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Mapping Election Results with R and Choroplethr

(This article was first published on R – AriLamstein.com , and kindly contributed toR-bloggers)

Today’s guest post is by R. Duncan McIntosh . Last week Duncan tweeted about using choroplethr to map the 2016 Florida primary election results. I’ve been wanting to analyze election results in R for some time, and asked Duncan to share with my readers how he did his analysis. This is his reply. 

Election season is providing plenty of data to explore. Today I will demonstrate how to make a choropleth map of recent presidential primary election results in R. The final map we will produce compares the democratic candidates’ percent of total votes by county:

Mapping Election Results with R and Choroplethr

The Data

The election results for Florida are made available by Florida Election Watch . Using r ead.delim() , you can read directly from the tab delimited file online, which allows for a completely reproducible analysis from start to finish, though you might want to also download the file for offline use. Setting the argument  strip.white = TRUE  removes the problematic white spaces in the  CountyNames column.

# Load required packages library(ggplot2) library(dplyr) library(reshape2) library(choroplethr) library(choroplethrMaps) library(gridExtra) library(knitr)  # Read election results file from the web, and strip the white spaces fl <- read.delim("http://fldoselectionfiles.elections.myflorida.com/enightfilespublic/20160315_ElecResultsFL.txt", strip.white = T)

Using the dplyr package, I filtered the data frame leaving only one party and selected only the columns I’m interested in. Using the  reshape2 package’s  dcast function, I then cast the data frame from long to wide format (i.e., with each candidate’s vote counts in a separate column). I also changed the datatype of the  CountyName column to facilitate joining it with the  county.regions data frame in a later step.

# Filter leaving only one party, and select desired columns dem <- filter(fl, PartyCode == "DEM") %>% select(CountyName, CanNameLast, CanVotes)  # Cast dem dataframe from long to wide using dcast  dem_cast <- dcast(dem, CountyName ~ CanNameLast, sum)  # Now we can see each candidate's votes per county colnames(dem_cast)[3] <- "OMalley" # Remove apostrophe from O'Malley  # Change CountyName column from Factor to lowercase Character   dem_cast$CountyName <- tolower(as.character(dem_cast$CountyName))

Then, I created a new column for each county’s total vote count and columns for each candidate’s percentage of those totals.

# Create columns for total votes in each county dem_cast <- mutate(dem_cast, total = Clinton + OMalley + Sanders)  # Create columns for percentage variables dem_cast <- mutate(dem_cast, hc = (Clinton/total)*100, bs = (Sanders/total)*100, mo = (OMalley/total)*100) dem_cast[,6:8] <- round(dem_cast[,6:8], digits = 1)  # Round new variables to 1 decimal place

In order to map these county-level data with the choroplethr package, our data frame needs a column containing each county’s FIPS code. We can get this vector from the  county.regions data frame supplied with the  choroplethrMaps package. I  filtered the county.regions data frame leaving only Florida counties, then selected the  region column and the  county.name column while renaming the latter to  CountyName to match the analogous column in the   dem_cast data frame. After joining these FIPS codes to our election results dataframe with a  left_join(),  our data frame is now ready for mapping.

# Read county.regions dataframe supplied by choroplethrMaps package data("county.regions")  # Filter leaving only florida counties, and select only the 2 needed columns fl.regions <- filter(county.regions, state.name == "florida") %>% select(region, "CountyName" = county.name)  # Join regions column from fl.regions dataframe to election results dataframe df <- left_join(dem_cast, fl.regions)

A table view of counties won by Sanders:

bs.counties <- filter(df, Sanders > Clinton & Sanders > OMalley) kable(bs.counties, caption = "Counties won by Sanders")
Counties won by Sanders
CountyName Clinton OMalley Sanders total hc bs mo region
baker 654 240 805 1699 38.5 47.4 14.1 12003
calhoun 437 225 545 1207 36.2 45.2 18.6 12013
dixie 409 150 459 1018 40.2 45.1 14.7 12029
gilchrist 428 134 578 1140 37.5 50.7 11.8 12041
holmes 339 239 619 1197 28.3 51.7 20.0 12059
lafayette 204 136 363 703 29.0 51.6 19.3 12067
liberty 316 124 392 832 38.0 47.1 14.9 12077
suwannee 1475 475 1551 3501 42.1 44.3 13.6 12121
union 336 107 472 915 36.7 51.6 11.7 12125

