GIS Tutorial
Mais Alraee
07/03/2022
This tutorial is part of Computerworld’s How to Make a Map with R
In 10 (fairly) Easy Steps https://www.computerworld.com/article/3038270/data-analytics/create-maps-in-r-in-10-fairly-easy-steps.html by Sharon Machlis sharon_machlis@idg.com. Edited and modified by Prof. Saidi.
# 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"load the needed libraries:
library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.1.3## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.6 v purrr 0.3.4
## v tibble 3.1.7 v dplyr 1.0.9
## v tidyr 1.2.0 v stringr 1.4.0
## v readr 2.1.2 v forcats 0.5.1## Warning: package 'ggplot2' was built under R version 4.1.3## Warning: package 'tibble' was built under R version 4.1.3## Warning: package 'tidyr' was built under R version 4.1.3## Warning: package 'readr' was built under R version 4.1.3## Warning: package 'purrr' was built under R version 4.1.3## Warning: package 'dplyr' was built under R version 4.1.3## Warning: package 'stringr' was built under R version 4.1.3## Warning: package 'forcats' was built under R version 4.1.3## -- 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 4.1.3library(scales)## Warning: package 'scales' was built under R version 4.1.3##
## Attaching package: 'scales'## The following object is masked from 'package:purrr':
##
## discard## The following object is masked from 'package:readr':
##
## col_factorlibrary(htmlwidgets)## Warning: package 'htmlwidgets' was built under R version 4.1.3Step 1: Read in the NH election results file:
setwd("C:/Users/mayss/Desktop/Data Science/Data 110/GIS")
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)
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")
setwd("C:/Users/mayss/Desktop/Data Science/Data 110/GIS")
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: 1.5-32, (SVN revision 1176)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 3.4.1, released 2021/12/27
## Path to GDAL shared files: C:/Users/mayss/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/mayss/Documents/R/win-library/4.1/rgdal/proj
## PROJ CDN enabled: FALSE
## Linking to sp version:1.5-0
## 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 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" ...
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## ..@ 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),]
if (identical(nhgeo$NAME,nhdata$County)) {
nhmap <- merge(nhgeo, nhdata, by.x = "NAME", by.y = "County") # Merge geo and vote datasets
} else {stop}Step 4: Merge geo data with results data using the merge function
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 ...
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## ..@ 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\mayss\Desktop\Data Science\Data 110\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$ClintonPct) # color palette for Clintonand a pop-up window
library(scales)
nhpopup <- paste0("County: ", nhmap$NAME,
"<br /><br /> Sanders, ", percent(nhmap$SandersPct), " <br />Clinton ", percent(nhmap$ClintonPct)) # popup contentStep 7: Now generate the interactive map:
For more information on CRS (coordinate reference systems) projection, see this document: https://rspatial.org/raster/spatial/6-crs.html
# 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$ClintonPct))South Carolina data
setwd("C:/Users/mayss/Desktop/Data Science/Data 110/GIS")
scdata <- rio::import(scdatafile)South Carolina shapefile and Quick plot of scgeo SC geospatial object:
scgeo <- usgeo[usgeo@data$STATEFP=="45",]
qtm(scgeo)
Add a column with percent of votes for each candidate. Candidates are in columns 2-7:
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/mayss/Desktop/Data Science/Data 110/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),]
# check 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:
scgeo$NAME <- as.character(scgeo$NAME)
# Order each data set by county 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") 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$NAME, "<br />Winner: ", scmap$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$winner),
group="Winners" ) %>%
addLegend(position="bottomleft", colors=c("#984ea3", "#e41a1c"), labels=c("Trump", "Rubio"))Put top 3 candidates in their own layers and add education layer, store in scGOPmap2 variable
scGOPmap <- leaflet(scmap) %>%
addProviderTiles("CartoDB.Positron") %>%
addPolygons(stroke=TRUE,
weight=1,
smoothFactor = 0.2,
fillOpacity = .75,
popup=scpopup,
color= ~winnerPalette(scmap$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")