Problem Set 2
Joshua Osula
10/15/2020
Problem Set 2
The object us_states from the spData package is a simple feature data frame from the U.S. Census Bureau. The variables include the name, region, area, and population.
- Create a new data frame containing only the population information. (10)
library(spData)## To access larger datasets in this package, install the spDataLarge
## package with: `install.packages('spDataLarge',
## repos='https://nowosad.github.io/drat/', type='source')`library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(tmap)
library(sf)## Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1library(sp)
library(RColorBrewer)
library(tmaptools)
library(rgeos)## rgeos version: 0.5-5, (SVN revision 640)
## GEOS runtime version: 3.8.0-CAPI-1.13.1
## Linking to sp version: 1.4-2
## Polygon checking: TRUElibrary(rgdal)## rgdal: version: 1.5-16, (SVN revision 1050)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 3.0.4, released 2020/01/28
## Path to GDAL shared files: C:/Users/JoshuaOsula/OneDrive - Jacksonville State University/Documents/R/win-library/4.0/rgdal/gdal
## GDAL binary built with GEOS: TRUE
## Loaded PROJ runtime: Rel. 6.3.1, February 10th, 2020, [PJ_VERSION: 631]
## Path to PROJ shared files: C:/Users/JoshuaOsula/OneDrive - Jacksonville State University/Documents/R/win-library/4.0/rgdal/proj
## Linking to sp version:1.4-2
## To mute warnings of possible GDAL/OSR exportToProj4() degradation,
## use options("rgdal_show_exportToProj4_warnings"="none") before loading rgdal.library(ggplot2)
usppl = us_states %>%
select(total_pop_10, total_pop_15)
usppl## Simple feature collection with 49 features and 2 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -124.7042 ymin: 24.55868 xmax: -66.9824 ymax: 49.38436
## geographic CRS: NAD83
## First 10 features:
## total_pop_10 total_pop_15 geometry
## 1 4712651 4830620 MULTIPOLYGON (((-88.20006 3...
## 2 6246816 6641928 MULTIPOLYGON (((-114.7196 3...
## 3 4887061 5278906 MULTIPOLYGON (((-109.0501 4...
## 4 3545837 3593222 MULTIPOLYGON (((-73.48731 4...
## 5 18511620 19645772 MULTIPOLYGON (((-81.81169 2...
## 6 9468815 10006693 MULTIPOLYGON (((-85.60516 3...
## 7 1526797 1616547 MULTIPOLYGON (((-116.916 45...
## 8 6417398 6568645 MULTIPOLYGON (((-87.52404 4...
## 9 2809329 2892987 MULTIPOLYGON (((-102.0517 4...
## 10 4429940 4625253 MULTIPOLYGON (((-92.01783 2...- Create a new data frame containing only states from the South region. (10)
Sreg = us_states %>%
filter(REGION == "South")
head(Sreg)## Simple feature collection with 6 features and 6 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -103.0024 ymin: 24.55868 xmax: -78.54109 ymax: 37.0001
