Lab 1- Homework 2
Debarati Chakraborty
2023-01-26
#tidycensus::census_api_key(key = "74b7705f240cd694220b5d61b47fd7f68b0f62c1", install = T)
library (foreign)
library(tidycensus)
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.7 ✔ dplyr 1.0.9
## ✔ tidyr 1.2.0 ✔ stringr 1.4.0
## ✔ readr 2.1.2 ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(sf)## Linking to GEOS 3.9.3, GDAL 3.5.2, PROJ 8.2.1; sf_use_s2() is TRUElibrary(ggplot2)
library(classInt)
library(dplyr)
v15_Profile <- load_variables(year = 2019 ,
dataset = "acs5/profile",
cache = TRUE)
v15_Profile%>%
filter(grepl(pattern = "POVERTY", x = label))%>%
select(name, label)## # A tibble: 38 × 2
## name label
## <chr> <chr>
## 1 DP03_0119 Estimate!!PERCENTAGE OF FAMILIES AND PEOPLE WHOSE INCOME IN THE P…
## 2 DP03_0119P Percent!!PERCENTAGE OF FAMILIES AND PEOPLE WHOSE INCOME IN THE PA…
## 3 DP03_0120 Estimate!!PERCENTAGE OF FAMILIES AND PEOPLE WHOSE INCOME IN THE P…
## 4 DP03_0120P Percent!!PERCENTAGE OF FAMILIES AND PEOPLE WHOSE INCOME IN THE PA…
## 5 DP03_0121 Estimate!!PERCENTAGE OF FAMILIES AND PEOPLE WHOSE INCOME IN THE P…
## 6 DP03_0121P Percent!!PERCENTAGE OF FAMILIES AND PEOPLE WHOSE INCOME IN THE PA…
## 7 DP03_0122 Estimate!!PERCENTAGE OF FAMILIES AND PEOPLE WHOSE INCOME IN THE P…
## 8 DP03_0122P Percent!!PERCENTAGE OF FAMILIES AND PEOPLE WHOSE INCOME IN THE PA…
## 9 DP03_0123 Estimate!!PERCENTAGE OF FAMILIES AND PEOPLE WHOSE INCOME IN THE P…
## 10 DP03_0123P Percent!!PERCENTAGE OF FAMILIES AND PEOPLE WHOSE INCOME IN THE PA…
## # … with 28 more rowsv15_Profile%>%
filter(grepl(pattern = "Built 2000 to 2009", x = label))%>%
select(name, label)## # A tibble: 2 × 2
## name label
## <chr> <chr>
## 1 DP04_0019 Estimate!!YEAR STRUCTURE BUILT!!Total housing units!!Built 2000 to…
## 2 DP04_0019P Percent!!YEAR STRUCTURE BUILT!!Total housing units!!Built 2000 to …sa_acs<-get_acs(geography = "tract",
state="TX",
county = "Bexar",
year = 2019,
variables=c("DP05_0001E", "DP03_0062E") ,
geometry = T,
output = "wide")## Getting data from the 2015-2019 5-year ACS
## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
## Using the ACS Data Profile##
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|======================================================================| 100%head(sa_acs)## Simple feature collection with 6 features and 6 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -98.56606 ymin: 29.36662 xmax: -98.44832 ymax: 29.54044
## Geodetic CRS: NAD83
## GEOID NAME DP05_0001E DP05_0001M
## 1 48029190601 Census Tract 1906.01, Bexar County, Texas 4514 788
## 2 48029181100 Census Tract 1811, Bexar County, Texas 6359 523
## 3 48029140700 Census Tract 1407, Bexar County, Texas 4838 303
## 4 48029180602 Census Tract 1806.02, Bexar County, Texas 3751 536
## 5 48029140800 Census Tract 1408, Bexar County, Texas 5124 453
## 6 48029170200 Census Tract 1702, Bexar County, Texas 5599 617
## DP03_0062E DP03_0062M geometry
## 1 46818 14436 MULTIPOLYGON (((-98.52602 2...
## 2 64930 6521 MULTIPOLYGON (((-98.56225 2...
## 3 36222 6656 MULTIPOLYGON (((-98.47348 2...
## 4 38389 7038 MULTIPOLYGON (((-98.56606 2...
## 5 43299 3656 MULTIPOLYGON (((-98.4778 29...
## 6 25755 7141 MULTIPOLYGON (((-98.5283 29...# create a county FIPS code - 5 digit
sa_acs$county<-substr(sa_acs$GEOID, 1, 5)
# rename variables and filter missing cases
sa_acs2<-sa_acs%>%
mutate(totpop= DP05_0001E,
medianinc=DP03_0062E) %>%
st_transform(crs = 2919)%>%
na.omit()library(tmap)
library(tmaptools)
tm_shape(sa_acs2)+
tm_polygons("medianinc",
title="Estimates of Median Household Income",
palette="Blues",
style="quantile", n=5 )+
tm_format("World",
title="San Antonio Bexar County Median Household Estimates - Quantile Breaks",
legend.outside=T)+
tm_scale_bar()+
tm_compass()
library(patchwork)
library(ggsn)## Loading required package: gridlibrary(xplorerr)
source("https://raw.githubusercontent.com/coreysparks/Rcode/master/mutate_map_funs.R")
medianinc_map<-sa_acs2 %>%
mutate_map_brks(medianinc, n=6, style="quantile")%>%
mutate_map_brks(medianinc, n=6, style="jenks")
p1<-ggplot(medianinc_map, aes(fill = medianinc_qbrks)) +
geom_sf(color="grey50") +
ggtitle("Estimates of Median Household Income",
subtitle = "Bexar County Texas, 2017 - Quantile Breaks")+
scale_fill_brewer(palette = "Reds") +
scale_color_brewer(palette = "Reds")+
theme(axis.text.x = element_blank(),
axis.text.y = element_blank())+
north(medianinc_map)+
scalebar(medianinc_map, location="bottomleft",
dist=5, transform = T,
dist_unit = "km",
model="WGS84",
st.size =2 )
p1