A table view of counties won by Clinton:

hc.counties <- filter(df, Clinton > Sanders & Clinton > OMalley) kable(hc.counties, caption = "Counties won by Clinton")
Counties won by Clinton
CountyName Clinton OMalley Sanders total hc bs mo region
alachua 17777 708 17730 36215 49.1 49.0 2.0 12001
bay 5218 571 4134 9923 52.6 41.7 5.8 12005
bradford 1056 206 908 2170 48.7 41.8 9.5 12007
brevard 31862 1392 20100 53354 59.7 37.7 2.6 12009
broward 134328 1901 49054 185283 72.5 26.5 1.0 12011
charlotte 8126 321 4636 13083 62.1 35.4 2.5 12015
citrus 6865 555 4786 12206 56.2 39.2 4.5 12017
clay 5346 323 3699 9368 57.1 39.5 3.4 12019
collier 12719 390 6134 19243 66.1 31.9 2.0 12021
columbia 2304 372 1676 4352 52.9 38.5 8.5 12023
desoto 988 165 728 1881 52.5 38.7 8.8 12027
duval 59511 1982 27232 88725 67.1 30.7 2.2 12031
escambia 16770 853 9326 26949 62.2 34.6 3.2 12033
flagler 6160 215 2980 9355 65.8 31.9 2.3 12035
franklin 666 104 647 1417 47.0 45.7 7.3 12037
gadsden 7449 354 1945 9748 76.4 20.0 3.6 12039
glades 387 76 313 776 49.9 40.3 9.8 12043
gulf 568 111 520 1199 47.4 43.4 9.3 12045
hamilton 758 148 479 1385 54.7 34.6 10.7 12047
hardee 530 82 393 1005 52.7 39.1 8.2 12049
hendry 1157 104 647 1908 60.6 33.9 5.5 12051
hernando 8946 510 5549 15005 59.6 37.0 3.4 12053
highlands 3715 276 2056 6047 61.4 34.0 4.6 12055
hillsborough 69060 2402 38590 110052 62.8 35.1 2.2 12057
indian river 6901 228 3928 11057 62.4 35.5 2.1 12061
jackson 2805 551 1842 5198 54.0 35.4 10.6 12063
jefferson 1671 152 762 2585 64.6 29.5 5.9 12065
lake 15932 696 8482 25110 63.4 33.8 2.8 12069
lee 27993 1029 15673 44695 62.6 35.1 2.3 12071
leon 27401 1150 19930 48481 56.5 41.1 2.4 12073
levy 1570 215 1356 3141 50.0 43.2 6.8 12075
madison 1548 188 743 2479 62.4 30.0 7.6 12079
manatee 18129 696 10181 29006 62.5 35.1 2.4 12081
marion 18224 934 9896 29054 62.7 34.1 3.2 12083
martin 6526 278 4105 10909 59.8 37.6 2.5 12085
miami-dade 129546 1756 42052 173354 74.7 24.3 1.0 12086
monroe 4846 172 3755 8773 55.2 42.8 2.0 12087
nassau 2912 205 2062 5179 56.2 39.8 4.0 12089
okaloosa 4563 428 3788 8779 52.0 43.1 4.9 12091
okeechobee 1152 149 787 2088 55.2 37.7 7.1 12093
orange 66677 1148 36664 104489 63.8 35.1 1.1 12095
osceola 16533 431 7285 24249 68.2 30.0 1.8 12097
palm beach 103792 1957 39533 145282 71.4 27.2 1.3 12099
pasco 21772 1052 14505 37329 58.3 38.9 2.8 12101
pinellas 63716 2160 39767 105643 60.3 37.6 2.0 12103
polk 29345 1715 15492 46552 63.0 33.3 3.7 12105
putnam 3183 511 2747 6441 49.4 42.6 7.9 12107
santa rosa 3941 460 3612 8013 49.2 45.1 5.7 12113
sarasota 25896 681 15793 42370 61.1 37.3 1.6 12115
seminole 22089 688 15112 37889 58.3 39.9 1.8 12117
st. johns 9737 405 6956 17098 56.9 40.7 2.4 12109
st. lucie 17559 595 8098 26252 66.9 30.8 2.3 12111
sumter 7023 272 3022 10317 68.1 29.3 2.6 12119
taylor 987 251 908 2146 46.0 42.3 11.7 12123
volusia 26310 1174 16182 43666 60.3 37.1 2.7 12127
wakulla 1659 309 1424 3392 48.9 42.0 9.1 12129
walton 1515 158 1365 3038 49.9 44.9 5.2 12131
washington 858 182 781 1821 47.1 42.9 10.0 12133

