My adopted home state of Utah has been a weird place this election cycle. For the unfamiliar, Utah is extremely conservative when it comes to politics; it is one of the reddest of the red states and has backed the Republican candidate for president for the past many decades. In 2012, about 3/4 of the popular vote went to Mitt Romney (who is LDS, like many here in the state) and there were no counties where Mitt Romney did not win.
The story during the 2016 election cycle has been very different, and well, kind of strange, if you ask me. For starters, Utah voters did not vote for the winning candidate in either party’s primary. Donald Trump is extremely unpopular here because his personality and values do not align with the typical Utah Republican. At the same time, Hillary Clinton is also quite unpopular here; the Clintons have just generally been unpopular here for a quarter decade with traditional Utah Republicans and she was not the first choice of Utah’s liberal voters. The winners of Utah’s primaries were Ted Cruz and Bernie Sanders. To complicate this situation, in the last weeks of our long, national nightmare that has been the 2016 campaign, Evan McMullin rose to some prominence as an independent candidate in Utah.
Utah isn’t particularly large or populous or really important, but it is where I live, so let’s look at the results of last Tuesday’s election!
Utah makes election results available online, but also Mike Kearney has gathered county-level election results for the U.S. and put it on GitHub. Let’s load the libraries I’ll use here, and then open the data that Mike has gathered.
library(readr) library(choroplethr) library(choroplethrMaps) library(ggplot2) library(ggthemes) library(RColorBrewer) library(gridExtra) library(dplyr) library(stringr) library(tidyr) all_results <- read_csv("https://raw.githubusercontent.com/mkearney/presidential_election_county_results_2016/master/pres16results.csv")
What does this look like?
## # A tibble: 6 × 8 ## X1 cand_id cand_name votes total state fips pct ## <int> <chr> <chr> <int> <int> <chr> <chr> <dbl> ## 1 1 US8639 Donald Trump 59821874 126061003 US US 0.474547025 ## 2 2 US1746 Hillary Clinton 60122876 126061003 US US 0.476934774 ## 3 3 US31708 Gary Johnson 4087972 126061003 US US 0.032428522 ## 4 4 US895 Jill Stein 1223828 126061003 US US 0.009708220 ## 5 5 US65775 Evan McMullin 425991 126061003 US US 0.003379245 ## 6 6 US59414 Darrell Castle 175956 126061003 US US 0.001395800
Counties in Utah have FIPS codes that start with 49.
utah <- all_results %>% filter(str_detect(fips, "^49")) utah
## # A tibble: 290 × 8 ## X1 cand_id cand_name votes total state fips pct ## <int> <chr> <chr> <int> <int> <chr> <chr> <dbl> ## 1 16087 UT55311 Donald Trump 1800 2434 UT 49001 0.7395234182 ## 2 16088 UT55316 Evan McMullin 316 2434 UT 49001 0.1298274445 ## 3 16089 UT55310 Hillary Clinton 258 2434 UT 49001 0.1059983566 ## 4 16090 UT55313 Gary Johnson 36 2434 UT 49001 0.0147904684 ## 5 16091 UT55312 Darrell Castle 12 2434 UT 49001 0.0049301561 ## 6 16092 UT55318 Jill Stein 10 2434 UT 49001 0.0041084634 ## 7 16093 UT55315 Alyson Kennedy 1 2434 UT 49001 0.0004108463 ## 8 16094 UT55317 Monica Moorehead 1 2434 UT 49001 0.0004108463 ## 9 16095 UT55314 Rocky De La Fuente 0 2434 UT 49001 0.0000000000 ## 10 16096 UT55319 Rocky Giordani 0 2434 UT 49001 0.0000000000 ## # ... with 280 more rows
There are 29 counties in Utah. Let’s just check to make sure they’re all here.
##  29
This is great. I would just like to say a big THANK YOU to Mike Kearney because this CSV is so well-formated and tidy!
In election results, we see a lot of maps like this one, where each county is colored with who won and the color intensity scales with the margin by which the candidate won that county; these maps are great and communicate a certain kind of information. However, it seemed like Utah might be a three-way race (and you can see from that NYTimes link that McMullin did in fact get 20% of the vote here) so let’s try a different way of visualizing these results, just to get a different view.
I’m going to use Ari Lamstein’s choroplethr package for mapping the results. Ari has a lot of exciting stuff going on over at his website these days, with his free email course and a membership community. To use the data with the choroplethr package, I actually need to convert it to a wide, “un-tidy” format, so let’s work on that.
utah_spread <- utah %>% filter(cand_name %in% c("Donald Trump", "Hillary Clinton", "Evan McMullin")) %>% select(fips, cand_name, pct) %>% mutate(pct = pct * 100, region = as.numeric(fips)) %>% spread(cand_name, pct) %>% select(-fips) utah_spread
## # A tibble: 29 × 4 ## region `Donald Trump` `Evan McMullin` `Hillary Clinton` ## * <dbl> <dbl> <dbl> <dbl> ## 1 49001 73.95234 12.982744 10.599836 ## 2 49003 62.38820 21.700508 10.962050 ## 3 49005 46.74996 27.672505 20.515627 ## 4 49007 66.04390 7.747583 21.857852 ## 5 49009 70.32258 8.602151 16.344086 ## 6 49011 44.94619 27.521151 22.397698 ## 7 49013 79.50689 10.427846 7.222625 ## 8 49015 79.80296 8.491673 8.890453 ## 9 49017 68.74455 11.987794 15.344377 ## 10 49019 43.46058 6.189293 43.370228 ## # ... with 19 more rows
That will do it!
