hw8 map
Sue Zeng
2023-03-22
Part 1: Map presidential elections results with the maps package
1. Download the elections file from harvard database
https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/42MVDX
d <- read.csv('hw8/dataverse_files/1976-2020-president.csv')
head(d)## year state state_po state_fips state_cen state_ic office
## 1 1976 ALABAMA AL 1 63 41 US PRESIDENT
## 2 1976 ALABAMA AL 1 63 41 US PRESIDENT
## 3 1976 ALABAMA AL 1 63 41 US PRESIDENT
## 4 1976 ALABAMA AL 1 63 41 US PRESIDENT
## 5 1976 ALABAMA AL 1 63 41 US PRESIDENT
## 6 1976 ALABAMA AL 1 63 41 US PRESIDENT
## candidate party_detailed writein candidatevotes
## 1 CARTER, JIMMY DEMOCRAT FALSE 659170
## 2 FORD, GERALD REPUBLICAN FALSE 504070
## 3 MADDOX, LESTER AMERICAN INDEPENDENT PARTY FALSE 9198
## 4 BUBAR, BENJAMIN ""BEN"" PROHIBITION FALSE 6669
## 5 HALL, GUS COMMUNIST PARTY USE FALSE 1954
## 6 MACBRIDE, ROGER LIBERTARIAN FALSE 1481
## totalvotes version notes party_simplified
## 1 1182850 20210113 NA DEMOCRAT
## 2 1182850 20210113 NA REPUBLICAN
## 3 1182850 20210113 NA OTHER
## 4 1182850 20210113 NA OTHER
## 5 1182850 20210113 NA OTHER
## 6 1182850 20210113 NA LIBERTARIAN2. Load the data, and clean it up
library(dplyr)
# group_by then top_n, both from the dplyr package.
d1 <- d %>%
group_by(state,year) %>%
top_n(1,candidatevotes)
d2 <- d1 %>% select(year,state,state_fips,party_detailed,candidate)
# define the color
d2 <- mutate(d2,color = ifelse(party_detailed %in% "DEMOCRAT", "blue",
ifelse(party_detailed %in% "REPUBLICAN", "red", "white" )))
d2$color <- as.character(d2$color)
#str(d2)
d2 <- d2 %>% rename(party = party_detailed)
head(d2)## # A tibble: 6 × 6
## # Groups: state, year [6]
## year state state_fips party candidate color
## <int> <chr> <int> <chr> <chr> <chr>
## 1 1976 ALABAMA 1 DEMOCRAT CARTER, JIMMY blue
## 2 1976 ALASKA 2 REPUBLICAN FORD, GERALD red
## 3 1976 ARIZONA 4 REPUBLICAN FORD, GERALD red
## 4 1976 ARKANSAS 5 DEMOCRAT CARTER, JIMMY blue
## 5 1976 CALIFORNIA 6 REPUBLICAN FORD, GERALD red
## 6 1976 COLORADO 8 REPUBLICAN FORD, GERALD red3. Try to make a map just for one year
library(maps)
#subset data for just one a year
d2000 <- subset(d2,year==2000)
#select the observsations that match state.fips$fips
d2000 <- d2000[match(state.fips$fips,d2000$state_fips),]
#map
map("state",col=d2000$color,fill=TRUE)
title("Presidential Elections Results by States in 2000")
legend("bottomright", legend = c("Democrat", "Republican","other"), fill = c("blue", "red","white"))
library(ggplot2)
library(usmap)
states <- map_data("state")
p2000<- plot_usmap(regions="states",exclude = c("Alaska","Hawaii","Puerto Rico"), data = d2000, values = "color") +
scale_fill_manual(name = "", values =c("red"= "red","blue"= "blue","white"= "white"),
labels = c("Democrat", "Republican","other") ) +
labs(titles = "Presidential Elections Results by States in 2000")+
theme(legend.position = "right")
p2000
4. Now loop that code and map it over time
year <- seq(min(d2$year),max(d2$year),4)
mapf <- list()
library(gridExtra)
for (i in year) {
#subset data for just one a year
di <- subset(d2,year==i)
#select the observsations that match state.fips$fips
di <- di[match(state.fips$fips,di$state_fips),]
#map
map("state",col=di$color,fill=TRUE)
title(paste("Presidential Elections Results by States in",i))
}
legend("bottomright", legend = c("Democrat", "Republican","other"), fill = c("blue", "red","white"))
#plot with ggplot
