library(tidyverse)
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library(tmap)
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library(tmaptools)
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library(leaflet)
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library(sf)
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library(leaflet.extras)
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library(dplyr)
library(rio)
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library(sp)
library(raster)
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library(rgdal)
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## Loaded GDAL runtime: GDAL 3.2.1, released 2020/12/29
## Path to GDAL shared files: C:/Users/Haley/Documents/R/win-library/4.0/rgdal/gdal
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library(scales)
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#Set variables needed
#read in the files from the GIS folder and assign to usable variables
nhdatafile <- "NHD2016.xlsx"
nhdatafilecsv <- "NHD2016.csv"
usshapefile <- "cb_2014_us_county_5m/cb_2014_us_county_5m.shp"
nhfipscode <- "33"
scdatafile <- "SCGOP2016.csv"
scfipscode <- "45"

Make sure all libraries are loaded in

#Read in NH election results
setwd("C:/Users/Haley/Desktop/GIS")
#import data from the GIS folder
nhdata <- rio::import(nhdatafilecsv)

Eliminate columns for minor candidates: only include County, Clinton, and Sanders

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

Decide what data to map

#The margin of votes of sanders over clinton
nhdata$SandersMarginVotes <- nhdata$Sanders - nhdata$Clinton

#The percentage point of sanders votes out of all votes
nhdata$SandersPct <- (nhdata$Sanders) / (nhdata$Sanders + nhdata$Clinton)


#Percentage points of clinton votes compared to all votes
nhdata$ClintonPct <- (nhdata$Clinton) / (nhdata$Sanders + nhdata$Clinton)

#Percentage of points sanders had over clinton
nhdata$SandersMarginPctgPoints <- nhdata$SandersPct - nhdata$ClintonPct

Get geographic data files

#Set the working directory to be the GIS folder on my desktop
setwd("C:/Users/Haley/Desktop/GIS")
#shapefile stores geospatial data
usgeo <- shapefile("cb_2014_us_county_5m/cb_2014_us_county_5m.shp")
## Warning in rgdal::readOGR(dirname(x), fn, stringsAsFactors = stringsAsFactors, :
## Z-dimension discarded

Plot the shapefile, qtm - quick thematic map

qtm(usgeo)

#review the data in spreadsheet format
#View(usgeo)
#subset NH data from the US shapefile
#upload new hampshire data with the fips codes which are indentifying the geographic areas
nhgeo <- usgeo[usgeo$STATEFP==nhfipscode,]
#make sure tmaps package is picked
#display new hampshire divided by county - levels
qtm(nhgeo)