Now let’s set up the code to make the maps. I will make one map for each candidate and then stitch them together, because I want to see how each candidate did across the state.
utah_spread$value <- utah_spread$`Donald Trump` choro1 = CountyChoropleth$new(utah_spread) choro1$set_zoom("utah") choro1$title = "Donald Trump" choro1$set_num_colors(1) choro1$ggplot_polygon = geom_polygon(aes(fill = value), color = NA) choro1$ggplot_scale = scale_fill_gradientn(name = "Percent", colours = brewer.pal(8, "Reds")) utah_spread$value <- utah_spread$`Hillary Clinton` choro2 = CountyChoropleth$new(utah_spread) choro2$set_zoom("utah") choro2$title = "Hillary Clinton" choro2$set_num_colors(1) choro2$ggplot_polygon = geom_polygon(aes(fill = value), color = NA) choro2$ggplot_scale = scale_fill_gradientn(name = "Percent", colours = brewer.pal(8, "Blues")) utah_spread$value <- utah_spread$`Evan McMullin` choro3 = CountyChoropleth$new(utah_spread) choro3$set_zoom("utah") choro3$title = "Evan McMullin" choro3$set_num_colors(1) choro3$ggplot_polygon = geom_polygon(aes(fill = value), color = NA) choro3$ggplot_scale = scale_fill_gradientn(name = "Percent", colours = brewer.pal(8, "Greens")) grid.arrange(choro1$render() + theme(text=element_text(family="KerkisSans")), choro2$render() + theme(text=element_text(family="KerkisSans")), choro3$render() + theme(text=element_text(family="KerkisSans")), ncol = 3)
Gosh, as someone who lives here, it is so satisfying to see this set of maps. First of all, that county where Evan McMullin did the best is Utah County, Utah’s second most populous county and the seat of Brigham Young University. Hillary Clinton did well in Salt Lake County (the most populous county, where I live and the site of protests last night) and Summit County (where Park City and the ski resorts are); in fact, she won the popular vote in both of those counties. Donald Trump won the rest of the counties. Talk about your urban/rural divide right there. He won by enough overall that, as we know of course, he won the state’s electoral votes. Let’s make a plot of that, just for kicks.
votes <- utah %>% filter(cand_name %in% c("Donald Trump", "Hillary Clinton", "Evan McMullin")) %>% mutate(cand_name = factor(cand_name, levels = c("Donald Trump", "Hillary Clinton", "Evan McMullin"))) %>% group_by(cand_name) %>% summarise(sum = sum(votes)) votes
## # A tibble: 3 × 2 ## cand_name sum ## <fctr> <int> ## 1 Donald Trump 375006 ## 2 Hillary Clinton 222858 ## 3 Evan McMullin 163573
These total numbers are a bit lower than what is currently being reported on Utah’s official election results website, but just proportionately a little lower overall. Utah’s election results won’t be officially certified until November 22.
ggplot(votes, aes(cand_name, sum, fill = cand_name)) + geom_bar(stat = "identity", alpha = 0.8) + theme_tufte(base_family = "KerkisSans") + scale_fill_manual(values = c("red3", "navyblue", "darkgreen")) + theme(legend.position="none") + labs(title = "Total Votes Cast in Utah", y = "Number of votes", x = NULL)
This is a big change from 2012. Hillary Clinton did several percentage points better than Barack Obama; Utah is one of the few places where this is true. Trump’s showing around 45% of the vote is much, much worse (around 30 percentage points worse) than how Mitt Romney did here in 2012. It is perhaps not news that Trump is this unpopular here in Utah, and enough people still voted for him in this intensely Republican state for him to win the electoral votes.
What about total turnout?
utah %>% summarise(total = sum(votes))
## # A tibble: 1 × 1 ## total ## <int> ## 1 801248
This is more than 20% lower than the 2012 turnout; that is a BIG DROP! I do want to note that this number is lower than what is currently being reported on Utah’s official election results site so the total number of votes cast is higher than this. I’m not sure what the total number will eventually be once the vote is certified, but surely there are not 20% more votes still out there to be counted. Assuming there is a drop in turnout, I’m sure it reflects Utah’s unhappiness with both of the major party candidates. I am concerned, like many others, with problems with voter suppression and the gutting of the Voting Rights Act but I don’t think these were big issues in Utah. I think this is an issue of turnout and people not being excited to vote for either candidate.
Politics has not been one of my life’s great passions, but I do care about my community and my neighbors and with that comes politics, I guess. I am an ex-academic with a PhD who has always lived in cities, so it is probably not a big shock to anybody that I am disappointed by the results of Tuesday’s election and concerned about what it means for our country. I am at a bit of a loss about what to say about all of that, to be honest, but I have appreciated the perspectives of other people on how we talk about this election, or maybe just the Onion. The R Markdown file used to make this blog post is available here. I am very happy to hear feedback or questions!
- Posted on:
- November 11, 2016
- 8 minute read, 1662 words