Spatial Figures Review
Matthew Kusen
Problem 1
Create the following figure, using the data included in the R Markdown file.
####HINT: Use the following code to get the colors right
#scale_fill_distiller(palette=5)
####HINT: make sure you use left_join to combine the data_values above to the world map data in my_world_map
####PUT YOUR CODE HERE
map <- left_join(my_world_map,some_data_values,by="region")
ggplot(data = map, mapping = aes(x= long, y= lat,
group=group,
fill=Score))+
geom_polygon()+
labs(title="Problem 1")+
scale_fill_distiller(palette = "Greens")
Problem 2
Create the following figure, using the data included in the R Markdown file.
####Make sure you load any necessary libraries
####HINT: Use a filter command to get just maps of Costa Rica, Panama, and Nicaragua
####Hint: Use a filter command to put in points only for cities with a population of greater than 40,000. This should leave you with 32 cities.
####Hint: Use add_column to attach the "Measurement" variable to your data, and set that to the color aesthetic when you draw the points.
####Hint: set the colors for the city points with scale_color_distiller(palette=7)
####Hint: set the size of all points to the value 5
world <- map_data("world")
CentralAmerica1 <- filter(world,region=="Costa Rica"|
region=="Nicaragua"|
region=="Panama")
my_cities <-maps::world.cities
CA_big_cities <- filter(my_cities,country.etc=="Costa Rica"|
country.etc=="Nicaragua"|
country.etc=="Panama",pop>40000)
add_column(CA_big_cities,Measurement)## name country.etc pop lat long capital Measurement
## 1 Alajuela Costa Rica 48366 10.02 -84.23 0 50.25882
## 2 Arraijan Panama 81118 8.95 -79.65 0 51.83112
## 3 Bluefields Nicaragua 45703 12.01 -83.77 0 49.66038
## 4 Chinandega Nicaragua 129730 12.63 -87.13 0 50.89720
## 5 Chitre Panama 44735 7.97 -80.42 0 50.48802
## 6 Ciudad Sandino Nicaragua 72109 12.16 -86.34 0 48.74461
## 7 Colon Panama 77983 9.36 -79.90 0 50.02279
## 8 David Panama 84013 8.44 -82.43 0 51.09077
## 9 El Viejo Nicaragua 55268 12.67 -87.18 0 49.86788
## 10 Esteli Nicaragua 99479 13.09 -86.36 0 48.92500
## 11 Granada Nicaragua 90868 11.94 -85.96 0 50.85501
## 12 Jinotega Nicaragua 53055 13.10 -86.00 0 49.63502
## 13 Juigalpa Nicaragua 56712 12.11 -85.38 0 50.16555
## 14 La Chorrera Panama 62359 8.88 -79.78 0 48.75722
## 15 Las Cumbres Panama 73219 9.08 -79.53 0 51.45929
## 16 Leon Nicaragua 146685 12.43 -86.89 0 49.99639
## 17 Liberia Costa Rica 47906 10.64 -85.45 0 49.97912
## 18 Limon Costa Rica 64285 9.99 -83.04 0 50.03211
## 19 Managua Nicaragua 990417 12.15 -86.27 1 48.83272
## 20 Masaya Nicaragua 134516 11.98 -86.10 0 49.48043
## 21 Matagalpa Nicaragua 114628 12.93 -85.93 0 51.37389
## 22 Nueva Guinea Nicaragua 55339 11.69 -84.46 0 51.41233
## 23 Pacora Panama 56414 9.08 -79.28 0 49.59783
## 24 Panama Panama 406070 8.97 -79.53 1 49.56086
## 25 Paraiso Costa Rica 41936 9.83 -83.87 0 51.01061
## 26 San Francisco Costa Rica 59484 9.99 -84.13 0 50.43082
## 27 San Jose Costa Rica 339588 9.93 -84.08 1 50.73393
## 28 San Miguelito Panama 326951 9.03 -79.50 0 49.31933
## 29 Santiago Panama 46284 8.10 -80.97 0 50.32620
## 30 Tipitapa Nicaragua 132672 12.20 -86.10 0 50.90703
## 31 Tocumen Panama 89951 9.08 -79.38 0 49.53732
## 32 Vista Alegre Panama 42451 8.93 -79.70 0 50.00470ggplot(data = CentralAmerica1, mapping = aes(x= long,
y= lat,group =group))+
geom_polygon(color="black",fill="white")+
geom_point(data = CA_big_cities,aes(x=long,y=lat,
group=NULL,
color=Measurement),
size=5, alpha=.8)+
scale_size_continuous() +
scale_color_distiller(palette=3) +
theme_classic() +
theme(axis.text.x = element_blank(),axis.text.y=element_blank(),
axis.line = element_blank(),axis.ticks = element_blank())+
labs(x="",y="", title="Panama, Nicaragua, & Costa Rica major cities",
caption ="Population > 40,000")
Problem 3
Create the following figure, using the data included in the R Markdown file.
Note that the code in the .rmd file will import a set of simple features data for South America. Make sure you install any necessary packages.
####Make sure you load any necessary libraries
####HINT: the s_america object created in the chunk above is a simple features object with many columns. Identify the correct column based on the solution figure and use it to color in the choropleth.
####HINT: Use scale_fill_distiller and palette 10 to the match the colors.
####PUT YOUR CODE HERE
library(ggrepel)
ggplot()+geom_sf(data= s_america,aes(fill=pop_est))+
scale_fill_distiller(palette = 10)
Data Wrangling
Simplify GDP data
#Additional Examples
#Asia countries by GDP
Asia <-ne_countries(scale="medium",continent='asia',returnclass="sf")
ggplot()+geom_sf(data= Asia,aes(fill=economy))+
scale_fill_brewer(palette="Set2")+
theme_classic()+
labs(x="",y="",title="Asia Countries by Economy")+
theme(axis.text.x = element_blank(),axis.text.y=element_blank(),
axis.line = element_blank(),axis.ticks = element_blank())
ESEAP <-Asia %>% filter(region_wb=="South Asia"|
region_wb=="East Asia & Pacific")
table(Asia$region_wb)##
## East Asia & Pacific Europe & Central Asia
## 20 11
## Middle East & North Africa South Asia
## 14 8SPRINT <- tibble("admin"=c("Myanmar","Pakistan","Afghanistan","Vietnam","Sri Lanka","Nepal","Indonesia","Philippines"),"SPRINT"=rep("SPRINT"))
ESEAP <- left_join(ESEAP,SPRINT,by="admin")
seAsia <- c("Myanmar","Thailand","Cambodia","Vietnam","Laos","Malaysia","Indonesia","Philippines")
SEA <- Asia %>% filter(name %in% seAsia)
ggplot()+geom_sf(data= ESEAP,aes(fill=SPRINT))+
scale_fill_brewer()+
theme_classic()+
labs(x="",y="",title="SPRINT Countries")+
theme(axis.text.x = element_blank(),axis.text.y=element_blank(),
axis.line = element_blank(),axis.ticks = element_blank())+
scale_fill_manual(values="deepskyblue")## Scale for 'fill' is already present. Adding another scale for 'fill', which
## will replace the existing scale.
ggplot()+geom_sf(data= SEA,aes(fill=economy))+
theme_classic()+
labs(x="",y="",title="SEA countries by Economy")+
theme(axis.text.x = element_blank(),axis.text.y=element_blank(),
axis.line = element_blank(),axis.ticks = element_blank())+
scale_fill_brewer(palette=4)