Retirement Locator

Introduction

The goal of the retirementData package was to consolidate some diverse and relevant factors on where to locate for retirement. The data is located in the retirementData package but displayed on a separate dashboard here. Within the package, the main dataset is retirementLoc. Its variables are:

fips	lon	lat
state	county	pop_2020
pct_pop_change	cbsa_desig	rucc_2013
partisan_lean	med_hh_inc_2019	pct_bachelor
broadband_2017	life_exp	violent_crime_rate
average_daily_pm2_5	prim_care_dr_rate	avg_annual_temp
median_home_price	yoy_price_chg_pct	years_to_payoff

Installation

You can install the development and experimental version of retirementData from its repository with:

# Or the development version from GitHub:
# install.packages("devtools")
install_github("RobWiederstein/retirementData")

Load Data

Once the development package is installed, it can be loaded via:

data("retirementData")
#> Warning in data("retirementData"): data set 'retirementData' not found

Common Questions

The data can provide some practical guidance as to candidate locations by identifying high growth counties, reasonable housing costs, air quality and life expectancy. For example, the code below might be used to answer where housing costs are reasonable:

library(retirementData)
retirementLoc |>
        dplyr::arrange(years_to_payoff) |>
        dplyr::select(state, county, pop_2020, years_to_payoff) |>
        dplyr::slice(1:5)
#> # A tibble: 5 × 4
#>   state county   pop_2020 years_to_payoff
#>   <chr> <chr>       <dbl>           <dbl>
#> 1 OK    Harmon       2557             0.9
#> 2 OK    Tillman      7229             0.9
#> 3 WV    Wyoming     20123             0.9
#> 4 AR    Phillips    17299             1  
#> 5 MN    Traverse     3218             1

Code of Conduct

Please note that the retirementData project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.