#structore of the object
str(nhgeo)
## Formal class 'SpatialPolygonsDataFrame' [package "sp"] with 5 slots
##   ..@ data       :'data.frame':  10 obs. of  9 variables:
##   .. ..$ STATEFP : chr [1:10] "33" "33" "33" "33" ...
##   .. ..$ COUNTYFP: chr [1:10] "009" "011" "007" "001" ...
##   .. ..$ COUNTYNS: chr [1:10] "00873178" "00873179" "00873177" "00873174" ...
##   .. ..$ AFFGEOID: chr [1:10] "0500000US33009" "0500000US33011" "0500000US33007" "0500000US33001" ...
##   .. ..$ GEOID   : chr [1:10] "33009" "33011" "33007" "33001" ...
##   .. ..$ NAME    : chr [1:10] "Grafton" "Hillsborough" "Coos" "Belknap" ...
##   .. ..$ LSAD    : chr [1:10] "06" "06" "06" "06" ...
##   .. ..$ ALAND   : chr [1:10] "4425927252" "2269220216" "4648216798" "1036582289" ...
##   .. ..$ AWATER  : chr [1:10] "105375486" "41604851" "90773891" "177039345" ...
##   ..@ polygons   :List of 10
##   .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
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##   .. .. .. .. .. .. ..@ area   : num 0.539
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##   .. .. .. .. .. .. ..@ labpt  : num [1:2] -71.2 43.9
##   .. .. .. .. .. .. ..@ area   : num 0.288
##   .. .. .. .. .. .. ..@ hole   : logi FALSE
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##   .. .. .. .. .. .. ..@ labpt  : num [1:2] -72.2 43.4
##   .. .. .. .. .. .. ..@ area   : num 0.159
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##   .. .. .. ..@ ID       : chr "3167"
##   .. .. .. ..@ area     : num 0.159
##   ..@ plotOrder  : int [1:10] 3 1 9 8 2 6 5 10 4 7
##   ..@ bbox       : num [1:2, 1:2] -72.6 42.7 -70.7 45.3
##   .. ..- attr(*, "dimnames")=List of 2
##   .. .. ..$ : chr [1:2] "x" "y"
##   .. .. ..$ : chr [1:2] "min" "max"
##   ..@ proj4string:Formal class 'CRS' [package "sp"] with 1 slot
##   .. .. ..@ projargs: chr "+proj=longlat +datum=NAD83 +no_defs"
#check if county names are in the same format in both files
str(nhgeo$NAME)
##  chr [1:10] "Grafton" "Hillsborough" "Coos" "Belknap" "Rockingham" ...
str(nhdata$County)
##  chr [1:10] "Belknap" "Carroll" "Cheshire" "Coos" "Grafton" "Hillsborough" ...
#Change te 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),]
if (identical(nhgeo$NAME,nhdata$County)) {
  nhmap <- merge(nhgeo, nhdata, by.x = "NAME", by.y = "County")
} else {stop}

Merge geo data

nhmap <- merge(nhgeo, nhdata,by.x = "NAME", by.y = "County")
str(nhmap)
## Formal class 'SpatialPolygonsDataFrame' [package "sp"] with 5 slots
##   ..@ data       :'data.frame':  10 obs. of  15 variables:
##   .. ..$ NAME                   : chr [1:10] "Belknap" "Carroll" "Cheshire" "Coos" ...
##   .. ..$ STATEFP                : chr [1:10] "33" "33" "33" "33" ...
##   .. ..$ COUNTYFP               : chr [1:10] "001" "003" "005" "007" ...
##   .. ..$ COUNTYNS               : chr [1:10] "00873174" "00873175" "00873176" "00873177" ...
##   .. ..$ AFFGEOID               : chr [1:10] "0500000US33001" "0500000US33003" "0500000US33005" "0500000US33007" ...
##   .. ..$ GEOID                  : chr [1:10] "33001" "33003" "33005" "33007" ...
##   .. ..$ LSAD                   : chr [1:10] "06" "06" "06" "06" ...
##   .. ..$ ALAND                  : chr [1:10] "1036582289" "2411458935" "1830366195" "4648216798" ...
##   .. ..$ AWATER                 : chr [1:10] "177039345" "158933434" "57990901" "90773891" ...
##   .. ..$ Clinton                : int [1:10] 3495 3230 5132 2013 6918 28147 12250 22829 8813 2497
##   .. ..$ Sanders                : int [1:10] 6005 5638 12441 3639 14245 39245 18107 31065 15881 5915
##   .. ..$ SandersMarginVotes     : int [1:10] 2510 2408 7309 1626 7327 11098 5857 8236 7068 3418
##   .. ..$ SandersPct             : num [1:10] 0.632 0.636 0.708 0.644 0.673 ...
##   .. ..$ ClintonPct             : num [1:10] 0.368 0.364 0.292 0.356 0.327 ...
##   .. ..$ SandersMarginPctgPoints: num [1:10] 0.264 0.272 0.416 0.288 0.346 ...
##   ..@ polygons   :List of 10
##   .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
##   .. .. .. ..@ Polygons :List of 1
##   .. .. .. .. ..$ :Formal class 'Polygon' [package "sp"] with 5 slots
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##   .. .. .. .. .. .. ..@ area   : num 0.136
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##   .. .. .. .. .. .. ..@ coords : num [1:118, 1:2] -72.5 -72.4 -72.4 -72.4 -72.4 ...
##   .. .. .. ..@ plotOrder: int 1
##   .. .. .. ..@ labpt    : num [1:2] -72.2 43.4
##   .. .. .. ..@ ID       : chr "3167"
##   .. .. .. ..@ area     : num 0.159
##   ..@ plotOrder  : int [1:10] 4 5 2 7 6 3 8 10 1 9
##   ..@ bbox       : num [1:2, 1:2] -72.6 42.7 -70.7 45.3
##   .. ..- attr(*, "dimnames")=List of 2
##   .. .. ..$ : chr [1:2] "x" "y"
##   .. .. ..$ : chr [1:2] "min" "max"
##   ..@ proj4string:Formal class 'CRS' [package "sp"] with 1 slot
##   .. .. ..@ projargs: chr "+proj=longlat +datum=NAD83 +no_defs"
qtm(nhmap, "SandersMarginVotes")
## Some legend labels were too wide. These labels have been resized to 0.62, 0.62, 0.62, 0.57, 0.53. Increase legend.width (argument of tm_layout) to make the legend wider and therefore the labels larger.

