Storing Data

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"

Loading Libraries

library("tidyverse")
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5     v purrr   0.3.4
## v tibble  3.1.5     v dplyr   1.0.7
## v tidyr   1.1.4     v stringr 1.4.0
## v readr   2.0.2     v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library("tmap")
## Registered S3 methods overwritten by 'stars':
##   method             from
##   st_bbox.SpatRaster sf  
##   st_crs.SpatRaster  sf
library("tmaptools")
library("sf")
## Linking to GEOS 3.9.1, GDAL 3.2.1, PROJ 7.2.1
library("leaflet")
library("leaflet.extras")
library("dplyr")
library("rio")
library("raster")
## Loading required package: sp
## 
## Attaching package: 'raster'
## The following object is masked from 'package:dplyr':
## 
##     select
library("rgdal")
## Please note that rgdal will be retired by the end of 2023,
## plan transition to sf/stars/terra functions using GDAL and PROJ
## at your earliest convenience.
## 
## rgdal: version: 1.5-27, (SVN revision 1148)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 3.2.1, released 2020/12/29
## Path to GDAL shared files: C:/Users/Jerem/OneDrive/Documents/R/win-library/4.1/rgdal/gdal
## GDAL binary built with GEOS: TRUE 
## Loaded PROJ runtime: Rel. 7.2.1, January 1st, 2021, [PJ_VERSION: 721]
## Path to PROJ shared files: C:/Users/Jerem/OneDrive/Documents/R/win-library/4.1/rgdal/proj
## PROJ CDN enabled: FALSE
## Linking to sp version:1.4-5
## To mute warnings of possible GDAL/OSR exportToProj4() degradation,
## use options("rgdal_show_exportToProj4_warnings"="none") before loading sp or rgdal.
## Overwritten PROJ_LIB was C:/Users/Jerem/OneDrive/Documents/R/win-library/4.1/rgdal/proj

Step 1: Read in the NH election results file:

# Setting directory
setwd("C:/Users/Jerem/OneDrive/Documents/Montgomery College/Fall 2021/DATA 110/Datasets/GIS")

nhdata <- import(nhdatafilecsv)

Eliminate columns for minor condidates 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$Clinton) 
# Will use formatting later to multiply by a hundred
nhdata$ClintonPct <- (nhdata$Clinton) / (nhdata$Sanders + nhdata$Clinton)
nhdata$SandersMarginPctgPoints <- nhdata$SandersPct - nhdata$ClintonPct

Step 3: Add columns for percents and margins:

Read in the shapefile for US states and counties:

• If libraries with raster and rgdal don’t work (see next chunk), try library(sf) with the command st_read • All these options are here and should help you get the qtm command in the next chunk

#install.packages("rgdal")
#install.packages("raster")
setwd("C:/Users/Jerem/OneDrive/Documents/Montgomery College/Fall 2021/DATA 110/Datasets/GIS")
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

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   : chr [1:10] "4425927252" "2269220216" "4648216798" "1036582289" ...
##   .. ..$ AWATER  : chr [1:10] "105375486" "41604851" "90773891" "177039345" ...
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##   .. .. .. ..@ ID       : chr "3167"
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##   .. .. .. ..$ comment: chr "0"
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##   ..@ 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"
##   .. .. ..$ comment: chr "GEOGCRS[\"NAD83\",\n    DATUM[\"North American Datum 1983\",\n        ELLIPSOID[\"GRS 1980\",6378137,298.257222"| __truncated__

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)
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                  : 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
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##   .. .. .. .. .. .. ..@ area   : num 0.159
##   .. .. .. .. .. .. ..@ hole   : logi FALSE
##   .. .. .. .. .. .. ..@ ringDir: int 1
##   .. .. .. .. .. .. ..@ 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
##   .. .. .. ..$ comment: chr "0"
##   ..@ 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"
##   .. .. ..$ comment: chr "GEOGCRS[\"NAD83\",\n    DATUM[\"North American Datum 1983\",\n        ELLIPSOID[\"GRS 1980\",6378137,298.257222"| __truncated__

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.

## 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 larg
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)
## 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.

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
## 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.

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

tmap_save(nhstaticmap, 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\Jerem\OneDrive\Documents\Montgomery College\Fall 2021\DATA 110\Homework\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 = "Blues", domain=nhmap$ClintonPct)

Create 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("<b>County: ", nhmap$NAME,
"<br />Sanders ", percent(nhmap$SandersPct), " - Clinton ", percent(nhmap$ClintonPct))

Step 7: Now generate the interactive map:

For more information on CRS (coordinate reference systems) projection, see this document: https://rspatial.org/raster/spatial/6-crs.html

# 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

setwd("C:/Users/Jerem/OneDrive/Documents/Montgomery College/Fall 2021/DATA 110/Datasets/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("C:/Users/Jerem/OneDrive/Documents/Montgomery College/Fall 2021/DATA 110/Datasets/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)

# 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")

Step 8: Add palettes for a multi-layer map

# Use same intensity for all - get minimum and maximum for the top 3 combined
minpct <- suppressWarnings(min(c(scdata$`Donald J Trump`, scdata$`Marco Rubio`, scdata$`Ted Cruz`)))
maxpct <- suppressWarnings(max(c(scdata$`Donald J Trump`, scdata$`Marco Rubio`, scdata$`Ted Cruz`)))

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", "Rubio"))

Step 9: 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", "#e41a1c"), labels=c("Trump", "Rubio"))  %>%

  addPolygons(stroke=TRUE,
     weight=1,
     smoothFactor = 0.2, 
     fillOpacity = .75, 
     popup=scpopup, 
     color= ~trumpPalette(scmap$`Donald J Trump`),
     group="Trump") %>%

  addPolygons(stroke=TRUE,
              weight=1,
              smoothFactor = 0.2, 
              fillOpacity = .75, 
              popup=scpopup, 
              color= ~rubioPalette(scmap$`Marco Rubio`),
              group="Rubio") %>%

  addPolygons(stroke=TRUE,
              weight=1,
              smoothFactor = 0.2, 
              fillOpacity = .75, 
              popup=scpopup, 
              color= ~cruzPalette(scmap$`Ted Cruz`),
              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

Step 10: Save as a self-contained HTML file

#install.packages("htmlwidgets")
library("htmlwidgets")

Save as a self-contained HTML file

htmlwidgets::saveWidget(scGOPmap, file="scGOPwidget2.html")

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