This is my first attempt at an R Markdown document.
When you click the Knit HTML button a web page will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:
This code loads the relevant packages into R.
## Enable required libraries
library(Hmisc)## Loading required package: survival
## Loading required package: splines
## Hmisc library by Frank E Harrell Jr
##
## Type library(help='Hmisc'), ?Overview, or ?Hmisc.Overview') to see overall
## documentation.
##
## NOTE:Hmisc no longer redefines [.factor to drop unused levels when
## subsetting. To get the old behavior of Hmisc type dropUnusedLevels().
## Attaching package: 'Hmisc'
## The following object(s) are masked from 'package:survival':
##
## untangle.specials
## The following object(s) are masked from 'package:base':
##
## format.pval, round.POSIXt, trunc.POSIXt, units
library(foreign)
library(XML)This code reads in the first set of data.
## READ IN DATA
## FIRST PREFERENCE VOTES 2010 BY _Candidate_ CSV file Code from Simon
## Jackman - source is comments section of:
## http://jackman.stanford.edu/blog/?p=1760
## 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)
## READ IN DATA
## FIRST PREFERENCE VOTES 2010 BY _Candidate_ CSV file Code from Simon
## Jackman - source is comments section of:
## http://jackman.stanford.edu/blog/?p=1760
## Define the location of the data (AEC website)
theURL <- "http://results.aec.gov.au/15508/Website/Downloads/HouseFirstPrefsByCandidateByVoteTypeDownload-15508.csv"
## Vote counts occuring by type
vars <- c("OrdinaryVotes", "AbsentVotes", "ProvisionalVotes", "PrePollVotes",
"PostalVotes", "TotalVotes")
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$DivisionNm,
sum, na.rm = TRUE))
}
outvars <- c("OrdinaryVotes", "AbsentVotes", "ProvisionalVotes", "PrePollVotes",
"PostalVotes", "TotalVotes", "DivisionName")
z <- apply(out, 2, function(x) sum(x != 0))
names(z) <- vars
print(z)## OrdinaryVotes AbsentVotes ProvisionalVotes PrePollVotes
## 150 150 150 150
## PostalVotes TotalVotes
## 150 150
## Swing against ALP candidates THIS CODE TO GENERATE SWING DOESNT WORK??
## swing <- aggregate(First_Preferences_By_Candidate_By_Vote_Type,
## list(Party = First_Preferences_By_Candidate_By_Vote_Type['PartyAb' =
## 'ALP']), mean) print(swing)
# Convert out data matrix to a data frame
outdf <- as.data.frame(out)
# Transform division from row.names to a variable
# http://stackoverflow.com/questions/11427434/how-to-create-a-variable-of-rownames
outdf$Division = rownames(outdf)
rownames(outdf) = NULL
# Assign column (variable) names
colnames(outdf) <- outvars
# Describe 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)Plot the vote distribution by division
library(ggplot2)
qplot(jackman_votetype_by_division$TotalVotes, main = "Total Votes by Division, 2010 Federal Election",
xlab = "Total votes")## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust
## this.
End of my first file