# Set various values needed, including names of files and FIPS codes for New Hampshire and South Carolina
nhdatafilecsv <- "NHD2016.csv"
usshapefile <- "cb_2014_us_county_5m/cb_2014_us_county_5m.shp"
nhfipscode <- "33"
scdatafile <- "SCGOP2016.csv"
scfipscode <- "45"

Load the tmap, tmaptools, and leaflet packages into your working session:

library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.2     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ ggplot2   3.4.2     ✔ tibble    3.2.1
## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ✔ purrr     1.0.1     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(tmap)
## The legacy packages maptools, rgdal, and rgeos, underpinning the sp package,
## which was just loaded, will retire in October 2023.
## Please refer to R-spatial evolution reports for details, especially
## https://r-spatial.org/r/2023/05/15/evolution4.html.
## It may be desirable to make the sf package available;
## package maintainers should consider adding sf to Suggests:.
## The sp package is now running under evolution status 2
##      (status 2 uses the sf package in place of rgdal)
library(tmaptools)
library(leaflet)
library(sf)
## Linking to GEOS 3.11.0, GDAL 3.5.3, PROJ 9.1.0; sf_use_s2() is TRUE
library(leaflet.extras)
library(rio)
library(sp)

Step 1: Read in the NH election results file:

setwd("/Users/smhenderson/Desktop/DATA110/R/Datasets/GIS")
nhdata <- import(nhdatafilecsv)

Eliminate columns for minor candidates and just use County, Clinton and Sanders columns:

nhdata <- nhdata[,c("County", "Clinton", "Sanders")]

Step 2: Decide what data to map

#Add columns for percents and margins:
nhdata$SandersMarginVotes <- nhdata$Sanders - nhdata$Clinton
nhdata$SandersPct <- (nhdata$Sanders) / (nhdata$Sanders + nhdata$Clinton)

# Will use formatting later to multiply by a hundred
nhdata$ClintonPct <- (nhdata$Clinton) / (nhdata$Sanders + nhdata$Clinton)
nhdata$SandersMarginPctgPoints <- nhdata$SandersPct - nhdata$ClintonPct

Step 3: Get geographic data files

#Read in the shapefile for US states and counties: ## If libraries with raster and rgdal don’t work (see next chunk), try library(sf) with the command st_read ## All these options are here and should help you get the qtm command in the next chunk

setwd("/Users/smhenderson/Desktop/DATA110/R/Datasets/GIS")
library(raster)
## 
## Attaching package: 'raster'
## The following object is masked from 'package:dplyr':
## 
##     select
library(rgdal)
## Please note that rgdal will be retired during October 2023,
## plan transition to sf/stars/terra functions using GDAL and PROJ
## at your earliest convenience.
## See https://r-spatial.org/r/2023/05/15/evolution4.html and https://github.com/r-spatial/evolution
## rgdal: version: 1.6-7, (SVN revision 1203)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 3.5.3, released 2022/10/21
## Path to GDAL shared files: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library/rgdal/gdal
##  GDAL does not use iconv for recoding strings.
## GDAL binary built with GEOS: TRUE 
## Loaded PROJ runtime: Rel. 9.1.0, September 1st, 2022, [PJ_VERSION: 910]
## Path to PROJ shared files: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library/rgdal/proj
## PROJ CDN enabled: FALSE
## Linking to sp version:1.6-1
## To mute warnings of possible GDAL/OSR exportToProj4() degradation,
## use options("rgdal_show_exportToProj4_warnings"="none") before loading sp or rgdal.
usgeo <- shapefile("cb_2014_us_county_5m/cb_2014_us_county_5m.shp")
## Warning: [vect] Z coordinates ignored

Do a quick plot (qtm stands for quick thematic map) of the shapefile and check its structure:

qtm(usgeo)

Subset just the NH data from the US shapefile

nhgeo <- usgeo[usgeo$STATEFP==nhfipscode,]

tmap test plot of the New Hampshire data

qtm(nhgeo)

# Structure of the object

#str(nhgeo)
#str(nhdata$County)
# They're not. Change the county names to plain characters in nhgeo:
nhgeo$NAME <- as.character(nhgeo$NAME)

Order each data set by county name

nhgeo <- nhgeo[order(nhgeo$NAME),]
nhdata <- nhdata[order(nhdata$County),]
# Are the two county columns identical now? They should be:
identical(nhgeo$NAME,nhdata$County)
## [1] TRUE