qtm(nhmap, "SandersMarginPctgPoints")

## To control the look and feel, use tm_shape()

tm_shape(nhmap) + tm_fill("SandersMarginVotes", title = "Sanders Margin, Total Votes", palette = "PRGn")+ tm_borders(alpha = .5)+ tm_text("NAME", size = .8)
## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output
## Some legend labels were too wide. These labels have been resized to 0.62, 0.62, 0.62, 0.57, 0.53. Increase legend.width (argument of tm_layout) to make the legend wider and therefore the labels larger.

nhstaticcmap<- tm_shape(nhmap) + tm_fill("SandersMarginVotes", title = "Sanders Margin, Total Votes", palette = "viridis")+ tm_borders(alpha = .5) + tm_text("NAME", size = .8)+ tm_style("classic")
nhstaticcmap
## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output
## Some legend labels were too wide. These labels have been resized to 0.62, 0.62, 0.62, 0.57, 0.53. Increase legend.width (argument of tm_layout) to make the legend wider and therefore the labels larger.

Save map to a jpg

tmap_save(nhstaticcmap, filename = "nhdemprimary.jpg")
## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output

## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output
## Map saved to C:\Users\Haley\Desktop\nhdemprimary.jpg
## Resolution: 1501.336 by 2937.385 pixels
## Size: 5.004452 by 9.791282 inches (300 dpi)

Create a palette

#Make sure leaflet packages are open to use
clintonPalette <- colorNumeric(palette = "Blues", domain=nhmap$ClintonPct)

Create a pop-up window

#Make sure scales and raster packages 
nhpopup <- paste0("County: ", nhmap$County,"Sanders",percent(nhmap$SandersPct), "- Clinton", percent(nhmap$ClintonPct))

Generate the interactive map

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 New data

scdata <- rio::import(scdatafile)

South Carolina shapefile and plot of scgeo SC geospatial

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

## Add columns with percent of votes for each candidates 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 sheet with percent of adult popultion holding at least a 4 - year college degree

sced <- rio::import("SCdegree.xlsx")

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

str(scgeo$NAME)
##  chr [1:46] "Edgefield" "Lee" "Horry" "Allendale" "Marion" "Dorchester" ...
str(scdata$County)
##  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),]
# Make sure the two columns are identical
identical(scgeo$NAME,scdata$County)
## [1] TRUE

Add election results and rename county columns

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

Color by strength of win using multiple layers

#get min and max for 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$County, "<br />Winner: ", scmap$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 projection we know from NH map we’ll need for this data on leaflet map

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

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", "Rubio"))

Put 3 candidates in their own layers, add education layer, store scGOPmap2 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))


scGOPmap