GIS

Lets get started

# 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" #keep entire name
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)

## Warning: package 'tmap' was built under R version 4.3.1

## 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)

## Warning: package 'tmaptools' was built under R version 4.3.1

library(leaflet)

## Warning: package 'leaflet' was built under R version 4.3.1

library(sf)

## Warning: package 'sf' was built under R version 4.3.1

## Linking to GEOS 3.11.2, GDAL 3.6.2, PROJ 9.2.0; sf_use_s2() is TRUE

library(leaflet.extras)

## Warning: package 'leaflet.extras' was built under R version 4.3.1

library(dplyr)
library(rio)

## Warning: package 'rio' was built under R version 4.3.1

library(sp) 

## Warning: package 'sp' was built under R version 4.3.1

Step 1: Read in the NH election results file

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

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

library(raster)

## Warning: package 'raster' was built under R version 4.3.1

## 
## Attaching package: 'raster'

## The following object is masked from 'package:dplyr':
## 
##     select

library(rgdal)

## Warning: package 'rgdal' was built under R version 4.3.1

## 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 3.6.2, released 2023/01/02
## Path to GDAL shared files: C:/Users/andre/AppData/Local/R/win-library/4.3/rgdal/gdal
##  GDAL does not use iconv for recoding strings.
## GDAL binary built with GEOS: TRUE 
## Loaded PROJ runtime: Rel. 9.2.0, March 1st, 2023, [PJ_VERSION: 920]
## Path to PROJ shared files: C:/Users/andre/AppData/Local/R/win-library/4.3/rgdal/proj
## PROJ CDN enabled: FALSE
## Linking to sp version:2.0-0
## 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)

## 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   : int [1:10] 130959956 -2025747080 353249502 1036582289 1799805954 1830366195 955401980 -1875570227 -1883508361 1391587566
##   .. ..$ AWATER  : int [1:10] 105375486 41604851 90773891 177039345 259517418 57990901 39157548 57788894 158933434 38070546
##   ..@ polygons   :List of 10
##   .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
##   .. .. .. ..@ Polygons :List of 1
##   .. .. .. .. ..$ :Formal class 'Polygon' [package "sp"] with 5 slots
##   .. .. .. .. .. .. ..@ labpt  : num [1:2] -71.8 43.9
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##   .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
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##   .. .. .. .. .. .. ..@ area   : num 0.255
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##   .. .. .. .. .. .. ..@ labpt  : num [1:2] -71.3 44.7
##   .. .. .. .. .. .. ..@ area   : num 0.539
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##   .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
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##   .. .. .. .. .. .. ..@ labpt  : num [1:2] -71.4 43.5
##   .. .. .. .. .. .. ..@ area   : num 0.136
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##   .. .. .. ..@ ID       : chr "1101"
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##   .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
##   .. .. .. ..@ Polygons :List of 1
##   .. .. .. .. ..$ :Formal class 'Polygon' [package "sp"] with 5 slots
##   .. .. .. .. .. .. ..@ labpt  : num [1:2] -71.1 43
##   .. .. .. .. .. .. ..@ area   : num 0.208
##   .. .. .. .. .. .. ..@ hole   : logi FALSE
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##   .. .. .. .. .. .. ..@ labpt  : num [1:2] -72.3 42.9
##   .. .. .. .. .. .. ..@ area   : num 0.208
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##   .. .. .. ..@ ID       : chr "1878"
##   .. .. .. ..@ area     : num 0.208
##   .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
##   .. .. .. ..@ Polygons :List of 1
##   .. .. .. .. ..$ :Formal class 'Polygon' [package "sp"] with 5 slots
##   .. .. .. .. .. .. ..@ labpt  : num [1:2] -71 43.3
##   .. .. .. .. .. .. ..@ area   : num 0.11
##   .. .. .. .. .. .. ..@ hole   : logi FALSE
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##   .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
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##   .. .. .. .. .. .. ..@ area   : num 0.274
##   .. .. .. .. .. .. ..@ hole   : logi FALSE
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##   .. .. .. ..@ ID       : chr "2774"
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##   .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
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##   .. .. .. .. .. .. ..@ labpt  : num [1:2] -71.2 43.9
##   .. .. .. .. .. .. ..@ area   : num 0.288
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##   .. .. .. ..@ ID       : chr "3078"
##   .. .. .. ..@ area     : num 0.288
##   .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
##   .. .. .. ..@ Polygons :List of 1
##   .. .. .. .. ..$ :Formal class 'Polygon' [package "sp"] with 5 slots
##   .. .. .. .. .. .. ..@ labpt  : num [1:2] -72.2 43.4
##   .. .. .. .. .. .. ..@ area   : num 0.159
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##   .. ..- 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"
##   .. .. ..$ comment: chr "GEOGCRS[\"unknown\",\n    DATUM[\"North American Datum 1983\",\n        ELLIPSOID[\"GRS 1980\",6378137,298.2572"| __truncated__
##   ..$ comment: chr "FALSE"

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

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)

## 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                  : int [1:10] 1036582289 -1883508361 1830366195 353249502 130959956 -2025747080 -1875570227 1799805954 955401980 1391587566
##   .. ..$ AWATER                 : int [1:10] 177039345 158933434 57990901 90773891 105375486 41604851 57788894 259517418 39157548 38070546
##   .. ..$ 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
##   .. .. .. .. .. .. ..@ labpt  : num [1:2] -71.4 43.5
##   .. .. .. .. .. .. ..@ area   : num 0.136
##   .. .. .. .. .. .. ..@ hole   : logi FALSE
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##   .. .. .. ..@ ID       : chr "1101"
##   .. .. .. ..@ area     : num 0.136
##   .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
##   .. .. .. ..@ Polygons :List of 1
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##   .. .. .. .. .. .. ..@ labpt  : num [1:2] -71.2 43.9
##   .. .. .. .. .. .. ..@ area   : num 0.288
##   .. .. .. .. .. .. ..@ hole   : logi FALSE
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##   .. .. .. ..@ ID       : chr "3078"
##   .. .. .. ..@ area     : num 0.288
##   .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
##   .. .. .. ..@ Polygons :List of 1
##   .. .. .. .. ..$ :Formal class 'Polygon' [package "sp"] with 5 slots
##   .. .. .. .. .. .. ..@ labpt  : num [1:2] -72.3 42.9
##   .. .. .. .. .. .. ..@ area   : num 0.208
##   .. .. .. .. .. .. ..@ hole   : logi FALSE
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##   .. .. .. ..@ ID       : chr "1878"
##   .. .. .. ..@ area     : num 0.208
##   .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
##   .. .. .. ..@ Polygons :List of 1
##   .. .. .. .. ..$ :Formal class 'Polygon' [package "sp"] with 5 slots
##   .. .. .. .. .. .. ..@ labpt  : num [1:2] -71.3 44.7
##   .. .. .. .. .. .. ..@ area   : num 0.539
##   .. .. .. .. .. .. ..@ hole   : logi FALSE
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##   .. .. .. ..@ ID       : chr "923"
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##   .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
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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 0.62, 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") 

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 0.62, 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.

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") 

View the map

nhstaticmap

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

tmap_save(nhstaticmap, filename="nhdemprimary.jpg") 

## Map saved to C:\Users\andre\OneDrive\Documents\School\Data 110\nhdemprimary.jpg

## Resolution: 1501.336 by 2937.385 pixels

## Size: 5.004452 by 9.791282 inches (300 dpi)

Part 6 Create a palette

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

library(raster)
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

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

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),]
# 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