R/git-download.R

In eechidna: Exploring Election and Census Highly Informative Data Nationally for Australia

#' Download a data frame containing the polygons of Australian federal electorates
#' 
#' Downloads and returns a DataFrame containing the points that outline the polygons 
#' for each of the Australian electorates in the desired federal election. The data were obtained from the 
#' Australian Electoral Commission and the Australian Bureau of Statistics.

#' 
#' @param year Desired year, must be one of 2001, 2004, 2007, 2010, 2011, 2013, 2016, 2019
#' @param ... Additional arguments passed to `download.file`
#'
#' @return A data frame consisting of points with the following variables:
#' \itemize{
#'     \item id: Numeric identifier for the polygon
#'     \item long: longitude coordinate of point in polygon
#'     \item lat: latitude coordinate of point in polygon
#'     \item order: order for polygon points
#'     \item hole: whether polygon has a hole
#'     \item piece: piece for polygon
#'     \item group: group for polygon
#'     \item elect_div: Electoral division name
#'     \item state: Abbreviation for state name
#' }
#' 
#' @examples
#' library(eechidna)
#' library(dplyr)
#' library(ggmap)
#'
#' nat_map16 <- nat_map_download(2016)
#'
#' nat_map16 %>%
#'  filter(elect_div=='MELBOURNE') %>%
#'  qmplot(long, lat, data=., color='red', size=5,
#'         xlab=NA,ylab=NA) +
#'  theme(legend.position = 'none')
#'@export
nat_map_download <- function(year, ...){
  

  year = as.numeric(year)
  suffix = substr(year, 3, 4)
  url_git = paste0("https://github.com/jforbes14/eechidna/raw/master/extra-data/nat_map", suffix, ".rda")
  
  if (year == 2001) {
    nat_map01 <- 1
    tmp <- tempfile()
    utils::download.file(url_git, tmp, ...)
    load(tmp)
    nat_map01      
  } else if (year == 2004) {
    nat_map04 <- 1
    tmp <- tempfile()
    utils::download.file(url_git, tmp, ...)
    load(tmp)
    nat_map04      
  } else if (year == 2007) {
    nat_map07 <- 1
    tmp <- tempfile()
    utils::download.file(url_git, tmp, ...)
    load(tmp)
    nat_map07      
  } else if (year == 2010) {
    nat_map10 <- 1
    tmp <- tempfile()
    utils::download.file(url_git, tmp, ...)
    load(tmp)
    nat_map10      
  } else if (year == 2011) {
    nat_map11 <- 1
    tmp <- tempfile()
    utils::download.file(url_git, tmp, ...)
    load(tmp)
    nat_map11      
  } else if (year == 2013) {
    nat_map13 <- 1
    tmp <- tempfile()
    utils::download.file(url_git, tmp, ...)
    load(tmp)
    nat_map13      
  } else if (year == 2016) {
    nat_map16 <- 1
    tmp <- tempfile()
    utils::download.file(url_git, tmp, ...)
    load(tmp)
    nat_map16   
  } else if (year == 2019) {
    nat_map19 <- 1
    tmp <- tempfile()
    utils::download.file(url_git, tmp, ...)
    load(tmp)
    nat_map19      
  } else {
    warning("Desired year are not available: select from 2001, 2004, 2007, 2010, 2011, 2013, 2016, 2019") 
    return()
  }
  
}

#' Download a data frame containing electorate centroids
#' 
#' @description
#' Downloads and returns a data frame containing the points that make up the centroids
#' for each of the Australian electorates in the desired federal election. 
#' 
#' @param year Desired year, must be one of 2001, 2004, 2007, 2010, 2011, 2013, 2016, 2019
#' @param ... Additional arguments passed to `download.file`
#'
#'
#' @return A data frame with data associated with each of the Australian federal electorates
#' 
#' \itemize{
#'     \item id: Numeric identifier for the polygon
#'     \item elect_div: Electorate division name   
#'     \item state: abbreviation of the state name
#'     \item numccds: AEC variable that might be filled with meaning or a description down the road
#'     \item area_sqkm: combined square kilometers of each electorate
#'     \item long_c: longitude coordinate of electorate (polygon) centroid
#'     \item lat_c: latitude coordinate of electorate (polygon) centroid
#'     \item x: latitude coordinate for plotting a cartogram
#'     \item y: longitude coordinate for plotting a cartogram
#'     \item radius: variable used in the construction of cartogram points
#' }
#' 
#' @examples
#' library(eechidna)
#' library(dplyr)
#' library(ggmap)
#'
#' nat_data19 <- nat_data_download(2019)
#'
#' nat_data19 %>%
#'   qmplot(long_c, lat_c, data=.)
#' @export
nat_data_download <- function(year, ...){
  
