Author: Steve McEachern Date:
date()## [1] "Mon Sep 3 11:08:13 2012"
github: [email protected]:stevenmce/aec_analysis.git This is a special R script which can be used to generate a report. You can write normal text in roxygen comments.
First we set up some options (you do not have to do this):
The report begins here.
# This code reads in the dataCode from Simon Jackman - source is comments section of: http://jackman.stanford.edu/blog/?p=1760 LOAD
Define the location of the data (AEC website)
theURL <- "http://results.aec.gov.au/15508/Website/Downloads/HouseFirstPrefsByCandidateByVoteTypeDownload-15508.csv"Read in the data
First_Preferences_By_Candidate_By_Vote_Type <- read.csv(url(theURL), skip = 1,
header = TRUE, quote = "\"", stringsAsFactors = FALSE)Vote counts occuring by type Define variables
vars <- c("OrdinaryVotes", "AbsentVotes", "ProvisionalVotes", "PrePollVotes",
"PostalVotes", "TotalVotes")Aggregate votes by division (sum all candidates)
out <- NULL
for (v in vars) {
theVar <- First_Preferences_By_Candidate_By_Vote_Type[, match(v, names(First_Preferences_By_Candidate_By_Vote_Type))]
out <- cbind(out, tapply(theVar, First_Preferences_By_Candidate_By_Vote_Type$DivisionNm,
sum, na.rm = TRUE))
}Define variable names
outvars <- c("OrdinaryVotes", "AbsentVotes", "ProvisionalVotes", "PrePollVotes",
"PostalVotes", "TotalVotes", "DivisionName")
z <- apply(out, 2, function(x) sum(x != 0))
names(z) <- vars
print(z)## OrdinaryVotes AbsentVotes ProvisionalVotes PrePollVotes
## 150 150 150 150
## PostalVotes TotalVotes
## 150 150
## Swing against ALP candidates THIS CODE TO GENERATE SWING DOESNT WORK??
## swing <- aggregate(First_Preferences_By_Candidate_By_Vote_Type,
## list(Party = First_Preferences_By_Candidate_By_Vote_Type['PartyAb' =
## 'ALP']), mean) print(swing)Convert out data matrix to a data frame
outdf <- as.data.frame(out)Transform division from row.names to a variable http://stackoverflow.com/questions/11427434/how-to-create-a-variable-of-rownames
outdf$Division = rownames(outdf)
rownames(outdf) = NULLAssign column (variable) names
colnames(outdf) <- outvarsDescribe the data
describe(outdf)## outdf
##
## 7 Variables 150 Observations
## ---------------------------------------------------------------------------
## OrdinaryVotes
## n missing unique Mean .05 .10 .25 .50 .75
## 150 0 149 73852 61940 65536 69770 74234 78000
## .90 .95
## 81693 83883
##
## lowest : 42257 47881 57272 57375 57801
## highest: 88520 89736 94172 103628 105358
## ---------------------------------------------------------------------------
## AbsentVotes
## n missing unique Mean .05 .10 .25 .50 .75
## 150 0 149 4821 2561 3042 4108 4878 5761
## .90 .95
## 6488 7078
##
## lowest : 791 924 1547 1688 1708, highest: 7279 7523 7618 7902 8826
## ---------------------------------------------------------------------------
## ProvisionalVotes
## n missing unique Mean .05 .10 .25 .50 .75
## 150 0 114 248.9 118.5 143.9 171.5 229.0 299.5
## .90 .95
## 402.4 446.2
##
## lowest : 77 81 84 99 101, highest: 512 522 609 615 656
## ---------------------------------------------------------------------------
## PrePollVotes
## n missing unique Mean .05 .10 .25 .50 .75
## 150 0 150 3240 1768 1967 2466 3053 3791
## .90 .95
## 4808 5206
##
## lowest : 1393 1653 1663 1668 1677, highest: 5663 5711 6174 6682 6745
## ---------------------------------------------------------------------------