Step 4: Merge geo data with results data using the merge function

library(sf) # sf stands for simple features#
nhmap <- merge(nhgeo, nhdata, by.x = "NAME", by.y = "County")
# See the new data structure with
#str(nhmap)

Step 5: Create a static map with tmap’s qtm() function:

qtm(nhmap, "SandersMarginVotes")
## Some legend labels were too wide. These labels have been resized to 0.63, 0.63, 0.63, 0.58, 0.54. Increase legend.width (argument of tm_layout) to make the legend wider and therefore the labels larger.

qtm(nhmap, "SandersMarginPctgPoints")

For more control over look and feel, use the tm_shape() function:

tm_shape(nhmap) +
 tm_fill("SandersMarginVotes", title="Sanders Margin, Total Votes", palette = "PRGn") +
 tm_borders(alpha=.5) +
 tm_text("NAME", size=0.8)
## Some legend labels were too wide. These labels have been resized to 0.63, 0.63, 0.63, 0.58, 0.54. Increase legend.width (argument of tm_layout) to make the legend wider and therefore the labels larger.

Same code as above, but store the static map in a variable, and change the theme to “classic” style:

nhstaticmap <- tm_shape(nhmap) +
tm_fill("SandersMarginVotes", title="Sanders Margin, Total Votes", palette = "viridis") +
#I like viridis
tm_borders(alpha=.5) +
tm_text("NAME", size=0.8) +
tm_style("classic")
nhstaticmap
## Some legend labels were too wide. These labels have been resized to 0.63, 0.63, 0.63, 0.58, 0.54. Increase legend.width (argument of tm_layout) to make the legend wider and therefore the labels larger.

Save the map to a jpg file with tmap’s tmap_save():

tmap_save(nhstaticmap, filename="nhdemprimary.jpg") 
## Map saved to /Users/smhenderson/Desktop/DATA110/Assignments/nhdemprimary.jpg
## Resolution: 1501.336 by 2937.385 pixels
## Size: 5.004452 by 9.791282 inches (300 dpi)

Part 6 - Next up: Code for a basic interactive map, this time for Clinton percentages in NH

clintonPalette <- colorNumeric(palette = "Blues", domain=nhmap$ClintonPct)

And a pop-up window

library(scales)
## 
## Attaching package: 'scales'
## The following object is masked from 'package:purrr':
## 
##     discard
## The following object is masked from 'package:readr':
## 
##     col_factor
nhpopup <- paste0("County: ", nhmap$NAME, "<br /><br /> Sanders: ", percent(nhmap$SandersPct), " Clinton: ", percent(nhmap$ClintonPct))

Step 7: Now generate the interactive map:

# re-project
nhmap_projected <- sp::spTransform(nhmap, "+proj=longlat +datum=WGS84")
leaflet(nhmap_projected) %>%
 addProviderTiles("CartoDB.Positron") %>%
 addPolygons(stroke=FALSE,
 smoothFactor = 0.2,
 fillOpacity = .8,
 popup=nhpopup,
 color= ~clintonPalette(nhmap$ClintonPct)
 )

South Carolina data

setwd("/Users/smhenderson/Desktop/DATA110/R/Datasets/GIS")
scdata <- rio::import(scdatafile)

South Carolina shapefile and Quick plot of scgeo SC geospatial object:

scgeo <- usgeo[usgeo@data$STATEFP=="45",]
qtm(scgeo)

Add a column with percent of votes for each candidate. Candidates are in columns 2-7:

candidates <- colnames(scdata[2:7])
for(i in 2:7){
 j = i + 7
 temp <- scdata[[i]] / scdata$Total
 scdata[[j]] <- temp
 colnames(scdata)[j] <- paste0(colnames(scdata)[i], "Pct")
}
winner <- colnames(scdata[2:7])

Get winner in each precinct

for(i in 1:nrow(scdata)){
 scdata$winner[i] <- names(which.max(scdata[i,2:7]))
19
}

Import spreadsheet with percent of adult population holding at least a 4-yr college degree

setwd("/Users/smhenderson/Desktop/DATA110/R/Datasets/GIS")
sced <- rio::import("SCdegree.xlsx")