  year = as.numeric(year)
  suffix = substr(year, 3, 4)
  url_git = paste0("https://github.com/jforbes14/eechidna/raw/master/extra-data/nat_data", suffix, ".rda")
  
  if (year == 2001) {
    nat_data01 <- 1
    tmp <- tempfile()
    utils::download.file(url_git, tmp, ...)
    load(tmp)
    nat_data01      
  } else if (year == 2004) {
    nat_data04 <- 1
    tmp <- tempfile()
    utils::download.file(url_git, tmp, ...)
    load(tmp)
    nat_data04      
  } else if (year == 2007) {
    nat_data07 <- 1
    tmp <- tempfile()
    utils::download.file(url_git, tmp, ...)
    load(tmp)
    nat_data07      
  } else if (year == 2010) {
    nat_data10 <- 1
    tmp <- tempfile()
    utils::download.file(url_git, tmp, ...)
    load(tmp)
    nat_data10      
  } else if (year == 2011) {
    nat_data11 <- 1
    tmp <- tempfile()
    utils::download.file(url_git, tmp, ...)
    load(tmp)
    nat_data11      
  } else if (year == 2013) {
    nat_data13 <- 1
    tmp <- tempfile()
    utils::download.file(url_git, tmp, ...)
    load(tmp)
    nat_data13      
  } else if (year == 2016) {
    nat_data16 <- 1
    tmp <- tempfile()
    utils::download.file(url_git, tmp, ...)
    load(tmp)
    nat_data16   
  } else if (year == 2019) {
    nat_data19 <- 1
    tmp <- tempfile()
    utils::download.file(url_git, tmp, ...)
    load(tmp)
    nat_data19      
  } else {
    warning("Desired year are not available: select from 2001, 2004, 2007, 2010, 2011, 2013, 2016, 2019") 
    return()
  }
  
}

#' Download SpatialPolygonsDataFrame containing polygons of Australian federal electorates
#' 
#' @param year Desired year, must be one of 2001, 2004, 2007, 2010, 2011, 2013, 2016, 2019
#' @param ... Additional arguments passed to `download.file`
#'
#' Downloads and returns a large SpatialPolygonsDataFrame containing the polygons 
#' and associated data for each of the Australian electorates in the desired federal election. 
#' This object is obtained using the `sF_download` function. The data were obtained from the 
#' Australian Electoral Commission and the Australian Bureau of Statistics.
#'
#' @return A SpatialPolygonsDataFrame containing polygons of the Australian federal electorates
#' 
#' @examples
#' \dontrun{
#' sF_16 <- sF_download(year = 2016)
#' # Plot a map of the electorates
#' library(sp)
#' plot(sF_16)
#' }
#' 
#' @export
sF_download <- function(year, ...){
  
  year = as.numeric(year)
  suffix = substr(year, 3, 4)
  url_git = paste0("https://github.com/jforbes14/eechidna/raw/master/extra-data/sF_", suffix, ".rda")
  
