Homework 2
Asiya
1/30/2021
library(tidycensus)
library(tidyverse)## ── Attaching packages ────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──## ✓ ggplot2 3.3.2 ✓ purrr 0.3.4
## ✓ tibble 3.0.3 ✓ dplyr 1.0.2
## ✓ tidyr 1.1.2 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0## ── Conflicts ───────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(sf)## Linking to GEOS 3.8.1, GDAL 3.1.4, PROJ 6.3.1library(ggplot2)
v15_Profile <- load_variables(2017 , "acs5/profile", cache = TRUE) #demographic profile tables
#Search for variables by keywords in the label
v15_Profile[grep(x = v15_Profile$label, "POVERTY"), c("name", "label")]## # A tibble: 38 x 2
## name label
## <chr> <chr>
## 1 DP03_0119 Estimate!!PERCENTAGE OF FAMILIES AND PEOPLE WHOSE INCOME IN THE P…
## 2 DP03_0119P Percent Estimate!!PERCENTAGE OF FAMILIES AND PEOPLE WHOSE INCOME …
## 3 DP03_0120 Estimate!!PERCENTAGE OF FAMILIES AND PEOPLE WHOSE INCOME IN THE P…
## 4 DP03_0120P Percent Estimate!!PERCENTAGE OF FAMILIES AND PEOPLE WHOSE INCOME …
## 5 DP03_0121 Estimate!!PERCENTAGE OF FAMILIES AND PEOPLE WHOSE INCOME IN THE P…
## 6 DP03_0121P Percent Estimate!!PERCENTAGE OF FAMILIES AND PEOPLE WHOSE INCOME …
## 7 DP03_0122 Estimate!!PERCENTAGE OF FAMILIES AND PEOPLE WHOSE INCOME IN THE P…
## 8 DP03_0122P Percent Estimate!!PERCENTAGE OF FAMILIES AND PEOPLE WHOSE INCOME …
## 9 DP03_0123 Estimate!!PERCENTAGE OF FAMILIES AND PEOPLE WHOSE INCOME IN THE P…
## 10 DP03_0123P Percent Estimate!!PERCENTAGE OF FAMILIES AND PEOPLE WHOSE INCOME …
## # … with 28 more rowsv15_Profile[grep(x = v15_Profile$label, "Built 2000 to 2009"), c("name", "label")]## # A tibble: 2 x 2
## name label
## <chr> <chr>
## 1 DP04_0019 Estimate!!YEAR STRUCTURE BUILT!!Total housing units!!Built 2000 to…
## 2 DP04_0019P Percent Estimate!!YEAR STRUCTURE BUILT!!Total housing units!!Built…Extract from ACS summary file data
sa_acs<-get_acs(geography = "tract",
state="TX",
county = c("Bexar"),
year = 2017,
variables=c( "DP03_0062E") ,
geometry = T, output = "wide")## Getting data from the 2013-2017 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## Warning: `funs()` is deprecated as of dplyr 0.8.0.
## Please use a list of either functions or lambdas:
##
## # Simple named list:
## list(mean = mean, median = median)
##
## # Auto named with `tibble::lst()`:
## tibble::lst(mean, median)
##
## # Using lambdas
## list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.##
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|======================================================================| 100%#create a county FIPS code - 5 digit, making county identifier
sa_acs$county<-substr(sa_acs$GEOID, 1, 5)
#rename variables and filter missing cases
sa_acs2<-sa_acs%>%
mutate(medianinc= DP03_0062E) %>%
na.omit()Write data out to shapefile
#change the directory
#sf::st_write(sa_acs2,dsn="/Users/asiyavalidova/Dropbox/Demography/GIS/w2",layer="sa_tr_dp03", driver="ESRI Shapefile")Some basic mapping of variables
library(classInt)
library(patchwork)
library(dplyr)
medianinc_map<-sa_acs2 %>%
mutate(cmedian=cut(medianinc,breaks = quantile(medianinc, na.rm=T, p=seq(0,1,length.out = 6)),include.lowest = T),
jmedian = cut(medianinc,breaks=data.frame(classIntervals(var=sa_acs2$medianinc, n=5, style="pretty")[2])[,1], include.lowest = T))Using tmap
#install.packages("tmap")
library(tmap)
library(tmaptools)
tm_shape(sa_acs2)+
tm_polygons("medianinc", title="Median Household Income", palette="Blues", style="quantile", n=5 ,legend.hist=T)+
tm_format("World", title="San Antonio Median Household Income Estimates - Quantile Breaks", legend.outside=T)+
tm_scale_bar()+
tm_compass()
tm_shape(sa_acs2)+
tm_polygons("medianinc", title="Median Household Income", palette="Blues", style="jenks", n=5,legend.hist=T )+
tm_format("World", title="San Antonio Median Household Income Estimates - Jenks Breaks", legend.outside=T)+
tm_scale_bar()+
tm_compass()
ggsave(filename="/Users/asiyavalidova/Dropbox/Demography/GIS/homework1map1.png")## Saving 7 x 5 in imageInteractive map with mapview
#install.packages("mapview")
#library(mapview)
#library(RColorBrewer)
#pal <- colorRampPalette(brewer.pal(6, "Blues")) #set colors
#mapview(medianinc_map["jmedian"], col.regions=pal, legend=T,map.types="OpenStreetMap", layer.name="Median Household #Income")