Get the development version of ggplot2

# devtools::install_github("tidyverse/ggplot2")
library(tidycensus)
## Warning: package 'tidycensus' was built under R version 3.4.2
library(tidyverse)
## Loading tidyverse: ggplot2
## Loading tidyverse: tibble
## Loading tidyverse: tidyr
## Loading tidyverse: readr
## Loading tidyverse: purrr
## Loading tidyverse: dplyr
## Conflicts with tidy packages ----------------------------------------------
## filter(): dplyr, stats
## lag():    dplyr, stats
library(viridis)
## Loading required package: viridisLite
library(stringr)
census_api_key("7491cf955005ef7221778ebe1228115c53a12df6")
## To install your API key for use in future sessions, run this function with `install = TRUE`.
options(tigris_use_cache = TRUE)

Get a list of the ACS Variables

v15 <- load_variables(2015, "acs5", cache = TRUE)

Let’s look at median household income in the last 12 months for the State of Washington using ACS 2015 5 year estimates.

options(tigris_use_cache = TRUE)

hcs <- get_acs(state = "WA", geography = "county", 
                  variables = "B27001_001", geometry = TRUE)

head(hcs)
## Simple feature collection with 6 features and 5 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -124.7631 ymin: 45.83596 xmax: -117.8194 ymax: 48.55072
## epsg (SRID):    4269
## proj4string:    +proj=longlat +datum=NAD83 +no_defs
## # A tibble: 6 x 6
##   GEOID                        NAME   variable estimate   moe
##   <chr>                       <chr>      <chr>    <dbl> <dbl>
## 1 53005   Benton County, Washington B27001_001   183388   482
## 2 53007   Chelan County, Washington B27001_001    73694   154
## 3 53009  Clallam County, Washington B27001_001    71421   240
## 4 53015  Cowlitz County, Washington B27001_001   101330   250
## 5 53037 Kittitas County, Washington B27001_001    41897   143
## 6 53043  Lincoln County, Washington B27001_001    10234    43
## # ... with 1 more variables: geometry <S3: sfc_MULTIPOLYGON>
hcs %>%
  mutate(NAME = str_replace(NAME,'County, Washington','')) %>% 
  ggplot(aes(x = estimate, y = reorder(NAME, estimate))) + 
  geom_point()

# Focus on Thurston County

thursinc <- get_acs(state = "WA", county = "Thurston", geography = "tract", variables = "B19013_001", geometry = TRUE)

head(thursinc)
## Simple feature collection with 6 features and 5 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -123.0239 ymin: 46.89342 xmax: -122.7017 ymax: 47.168
## epsg (SRID):    4269
## proj4string:    +proj=longlat +datum=NAD83 +no_defs
## # A tibble: 6 x 6
##         GEOID                                             NAME   variable
##         <chr>                                            <chr>      <chr>
## 1 53067010400    Census Tract 104, Thurston County, Washington B19013_001
## 2 53067010510 Census Tract 105.10, Thurston County, Washington B19013_001
## 3 53067011100    Census Tract 111, Thurston County, Washington B19013_001
## 4 53067011822 Census Tract 118.22, Thurston County, Washington B19013_001
## 5 53067012330 Census Tract 123.30, Thurston County, Washington B19013_001
## 6 53067990100   Census Tract 9901, Thurston County, Washington B19013_001
## # ... with 3 more variables: estimate <dbl>, moe <dbl>, geometry <S3:
## #   sfc_MULTIPOLYGON>
thursinc %>%
  mutate(NAME = str_replace(NAME,', Thurston County, Washington','')) %>% 
  mutate(NAME = str_replace(NAME, "Census Tract","")) %>% 
  ggplot(aes(x = estimate, y = reorder(NAME, estimate))) + 
  geom_point()
## Warning: Removed 1 rows containing missing values (geom_point).