GIS_HOMEWORK
Salifou Sylla
7/7/2020
Load the tmap, tmaptools, and leaflet packages into your working session:
library(tmap)## Warning: package 'tmap' was built under R version 4.0.2library(tmaptools)## Warning: package 'tmaptools' was built under R version 4.0.2library(leaflet)## Warning: package 'leaflet' was built under R version 4.0.2library(sf)## Warning: package 'sf' was built under R version 4.0.2## Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1library(leaflet.extras)## Warning: package 'leaflet.extras' was built under R version 4.0.2library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(rio)## Warning: package 'rio' was built under R version 4.0.2WORKING DIRECTORY
# 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"Step 1: Read in the NH election results file:
nhdata <- rio::import(nhdatafile)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:
#install.packages("raster")
#install.packages("rgdal")
library(raster)## Warning: package 'raster' was built under R version 4.0.2## Loading required package: sp## Warning: package 'sp' was built under R version 4.0.2##
## Attaching package: 'raster'## The following object is masked from 'package:dplyr':
##
## selectlibrary(rgdal)## Warning: package 'rgdal' was built under R version 4.0.2## rgdal: version: 1.5-12, (SVN revision 1018)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 3.0.4, released 2020/01/28
## Path to GDAL shared files: C:/Users/Salifou Sylla/Documents/R/win-library/4.0/rgdal/gdal
## GDAL binary built with GEOS: TRUE
## Loaded PROJ runtime: Rel. 6.3.1, February 10th, 2020, [PJ_VERSION: 631]
## Path to PROJ shared files: C:/Users/Salifou Sylla/Documents/R/win-library/4.0/rgdal/proj
## Linking to sp version:1.4-2
## To mute warnings of possible GDAL/OSR exportToProj4() degradation,
## use options("rgdal_show_exportToProj4_warnings"="none") before loading rgdal.usgeo <- shapefile("cb_2014_us_county_5m/cb_2014_us_county_5m.shp")## Warning in rgdal::readOGR(dirname(x), fn, stringsAsFactors = stringsAsFactors, :
## Z-dimension discardedQuick plot of the shapefile
qtm(usgeo)
check its structure
# (pause to wait for map to render, may take a few seconds)
str(usgeo)## Formal class 'SpatialPolygonsDataFrame' [package "sp"] with 5 slots
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## .. ..$ STATEFP : chr [1:3233] "01" "13" "19" "40" ...
## .. ..$ COUNTYFP: chr [1:3233] "059" "111" "109" "115" ...
## .. ..$ COUNTYNS: chr [1:3233] "00161555" "00351094" "00465243" "01101845" ...
## .. ..$ AFFGEOID: chr [1:3233] "0500000US01059" "0500000US13111" "0500000US19109" "0500000US40115" ...
## .. ..$ GEOID : chr [1:3233] "01059" "13111" "19109" "40115" ...
## .. ..$ NAME : chr [1:3233] "Franklin" "Fannin" "Kossuth" "Ottawa" ...
## .. ..$ LSAD : chr [1:3233] "06" "06" "06" "06" ...
## .. ..$ ALAND : chr [1:3233] "1641580723" "1002370118" "2519332669" "1219467406" ...
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## ..@ proj4string:Formal class 'CRS' [package "sp"] with 1 slot
## .. .. ..@ projargs: chr "+proj=longlat +datum=NAD83 +no_defs"NH data from the US shapefile
nhgeo <- usgeo[usgeo$STATEFP==nhfipscode,]
# or
#nhgeo <- dplyr::filter(usgeo, STATEFP == nhfipscode)tmap test plot of the New Hampshire data
qtm(nhgeo)
## structure of the object
str(nhgeo)## Formal class 'SpatialPolygonsDataFrame' [package "sp"] with 5 slots
## ..@ data :'data.frame': 10 obs. of 9 variables:
## .. ..$ 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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## .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
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## .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
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## .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
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## .. .. .. ..@ ID : chr "3167"
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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"# 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] TRUEStep 4: Merge geo data with results data using tmaptool’s append_data function
#nhmap <- append_data(nhgeo, nhdata, key.shp = "NAME", key.data="County")
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 : num [1:10] 3495 3230 5132 2013 6918 ...