Check if county names are in the same format in both files

#str(scgeo$NAME)
#str(scdata$County)
# Change the county names to plain characters in scgeo:
scgeo$NAME <- as.character(scgeo$NAME)
# Order each data set by county name
scgeo <- scgeo[order(scgeo$NAME),]
scdata <- scdata[order(scdata$County),]
# Are the two county columns identical now? They should be:
identical(scgeo$NAME,scdata$County )
## [1] TRUE

Add the election results and rename county column

scmap <- merge(scgeo, scdata, by.x = "NAME", by.y = "County")

Instead of just coloring the winner, let’s color by strength of win with multiple layers

# Use same intensity for all - get minimum and maximum for the top 3 combined
minpct <- min(c(scdata$`Donald J TrumpPct`, scdata$`Marco RubioPct`, scdata$`Ted CruzPct`))
maxpct <- max(c(scdata$`Donald J TrumpPct`, scdata$`Marco RubioPct`, scdata$`Ted CruzPct`))

Create leaflet palettes for each layer of the map:

trumpPalette <- colorNumeric(palette = "Purples", domain=c(minpct, maxpct))
rubioPalette <- colorNumeric(palette = "Reds", domain = c(minpct, maxpct))
cruzPalette <- colorNumeric(palette = "Oranges", domain = c(minpct, maxpct))
winnerPalette <- colorFactor(palette=c("#984ea3", "#e41a1c"), domain = scmap$winner)
edPalette <- colorNumeric(palette = "Blues", domain=scmap$PctCollegeDegree)

Create a pop-up:

scpopup <- paste0("<b>County: ", scmap$NAME, "<br />Winner: ", scmap$winner, "</b><br /><
br />Trump: ", percent(scmap$`Donald J TrumpPct`), "<br />Rubio: ", percent(scmap$`Marco
RubioPct`), "<br />Cruz: ", percent(scmap$`Ted CruzPct`), "<br /><br />Pct w college ed:
", sced$PctCollegeDegree, "% vs state-wide avg of 25%")

Add the projection we know from the NH map we’ll need for this data on a Leaflet map:

scmap <- sp::spTransform(scmap, "+proj=longlat +datum=WGS84")

Basic interactive map showing winner in each county:

leaflet(scmap) %>%
 addProviderTiles("CartoDB.Positron") %>%
 addPolygons(stroke=TRUE,
 weight=1,
 smoothFactor = 0.2,
 fillOpacity = .75,
 popup=scpopup,
 color= ~winnerPalette(scmap$winner),
 group="Winners" ) %>%
 addLegend(position="bottomleft", colors=c("#984ea3", "#e41a1c"), labels=c("Trump", "R
ubio"))

Put top 3 candidates in their own layers and add education layer, store in scGOPmap variable

scGOPmap <- leaflet(scmap) %>%
 addProviderTiles("CartoDB.Positron") %>%
 addPolygons(stroke=TRUE,
 weight=1,
 smoothFactor = 0.2,
 fillOpacity = .75,
 popup=scpopup,
 color= ~winnerPalette(scmap$winner),
 group="Winners" ) %>%
 addLegend(position="bottomleft", colors=c("#984ea3", "#e41a1c"), labels=c("Trump", "Rubio")) %>%
 addPolygons(stroke=TRUE,
 weight=1,
 smoothFactor = 0.2,
 fillOpacity = .75,
 popup=scpopup,
 color= ~trumpPalette(scmap$`Donald J TrumpPct`),
 group="Trump") %>%
 addPolygons(stroke=TRUE,
 weight=1,
 smoothFactor = 0.2,
 fillOpacity = .75,
 popup=scpopup,
 color= ~rubioPalette(scmap$`Marco RubioPct`),
 group="Rubio") %>%
 addPolygons(stroke=TRUE,
 weight=1,
 smoothFactor = 0.2,
 fillOpacity = .75, 
 popup=scpopup,
 color= ~cruzPalette(scmap$`Ted CruzPct`),
 group="Cruz") %>%
 addPolygons(stroke=TRUE,
 weight=1,
 smoothFactor = 0.2,
 fillOpacity = .75,
 popup=scpopup,
 color= ~edPalette(sced$PctCollegeDegree), #this data is in the sced table, not scmaps
 group="College degs") %>%
 addLayersControl(
 baseGroups=c("Winners", "Trump", "Rubio", "Cruz", "College degs"),
 position = "bottomleft",
 options = layersControlOptions(collapsed = FALSE))
# Now display the map
scGOPmap