## geographic CRS: NAD83
## GEOID NAME REGION AREA total_pop_10 total_pop_15
## 1 01 Alabama South 133709.27 [km^2] 4712651 4830620
## 2 12 Florida South 151052.01 [km^2] 18511620 19645772
## 3 13 Georgia South 152725.21 [km^2] 9468815 10006693
## 4 22 Louisiana South 122345.76 [km^2] 4429940 4625253
## 5 40 Oklahoma South 180971.40 [km^2] 3675339 3849733
## 6 45 South Carolina South 80903.58 [km^2] 4511428 4777576
## geometry
## 1 MULTIPOLYGON (((-88.20006 3...
## 2 MULTIPOLYGON (((-81.81169 2...
## 3 MULTIPOLYGON (((-85.60516 3...
## 4 MULTIPOLYGON (((-92.01783 2...
## 5 MULTIPOLYGON (((-103.0022 3...
## 6 MULTIPOLYGON (((-83.10861 3...- Create a new data frame containing only states from the West region having area less then 250,000 square km and a 2015 population more than 5,000,000 residents. Hint: you will need to use
as.numeric(AREA)to remove the units. (10)
Wreg = us_states %>%
filter(REGION == "West", as.numeric(AREA) < 250000, total_pop_15 > 5000000)
Wreg## Simple feature collection with 1 feature and 6 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -124.7042 ymin: 45.54774 xmax: -116.916 ymax: 49.00236
## geographic CRS: NAD83
## GEOID NAME REGION AREA total_pop_10 total_pop_15
## 1 53 Washington West 175436 [km^2] 6561297 6985464
## geometry
## 1 MULTIPOLYGON (((-122.7699 4...- What was the total population of the Midwest region in 2010 and 2015? (20)
USppl = us_states %>%
select(total_pop_10, total_pop_15, "REGION") %>%
filter (REGION == "Midwest") %>%
summarise(t10 = sum(total_pop_10),
t15 = sum(total_pop_15)) %>%
glimpse()## Rows: 1
## Columns: 3
## $ t10 <dbl> 66514091
## $ t15 <dbl> 67546398
## $ geometry <MULTIPOLYGON [°]> MULTIPOLYGON (((-87.80048 4...- How many states are in each region? (20)
us_states %>%
group_by(REGION) %>%
summarize(number_of_states = n())## `summarise()` ungrouping output (override with `.groups` argument)## Simple feature collection with 4 features and 2 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -124.7042 ymin: 24.55868 xmax: -66.9824 ymax: 49.38436
## geographic CRS: NAD83
## # A tibble: 4 x 3
## REGION number_of_states geometry
## <fct> <int> <MULTIPOLYGON [°]>
## 1 Norteast 9 (((-75.55945 39.62981, -75.50974 39.68611, -75.4150~
## 2 Midwest 12 (((-87.80048 42.49192, -87.83477 42.30152, -87.8000~
## 3 South 17 (((-81.81169 24.56874, -81.74565 24.65988, -81.4435~
## 4 West 11 (((-118.6055 33.031, -118.37 32.83927, -118.4963 32~Extra Credit. Make a bar chart showing the total area in millions of square kilometers by region. Hint: include stat = "identity" in the geom_bar() function. (10)
us_states %>%
group_by(REGION) %>%
summarize(AREA = n()) %>%
ggplot(aes(x = REGION, y = AREA, fill = AREA)) +
geom_bar(stat="identity", fill = "yellowgreen") +
xlab("Region") +
ylab("Total Area in Millions km^2") +
geom_text(aes(label = AREA), just = -.5, size = 3.5) +
theme_minimal() +
theme(legend.position = "none")## `summarise()` ungrouping output (override with `.groups` argument)## Warning: Ignoring unknown parameters: just
- Create a map using the Sac.metro.tracts data from lesson 7 of the medincome by county and then compare it with the map of total population for the counties. Include a north arrow, legend, neatline, and scale bar. (30)
download.file(url = "https://raw.githubusercontent.com/crd150/data/master/SacramentoMetroTracts.zip", destfile = "SacramentoMetroTracts.zip")
unzip(zipfile = "SacramentoMetroTracts.zip")
sac.metro.tracts <- st_read("SacMetroTractsIncome.shp", stringsAsFactors = FALSE)## Reading layer `SacMetroTractsIncome' from data source `C:\R\SacMetroTractsIncome.shp' using driver `ESRI Shapefile'
