---
title: "house"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{house}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
## Setup
```{r eval = FALSE}
devtools::install()
```
```{r setup, message = FALSE, warning = FALSE}
library(tripaccess)
library(tidyverse)
```
## Exploratory Data Analysis Example
This example uses the `house` dataset to explore whether households with more drivers tend to have more vehicles.
```{r, message = FALSE, warning = FALSE, out.width = "70%"}
#> Filtered to households with at least one driver
house_with_drivers <- house |>
filter(number_drivers > 0)
#> Summary statistics of vehicles by number of drivers
house_with_drivers |>
group_by(number_drivers) |>
summarize(
households = n(),
mean_vehicles = mean(number_vehicles),
median_vehicles = median(number_vehicles),
sd_vehicles = sd(number_vehicles)
)
#> Filtered to households with at least one vehicle
house_with_vehicles <- house_with_drivers |>
filter(number_vehicles > 0)
#> Plot household vehicles by number of drivers
ggplot(data = house_with_vehicles,
aes(x = number_drivers,
y = number_vehicles)) +
geom_jitter(alpha = 0.08, width = 0.15, height = 0.15) +
geom_smooth(method = lm, se = FALSE, formula = y ~ x, color = "blue") +
labs(title = "Household Vehicles \nby Number of Drivers",
x = "Number of Drivers in Household",
y = "Number of Household Vehicles") +
theme_bw()
#> Fit a simple linear regression model
vehicles_drivers_model <- lm(number_vehicles ~ number_drivers,
data = house_with_vehicles)
summary(vehicles_drivers_model)
#> Correlation between number of drivers and number of vehicles
cor(house_with_vehicles$number_drivers, house_with_vehicles$number_vehicles)
#> The slope estimates the average change in household vehicles for one
#> additional driver in the household.
coef(vehicles_drivers_model)["number_drivers"]
```