DEM 7093 HOMEWORK 3

library(tidycensus)
library(sf)

## Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1

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, union

Read in Bexar county tracts

sa_acs<-get_acs(geography = "tract",
                state="TX",
                county = c("Bexar"),
                year = 2017,
                variables=c( "DP05_0001E", 
                            "DP03_0119PE") ,
                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

## 
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#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, ppov=DP03_0119PE) %>%
#  st_transform(crs = 102740)%>%
  na.omit()

find 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]]

create basic map

library(tmap)
library(tmaptools)

tm_shape(sa_acs2)+
  tm_polygons("ppov", title="% in Poverty", palette="Blues", style="quantile", n=5 )+
  tm_format("World", title="San Antonio Poverty Estimates - Quantile Breaks", legend.outside=T)+
  tm_scale_bar()+
  tm_compass()                

### re-project map into South Central Texas projection

new_sa<-st_transform(sa_acs2, crs = 2278)

#Extract two tracts
twtr<-new_sa%>%
  filter(GEOID %in% c(48029181820, 48029110600))

# get centroid coordinates for two tracts
tr_co<-st_centroid(twtr)

## Warning in st_centroid.sf(twtr): st_centroid assumes attributes are constant
## over geometries of x

#Measure feet apart
st_distance(tr_co)

## Units: [US_survey_foot]
##          [,1]     [,2]
## [1,]     0.00 64043.26
## [2,] 64043.26     0.00

re-project map into NAD83 projection

new_sa<-st_transform(sa_acs2, crs = 4269)

#Extract two tracts
twtr<-new_sa%>%
  filter(GEOID %in% c(48029181820, 48029110600))

# get centroid coordinates for two tracts
tr_co<-st_centroid(twtr)

## Warning in st_centroid.sf(twtr): st_centroid assumes attributes are constant
## over geometries of x

## Warning in st_centroid.sfc(st_geometry(x), of_largest_polygon =
## of_largest_polygon): st_centroid does not give correct centroids for longitude/
## latitude data

#Measure feet apart
st_distance(tr_co)

## Units: [m]
##          [,1]     [,2]
## [1,]     0.00 19522.95
## [2,] 19522.95     0.00

re-project map into NAD83 Texas Centric Albers Equal Area projection

new_sa<-st_transform(sa_acs2, crs = 3083)

#Extract two tracts
twtr<-new_sa%>%
  filter(GEOID %in% c(48029181820, 48029110600))

# get centroid coordinates for two tracts
tr_co<-st_centroid(twtr)

## Warning in st_centroid.sf(twtr): st_centroid assumes attributes are constant
## over geometries of x

#Measure feet apart
st_distance(tr_co)

## Units: [m]
##          [,1]     [,2]
## [1,]     0.00 19536.81
## [2,] 19536.81     0.00