  if (year == 2001) {
    sF_01 <- 1
    tmp <- tempfile()
    utils::download.file(url_git, tmp, ...)
    load(tmp)
    sF_01      
  } else if (year == 2004) {
    sF_04 <- 1
    tmp <- tempfile()
    utils::download.file(url_git, tmp, ...)
    load(tmp)
    sF_04      
  } else if (year == 2007) {
    sF_07 <- 1
    tmp <- tempfile()
    utils::download.file(url_git, tmp, ...)
    load(tmp)
    sF_07      
  } else if (year == 2010) {
    sF_10 <- 1
    tmp <- tempfile()
    utils::download.file(url_git, tmp, ...)
    load(tmp)
    sF_10      
  } else if (year == 2011) {
    sF_11 <- 1
    tmp <- tempfile()
    utils::download.file(url_git, tmp, ...)
    load(tmp)
    sF_11      
  } else if (year == 2013) {
    sF_13 <- 1
    tmp <- tempfile()
    utils::download.file(url_git, tmp, ...)
    load(tmp)
    sF_13      
  } else if (year == 2016) {
    sF_16 <- 1
    tmp <- tempfile()
    utils::download.file(url_git, tmp, ...)
    load(tmp)
    sF_16      
  } else if (year == 2019) {
    sF_19 <- 1
    tmp <- tempfile()
    utils::download.file(url_git, tmp, ...)
    load(tmp)
    sF_19      
  } else {
    warning("Electoral boundaries for desired year are not available: select from 2001, 2004, 2007, 2010, 2011, 2013, 2016, 2019") 
    return()
  }
  
}

#' Download first preference voting data from each polling booth, from the six 
#' Australian Federal elections between 2001 and 2016.
#' 
#' @param ... Additional arguments passed to `download.file`
#'
#' Downloads and returns first preference votes for candidates in the House of 
#' Representatives, for each polling both, in the seven Australian Federal elections 
#' between 2001 and 2016. 
#'
#' @return A data frame containing first preference votes
#' 
#' A dataset containing first preference vote counts, candidate names, polling place locations,
#' and other results for the House of Representatives from the 2001, 2004, 2007, 2010,
#' 2013 and 2016 Australian federal elections. This data set is obtained using the 
#' `firstpref_pollingbooth_download` function. The data were obtained from the Australian Electoral 
#' Commission.
#' 
#' @format A data frame with the following variables:
#' \itemize{
#'     \item StateAb: Abbreviation for state name    
#'     \item DivisionID: Electoral division ID    
#'     \item DivisionNm:  Electoral division name   
#'     \item PollingPlaceID: Polling place ID  
#'     \item PollingPlace: Polling place name     
#'     \item CandidateID: Candidate ID       
#'     \item Surname: Candidate surname          
#'     \item GivenNm: Candidate given name            
#'     \item BallotPosition: Candidate's position on the ballot    
#'     \item Elected: Whether the candidate was elected (Y/N)           
#'     \item HistoricElected: Whether the candidate is the incumbent member  
#'     \item PartyAb: Abbreviation for political party name           
#'     \item PartyNm: Political party name           
#'     \item OrdinaryVotes: Number of ordinary votes cast at the polling place
#'     for the candidate     
#'     \item Swing: Percentage point change in ordinary votes for the party 
#'     from the previous election
#'     \item PremisesPostCode: Post code of polling booth  
#'     \item Latitude: Coordinates        
#'     \item Longitude: Coordinates
#'     \item year: Election year
#'     }
#' 
#' @examples
#' \dontrun{
#' fp_pp <- firstpref_pollingbooth_download()
#' library(dplyr)
#' fp_pp %>% filter(year == 2016) %>% arrange(-OrdinaryVotes) %>% head
#' }
#' 
#' @export
firstpref_pollingbooth_download <- function(...){
  fp_pp <- 1
  tmp <- tempfile()
  utils::download.file("https://github.com/jforbes14/eechidna/raw/master/extra-data/fp_pp.rda", tmp)
  load(tmp)
  fp_pp 
}

