---

output: html_notebook

title: “DEM 7093 Homework 2 using R” author: “Steve Weeks” date: “2/20/2020”

```

library(tidycensus)
library(sf)

## Linking to GEOS 3.7.2, GDAL 2.4.2, PROJ 5.2.0

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

#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:
##   EPSG: 4269 
##   proj4string: "+proj=longlat +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +no_defs"

create basic map

ppov_map<-sa_acs2 %>%
  mutate(cpov=cut(ppov,breaks = quantile(ppov, na.rm=T, p=seq(0,1,length.out = 6)),include.lowest = T))

library(ggsn)

## Loading required package: ggplot2

## Loading required package: grid

p1<-ggplot(ppov_map, aes(fill = cpov, color = cpov)) + 
  geom_sf() + 
  ggtitle("Proportion in poverty", 
          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(ppov_map)+
  scalebar(ppov_map, location="bottomleft", dist=5, transform = T,dist_unit = "km",  model="WGS84", st.size =2 )
p1

re-project map into South Central Texas Centric Albers Equal Area

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

##What is the distance between the two points? Is this distance interpretable?

#The distance is 64043 ft using Texas South and 64097 ft using Texas Centric. Yes, the results are interpretable.

##Reproject the layer into a new coordinate system, use NAD83/Texas Centric Albers Equal Area. How do the distances compare between the Texas Centric and Texas South?

#The distances are very similar. The difference between the two is 54 feet.

##In general, why is it important to have an accurate system of projection? How could your results be sensitive to this?

#It is important to have an accurate system of projection so that the map will display correctly. I.e., a projection for Australia used for the United States would have the United States looking very funky.