Untitled
Nate Jack
4/4/2022
# 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"Run any of the install.packages() commands below for packages that are not yet on your system
install.packages(“shiny”) install.packages(“urltools”) install.packages(“tmap”) install.packages(“tmaptools”)
install.packages(“leaflet”) install.packages(“scales”) install.packages(“leaflet.extras”) install.packages(“rio”)
install.packages(“htmlwidgets”) install.packages(“sf”) install.packages(“dplyr”)
Load the tmap, tmaptools, and leaflet packages into your working session:
library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.1.3## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.6 v dplyr 1.0.7
## v tidyr 1.1.4 v stringr 1.4.0
## v readr 2.1.1 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(tmap)## Warning: package 'tmap' was built under R version 4.1.3library(tmaptools)## Warning: package 'tmaptools' was built under R version 4.1.3library(leaflet)## Warning: package 'leaflet' was built under R version 4.1.3library(sf)## Warning: package 'sf' was built under R version 4.1.3## Linking to GEOS 3.9.1, GDAL 3.2.1, PROJ 7.2.1; sf_use_s2() is TRUElibrary(leaflet.extras)## Warning: package 'leaflet.extras' was built under R version 4.1.3library(dplyr)
library(rio)## Warning: package 'rio' was built under R version 4.1.3library(sp)## Warning: package 'sp' was built under R version 4.1.3Step 1: Read in the NH election results file:
setwd("~/Data 110 Folder")
#nhdata <- import(nhdatafile)
nhdata <- import(nhdatafilecsv)If you have any problems with this, there is also a CSV version of the file – sometimes reading Excel between Mac and Windows can be tricky. Try # nhdata <- rio::import(nhdatafilecsv)
view(nhdata)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$Clinton)
# Will use formatting later to multiply by a hundred
nhdata$ClintonPct <- (nhdata$Clinton) / (nhdata$Sanders + nhdata$Clinton)
nhdata$SandersMarginPctgPoints <- nhdata$SandersPct - nhdata$ClintonPctStep 3: Get geographic data files
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("raster")
#install.packages("rgdal")
setwd("~/Data 101")
library(raster)## Warning: package 'raster' was built under R version 4.1.3##
## Attaching package: 'raster'## The following object is masked from 'package:dplyr':
##
## selectlibrary(rgdal)## Warning: package 'rgdal' was built under R version 4.1.3## 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: 1.5-29, (SVN revision 1165M)
## 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/natha/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/natha/OneDrive/Documents/R/win-library/4.1/rgdal/proj
## PROJ CDN enabled: FALSE
## Linking to sp version:1.4-6
## 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/natha/OneDrive/Documents/R/win-library/4.1/rgdal/projusgeo <- shapefile("cb_2014_us_county_5m/cb_2014_us_county_5m.shp")## Warning in rgdal::readOGR(dirname(x), fn, stringsAsFactors = stringsAsFactors, :
## Z-dimension discardedDo a quick plot (qtm stands for quick thematic map) of the shapefile and check its structure:
qtm(usgeo)
# (pause to wait for map to render, may take a few seconds)
view(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" ...
## ..@ polygons :List of 10
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## .. .. .. ..@ ID : chr "3167"
## .. .. .. ..@ area : num 0.159
## .. .. .. ..$ comment: chr "0"
## ..@ plotOrder : int [1:10] 3 1 9 8 2 6 5 10 4 7
## ..@ 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__
## ..$ comment: chr "TRUE"# Check if county names are in the same format in both files
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)
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
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## .. .. .. ..@ Polygons :List of 1
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## .. .. .. .. .. .. ..@ 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__
## ..$ comment: chr "TRUE"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\natha\OneDrive\Documents\Data 110 Folder\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$ClintonPct)and a pop-up window
library(scales)##
## Attaching package: 'scales'## The following object is masked from 'package:purrr':
##
## discard## The following object is masked from 'package:readr':
##
## col_factorlibrary(raster)
nhpopup <- paste0("County: ", nhmap$County,
"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$ClintonPct)
)South Carolina data
setwd("~/Data 110 Folder")
scdata <- rio::import(scdatafile)South Carolina shapefile and Quick plot of scgeo SC geospatial object:
scgeo <- usgeo[usgeo@data$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("~/Data 110 Folder")
