv… -33.8 151. TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#> # … with 116 more rows, and abbreviated variable names ¹PollingPlaceNm, ²Latitude, ³Longitude