year <- seq(min(d2$year),max(d2$year),4)
mapf <- list()
library(gridExtra)
his <- list()
for (i in year) {
di <- subset(d2,year==i)
di <- di[match(state.fips$fips,di$state_fips),]
p<-plot_usmap(regions="states",
exclude = c("Alaska","Hawaii","Puerto Rico"),
data = di, values = "color") +
scale_fill_manual(name = "", values =c("red"= "red","blue"= "blue","white"= "white"),
labels = c("Democrat", "Republican","other") ) +
labs(titles = paste(i))+
theme(legend.position = "none")
t <- which(year == i)
# add the plot to the list
his[[t]]<- p
}
#extract legend
library(gtable)
legend1 = gtable_filter(ggplot_gtable(ggplot_build(p2000)), "guide-box")
grid.arrange(arrangeGrob(grobs = his,nrow = 4),legend1, heights=c(10,1),ncol = 2)
Part 2: Interactive Maps with Leaflet
1. Get familiar with the leaflet package
library(leaflet)
myMap <- leaflet() %>%
addProviderTiles(providers$OpenStreetMap) %>%
setView(lat=33.947474, lng=-83.373671, zoom = 12)
myMaplibrary(leaflet)
beijing <- leaflet() %>%
addProviderTiles(providers$OpenStreetMap) %>%
setView(lat=39.904202, lng=116.407394, zoom = 12)
beijing2. Add your own shapefiles
library(sf)
library(raster)
library(rgdal)
sp <- shapefile('hw8/220130_RRC_Outline_Camp_AL1/T220130_RRC_Outline_Camp_AL1.shp')
# Projection is necessary for R to place the coordinates correctly
campShapeFile <- spTransform(sp, CRS("+proj=longlat +datum=WGS84 +no_defs"))
head(campShapeFile)## OBJECTID District Upazila Settlement Union Name_Alias
## 1 1 Cox's Bazar Ukhia Collective site Palong Khali Bagghona-Putibonia
## 2 2 Cox's Bazar Ukhia Collective site Palong Khali <NA>
## 3 3 Cox's Bazar Ukhia Collective site Palong Khali Jamtoli-Baggona
## 4 4 Cox's Bazar Ukhia Collective site Raja Palong Kutupalong RC
## 5 5 Cox's Bazar Ukhia Collective site Palong Khali <NA>
## 6 6 Cox's Bazar Ukhia Collective site Palong Khali <NA>
## SSID SMSD__Cnam NPM_Name Area_Acres PeriMe_Met Camp_Name
## 1 CXB-224 Camp 16 Camp 16 (Potibonia) 130.57004 4136.730 Camp 16
## 2 CXB-203 Camp 02E Camp 02E 96.58179 4803.162 Camp 2E
## 3 CXB-223 Camp 15 Camp 15 (Jamtoli) 243.25346 4722.267 Camp 15
## 4 CXB-221 Camp KRC Kutupalong RC 95.70851 3094.872 Kutupalong RC
## 5 CXB-213 Camp 09 Camp 09 160.39225 4116.455 Camp 9
## 6 CXB-214 Camp 10 Camp 10 122.60020 3732.526 Camp 10
## Area_SqM Latitude Longitude Shape_Leng Shape_Area
## 1 528946.95881724 21.1563813298438 92.1490685817901 0.03853094 4.597769e-05
## 2 391267.799744003 21.2078084302778 92.1643360947381 0.04481152 3.402118e-05
## 3 985424.393160958 21.1606399787906 92.1428956454661 0.04423700 8.565911e-05
## 4 387729.666427279 21.2120281895357 92.1638095873048 0.02878576 3.371450e-05
## 5 649769.878002447 21.1899911992394 92.1603127317644 0.03853989 5.649182e-05
## 6 496664.566973549 21.1897434585344 92.1542587569213 0.03475873 4.318088e-05library(leaflet)
library(rgeos)
myMap <- leaflet() %>%
addProviderTiles(providers$OpenStreetMap) %>%
setView(92.14871, 21.18780, zoom = 12) %>%
addPolygons(data=campShapeFile, fill=TRUE, stroke=T, weight=1,highlight = highlightOptions(fillOpacity = 0.7),label = campShapeFile$Block_No)
myMap3. Add tiles from the web
myMap <- leaflet() %>%
addProviderTiles(providers$OpenStreetMap) %>%
addWMSTiles(
"http://mesonet.agron.iastate.edu/cgi-bin/wms/nexrad/n0r.cgi",
layers = "nexrad-n0r-900913",
options = WMSTileOptions(format = "image/png", transparent = TRUE),attribution = "Weather data 2012 IEM Nexrad") %>%
setView(-95.712891, 37.090240, zoom =4)
myMap