# 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"

#Run any of the install.packages() commands below for packages that are not yet on your system install.packages(“tmap”) install.packages(“tmaptools”) install.packages(“leaflet”) install.packages(“scales”) install.packages(“leaflet.extras”) install.packages(“rio”) install.packages(“htmlwidgets”) install.packages(“sf”) install.packages(“dplyr”) install.packages(“sp”)

#Load the tmap, tmaptools, and leaflet packages into your
#working session:
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.1     ✔ 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)
library(tmaptools)
library(leaflet)
library(sf)
## Linking to GEOS 3.10.2, GDAL 3.4.2, PROJ 8.2.1; sf_use_s2() is TRUE
library(leaflet.extras)
library(dplyr)
library(rio)
library(sp)
#Get the working directory 
getwd()
## [1] "/Users/grayce/Desktop/GIS "
#Set working directory and Read the New Hampshire Election Results File
setwd("/Users/grayce/Desktop/GIS ")
nhdata <- import ("nhd2016.csv")
# Eliminate columns for minor candidates and just use County, Clinton and Sanders columns:
nhdata <- nhdata[,c("County", "Clinton", "Sanders")]
# 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

Install(raster) Install(rgdal)

setwd("/Users/grayce/Desktop/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 2023,
## plan transition to sf/stars/terra functions using GDAL and PROJ
## at your earliest convenience.
## See https://r-spatial.org/r/2022/04/12/evolution.html and https://github.com/r-spatial/evolution
## rgdal: version: 1.6-5, (SVN revision 1199)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 3.4.2, released 2022/03/08
## Path to GDAL shared files: /Library/Frameworks/R.framework/Versions/4.2/Resources/library/rgdal/gdal
## GDAL binary built with GEOS: FALSE 
## Loaded PROJ runtime: Rel. 8.2.1, January 1st, 2022, [PJ_VERSION: 821]
## Path to PROJ shared files: /Library/Frameworks/R.framework/Versions/4.2/Resources/library/rgdal/proj
## PROJ CDN enabled: FALSE
## Linking to sp version:1.6-0
## To mute warnings of possible GDAL/OSR exportToProj4() degradation,
## use options("rgdal_show_exportToProj4_warnings"="none") before loading sp or rgdal.
#Read in the shapefile for U.S. states and counties
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$NAME)
##  chr [1:10] "Grafton" "Hillsborough" "Coos" "Belknap" "Rockingham" ...
str(nhdata$County)
##  chr [1:10] "Belknap" "Carroll" "Cheshire" "Coos" "Grafton" "Hillsborough" ...
# 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
#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:

#Some labels were too wide. These labels have been resized (see below)
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 = "BrBG") +
 tm_borders(alpha=.8) +
 tm_text("NAME", size=0.5)
## 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.

#These legend labels were also too wide.  They have been resized(see below).  I also changed the color palette.

Same code as above, but store the static map in a variable, and change the theme to “classic” style: The color palette was changed.

nhstaticmap <- tm_shape(nhmap) +
tm_fill("SandersMarginVotes", title="Sanders Margin, Total Votes", palette = "plasma") +
#I like viridis
tm_borders(alpha=.5) +
tm_text("NAME", size=0.5) +
tm_style("classic")

View the map

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/grayce/Desktop/GIS /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

Create a palette

clintonPalette <- colorNumeric(palette = "Reds", 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(nhmap$NAME, " County: ",  "Sanders ", percent(nhmap$SandersPct, accuracy = 0.01), " - Clinton ", percent(nhmap$ClintonPct, accuracy = 0.01))

Step 7: Now generate the interactive map:

#The color palette was changed to show emphasis. 
nhmap_projected <- sp::spTransform(nhmap, "+proj=longlat +datum=WGS84") 
leaflet(nhmap_projected) %>%   
addProviderTiles("CartoDB.Positron") %>%   
addPolygons(stroke=FALSE,                
smoothFactor = 0.2,                
fillOpacity = .4,                
popup=nhpopup,                
color= ~clintonPalette(nhmap$ClintonPct))

South Carolina data

#Set working directory and import the South Carolina data file
setwd("/Users/grayce/Desktop/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]))}

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

setwd("/Users/grayce/Desktop/GIS ")
sced <- rio::import("SCdegree.xlsx")

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

str(scgeo$NAME)
##  chr [1:46] "Edgefield" "Lee" "Horry" "Allendale" "Marion" "Dorchester" ...
## chr [1:46] "Edgefield" "Lee" "Horry" "Allendale" "Marion" "Dorchester" ...
str(scdata$County)
##  chr [1:46] "Abbeville" "Aiken" "Allendale" "Anderson" "Bamberg" "Barnwell" ...
## chr [1:46] "Abbeville" "Aiken" "Allendale" "Anderson" "Bamberg" "Barnwell" ...
# 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$winner)
edPalette <- colorNumeric(palette = "Greens", domain=scmap$PctCollegeDegree)

Create a pop-up:

scpopup <- paste0("<b>County: ", scmap$NAME, "<br />Winner: ", scmap$winner, "<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="bottomright", colors=c("#984ea3", "#e41a1c"), labels=c("Trump", "Rubio"))

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="bottomright", 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 = "bottomright",
 options = layersControlOptions(collapsed = FALSE))
# Now display the map
scGOPmap %>%
addSearchOSM() # add a search for extra
#scGOPmap

Save as a self-contained HTML file

htmlwidgets::saveWidget(scGOPmap, file="scGOPwidget.html")
# save as an HTML file with dependencies in another directory:
htmlwidgets::saveWidget(widget=scGOPmap, file="scGOPprimary_withdependencies.html", selfcontained=FALSE, libdir = "js")