UK General Election 2017: May vs June
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
Pundits have been asking themselves over the last days how did Theresa May’s gamble fail? Although the Conservative party received 42% of the vote, Labour received 40%, and all the other parties received 18% between them. The Tories lost their parliamentary majority in an election they purposively called when they were told they were 20 points ahead in the polls. What went wrong this time (again)?
Two year ago, in the previous general election, Conservative beat Labour by 37%-30%. And the time before, the Conservatives beat Labour by 36%-29% of the vote.
The polls were certainly off, and the higher turnout seems to have favored the Labour party this time. But the inconvenient conclusion from my preliminary analysis is that the Conservatives would have received at least 20 fewer seats, so taking them well below the required majority, if Labour, Lib Dem, and SNP voters had coordinated.
This is not voters’ fault, however. It is very difficult to communicate with all voters supporting several different parties to get them to adopt a strategy. In fact, this is a known failure of First-past-the-post voting systems. Other voting systems would accomplish the strategic outcome automatically, but perhaps would impose new concerns as well.
The data
The data were collected from The Economist’s Infographics following the steps put forward by Thiemo Fetzer, and further stored as a CSV file. I didn’t upload it anywhere, but I’d be happy to share it with you if really needed, just send me an e-mail.
Load the data
library(dplyr)
data <- read.csv("UK2017.csv", skip = 1, header=T, stringsAsFactors = F)
data[,7:15] <- lapply(data[,7:15],function(x){as.numeric(gsub(",", "", x))})
data[is.na(data)] <- 0
Con <- data %>% filter(X2017 == "Con")
Lab <- data %>% filter(X2017 == "Lab")
nrow(Con);
[1] 317
nrow(Lab)
[1] 260Labour’s weakest seats
So, among seats won by Labour, how many might Con + UKIP have won if their voters had coordinated? Just 13 more seats.
Lab.margin <- with(Lab, Con + UKIP - Lab)
sum(Lab.margin > 0)
[1] 13How many seats would have been won by a margin of 1000 or more votes?
sum(Lab.margin > 1000)
[1] 9Which seats are these?
print(Lab[which(Lab.margin > 1000),
c("Constituency", "Con", "UKIP", "Lab")],
row.names = FALSE)
Constituency Con UKIP Lab
Leicester East 35116 0 12668
Crewe & Nantwich 25880 1885 25928
Exeter 34336 0 18219
Dudley North 18068 2144 18090
Walsall South 25286 1805 16394
Wolverhampton North East 12623 1675 12623
Keighley 23817 1291 24066
Penistone & Stocksbridge 21485 3453 22807
Sheffield, Heeley 26524 1977 12696Tories weakest seats
So, among seats won by Conservatives, how many might Lab + Lib Dem + SNP have won if their voters had coordinated? The number of 52 seats is surprisingly high.
Con.margin <- with(Con, Lab + Lib + SNP - Con)
sum(Con.margin > 0)
[1] 52Similarly, how many seats would have been won by a margin of 1000 or more votes?
sum(Con.margin > 1000)
[1] 33Which seats are these?
print(Con[which(Con.margin > 1000),
c("Constituency", "Lab", "Lib", "SNP", "Con")],
row.names = FALSE)
Constituency Lab Lib SNP Con
Broxtowe 25120 2247 0 25983
Colchester 18888 9087 0 24565
St Albans 13137 18462 0 24571
Watford 24639 5335 0 26731
Chipping Barnet 25326 3012 0 25679
Cities of London & Westminster 14857 4270 0 18005
Finchley & Golders Green 22942 3463 0 24599
Putney 19125 5448 0 20679
Richmond Park 5773 26543 0 28588
Wimbledon 18324 7427 0 23946
Cheadle 10417 19824 0 24331
Hazel Grove 9036 14533 0 20047
Southport 15627 12661 0 18541
Aberdeen South 9143 2600 13994 18746
Angus 5233 1308 15504 18148
Ayr, Carrick & Cumnock 11024 872 15776 18550
Banff & Buchan 3936 1448 16283 19976
Dumfries & Galloway 10775 1241 16701 22344
East Renfrewshire 14346 1112 16784 21496
Gordon 6340 6230 19254 21861
Moray 5208 1078 18478 22637
Ochil & South Perthshire 10847 1742 19110 22469
Stirling 10902 1683 18143 18291
West Aberdeenshire & Kincardine 5706 4461 16754 24704
Hastings & Rye 25322 1885 0 25668
Southampton, Itchen 21742 1421 0 21773
Camborne & Redruth 21424 2979 0 23001
Cheltenham 5408 24046 0 26615
North Devon 7063 21185 0 25517
St Ives 7298 21808 0 22120
Truro & Falmouth 21331 8465 0 25123
Calder Valley 26181 1952 0 26790
Pudsey 25219 1761 0 25550R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.