## Simple feature collection with 486 features and 6 fields
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: -122.422 ymin: 38.01842 xmax: -119.8773 ymax: 39.31645
## geographic CRS: NAD83ca.race <- read.csv("https://raw.githubusercontent.com/crd150/data/master/californiatractsrace.csv")sac.metro.tracts$GEOID = as.numeric(sac.metro.tracts$GEOID)
sac.metro.tracts <- sac.metro.tracts %>%
left_join(ca.race, by = "GEOID")
sac.metro.tracts## Simple feature collection with 486 features and 10 fields
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: -122.422 ymin: 38.01842 xmax: -119.8773 ymax: 39.31645
## geographic CRS: NAD83
## First 10 features:
## GEOID tract county state medincome totp
## 1 6017030200 Census Tract 302 El Dorado County California 54071 5299
## 2 6017030301 Census Tract 303.01 El Dorado County California 48269 2874
## 3 6017030302 Census Tract 303.02 El Dorado County California 36782 2718
## 4 6017030401 Census Tract 304.01 El Dorado County California 56339 3710
## 5 6017030402 Census Tract 304.02 El Dorado County California 37868 3972
## 6 6017030502 Census Tract 305.02 El Dorado County California 89583 2638
## 7 6017030504 Census Tract 305.04 El Dorado County California 70435 2440
## 8 6017030505 Census Tract 305.05 El Dorado County California 63194 2352
## 9 6017030601 Census Tract 306.01 El Dorado County California 90517 5162
## 10 6017030602 Census Tract 306.02 El Dorado County California 67168 7325
## pnhwhite pnhasn pnhblk phisp geometry
## 1 0.5423665 0.040007549 0.010190602 0.36818268 POLYGON ((-120.0182 38.9383...
## 2 0.6022965 0.013569937 0.003479471 0.36986778 POLYGON ((-119.99 38.92282,...
## 3 0.7671082 0.009933775 0.009197940 0.20161884 POLYGON ((-119.9991 38.9376...
## 4 0.8652291 0.021563342 0.004043127 0.09326146 POLYGON ((-120.0229 38.9340...
## 5 0.6724572 0.045317221 0.019385700 0.23136959 POLYGON ((-120.0271 38.8884...
## 6 0.8931008 0.002274450 0.003032600 0.07429871 POLYGON ((-120.0458 38.9227...
## 7 0.7754098 0.043852459 0.000000000 0.11926230 POLYGON ((-120.0055 38.8751...
## 8 0.8728741 0.004676871 0.007227891 0.07738095 POLYGON ((-120.0509 38.7499...
## 9 0.9254165 0.007555211 0.000000000 0.02828361 POLYGON ((-121.141 38.71198...
## 10 0.8647099 0.003822526 0.004232082 0.10662116 POLYGON ((-120.9813 38.8511...spdf.sac <- as(sac.metro.tracts, "Spatial")
spdf.sac <- spTransform(spdf.sac,
CRS = CRS("+proj=merc +ellps+GRS80 +units=m"))
is.projected(spdf.sac)## [1] TRUEslotNames(spdf.sac)## [1] "data" "polygons" "plotOrder" "bbox" "proj4string"plot(spdf.sac)
spdf.sac@bbox## min max
## x -13627960 -13344679
## y 4555708 4740025Jmap1 = tm_shape(spdf.sac)+
tm_polygons("totp",
style = "quantile",
palette = "seq") +
tm_legend(legend.outside = TRUE)+
tm_layout(title = "Total Population",
title.size = 1.1,
title.position = c("center", "top")) +
tm_layout(inner.margins = c(0.06, 0.10, 0.18, 0.08)) +
tm_scale_bar(color.dark = "black",
position = c("right, bottom")) +
tm_compass(type = "4star", size = 4, text.size = 0.5,
color.dark = "gray40", text.color = "black",
position = c("left", "top"))
Jmap2 = tm_shape(spdf.sac)+
tm_polygons("medincome",
style = "quantile",
palette = "seq") +
tm_legend(legend.outside = TRUE) +
tm_layout(title = "medincome",
title.size = 1.1,
title.position = c("center", "top")) +
tm_layout(inner.margins = c(0.06, 0.10, 0.18, 0.08)) +
tm_scale_bar(color.dark = "black",
position = c("right, bottom")) +
tm_compass(type = "4star", size = 4, text.size = 0.5,
color.dark = "gray40", text.color = "black",
position = c("left", "top"))