Data 110 - Unit 10 GIS
Marilyn Macy
4/9/2021
Create Maps in R
Setup and get New Hampshire and USGEO data
# set file names and FIPS codes for NH and SC
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"
pacman::p_load(tidyverse, tmap, tmaptools, leaflet, leaflet.extras, sf, dplyr, rio, sp)Read New Hampshire data
setwd("C:/Users/Owner/Desktop/My Documents/School/DataFiles/GIS")
nhdata <- import(nhdatafilecsv)
nhdata <- nhdata[,c("County", "Clinton", "Sanders")] # select columns
nhdata$SandersMarginVotes <- nhdata$Sanders - nhdata$Clinton # add Sanders margins
nhdata$SandersPct <- (nhdata$Sanders) / (nhdata$Sanders + nhdata$Clinton) # add Sanders Pct
nhdata$ClintonPct <- (nhdata$Clinton) / (nhdata$Sanders + nhdata$Clinton) # add Clinton Pct
nhdata$SandersMarginPctgPoints <- nhdata$SandersPct - nhdata$ClintonPct # add Sanders margin PctRead USGEO data shapefile
# Read the shape file for US states and counties
pacman::p_load(raster, rgdal)
setwd("C:/Users/Owner/Desktop/My Documents/School/DataFiles/GIS/")
usgeo <- shapefile("cb_2014_us_county_5m/cb_2014_us_county_5m.shp")Quick plot view of USGEO shapes
qtm(usgeo) # test plot US geo shapes
#view(usgeo) # look at the usgeo dataQuickplot view of NH shapes & reconcile county names between files
nhgeo <- usgeo[usgeo$STATEFP==nhfipscode,] # get New Hampshire shapes
qtm(nhgeo) # test plot New Hampshire data
str(nhgeo$NAME) # look at nhgeo county name## chr [1:10] "Grafton" "Hillsborough" "Coos" "Belknap" "Rockingham" ...str(nhdata$County) # look at nhdata county name## chr [1:10] "Belknap" "Carroll" "Cheshire" "Coos" "Grafton" "Hillsborough" ...identical(nhgeo$NAME,nhdata$County) # check if they're the same## [1] FALSEnhgeo$NAME <- as.character(nhgeo$NAME) # make the county names plain char
nhgeo <- nhgeo[order(nhgeo$NAME),] # order nhgeo by county name
nhdata <- nhdata[order(nhdata$County),] # order nhdata by county name
identical(nhgeo$NAME,nhdata$County) # check if they're the same## [1] TRUECreate New Hampshire map data
nhmap <- merge(nhgeo, nhdata, by.x = "NAME", by.y = "County")
str(nhmap) # look at NH map data## Formal class 'SpatialPolygonsDataFrame' [package "sp"] with 5 slots
## ..@ data :'data.frame': 10 obs. of 15 variables:
## .. ..$ 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 ...
## ..@ polygons :List of 10
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## .. ..$ :Formal class 'Polygons' [package "sp"] with 5 slots
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## .. .. .. ..@ Polygons :List of 1
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## .. ..- 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"New Hampshire static maps
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")
NH Map with some formatting
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.
NH Map using Classic style format
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")
nhstaticmap # I like it with the PRGn palette## 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 NH map as a jpg
tmap_save(nhstaticmap, filename="nhdemprimary.jpg")## Map saved to C:\Users\Owner\Desktop\My Documents\School\RMD source files\nhdemprimary.jpg## Resolution: 1501.336 by 2937.385 pixels## Size: 5.004452 by 9.791282 inches (300 dpi)NH interactive map
clintonPalette <- colorNumeric(palette = "Blues", domain=nhmap$ClintonPct) # create palette
# create popup, round percentages to two decimal places
pacman::p_load(scales, raster)
nhpopup <- paste0(nhmap$NAME, " County: ", "Sanders ", percent(nhmap$SandersPct, accuracy = 0.01), " - Clinton ", percent(nhmap$ClintonPct, accuracy = 0.01))
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$ClintonPct)
)South Carolina map
Get South Carolina data and geo shapes
setwd("C:/Users/Owner/Desktop/My Documents/School/DataFiles/GIS")
scdata <- rio::import(scdatafile)
# sc shapefile
scgeo <- usgeo[usgeo@data$STATEFP==scfipscode,]
qtm(scgeo)
Create new columns for percentages and winners
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]))
}Get the SC college education data
setwd("C:/Users/Owner/Desktop/My Documents/School/DataFiles/GIS")
sced <- rio::import("SCdegree.xlsx")Reconcile the county names in SC data and SC geo data frames
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 scgeo county names to plain char
scgeo$NAME <- as.character(scgeo$NAME)
# Order by county name
scgeo <- scgeo[order(scgeo$NAME),]
scdata <- scdata[order(scdata$County),]
identical(scgeo$NAME, scdata$County) # check if identical## [1] TRUECreate the South Carolina map data frame, create palettes
# add election results
scmap <- merge(scgeo, scdata, by.x = "NAME", by.y = "County")
# get min max for 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 palettes
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$winner)
edPalette <- colorNumeric(palette = "Blues", domain = sced$PctCollegeDegree)Create popup
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%" )Create the projection of scmap data onto the World Geodetic System
scmap <- sp::spTransform(scmap, "+proj=longlat +datum=WGS84")Create the SC interactive map
leaflet(scmap) %>%
addProviderTiles("CartoDB.Positron") %>%
addPolygons(stroke = TRUE,
weight = 1,
smoothFactor = 0.2,
fillOpacity = .75,
popup = scpopup,
color = winnerPalette(scmap$winner),
group = "Winners") %>%
addLegend(position = "bottomleft", colors = c("#984ea3", "#e41a1c"), labels = c("Trump", "Rubio"))Add layers to SC interactive map for candidates, winners, and education
scGOPmap <- leaflet(scmap) %>%
addProviderTiles("CartoDB.Positron") %>%
addPolygons(stroke = TRUE,
weight = 1,
smoothFactor = 0.2,
fillOpacity = .75,
popup = scpopup,
color = winnerPalette(scmap$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 as shown in the tutorial
group="College degs") %>%
addLayersControl(
baseGroups=c("Winners", "Trump", "Rubio", "Cruz", "College degs"),
position = "bottomleft",
options = layersControlOptions(collapsed = FALSE))
# display the map
scGOPmapSave the SC map as an HTML file with dependencies in another directory
#htmlwidgets::saveWidget(widget = scGOPmap, file = "scGOPprimary.html", selfcontained = FALSE, libdir = "js")