Assigment Week 10 Revising

GIS Tutorial

This tutorial is part of Computerworld’s How to Make a Map with R

nhdatafile <- "NHD2016.xlsx"
nhdatafilecsv <- "NHD2016.csv"
usshapefile <- "cb_2014_us_county_5m/cb_2014_us_county_5m.shp" #keep entire name
nhfipscode <- "33"
scdatafile <- "SCGOP2016.csv"
scfipscode <- "45"

Run any of the install.packages() commands below for packages that are not yet on your system

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

library(tidyverse)

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

## Warning: package 'purrr' was built under R version 4.2.3

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.0     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ ggplot2   3.4.1     ✔ tibble    3.1.8
## ✔ 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 ]8;;http://conflicted.r-lib.org/conflicted package]8;; 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(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

getwd()

## [1] "C:/Users/aline/OneDrive/Desktop"

Step 1: Read in the NH election results file:

setwd("C:/Users/aline/Downloads/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)

nhdata$ClintonPct <- (nhdata$Clinton) / (nhdata$Sanders + nhdata$Clinton)
nhdata$SandersMarginPCtgPoints <- nhdata$SandersPct - nhdata$ClintonPct

glimpse(nhdata)

## Rows: 10
## Columns: 7
## $ County                  <chr> "Belknap", "Carroll", "Cheshire", "Coos", "Gra…
## $ Clinton                 <int> 3495, 3230, 5132, 2013, 6918, 28147, 12250, 22…
## $ Sanders                 <int> 6005, 5638, 12441, 3639, 14245, 39245, 18107, …
## $ SandersMarginVotes      <int> 2510, 2408, 7309, 1626, 7327, 11098, 5857, 823…
## $ SandersPct              <dbl> 0.6321053, 0.6357691, 0.7079611, 0.6438429, 0.…
## $ ClintonPct              <dbl> 0.3678947, 0.3642309, 0.2920389, 0.3561571, 0.…
## $ SandersMarginPCtgPoints <dbl> 0.2642105, 0.2715381, 0.4159222, 0.2876858, 0.…

Step 3: Get geographic data files

Read in the shapefile for US states and counties:

#install.packages("raster")
#install.packages("rgdal")
setwd("C:/Users/aline/Downloads/Gis")
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/aline/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/aline/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

Do a quick plot (qtm stands for quick thematic map)of the shapefile and check its structure:

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

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

# (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) 
#str(nhdata$County) 
# They're not. Change the county names to plain characters in nhgeo:
nhgeo$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 0.62, 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 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 = "Spectral") + 
 tm_borders(alpha=.5) + 
 tm_text("NAME", size=0.6)

## 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 avariable, and change the theme to “classic” style:

nhstaticmap <- tm_shape(nhmap) + 
tm_fill("SandersMarginVotes", title="Sanders Margin, Total Votes", palette = "magma") +
#I like viridis #I like viridis too Aline
tm_borders(alpha=.5) + 
tm_text("NAME", size=0.6) + 
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.

## 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")

## Map saved to C:\Users\aline\OneDrive\Desktop\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_factor

#library(raster) 
nhpopup <- paste0(nhmap$NAME, " County: ",  "Sanders ",
                  percent(nhmap$SandersPct, accuracy = 0.01), " - Clinton ",
                  percent(nhmap$ClintonPct, accuracy = 0.01))

Step 7: Now generate the interactive map:

# re-project
# add the project, WGS84
nhmap_projected <- sp::spTransform(nhmap, "+proj=longlat +datum=WGS84")
leaflet(nhmap_projected) %>% 
  addProviderTiles("CartoDB.Positron") %>%
  addPolygons(stroke=FALSE,
              smoothFactor = 0.2,
              fillOpacity = 0.8,
              popup=nhpopup,
              color= ~clintonPalette(nhmap$ClintonPct))

South Carolina data

setwd("C:/Users/aline/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. Candidates are in columns 2-7:

#ALine notes from data scdata column2 Jeb Bush - column7 Donald Thump candidates chuck name reads from colnames (data[column numbers]) 

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/aline/Downloads/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" ... 
# 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("#006699", "#ff00cc"), domain = scmap$winner) 
edPalette <- colorNumeric(palette = "Blues", domain=scmap$PctCollegeDegree)

Create a pop-up:

scpopup <- paste0("<b>County: ", scmap$NAME, "<br />Winner: ", scmap$winner, "<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="bottomright", colors=c("#006699", "#ff00cc"),
            labels=c("Trump", "Rubio"))

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="bottomright", colors=c("#006699", "#ff00cc"), 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 = "bottomright",
 options = layersControlOptions(collapsed = FALSE))
# Now display the map
scGOPmap  %>%
addSearchOSM()

Save as a self-contained HTML file

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

Assigment Week 10 Revising

Aline Menezes

2023-04-05

GIS Tutorial

This tutorial is part of Computerworld’s How to Make a Map with R

Run any of the install.packages() commands below for packages that are not yet on your system

Load the tmap, tmaptools, and leaflet packages into your 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

Read in the shapefile for US states and counties:

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 avariable, and change the theme to “classic” style:

View the map

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

Part 6

Create a palette

and a pop-up window

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