v15_Profile <- load_variables(year = 2019 ,
                              dataset =  "acs5/profile",
                              cache = TRUE)

v15_Profile%>%
  filter(grepl(pattern = "DOLLARS)!!Median", x = label))%>%
  select(name, label)
## # A tibble: 6 × 2
##   name       label                                                              
##   <chr>      <chr>                                                              
## 1 DP03_0092  Estimate!!INCOME AND BENEFITS (IN 2019 INFLATION-ADJUSTED DOLLARS)…
## 2 DP03_0092P Percent!!INCOME AND BENEFITS (IN 2019 INFLATION-ADJUSTED DOLLARS)!…
## 3 DP03_0093  Estimate!!INCOME AND BENEFITS (IN 2019 INFLATION-ADJUSTED DOLLARS)…
## 4 DP03_0093P Percent!!INCOME AND BENEFITS (IN 2019 INFLATION-ADJUSTED DOLLARS)!…
## 5 DP03_0094  Estimate!!INCOME AND BENEFITS (IN 2019 INFLATION-ADJUSTED DOLLARS)…
## 6 DP03_0094P Percent!!INCOME AND BENEFITS (IN 2019 INFLATION-ADJUSTED DOLLARS)!…
v15_Profile%>%
  filter(grepl(pattern = "Built 2000 to 2009", x = label))%>%
  select(name, label)
## # A tibble: 2 × 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 = "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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## Data Extraction and Variable 
head(sa_acs)
## Simple feature collection with 6 features and 6 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -98.56606 ymin: 29.36662 xmax: -98.44832 ymax: 29.54044
## Geodetic CRS:  NAD83
##         GEOID                                      NAME DP05_0001E DP05_0001M
## 1 48029190601 Census Tract 1906.01, Bexar County, Texas       4514        788
## 2 48029181100    Census Tract 1811, Bexar County, Texas       6359        523
## 3 48029140700    Census Tract 1407, Bexar County, Texas       4838        303
## 4 48029180602 Census Tract 1806.02, Bexar County, Texas       3751        536
## 5 48029140800    Census Tract 1408, Bexar County, Texas       5124        453
## 6 48029170200    Census Tract 1702, Bexar County, Texas       5599        617
##   DP03_0062E DP03_0062M                       geometry
## 1      46818      14436 MULTIPOLYGON (((-98.52602 2...
## 2      64930       6521 MULTIPOLYGON (((-98.56225 2...
## 3      36222       6656 MULTIPOLYGON (((-98.47348 2...
## 4      38389       7038 MULTIPOLYGON (((-98.56606 2...
## 5      43299       3656 MULTIPOLYGON (((-98.4778 29...
## 6      25755       7141 MULTIPOLYGON (((-98.5283 29...
# 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,
         med_hh_inc=DP03_0062E) %>%
  st_transform(crs = 2019)%>%
  na.omit()
library(tmap)
library(tmaptools)
tm_shape(sa_acs2)+
  tm_polygons("med_hh_inc",
  title="Median Household Income",
palette="Greens",
style="pretty", n=5)+
  tm_format("World",
            title="Author: Joshua Reyna",
            legend.outside=T)+
  tm_layout(main.title="Bexar County Median Income Estimatres:Pretty Breaks",
            main.title.position = "center")+
  tm_scale_bar()+
  tm_compass()

ggsave(filename="C:/Users/josha/Desktop/Fall 2020 Stats 1 Dem/lab1map1.png",
       dpi = "print")
## Saving 7 x 5 in image