--- title: "Listing the essential data" author: "Carlos YANEZ SANTIBANEZ" date: "2022-11-28" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Listing the essential data} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- 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: - list_years() - list_divisions() - list_parties() - list_polling_places() ## Years and divisions To figure out the election results included in the package, just run: ```r list_years() #> [1] 2022 2019 2016 2013 2010 2007 2004 ``` To retrieve the list of divisions for all the years, you can run *list_divisions*: ```r list_divisions() #> # A tibble: 167 × 10 #> StateAb DivisionID DivisionNm `2022` `2019` `2016` `2013` `2010` `2007` `2004` #> #> 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 rows ``` This function allows for some filtering by using a list containing any of the columns included: ```r list_divisions(filter=list(DivisionNm=c("Batman","Cooper"))) #> # A tibble: 2 × 10 #> StateAb DivisionID DivisionNm `2022` `2019` `2016` `2013` `2010` `2007` `2004` #> #> 1 VIC 199 Batman NA NA TRUE TRUE TRUE TRUE TRUE #> 2 VIC 320 Cooper TRUE TRUE NA NA NA NA NA ``` ## Political Parties It is recommended to explore the list of parties that have participated in the elections in the data. For this ** ```r list_parties() #> # A tibble: 387 × 10 #> StateAb PartyAb PartyNm `2016` `2019` `2004` `2010` `2013` `2022` `2007` #> #> 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 rows ``` Due 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](https://en.wikipedia.org/wiki/Regular_expression), e.g.: ```r list_parties(party_regex = "Greens") #> # A tibble: 17 × 10 #> StateAb PartyAb PartyNm `2004` `2007` `2010` `2013` `2016` `2019` `2022` #> #> 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 NA ``` ## Polling places Finally, it is possible to list all polling places, showing their active years. For example, for all the electorates in Bennelong ```r 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` #> #> 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 ¹​PollingPlaceNm, ²​Latitude, ³​Longitude ```