#' Download two party preference voting data from each polling booth, from the seven 
#' Australian Federal elections between 2001 and 2016.
#' 
#' @param ... Additional arguments passed to `download.file`
#'
#' Downloads and returns the two party preferred votes for candidates in the House of 
#' Representatives, for each polling both, in the six Australian Federal elections between
#' 2001 and 2016. 
#'
#' @return A data frame containing two party preference votes
#' 
#' A dataset containing two party preferred vote counts, winning candidate names, 
#' polling place locations, and other results for the House of Representatives from 
#' each of the 2001, 2004, 2007, 2010, 2013 and 2016 Australian federal elections.
#' Includes the count of votes for the Australian Labor Party and the count of votes 
#' for the Liberal-National Coalition for each polling place. This data set is obtained 
#' using the `twoparty_pollingbooth_download` function. The data were obtained from the 
#' Australian Electoral Commission.
#' 
#' @format A data frame with the following variables:
#' \itemize{
#'     \item StateAb: Abbreviation for state name    
#'     \item DivisionID: Electoral division ID    
#'     \item DivisionNm:  Electoral division name   
#'     \item PollingPlaceID: Polling place ID  
#'     \item PollingPlace: Polling place name  
#'     \item LNP_Votes: Count of two party preferred vote in favour of the 
#'     Liberal National coalition
#'     \item LNP_Percent: Percentage of two party preferred vote in favour 
#'     of the Liberal National coalition   
#'     \item ALP_Votes: Count of two party preferred vote in favour of the 
#'     Labor party
#'     \item ALP_Percent: Percentage of two party preferred vote in favour 
#'     of the Labor party     
#'     \item TotalVotes: Total number of votes cast     
#'     \item Swing: Percentage point change in two party preferred vote from 
#'     the previous election     
#'     \item PremisesPostCode: Post code of polling booth  
#'     \item Latitude: Coordinates        
#'     \item Longitude: Coordinates
#'     \item year: Election year
#'     }
#' 
#' @examples
#' \dontrun{
#' tpp_pp <- twoparty_pollingbooth_download()
#' library(dplyr)
#' tpp_pp %>% filter(year == 2016) %>% arrange(-LNP_Percent) %>% head
#' }
#' 
#' @export
twoparty_pollingbooth_download <- function(...){
  tpp_pp <- 1
  tmp <- tempfile()
  utils::download.file("https://github.com/jforbes14/eechidna/raw/master/extra-data/tpp_pp.rda", tmp)
  load(tmp)
  tpp_pp 
}

#' Download two candidate preference voting data from each polling booth, from the five 
#' Australian Federal elections between 2004 and 2016.
#' 
#' @param ... Additional arguments passed to `download.file`
#'
#' Downloads and returns the two candidate preferred votes for candidates in the House of 
#' Representatives, for each polling both, in the five Australian Federal elections between
#' 2004 and 2016. 
#'
#' @return A data frame containing two candidate preference votes
#' 
#' A dataset containing two candidate preferred vote counts, polling place locations,
#' and other results for the House of Representatives from each of the 2004, 
#' 2007, 2010, 2013 and 2016 Australian federal elections. Includes the count of votes
#' for the leading two candidates in the electorate after distribution of preferences 
#' for each polling place. Note that 2001 two candidate preferred vote is not available
#' in this package. This data set is obtained using the `twocand_pollingbooth_download` function.
#' The data were obtained from the Australian Electoral Commission,
#' 
#' @format A data frame with the following variables:
#' \itemize{
#'     \item StateAb: Abbreviation for state name    
#'     \item DivisionID: Electoral division ID    
#'     \item DivisionNm:  Electoral division name   
#'     \item PollingPlaceID: Polling place ID  
#'     \item PollingPlace: Polling place name     
#'     \item CandidateID: Candidate ID       
#'     \item Surname: Candidate surname          
#'     \item GivenNm: Candidate given name            
#'     \item BallotPosition: Candidate's position on the ballot    
#'     \item Elected: Whether the candidate was elected (Y/N)           
#'     \item HistoricElected: Whether the candidate is the incumbent member  
#'     \item PartyAb: Abbreviation for political party name           
#'     \item PartyNm: Political party name           
#'     \item OrdinaryVotes: Number of ordinary votes cast at the polling place 
#'     for the candidate     
#'     \item Swing: Percentage point change in ordinary votes for the party 
#'     from the previous election
#'     \item PremisesPostCode: Post code of polling booth  
#'     \item Latitude: Coordinates        
#'     \item Longitude: Coordinates
#'     \item year: Election year
#'     }
#'     
#' @examples
#' \dontrun{
#' tcp_pp <- twocand_pollingbooth_download()
#' library(dplyr)
#' tcp_pp %>% filter(year == 2016) %>% arrange(-OrdinaryVotes) %>% head
#' }
#' 
#' @export
twocand_pollingbooth_download <- function(...){
  tcp_pp <- 1
  tmp <- tempfile()
  utils::download.file("https://github.com/jforbes14/eechidna/raw/master/extra-data/tcp_pp.rda", tmp)
  load(tmp)
  tcp_pp 
}