Results from the US Census American Community Survey, 2012.

## Format

A data frame with 2000 observations on the following 13 variables.

- income
Annual income.

- employment
Employment status.

- hrs_work
Hours worked per week.

- race
Race.

- age
Age, in years.

- gender
Gender.

- citizen
Whether the person is a U.S. citizen.

- time_to_work
Travel time to work, in minutes.

- lang
Language spoken at home.

- married
Whether the person is married.

- edu
Education level.

- disability
Whether the person is disabled.

- birth_qrtr
The quarter of the year that the person was born, e.g.

`Jan thru Mar`

.

## Examples

```
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
library(ggplot2)
library(broom)
# employed only
acs12_emp <- acs12 %>%
filter(
age >= 30, age <= 60,
employment == "employed",
income > 0
)
# linear model
ggplot(acs12_emp, mapping = aes(x = age, y = income)) +
geom_point() +
geom_smooth(method = "lm")
#> `geom_smooth()` using formula 'y ~ x'
lm(income ~ age, data = acs12_emp) %>%
tidy()
#> # A tibble: 2 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 46579. 13600. 3.43 0.000664
#> 2 age 156. 297. 0.524 0.600
# log-transormed model
ggplot(acs12_emp, mapping = aes(x = age, y = log(income))) +
geom_point() +
geom_smooth(method = "lm")
#> `geom_smooth()` using formula 'y ~ x'
lm(log(income) ~ age, data = acs12_emp) %>%
tidy()
#> # A tibble: 2 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 9.81 0.256 38.3 2.47e-152
#> 2 age 0.0138 0.00559 2.46 1.41e- 2
```