GIS_ Create Maps in R

Set various values needed

nhdatafilecsv <- "NHD2016.csv" 
nhfipscode <- "33" 
scdatafile <- "SCGOP2016.csv" 
scfipscode <- "45"

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

library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──

## ✔ ggplot2 3.4.0     ✔ purrr   1.0.1
## ✔ tibble  3.1.8     ✔ dplyr   1.1.0
## ✔ tidyr   1.3.0     ✔ stringr 1.5.0
## ✔ readr   2.1.3     ✔ forcats 0.5.2

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

library(tmap) 
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(dplyr) 
library(rio) 
library(sp)

Step 1: Read in the NH election results file

setwd("/Users/Linh/Desktop/DATA 110/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

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

setwd("/Users/Linh/Desktop/DATA 110/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 3.5.3, released 2022/10/21
## Path to GDAL shared files: /Library/Frameworks/R.framework/Versions/4.2-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/4.2-arm64/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.

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

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")

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/Linh/Desktop/DATA 110/GIS/nhdemprimary.jpg

## Resolution: 1501.336 by 2937.385 pixels

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

Step 6: Create palette and pop-ups for interactive map

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

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$NAME, 
                  "Sanders ", percent(nhmap$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("/Users/Linh/Desktop/DATA 110/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/Linh/Desktop/DATA 110/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

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

save as a self-contained HTML file

#htmlwidgets::saveWidget(scGOPmap2, file="scGOPwidget2.html")  
# save as an HTML file with dependencies in another directory: 
#htmlwidgets::saveWidget(widget=scGOPmap2, file="scGOPprimary_withdependencies.html", sel fcontained=FALSE, libdir = "js")

GIS_ Create Maps in R

Linh Le

2023-04-07

Set various values needed

Load the tmap, tmaptools, and leaflet packages into the 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

Step 3: Get geographic data files

Do a quick plot (qtm stands for quick thematic map) of the shapefile and 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 results 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 change the theme to “classic” style:

View the map

Step 6: Create palette and pop-ups for interactive map

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 of 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 this data on a Leaflet map:

Basic interactive map showing winner in each county:

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

save as a self-contained HTML file