--- 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"] ```