Computerworld’s How to Make a Map with R Tutorial

In 10 (fairly) Easy Steps https://www.computerworld.com/article/3038270/data-analytics/create-maps-in-r-in-10-fairly-easy-steps.html by Sharon Machlis

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

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

#install.packages("tmap")
#install.packages("tmaptools")
#install.packages("sf")
#install.packages("leaflet")
#install.packages("leaflet.extras")
#install.packages("rio")
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 3.8.1, GDAL 3.2.1, PROJ 7.2.1
library("leaflet")
library("leaflet.extras")
library("tidyverse")
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✓ ggplot2 3.3.5     ✓ purrr   0.3.4
## ✓ tibble  3.1.5     ✓ dplyr   1.0.7
## ✓ tidyr   1.1.3     ✓ stringr 1.4.0
## ✓ readr   2.0.1     ✓ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library("dplyr")
library("rio")
library("sp")

Step 1: Read in the NH election results file:

nhdata <- read.csv("~/Downloads/GIS/NHD2016.csv")

Elimniate columns for minor candidates and just use County, Clinton, and Snaders 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$Sanders - nhdata$Clinton) / (nhdata$Sanders + nhdata$Clinton) 
# Will use formatting later to multiply by a hundred
nhdata$ClintonPct <- (nhdata$Clinton - nhdata$Sanders) / (nhdata$Sanders + nhdata$Clinton)
nhdata$SandersMarginPctPoints <- nhdata$SandersPct - nhdata$ClintonPct

Step 3: Get your geographic data

setwd("~/Downloads/GIS")
#install.packages("Raster")
#install.packages("rgdal")
usshapefile <- "GIS/cb_2014_us_county_5m/cb_2014_us_county_5m.shp"
library(raster)
## 
## 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: /Library/Frameworks/R.framework/Versions/4.1/Resources/library/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: /Library/Frameworks/R.framework/Versions/4.1/Resources/library/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 /Library/Frameworks/R.framework/Versions/4.1/Resources/library/rgdal/proj
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 Hammpshire data

qtm(nhgeo)

Step 4: Merge spatial and results data

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" ...
##   ..@ polygons   :List of 10
##   .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
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##   .. .. .. ..$ comment: chr "0"
##   .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
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##   .. .. .. .. .. .. ..@ area   : num 0.539
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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] -71.4 43.5
##   .. .. .. .. .. .. ..@ area   : num 0.136
##   .. .. .. .. .. .. ..@ hole   : logi FALSE
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##   .. .. .. ..@ ID       : chr "1100"
##   .. .. .. ..@ area     : num 0.136
##   .. .. .. ..$ comment: chr "0"
##   .. ..$ :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
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##   .. .. .. ..@ ID       : chr "1278"
##   .. .. .. ..@ area     : num 0.208
##   .. .. .. ..$ comment: chr "0"
##   .. ..$ :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 "1877"
##   .. .. .. ..@ area     : num 0.208
##   .. .. .. ..$ comment: chr "0"
##   .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
##   .. .. .. ..@ Polygons :List of 1
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##   .. .. .. .. .. .. ..@ area   : num 0.11
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##   .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
##   .. .. .. ..@ Polygons :List of 1
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##   .. .. .. .. .. .. ..@ labpt  : num [1:2] -71.2 43.9
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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] -72.2 43.4
##   .. .. .. .. .. .. ..@ area   : num 0.159
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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__
str(nhdata$County)
##  chr [1:10] "Belknap" "Carroll" "Cheshire" "Coos" "Grafton" "Hillsborough" ...
#Change the county name to plain characters in nhgeo:
nhgeo$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; lets check:
identical(nhgeo$NAME,nhdata$County)
## [1] TRUE
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                 : 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] 0.264 0.272 0.416 0.288 0.346 ...
##   .. ..$ ClintonPct            : num [1:10] -0.264 -0.272 -0.416 -0.288 -0.346 ...
##   .. ..$ SandersMarginPctPoints: num [1:10] 0.528 0.543 0.832 0.575 0.692 ...
##   ..@ 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 "1100"
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##   .. .. .. ..$ comment: chr "0"
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##   .. .. .. ..@ Polygons :List of 1
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##   .. .. .. ..@ area     : num 0.274
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##   .. .. .. ..@ labpt    : num [1:2] -71 43.3
##   .. .. .. ..@ ID       : chr "2676"
##   .. .. .. ..@ area     : num 0.11
##   .. .. .. ..$ comment: chr "0"
##   .. ..$ :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
##   .. .. .. .. .. .. ..@ 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

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, "SandersMarginPctPoints")

For more control over the map’s colors, borders and such, 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 0.63, 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.

There are also a few built-in tmap themes, such as tm_style_classic:

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")
## $tm_layout
## $tm_layout$style
## [1] "classic"
## 
## 
## attr(,"class")
## [1] "tm"

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 0.63, 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 static maps create by tmap by using the save_tmap() function:

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 /Users/owner/Downloads/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

Create a lafelet pallete with this syntax. This will represent where Hillary CLinton was strongest, the inverse ofthe Sanders maps.

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

Install packages

#install.packages("scales")
library("scales")
## 
## Attaching package: 'scales'
## The following object is masked from 'package:purrr':
## 
##     discard
## The following object is masked from 'package:readr':
## 
##     col_factor

Content for a pop-up window is just a simple combination of HTML and R variables, such as:

nhpopup <- paste0("County: ", nhmap$County,
"Sanders ", percent(nhmap$SandersPct), " - CLinton ", percent(nhmap$ClintonPct))

Step 7: Generate an 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)
  ) 

#Step 8: Add palettes for a multi-layer map

##South Carolina data

setwd("~/Downloads/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. Cnadidates 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 precint

for (i in 1: nrow(scdata)){
  scdata$winner[i] <- names(which.max(scdata[i,2:7]))
}

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

setwd("~/Downloads/GIS")
sced <- rio::import("SCdegree.xlsx")

Check if county names are inthe 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 resutls and rename county column

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

##South Carolina - Min and Max combined Trump, Rubio, and Cruz county results

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 a leaflet palette for each candidate using differnt colors but the same intensity range and also add palettes for hte winner and education layers:

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 basic 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 that projection from the NH Map to the scmap object:

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

Basic interactive map of winners by 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 map lyaers and controls

Add additional polygon layers for each cnadiate and a final layer for college educaiton, along with a layer control to wrap up the code. This time, we’ll store the map in a variabel and then display it:

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))

View Map

scGOPmap

Step 10: Save your interactive map

Install Packages

#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")