## PostalVotes
## n missing unique Mean .05 .10 .25 .50 .75
## 150 0 150 5383 3276 3529 4284 5166 6386
## .90 .95
## 7216 7874
##
## lowest : 1242 2049 2736 2849 2921
## highest: 8267 8295 8471 9627 11118
## ---------------------------------------------------------------------------
## TotalVotes
## n missing unique Mean .05 .10 .25 .50 .75
## 150 0 150 87544 75664 80588 84404 87798 91302
## .90 .95
## 94484 97121
##
## lowest : 46409 53672 67294 67896 68216
## highest: 105033 109950 110052 116712 117911
## ---------------------------------------------------------------------------
## DivisionName
## n missing unique
## 150 0 150
##
## lowest : Adelaide Aston Ballarat Banks Barker
## highest: Wentworth Werriwa Wide Bay Wills Wright
## ---------------------------------------------------------------------------
Rename the data frame
jackman_votetype_by_division <- outdf
remove(outdf)
remove(out)FIRST PREFERENCE RESULTS BY Division - HTML table IF REQUIRED: remove(HouseFirstPrefsTppByDivision)
READ IN DATA - NOTE StringsAsFactors - False to avoid FACTOR problems
HouseFirstPrefsTppByDivision <- readHTMLTable("http://results.aec.gov.au/15508/Website/HouseFirstPrefsTppByDivision-15508-NAT.htm",
header = c("Division", "State", "ALP_1Prf", "LP_1Pref", "LNQ_1Pref", "GRN_1Pref",
"NP_1Pref", "OTH_1Pref", "ALP_2PP", "LNP_2PP"), skip.rows = c(1, 2),
trim = TRUE, as.data.frame = TRUE, which = 5, stringsAsFactors = FALSE)Describe the data
describe(HouseFirstPrefsTppByDivision)## HouseFirstPrefsTppByDivision
##
## 10 Variables 151 Observations
## ---------------------------------------------------------------------------
## Division
## n missing unique
## 151 0 151
##
## lowest : Adelaide Aston Ballarat Banks Barker
## highest: Wentworth Werriwa Wide Bay Wills Wright
## ---------------------------------------------------------------------------
## NA
## n missing unique
## 151 0 9
##
## 37.99 ACT NSW NT QLD SA TAS VIC WA
## Frequency 1 2 48 2 30 11 5 37 15
## % 1 1 32 1 20 7 3 25 10
## ---------------------------------------------------------------------------
## NA
## n missing unique
## 151 0 150
##
## lowest : 13.49 17.11 19.22 19.27 19.89
## highest: 58.81 59.98 62.12 64.25 8.13
## ---------------------------------------------------------------------------
## NA
## n missing unique
## 151 0 110
##
## lowest : 0.00 16.52 19.08 19.90 21.00
## highest: 62.12 62.66 64.45 64.98 9.12
## ---------------------------------------------------------------------------
## NA
## n missing unique
## 151 0 32
##
## lowest : 0.00 11.76 26.60 35.80 37.10
## highest: 58.33 58.86 61.25 61.85 65.52
## ---------------------------------------------------------------------------
## NA
## n missing unique
## 151 0 145
##
## lowest : 10.01 10.09 10.15 10.19 10.33
## highest: 9.61 9.62 9.67 9.72 9.97
## ---------------------------------------------------------------------------
## NA
## n missing unique
## 151 0 18
##
## 0.00 17.70 2.54 21.21 25.22 28.85 31.05 34.39 42.47 44.77 5.32
## Frequency 134 1 1 1 1 1 1 1 1 1 1
## % 89 1 1 1 1 1 1 1 1 1 1
## 5.98 50.77 52.41 53.00 59.23 6.94 66.79
## Frequency 1 1 1 1 1 1 1
## % 1 1 1 1 1 1 1
## ---------------------------------------------------------------------------
## NA
## n missing unique
## 151 0 136
##
## lowest : 0.00 0.01 1.28 1.41 1.57, highest: 9.36 9.37 9.46 9.49 9.67
## ---------------------------------------------------------------------------
## NA
## n missing unique
## 151 0 147
##
## lowest : 25.59 27.00 27.11 29.65 31.14
## highest: 72.25 72.64 73.30 73.90 74.91
## ---------------------------------------------------------------------------
## NA
## n missing unique
## 150 1 146
##
## lowest : 25.09 26.10 26.70 27.36 27.75
## highest: 68.86 70.35 72.89 73.00 74.41
## ---------------------------------------------------------------------------
Apply variable names to columns
