Homework 2
Adolph Delgado
2/10/2021
library(tidycensus)
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.3 v purrr 0.3.4
## v tibble 3.0.5 v dplyr 1.0.4
## v tidyr 1.1.2 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.0## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()v15_Profile <- load_variables(2019 , "acs5/profile", cache = TRUE)
v15_Profile[grep(x = v15_Profile$label, "Income"), c("name", "label")]## # A tibble: 4 x 2
## name label
## <chr> <chr>
## 1 DP03_0070 Estimate!!INCOME AND BENEFITS (IN 2019 INFLATION-ADJUSTED DOLLARS)~
## 2 DP03_0070P Percent!!INCOME AND BENEFITS (IN 2019 INFLATION-ADJUSTED DOLLARS)!~
## 3 DP03_0071 Estimate!!INCOME AND BENEFITS (IN 2019 INFLATION-ADJUSTED DOLLARS)~
## 4 DP03_0071P Percent!!INCOME AND BENEFITS (IN 2019 INFLATION-ADJUSTED DOLLARS)!~v15_Profile[grep(x = v15_Profile$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!!YEAR STRUCTURE BUILT!!Total housing units!!Built 2000 to ~sa_acs<-get_acs(geography = "tract",
state="TX",
county = c("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%options(tigris_use_cache = TRUE)
sa_acs$county<-substr(sa_acs$GEOID, 1, 5)
sa_acs2<-sa_acs%>%
mutate(totpop= DP05_0001E, pinc=DP03_0062E) %>%
na.omit()library(tmap)
library(tmaptools)
tm_shape(sa_acs2)+
tm_polygons("pinc", title="% in Income", palette="Blues", style="quantile", n=5 ,legend.hist=T)+
tm_format("World", title="San Antonio Income Estimates - Quantile Breaks", legend.outside=T)+
tm_scale_bar()+
tm_compass()## Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1
tm_shape(sa_acs2)+
tm_polygons("pinc", title="% in Income", palette="Blues", style="jenks", n=5,legend.hist=T )+
tm_format("World", title="San Antonio income Estimates - Jenks Breaks", legend.outside=T)+
tm_scale_bar()+
tm_compass()
#Coordinate System of Current Map
st_crs(sa_acs2)## Coordinate Reference System:
## User input: NAD83
## wkt:
## GEOGCRS["NAD83",
## DATUM["North American Datum 1983",
## ELLIPSOID["GRS 1980",6378137,298.257222101,
## LENGTHUNIT["metre",1]]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["degree",0.0174532925199433]],
## CS[ellipsoidal,2],
## AXIS["latitude",north,
## ORDER[1],
## ANGLEUNIT["degree",0.0174532925199433]],
## AXIS["longitude",east,
## ORDER[2],
## ANGLEUNIT["degree",0.0174532925199433]],
## ID["EPSG",4269]]new_sa2<-st_transform(sa_acs2, crs = 2278)
twtr<-new_sa2%>%
filter(GEOID %in% c(48029181820, 48029110600))
tr_co<-st_centroid(twtr)## Warning in st_centroid.sf(twtr): st_centroid assumes attributes are constant
## over geometries of xst_distance(tr_co)## Units: [US_survey_foot]
## [,1] [,2]
## [1,] 0.00 64041.12
## [2,] 64041.12 0.00#convert
64041.12*.00018939393## [1] 12.129new_sa3<-st_transform(sa_acs2, crs = 3085)
twtr<-new_sa3%>%
filter(GEOID %in% c(48029181820, 48029110600))
tr_co<-st_centroid(twtr)## Warning in st_centroid.sf(twtr): st_centroid assumes attributes are constant
## over geometries of xst_distance(tr_co)## Units: [m]
## [,1] [,2]
## [1,] 0.00 19536.16
## [2,] 19536.16 0.00#convert
19536.16*.000621371## [1] 12.1392Summary: The Texas Centric Albers Equal Area projection shows slightly more mileage than the Texas South Central projection. It is important to have an accurate system of projection to have reliability and validity of your measurements. The results are sensitive due to the different distances and metrics each system uses to measure the data.