GIS Tutorial Assignment

Author

Shadeja Fuentes

Set up the working directory for all chunks with the function (root.dir=) Load packages: tidyverse, tmap, tmaptools, and leaflet

knitr::opts_knit$set(root.dir = "C:/Users/funne/OneDrive/Desktop/Data 110/GIS/GIS")
library(tidyverse)

Warning: package 'tidyverse' was built under R version 4.2.3

Warning: package 'ggplot2' was built under R version 4.2.3

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

Warning: package 'readr' was built under R version 4.2.3

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

Warning: package 'lubridate' was built under R version 4.2.3

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.1     ✔ readr     2.1.4
✔ forcats   1.0.0     ✔ stringr   1.5.0
✔ ggplot2   3.4.2     ✔ tibble    3.2.1
✔ lubridate 1.9.2     ✔ tidyr     1.3.0
✔ purrr     1.0.1     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

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(dplyr)
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

Set Values

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"

Read in the NH election results file, then eliminate columns for minor candidates. Keep the County, Clinton and Sanders columns.

nh_data <- import(nhdatafilecsv)
nhdata <- nh_data[,c("County", "Clinton", "Sanders")]

Choose what data will be mapped. Create 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

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/funne/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/funne/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.

usgeo <- shapefile("cb_2014_us_county_5m/cb_2014_us_county_5m.shp")

Warning: [vect] Z coordinates ignored

Create a quick plot of the shapefile and check its structure. qtm: quick thematic map

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

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

view(usgeo)

Subset just the NH data from the US shapefile.

nhgeo <- usgeo[usgeo$STATEFP==nhfipscode,]

Create a tmap test plot of the New Hampshire data.

qtm(nhgeo)

Display the 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   : int [1:10] 130959956 -2025747080 353249502 1036582289 1799805954 1830366195 955401980 -1875570227 -1883508361 1391587566
  .. ..$ AWATER  : int [1:10] 105375486 41604851 90773891 177039345 259517418 57990901 39157548 57788894 158933434 38070546
  ..@ polygons   :List of 10
  .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
  .. .. .. ..@ Polygons :List of 1
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  .. .. .. ..@ ID       : chr "686"
  .. .. .. ..@ area     : num 0.508
  .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
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  .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
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  .. .. .. .. .. .. ..@ labpt  : num [1:2] -71.1 43
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  .. .. .. .. .. .. ..@ labpt  : num [1:2] -72.3 42.9
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  .. .. .. .. .. .. ..@ labpt  : num [1:2] -71.2 43.9
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  .. .. .. .. .. .. ..@ labpt  : num [1:2] -72.2 43.4
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  .. .. .. ..@ ID       : chr "3168"
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  ..@ 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[\"unknown\",\n    DATUM[\"North American Datum 1983\",\n        ELLIPSOID[\"GRS 1980\",6378137,298.2572"| __truncated__
  ..$ comment: chr "FALSE"

Check that the 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" ...

Notice that the names are different. 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),]
if (identical(nhgeo$NAME,nhdata$County)) {
nhmap <- merge(nhgeo, nhdata, by.x = "NAME", by.y = "County")  # Merge geo and vote datasets
} else {stop}

Merge the geo data with results data using the merge function.