sced <- rio::import("SCdegree.xlsx")str(scdata$County)## chr [1:46] "Abbeville" "Aiken" "Allendale" "Anderson" "Bamberg" "Barnwell" ...Check if county names are in the same format in both files
# Change the county names to plain characters in scgeo:
scgeo$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$PctCollegeDegree)Create a pop-up:
scpopup <- paste0("<b>County: ", scmap$County, "<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"))Put top 3 candidates in their 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$winner),
group="Winners" ) %>%
addLegend(position="bottomleft", colors=c("#984ea3", "#e41a1c"), labels=c("Trump", "Rubio")) %>%
addPolygons(stroke=TRUE,
weight=1,
smoothFactor = 0.2,
fillOpacity = .75,
popup=scpopup,
color= ~trumpPalette(scmap$`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$PctCollegeDegree), #this data is in the sced table, not scmaps
group="College degs") %>%
addLayersControl(
baseGroups=c("Winners", "Trump", "Rubio", "Cruz", "College degs"),
position = "bottomleft",
options = layersControlOptions(collapsed = FALSE))
# Now display the map
scGOPmapsave as a self-contained HTML file
#htmlwidgets::saveWidget(scGOPmap2, file="scGOPwidget2.html")
# save as an HTML file with dependencies in another directory:
#htmlwidgets::saveWidget(widget=scGOPmap2, file="scGOPprimary_withdependencies.html", selfcontained=FALSE, libdir = "js")#Step 1: Read in SC files
setwd("~/Data 110 Folder")
#nhdata <- import(nhdatafile)
nhdata <- import(nhdatafilecsv)Eliminate columns for minor candidates and just use County, Clinton and Sanders columns:
nhdata <- nhdata[,c("County", "Adams", "Clinton")]Step 2: Decide what data to map
nhdata$ClintonMarginVotes <- nhdata$Clinton - nhdata$Adams
nhdata$ClintonPct <- (nhdata$Clinton) / (nhdata$Clinton + nhdata$Adams)
nhdata$AdamsPct <- (nhdata$Adams) / (nhdata$Clinton + nhdata$Adams)
nhdata$ClintonMarginPctgPoints <- nhdata$ClintonPct - nhdata$AdamsPct#Step 3: Get geographic data files
setwd("~/Data 110 Folder")
library(raster)
library(rgdal)
library(sf)
usgeo <- shapefile("cb_2014_us_county_5m/cb_2014_us_county_5m.shp")## Warning in rgdal::readOGR(dirname(x), fn, stringsAsFactors = stringsAsFactors, :
## Z-dimension discardedqtm(usgeo)
nhgeo <- usgeo[usgeo$STATEFP==nhfipscode,]qtm(nhgeo)
nhgeo$NAME <- as.character(nhgeo$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] TRUEstr(nhgeo$NAME)## chr [1:10] "Belknap" "Carroll" "Cheshire" "Coos" "Grafton" "Hillsborough" ...str(nhdata$County)## chr [1:10] "Belknap" "Carroll" "Cheshire" "Coos" "Grafton" "Hillsborough" ...nhgeo$NAME <- as.character(nhgeo$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") # Merge geo and vote datasets
} else {stop}library(sf)
nhmap <- merge(nhgeo, nhdata, by.x = "NAME", by.y = "County")qtm(nhmap, fill.palette="-Blues", "ClintonMarginVotes")## Some legend labels were too wide. These labels have been resized to 0.57, 0.53, 0.53, 0.53, 0.53. Increase legend.width (argument of tm_layout) to make the legend wider and therefore the labels larger.
qtm(nhmap, fill.palette="-Purples" ,"ClintonMarginPctgPoints")## Legend labels were too wide. The labels have been resized to 0.53, 0.53, 0.53, 0.53. Increase legend.width (argument of tm_layout) to make the legend wider and therefore the labels larger.
tm_shape(nhmap) +
tm_fill("ClintonMarginVotes", title="Clinton Margin, Total Votes", palette = "Set1") +
tm_borders(alpha=.5) +
tm_text("NAME", size=0.9)## Some legend labels were too wide. These labels have been resized to 0.57, 0.53, 0.53, 0.53, 0.53. Increase legend.width (argument of tm_layout) to make the legend wider and therefore the labels larger.
nhstaticmap <- tm_shape(nhmap) +
tm_fill("ClintonMarginVotes", title="Clinton Margin, Total Votes", palette = "Accent") + #I like viridis
tm_borders(alpha=.5) +
tm_text("NAME", size=0.8) +
tm_style("classic")nhstaticmap## Some legend labels were too wide. These labels have been resized to 0.57, 0.53, 0.53, 0.53, 0.53. Increase legend.width (argument of tm_layout) to make the legend wider and therefore the labels larger.
tmap_save(nhstaticmap, filename="nhdemprimary.jpg") ## Map saved to C:\Users\natha\OneDrive\Documents\Data 110 Folder\nhdemprimary.jpg## Resolution: 1501.336 by 2937.385 pixels## Size: 5.004452 by 9.791282 inches (300 dpi)AdamsPalette <- colorNumeric(palette = "Blues", domain=nhmap$AdamsPct) # color palette for Clintonlibrary(scales)nhpopup <- paste0("County: ", nhmap$NAME,
", Clinton ", percent(nhmap$ClintonPct), " - Adams ", percent(nhmap$AdamsPct)) # popup contentnhmap_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= ~AdamsPalette(nhmap$AdamsPct))