Analyzing US Census Data in R
Kyle Walker
Instructor
library(tidycensus)
library(tidyverse)
wa_income <- get_acs(geography = "county", state = "WA",
table = "B19001")
# A tibble: 663 x 5
GEOID NAME variable estimate moe
<chr> <chr> <chr> <dbl> <dbl>
1 53001 Adams County, Washington B19001_001 5733 124
2 53001 Adams County, Washington B19001_002 400 100
3 53001 Adams County, Washington B19001_003 252 87
4 53001 Adams County, Washington B19001_004 373 126
5 53001 Adams County, Washington B19001_005 456 133
6 53001 Adams County, Washington B19001_006 396 103
# ... with 657 more rows
race_vars <- c(White = "B03002_003", Black = "B03002_004", Native = "B03002_005", Asian = "B03002_006", HIPI = "B03002_007", Hispanic = "B03002_012")
tx_race <- get_acs(geography = "county", state = "TX", variables = race_vars, summary_var = "B03002_001") tx_race
# A tibble: 1,524 x 7
GEOID NAME variable estimate moe summary_est summary_moe
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 48001 Anderson County, Texas White 34680 5 57772 NA
2 48001 Anderson County, Texas Black 12246 146 57772 NA
3 48001 Anderson County, Texas Native 206 58 57772 NA
4 48001 Anderson County, Texas Asian 336 71 57772 NA
5 48001 Anderson County, Texas HIPI 8 14 57772 NA
6 48001 Anderson County, Texas Hispanic 9799 NA 57772 NA
# ... with 1,518 more rows
tx_race_pct <- tx_race %>%
mutate(pct = 100 * (estimate / summary_est)) %>%
select(NAME, variable, pct)
tx_race_pct
# A tibble: 1,524 x 3
NAME variable pct
<chr> <chr> <dbl>
1 Anderson County, Texas White 60.0
2 Anderson County, Texas Black 21.2
3 Anderson County, Texas Native 0.357
4 Anderson County, Texas Asian 0.582
5 Anderson County, Texas HIPI 0.0138
6 Anderson County, Texas Hispanic 17.0
# ... with 1,518 more rows
Analyzing US Census Data in R