HouseFirstPrefsTppByDivision_varnames <- c("Division", "State", "ALP_1Prf",
"LP_1Pref", "LNQ_1Pref", "GRN_1Pref", "NP_1Pref", "OTH_1Pref", "ALP_2PP",
"LNP_2PP")
colnames(HouseFirstPrefsTppByDivision) <- HouseFirstPrefsTppByDivision_varnamesRecode values of NATIONAL TOTAL due to missing State value select rows where v1 is 99 and recode column v1
HouseFirstPrefsTppByDivision[HouseFirstPrefsTppByDivision$Division == "National Total",
"State"] <- "National"
HouseFirstPrefsTppByDivision[HouseFirstPrefsTppByDivision$Division == "National Total",
"ALP_1Prf"] <- 37.99
HouseFirstPrefsTppByDivision[HouseFirstPrefsTppByDivision$Division == "National Total",
"LP_1Pref"] <- 30.46
HouseFirstPrefsTppByDivision[HouseFirstPrefsTppByDivision$Division == "National Total",
"LNQ_1Pref"] <- 9.12
HouseFirstPrefsTppByDivision[HouseFirstPrefsTppByDivision$Division == "National Total",
"GRN_1Pref"] <- 11.76
HouseFirstPrefsTppByDivision[HouseFirstPrefsTppByDivision$Division == "National Total",
"NP_1Pref"] <- 3.73
HouseFirstPrefsTppByDivision[HouseFirstPrefsTppByDivision$Division == "National Total",
"OTH_1Pref"] <- 6.94
HouseFirstPrefsTppByDivision[HouseFirstPrefsTppByDivision$Division == "National Total",
"ALP_2PP"] <- 50.12
HouseFirstPrefsTppByDivision[HouseFirstPrefsTppByDivision$Division == "National Total",
"LNP_2PP"] <- 49.88
# CHECK CHANGES APPLIED CORRECTLY VALUES: National Total [STATE] 37.99
# 30.46 9.12 11.76 3.73 6.94 50.12 49.88
tail(HouseFirstPrefsTppByDivision)## Division State ALP_1Prf LP_1Pref LNQ_1Pref GRN_1Pref NP_1Pref
## 146 Wentworth NSW 21.07 59.57 0.00 17.44 0.00
## 147 Werriwa NSW 48.57 38.72 0.00 12.70 0.00
## 148 Wide Bay QLD 24.10 0.00 58.86 11.00 0.00
## 149 Wills VIC 51.81 23.79 0.00 20.60 0.00
## 150 Wright QLD 27.78 0.00 53.09 11.95 0.00
## 151 National Total National 37.99 30.46 9.12 11.76 3.73
## OTH_1Pref ALP_2PP LNP_2PP
## 146 1.92 35.14 64.86
## 147 0.01 56.75 43.25
## 148 6.04 34.39 65.61
## 149 3.80 72.64 27.36
## 150 7.18 39.85 60.15
## 151 6.94 50.12 49.88
# CREATE A State FACTOR VARIABLE
HouseFirstPrefsTppByDivision$State_factor <- as.factor(HouseFirstPrefsTppByDivision$State)
# RENAME THE DIVISION VARIABLE (FOR FILE MERGING)
HouseFirstPrefsTppByDivision$DivisionNm <- as.character(HouseFirstPrefsTppByDivision$Division)
HouseFirstPrefsTppByDivision$Division <- NULLDefine the location of the data (AEC website)
theURL <- "http://results.aec.gov.au/15508/Website/Downloads/HouseVotesCountedByDivisionDownload-15508.csv"
## Read in the data
HouseVotesCountedByDivision <- read.csv(url(theURL), skip = 1, header = TRUE,
quote = "\"", stringsAsFactors = FALSE)
require(ggplot2)## Loading required package: ggplot2
quickplot(HouseVotesCountedByDivision$TotalVotes)## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust
## this.
# Run the following on the command line to generate R-markdown document
# spin(aec_read_data_knitr.r)END OF FILE