## .. ..$ Sanders : num [1:10] 6005 5638 12441 3639 14245 ...
## .. ..$ SandersMarginVotes : num [1:10] 2510 2408 7309 1626 7327 ...
## .. ..$ 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
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## .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
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## .. .. ..@ projargs: chr "+proj=longlat +datum=NAD83 +no_defs"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")
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 0.62, 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.
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) +
#Since version 2.0, tmap function names are prefixed with a tm_ or tmap_. Therefore, function
#names used by tmap 1.x such as animation_tmap have been renamed to tmap_animation.
#• tm_style_classic: replaced by tm_style("classic")
tm_style("classic")## 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 save_tmap():
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")
tmap_save(nhstaticmap, "nhdemprimary.jpg", width=1920, height=1080, asp=0)## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Map saved to C:\Users\Salifou Sylla\Desktop\DATA110\GIS\nhdemprimary.jpg## Resolution: 1920 by 1080 pixels## Size: 6.4 by 3.6 inches (300 dpi)Step 6: Create palette and pop-ups for interactive map
Create a palette
clintonPalette <- colorNumeric(palette = "Blues", domain=nhmap$ClintonPct)Create a pop-up window
library(scales)
nhpopup <- paste0("County: ", nhmap$County,
"Sanders ", percent(nhmap$SandersPct), " - Clinton ", percent(nhmap$ClintonPct))Now the interactive map:
leaflet(nhmap) %>%
addProviderTiles("CartoDB.Positron") %>%
addPolygons(stroke=FALSE,
smoothFactor = 0.2,
fillOpacity = .8,
popup=nhpopup,
color= ~clintonPalette(nhmap$ClintonPct))South Carolina data
scdata <- read.csv("SCGOP2016.csv")South Carolina shapefile:
scgeo <- usgeo[usgeo@data$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$Total
scdata[[j]] <- temp
colnames(scdata)[j] <- paste0(colnames(scdata)[i], "Pct")
} Get winner in each precinct
for(i in 1:nrow(scdata)){
scdata$winner[i] <- names(which.max(scdata[i,2:7]))
}Import spreadsheet with percent of adult population holding at least a 4-yr college degree
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] TRUEAdd the election results and rename county column
#scmap <- append_data(scgeo, scdata, key.data = "County", key.shp = "NAME")
scmap <- merge(scgeo, scdata, by.x = "NAME", by.y = "County")#scmap <- rename(scmap, County = NAME)
scgeo$NAME <- as.character(scgeo$NAME)
#scmap <- append_data(scmap, sced, key.shp = "County", key.data = "County")
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(scmap$Donald.J.TrumpPct, scmap$Marco.RubioPct , scmap$Ted.CruzPct))
maxpct <- max(c(scmap$Donald.J.TrumpPct, scmap$Marco.RubioPct , scmap$Ted.CruzPct))Create leaflet palettes for each layer of the map:
winnerPalette <- colorFactor(palette=c("#984ea3", "#e41a1c"), domain = scmap$winner)
trumpPalette <- colorNumeric(palette = "Purples", domain=c(minpct, maxpct))
rubioPalette <- colorNumeric(palette = "Reds", domain = c(minpct, maxpct))
cruzPalette <- colorNumeric(palette = "Oranges", domain = c(minpct, maxpct))
edPalette <- colorNumeric(palette = "Blues", domain=scmap$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$Ted.CruzPct), "<br /><br />Pct w college ed: ", scmap$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 <- sf::st_transform(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$winner),
group="Winners"
) %>%
addLegend(position="bottomleft", colors=c("#984ea3", "#e41a1c"), labels=c("Trump", "Rubio"))Put top 3 candidates in their own layers and add education layer, store in scGOPmap2 variable
scGOPmap2 <- leaflet(scmap) %>%
addProviderTiles("CartoDB.Positron") %>%
addPolygons(stroke=TRUE,
weight=1,
smoothFactor = 0.2,
fillOpacity = .75,
popup=scpopup,
color= ~winnerPalette(scmap$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$Ted.CruzPct),
group="Cruz"
) %>%
addPolygons(stroke=TRUE,
weight=1,
smoothFactor = 0.2,
fillOpacity = .75,
popup=scpopup,
color= ~edPalette(scmap$PctCollegeDegree),
group="College degs"
) %>%
addLayersControl(
baseGroups=c("Winners", "Trump", "Rubio", "Cruz", "College degs"),
position = "bottomleft",
options = layersControlOptions(collapsed = FALSE)
) %>%
addSearchOSM()
scGOPmap2save 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")