O’Malley did not win any counties.

Mapping with Choroplethr

To create choropleth maps, choroplethr requires:

A data.frame with a column named “region” and a column named “value”. Elements in the “region” column must exactly match how regions are named in the “region” column in ?country.map.

We have joined the regions directly from the county.map data frame, now we just need to add a column named value and assign it to equal the column we want to map. I do this with one line of base R immediately preceding each call of the county_choropleth() function. Below, I mapped each candidate’s percent of total vote by county in three separate maps, then all three in a row.

# For each candidate, map the percent of each counties' total vote using choroplethr package  df$value = df$bs  # Set the desired 'value' column for choroplethr choro_bs = county_choropleth(df, state_zoom="florida", legend = "%", num_colors=1) +    ggtitle("Bernie Sanders") +   coord_map()  # Adds a Mercator projection choro_bs

Mapping Election Results with R and Choroplethr

df$value = df$hc  # Set the desired 'value' column for choroplethr choro_hc = county_choropleth(df, state_zoom="florida", legend = "%", num_colors=1) +    ggtitle("Hillary Clinton") +   coord_map() choro_hc

Mapping Election Results with R and Choroplethr

df$value = df$mo  # Set the desired 'value' column for choroplethr choro_mo = county_choropleth(df, state_zoom="florida", legend = "%", num_colors=1) +    ggtitle("Martin O'Malley") +   coord_map() choro_mo

Mapping Election Results with R and Choroplethr

# Plot all three maps in a grid grid.arrange(choro_hc, choro_bs, choro_mo, ncol=3, top = "Florida Democratic Primary 2016n Percent of Total Votes by Countyn ")

Mapping Election Results with R and Choroplethr

Highlight Counties

In this post , Ari shared a function for highlighting a county. Here, it’s applied to our first map:

# Function for highlighting a county highlight_county = function(county_fips) {  library(choroplethrMaps)  data(county.map, package="choroplethrMaps", envir=environment())  df = county.map[county.map$region %in% county_fips, ]  geom_polygon(data=df, aes(long, lat, group = group), color = "yellow", fill = NA, size = 0.5) } # Filter counties won by Sanders bs.counties <- filter(df, Sanders > Clinton & Sanders > OMalley)  # Create list of counties won bs.fips <- bs.counties[[9]]  # Map using the highlight_county() function after calling county_choropleth() df$value = df$bs  # Set the desired 'value' column for choroplethr choro_bs = county_choropleth(df, state_zoom="florida", legend = "%", num_colors=1) +    highlight_county(bs.fips) +  # Highlight counties won   ggtitle("Bernie Sanders") +   coord_map()  # Adds a Mercator projection choro_bs

Mapping Election Results with R and Choroplethr

Update

Ari asked if I’d add a map showing who won each county:

# Add a new column to show each county's winner df$winner <- as.factor(ifelse(df$hc > df$bs, "Clinton", "Sanders"))  # Plot of winner by county</div> df$value = df$winner  # Set the desired 'value' column for choroplethr choro_winner = county_choropleth(df, state_zoom="florida", legend = "Winner", num_colors=2) +   ggtitle("Florida Presidential Primaryn 15 March 2016") +   coord_map() choro_winner

Mapping Election Results with R and Choroplethr

The post Mapping Election Results with R and Choroplethr appeared first on AriLamstein.com .

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