GIS_Homework_Jul_4_2021_Part2
Samuel Kalibala
7/5/2021
This is part2 of the homework. I had to split because the file was too big and was not uploading on Rpubs. This part starts from Step 8 onwards.
Load tidyverse, ggplot2 and fansi
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.3 v purrr 0.3.4
## v tibble 3.1.2 v dplyr 1.0.6
## v tidyr 1.1.3 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(ggplot2)
library(fansi)# Load the required packages
# install.packages("tmap")
# install.packages("tmaptools")
# install.packages("sf")
# install.packages("leaflet")
library("tmap")
library("tmaptools")
library("sf")## Linking to GEOS 3.9.0, GDAL 3.2.1, PROJ 7.2.1library("leaflet")# install.packages("rio")
library(rio)Read in the shapefile for US states and counties:
#install.packages("raster")
#install.packages("rgdal")
setwd("C:/Documents - Copy/PERSONAL/Data 110_MC_Class/GIS/GIS")
library(raster)## Loading required package: sp##
## Attaching package: 'raster'## The following object is masked from 'package:dplyr':
##
## select## The following object is masked from 'package:tidyr':
##
## extractlibrary(rgdal)## rgdal: version: 1.5-23, (SVN revision 1121)
## 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/user/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/user/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 rgdal.
## Overwritten PROJ_LIB was C:/Users/user/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 discardedStep 8: Add palettes for a multi-layer map
Import South Carolina data
Rename pre-existing file in GIS folder
scdatafile <- "SCGOP2016.csv"setwd("C:/Documents - Copy/PERSONAL/Data 110_MC_Class/GIS/GIS")
scdata <- rio::import(scdatafile)view(scdata)The data frame has 8 columns and 46 entries.
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$winner[i] <- names(which.max(scdata[i,2:7]))
}Take a look at the data frame scdata$winner
view(scdata$winner)Shows Trump winning most counties with Rubio winning just two.
Import spreadsheet with percent of adult population holding at least a 4-yr college degree
setwd("C:/Documents - Copy/PERSONAL/Data 110_MC_Class/GIS/GIS")
sced <- rio::import("SCdegree.xlsx")Take a look at the file
view(sced)Shows 46 entries with two columns, one showing names of counties, the other showing percent with college degree.
Check if county names are in the same format in both files
str(scgeo$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$County )## [1] TRUEAdd the election results and rename county column
scmap <- merge(scgeo, scdata, by.x = "NAME", by.y = "County") Instead of just coloring the winner, let’s color by strength of win with multiple layers
# Use same intensity for all - get minimum and maximum for the top 3 combined
minpct <- min(c(scdata$`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$PctCollegeDegree)Create a pop-up:
library(formattable)##
## Attaching package: 'formattable'## The following object is masked from 'package:raster':
##
## areaAt first I got an error message saying percent function not known. I had to install “formattable” package that contains the percent function.
scpopup <- paste0("<b>County: ", scmap$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 map layers and controls
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
scGOPmap Step 10: Save your interactive map
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=scGOPmap, file="scGOPprimary_withdependencies.html", selfcontained=FALSE, libdir = "js")