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 data

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()

Map coordinate system

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]]

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 Texas Centric Albers Equal Area

aea_sa<-st_transform(sa_acs2, crs = 3665)

cat<-aea_sa %>% 
  filter(GEOID %in% c(48029181820, 48029110600))

dog<-st_centroid(cat)
## Warning in st_centroid.sf(cat): st_centroid assumes attributes are constant over
## geometries of x
st_distance(dog)
## Units: [m]
##          [,1]     [,2]
## [1,]     0.00 19536.81
## [2,] 19536.81     0.00

Distance comparison

# Distance with south texas projection = 64043 ft

# Distance with albers equal area projection = 19536 m = 64097 ft

64097-64043
## [1] 54

Conclusion

# It's important to use appropriate projections when dealing with geospatial data since the accuracy of measurements, such as distance, depends on the chosen projection and how well it represents your area of interest.