GIS Tutorial

Set various values needed, including names of files and FIPS codes for New Hampshire and South Carolina

#nhdatafiile <- "NHD2016.xlsx"
nhdatafilecsv <- "NHD2016.csv"
usshapefile <- "cb_2014_us_county_5m/cb_2014_us_county_5m.shp"
nhfipscode <- "33"
scdatafile <- "SCGOP2016.csv"
scfipscode <- "45"

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

library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.4.0     ✔ purrr   1.0.1
## ✔ tibble  3.2.1     ✔ dplyr   1.1.1
## ✔ tidyr   1.3.0     ✔ stringr 1.5.0
## ✔ readr   2.1.3     ✔ forcats 1.0.0

## Warning: package 'tibble' was built under R version 4.2.3

## Warning: package 'dplyr' was built under R version 4.2.3

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

library(tmap)

## Warning: package 'tmap' was built under R version 4.2.3

library(tmaptools)

## Warning: package 'tmaptools' was built under R version 4.2.3

library(leaflet)

## Warning: package 'leaflet' was built under R version 4.2.3

library(sf)

## Warning: package 'sf' was built under R version 4.2.3

## Linking to GEOS 3.9.3, GDAL 3.5.2, PROJ 8.2.1; sf_use_s2() is TRUE

library(leaflet.extras)

## Warning: package 'leaflet.extras' was built under R version 4.2.3

library(rio)

## Warning: package 'rio' was built under R version 4.2.3

library(sp)

## Warning: package 'sp' was built under R version 4.2.3

Step 1: Read in the NH election results file:

setwd("C:/Users/Upsta/OneDrive/R Programming/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$Clinton)
nhdata$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:

library(raster)

## Warning: package 'raster' was built under R version 4.2.3

## 
## Attaching package: 'raster'

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

library(rgdal)

## Warning: package 'rgdal' was built under R version 4.2.3

## 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 3.5.2, released 2022/09/02
## Path to GDAL shared files: C:/Users/Upsta/AppData/Local/R/win-library/4.2/rgdal/gdal
## GDAL binary built with GEOS: TRUE 
## Loaded PROJ runtime: Rel. 8.2.1, January 1st, 2022, [PJ_VERSION: 821]
## Path to PROJ shared files: C:/Users/Upsta/AppData/Local/R/win-library/4.2/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.

setwd("C:/Users/Upsta/OneDrive/R Programming/GIS")
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 ands check its structure

tmap_options(check.and.fix = TRUE)
qtm(usgeo)

## Warning: The shape usgeo is invalid. See sf::st_is_valid

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(nhgeo$NAME)

##  chr [1:10] "Grafton" "Hillsborough" "Coos" "Belknap" "Rockingham" ...

str(nhdata$County)

##  chr [1:10] "Belknap" "Carroll" "Cheshire" "Coos" "Grafton" "Hillsborough" ...

nhgeo$NAME <- as.character(nhgeo$NAME)
nhgeo$NAME

##  [1] "Grafton"      "Hillsborough" "Coos"         "Belknap"      "Rockingham"  
##  [6] "Cheshire"     "Strafford"    "Merrimack"    "Carroll"      "Sullivan"

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}

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

#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 0.62, 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("SandersMarginPctgPoints", title ="Sanders Margin, Total Votes", palette = "PRGn") +
  tm_borders(alpha=.5) +
  tm_text("NAME", size=0.8)

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

nhstaticmap <- tm_shape(nhmap) +
  tm_fill("SandersMarginVotes", title ="Sanders Margin, Total Votes", palette = "PRGn") +
  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 the a jpg file with the tmap’s tmap_save():

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

## Map saved to C:\Users\Upsta\OneDrive\R Programming\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)

## Warning: package 'scales' was built under R version 4.2.3

## 
## 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

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("C:/Users/Upsta/OneDrive/R Programming/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 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")
}
winnner <- 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("C:/Users/Upsta/OneDrive/R Programming/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" ...

str(scdata$County)

##  chr [1:46] "Abbeville" "Aiken" "Allendale" "Anderson" "Bamberg" "Barnwell" ...

scgeo$NAME <- as.character(scgeo$NAME)
scgeo <- scgeo[order(scgeo$NAME),]
scdata <- scdata[order(scdata$County),]
if (identical(scgeo$NAME, scdata$County)) {
  scmap <- merge(scgeo, scdata, by.x = "NAME", by.y = "County")
} else {stop}

str(scgeo$NAME)

##  chr [1:46] "Abbeville" "Aiken" "Allendale" "Anderson" "Bamberg" "Barnwell" ...

str(scdata$County)

##  chr [1:46] "Abbeville" "Aiken" "Allendale" "Anderson" "Bamberg" "Barnwell" ...

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

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 the data on a Leaflet map:

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

Basic interactive map shwoing 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 top 3 candidates in there own layers and add education layer, store in 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", "#ea1a1c"), 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),
              group="College degs") %>%
  
  addLayersControl(
    baseGroups =c("Winners", "Trump", "Rubio", "Cruz", "College degs"),
    position = "bottomleft",
    options = layersControlOptions(collapsed = FALSE)
  )

scGOPmap

GIS Tutorial

Chandler Brown

2023-04-11

Set various values needed, including names of files and FIPS codes for New Hampshire and South Carolina

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

Step 1: Read in the NH election results file:

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

Step 2: Decide what data to map

Add columns for percents and margins:

Step 3: Get Geographic data files

Read in the shapefile for US states and counties:

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

Subset just the NH data from the US shapefile

tmap test plot of the New Hampshire data

Structure of the object

Order each data set by county name

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

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

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

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

View the map

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

Part 6

Create a palette

and a pop-up window

Step 7: Now generate the interactive map

South Carolina Data

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

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

Get winner in each precinct

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

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

Add the election results and rename county column

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

Create leaflet palettes for each layer of the map:

Create a pop-up:

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

Basic interactive map shwoing winner in each county

Put top 3 candidates in there own layers and add education layer, store in scGOPmap2 variable