nhmap <- merge(nhgeo, nhdata, by.x = "NAME", by.y = "County")
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                  : int [1:10] 1036582289 -1883508361 1830366195 353249502 130959956 -2025747080 -1875570227 1799805954 955401980 1391587566
  .. ..$ AWATER                 : int [1:10] 177039345 158933434 57990901 90773891 105375486 41604851 57788894 259517418 39157548 38070546
  .. ..$ 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
  .. .. .. .. ..$ :Formal class 'Polygon' [package "sp"] with 5 slots
  .. .. .. .. .. .. ..@ labpt  : num [1:2] -71.4 43.5
  .. .. .. .. .. .. ..@ area   : num 0.136
  .. .. .. .. .. .. ..@ hole   : logi FALSE
  .. .. .. .. .. .. ..@ ringDir: int 1
  .. .. .. .. .. .. ..@ coords : num [1:33, 1:2] -71.7 -71.7 -71.7 -71.7 -71.7 ...
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  .. .. .. ..@ plotOrder: int 1
  .. .. .. ..@ labpt    : num [1:2] -71.4 43.5
  .. .. .. ..@ ID       : chr "1101"
  .. .. .. ..@ area     : num 0.136
  .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
  .. .. .. ..@ Polygons :List of 1
  .. .. .. .. ..$ :Formal class 'Polygon' [package "sp"] with 5 slots
  .. .. .. .. .. .. ..@ labpt  : num [1:2] -71.2 43.9
  .. .. .. .. .. .. ..@ area   : num 0.288
  .. .. .. .. .. .. ..@ hole   : logi FALSE
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  .. .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. .. .. .. ..$ : chr [1:73] "199379" "199380" "199381" "199382" ...
  .. .. .. .. .. .. .. .. ..$ : chr [1:2] "x" "y"
  .. .. .. ..@ plotOrder: int 1
  .. .. .. ..@ labpt    : num [1:2] -71.2 43.9
  .. .. .. ..@ ID       : chr "3078"
  .. .. .. ..@ area     : num 0.288
  .. ..$ :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
  .. .. .. .. .. .. ..@ ringDir: int 1
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  .. .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. .. .. .. ..$ : chr [1:161] "112007" "112008" "112009" "112010" ...
  .. .. .. .. .. .. .. .. ..$ : chr [1:2] "x" "y"
  .. .. .. ..@ plotOrder: int 1
  .. .. .. ..@ labpt    : num [1:2] -72.3 42.9
  .. .. .. ..@ ID       : chr "1878"
  .. .. .. ..@ area     : num 0.208
  .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
  .. .. .. ..@ Polygons :List of 1
  .. .. .. .. ..$ :Formal class 'Polygon' [package "sp"] with 5 slots
  .. .. .. .. .. .. ..@ labpt  : num [1:2] -71.3 44.7
  .. .. .. .. .. .. ..@ area   : num 0.539
  .. .. .. .. .. .. ..@ hole   : logi FALSE
  .. .. .. .. .. .. ..@ ringDir: int 1
  .. .. .. .. .. .. ..@ coords : num [1:511, 1:2] -71.8 -71.8 -71.8 -71.7 -71.7 ...
  .. .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. .. .. .. ..$ : chr [1:511] "49011" "49012" "49013" "49014" ...
  .. .. .. .. .. .. .. .. ..$ : chr [1:2] "x" "y"
  .. .. .. ..@ plotOrder: int 1
  .. .. .. ..@ labpt    : num [1:2] -71.3 44.7
  .. .. .. ..@ ID       : chr "923"
  .. .. .. ..@ area     : num 0.539
  .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
  .. .. .. ..@ Polygons :List of 1
  .. .. .. .. ..$ :Formal class 'Polygon' [package "sp"] with 5 slots
  .. .. .. .. .. .. ..@ labpt  : num [1:2] -71.8 43.9
  .. .. .. .. .. .. ..@ area   : num 0.508
  .. .. .. .. .. .. ..@ hole   : logi FALSE
  .. .. .. .. .. .. ..@ ringDir: int 1
  .. .. .. .. .. .. ..@ coords : num [1:327, 1:2] -72.3 -72.3 -72.3 -72.3 -72.3 ...
  .. .. .. .. .. .. .. ..- attr(*, "dimnames")=List of 2
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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\funne\OneDrive\Desktop\Data 110\GIS\GIS\nhdemprimary.jpg

Resolution: 1501.336 by 2937.385 pixels

Size: 5.004452 by 9.791282 inches (300 dpi)

Create a palette.

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

Create 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$NAME,
", Sanders ", percent(nhmap$SandersPct), " - Clinton ", percent(nhmap$ClintonPct))

Generate an interactive map (add the appropriate projection, WGS84).

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))

Load the South Carolina data and shapefile, then create a quick plot of scgeo SC geospatial object.

scdata <- rio::import(scdatafile)
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 the winner in each precinct.

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

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

sced <- rio::import("SCdegree.xlsx")

Check that the 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" ...

Notice that the names are different. Change the county names to plain characters in scgeo. Then order each data set by county name.

scgeo$NAME <- as.character(scgeo$NAME)
scgeo <- scgeo[order(scgeo$NAME),]
scdata <- scdata[order(scdata$County),]

Check thatthe name and county columns are identical.

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

Change the county names to plain characters in scgeo. Then order each data set by county name.

scgeo$NAME <- as.character(scgeo$NAME)
scgeo <- scgeo[order(scgeo$NAME),]
scdata <- scdata[order(scdata$County),]
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")

Use color to indicate 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")

Create a 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),
              group="College degs") %>%

  addLayersControl(
      baseGroups=c("Winners", "Trump", "Rubio", "Cruz", "College degs"),
      position = "bottomleft",
      options = layersControlOptions(collapsed = FALSE))
scGOPmap

Save the map as a self-contained HTML file.

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

Save the map as an HTML file with dependencies in another directory.

htmlwidgets::saveWidget(widget=scGOPmap, file="scGOPprimary_withdependencies.html", selfcontained=FALSE, libdir = "js")