TidyCensus Stuff
Get the development version of ggplot2
# devtools::install_github("tidyverse/ggplot2")Get a list of the acs variables.
library(tidycensus)
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, statslibrary(viridis)## Loading required package: viridisLitelibrary(stringr)
census_api_key("7491cf955005ef7221778ebe1228115c53a12df6")## To install your API key for use in future sessions, run this function with `install = TRUE`.v15 <- load_variables(2015, "acs5", cache = TRUE)# Plot of race/ethnicity by county in Washington for 2010
library(tidycensus)
library(tidyverse)
library(viridis)
census_api_key("7491cf955005ef7221778ebe1228115c53a12df6")## To install your API key for use in future sessions, run this function with `install = TRUE`.vars10 <- c("P0050003", "P0050004", "P0050006", "P0040003")
WA <- get_decennial(geography = "county", variables = vars10, year = 2010,
summary_var = "P0010001", state = "WA", geometry = TRUE) %>%
mutate(pct = 100 * (value / summary_value))
ggplot(WA, aes(fill = pct, color = pct)) +
geom_sf() +
facet_wrap(~variable)
str(WA)## Classes 'tbl_df', 'tbl' and 'data.frame': 156 obs. of 7 variables:
## $ GEOID : chr "53001" "53001" "53001" "53001" ...
## $ NAME : chr "Adams County" "Adams County" "Adams County" "Adams County" ...
## $ variable : chr "P0050003" "P0050004" "P0050006" "P0040003" ...
## $ value : num 7262 47 99 11099 20026 ...
## $ summary_value: num 18728 18728 18728 18728 21623 ...
## $ geometry :sfc_MULTIPOLYGON of length 156; first list element: List of 1
## ..$ :List of 1
## .. ..$ : num [1:174, 1:2] -118 -118 -118 -118 -118 ...
## ..- attr(*, "class")= chr "XY" "MULTIPOLYGON" "sfg"
## $ pct : num 38.776 0.251 0.529 59.264 92.614 ...m90 <- get_decennial(geography = "state", variables = "H043A001", year = 1990)
m90## # A tibble: 51 x 4
## GEOID NAME variable value
## <chr> <chr> <chr> <dbl>
## 1 01 Alabama H043A001 325
## 2 02 Alaska H043A001 559
## 3 04 Arizona H043A001 438
## 4 05 Arkansas H043A001 328
## 5 06 California H043A001 620
## 6 08 Colorado H043A001 418
## 7 09 Connecticut H043A001 598
## 8 10 Delaware H043A001 495
## 9 11 District of Columbia H043A001 479
## 10 12 Florida H043A001 481
## # ... with 41 more rowsoptions(tigris_use_cache = TRUE)
Pierce <- get_acs(state = "WA", county = "Pierce", geography = "tract",
variables = "B19013_001", geometry = TRUE)
head(Pierce)## Simple feature collection with 6 features and 5 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -122.489 ymin: 47.09672 xmax: -122.2586 ymax: 47.27054
## epsg (SRID): 4269
## proj4string: +proj=longlat +datum=NAD83 +no_defs
## # A tibble: 6 x 6
## GEOID NAME variable
## <chr> <chr> <chr>
## 1 53053060700 Census Tract 607, Pierce County, Washington B19013_001
## 2 53053061500 Census Tract 615, Pierce County, Washington B19013_001
## 3 53053063100 Census Tract 631, Pierce County, Washington B19013_001
## 4 53053071208 Census Tract 712.08, Pierce County, Washington B19013_001
## 5 53053071403 Census Tract 714.03, Pierce County, Washington B19013_001
## 6 53053071504 Census Tract 715.04, Pierce County, Washington B19013_001
## # ... with 3 more variables: estimate <dbl>, moe <dbl>, geometry <S3:
## # sfc_MULTIPOLYGON>library(viridis)
Pierce %>%
ggplot(aes(fill = estimate, color = estimate)) +
geom_sf() +
coord_sf(crs = 26911) +
scale_fill_viridis(option = "magma") +
scale_color_viridis(option = "magma") +
ggsave("Pierce.pdf")## Saving 7 x 5 in image
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)
medhh <- get_acs(state = "WA", geography = "county",
variables = "B19013_001", geometry = TRUE)
head(medhh)## 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 B19013_001 60251 1370
## 2 53007 Chelan County, Washington B19013_001 51837 1886
## 3 53009 Clallam County, Washington B19013_001 47253 1606
## 4 53015 Cowlitz County, Washington B19013_001 47452 1861
## 5 53037 Kittitas County, Washington B19013_001 46458 3093
## 6 53043 Lincoln County, Washington B19013_001 46069 1791
## # ... with 1 more variables: geometry <S3: sfc_MULTIPOLYGON>medhh %>%
mutate(NAME = str_replace(NAME,'County, Washington','')) %>%
ggplot(aes(x = estimate, y = reorder(NAME, estimate))) +
geom_point()
medhh %>%
ggplot(aes(fill = estimate, color = estimate)) +
geom_sf() +
coord_sf(crs = 26911) +
scale_fill_viridis(option = "magma") +
scale_color_viridis(option = "magma")
# 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).
thursinc %>%
ggplot(aes(fill = estimate, color = estimate)) +
geom_sf() +
coord_sf(crs = 26911) +
scale_fill_viridis(option = "magma") +
scale_color_viridis(option = "magma")