GIS Tutorial
Samuel Kalibala
7/4/2021
Load ggplot2 and tidyverse
library(fansi)
library(ggplot2)
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## 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
## v purrr 0.3.4## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()Set values needed
# 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 tmap, tmaptools, and leaflet packages into your working session:
library(tidyverse)
library(tmap)
library(tmaptools)
library(leaflet)
library(sf)## Linking to GEOS 3.9.0, GDAL 3.2.1, PROJ 7.2.1library(leaflet.extras)
library(dplyr)
library(rio)
library(sp)Ten Steps
Step 1: Read in the NH election results file:
setwd("C:/Documents - Copy/PERSONAL/Data 110_MC_Class/GIS/GIS")
#nhdata <- import(nhdatafile)
nhdata <- rio::import(nhdatafilecsv)Take a look at the data
view(nhdata)Eliminate columns for minor candidates and just use County, Clinton and Sanders columns:
nhdatacs <- nhdata[,c("County", "Clinton", "Sanders")]Take a look at the data
view(nhdatacs)I had to rename the data frame as nhdatacs for it to show only data for Clinton and Sanders.
Step 2: Decide what data to map
Add columns for percents and margins:
nhdatacs$SandersMarginVotes <- nhdatacs$Sanders - nhdatacs$Clinton
nhdatacs$SandersPct <- (nhdatacs$Sanders) / (nhdatacs$Sanders + nhdatacs$Clinton)
# Will use formatting later to multiply by a hundred
nhdatacs$ClintonPct <- (nhdatacs$Clinton) / (nhdatacs$Sanders + nhdatacs$Clinton)
nhdatacs$SandersMarginPctgPoints <- nhdatacs$SandersPct - nhdatacs$ClintonPctStep 3: Get geographic data files
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)##
## 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 discardedTake a look at the data
view(usgeo)Do a quick plot (qtm stands for quick thematic map) of the shapefile and check its structure:
To get qtm I had to load tmap
qtm(usgeo)
#view(usgeo)Subset just the NH data from the US shapefile
nhgeo <- usgeo[usgeo$STATEFP==nhfipscode,]view(nhgeo)The data frame contains only 9 variables and 10 entries.
tmap test plot of the New Hampshire data
qtm(nhgeo)
structure of the object
str(nhgeo)## Formal class 'SpatialPolygonsDataFrame' [package "sp"] with 5 slots
## ..@ data :'data.frame': 10 obs. of 9 variables:
## .. ..$ 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__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),]
nhdatacs <- nhdatacs[order(nhdatacs$County),]
# Are the two county columns identical now? They should be:
identical(nhgeo$NAME,nhdatacs$County)## [1] TRUEview (nhdatacs)view(nhgeo)Yes, the county names are identical in both data frames.
Step 4: Merge geo data with results data using the merge function
library(sf)
nhmap <- merge(nhgeo, nhdatacs, by.x = "NAME", by.y = "County")
# See the new data structure with
str(nhmap)## Formal class 'SpatialPolygonsDataFrame' [package "sp"] with 5 slots
## ..@ data :'data.frame': 10 obs. of 15 variables:
## .. ..$ 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__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, add title and names of counties, use purple/green color palette:
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.
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## 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.
## 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") ## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output
## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Map saved to C:\Documents - Copy\PERSONAL\Data 110_MC_Class\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
clintonPalette <- colorNumeric(palette = "Blues", domain=nhmap$ClintonPct)I needed to make sure that leaflet and leaflet.extras are loaded in order to get the function colorNumeric.
and a pop-up windowand a pop-up window
library(scales)##
## Attaching package: 'scales'## The following object is masked from 'package:purrr':
##
## discard## The following object is masked from 'package:readr':
##
## col_factorlibrary(raster)
nhpopup <- paste0("County: ", nhmap$NAME,
"Sanders ", percent(nhmap$SandersPct), " - Clinton ", percent(nhmap$ClintonPct))Step 7: Now generate the interactive map:
# re-project
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))