TidyCensus

Kyle Walker’s tutorials introduce TidyCensus in R.

Here is a bar graph showing median income for Story, Grundy, Chickasaw, and Buchanan counties. Also included is each estimate’s margin of error.

library(tidycensus)
library(ggplot2)
library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.2     ✔ readr     2.1.4
✔ forcats   1.0.0     ✔ stringr   1.5.0
✔ lubridate 1.9.2     ✔ tibble    3.2.1
✔ purrr     1.0.1     ✔ tidyr     1.3.0
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

median_income <- get_acs(
  geography = "county",
  variables = "B19013_001",
  year = 2021
)

Getting data from the 2017-2021 5-year ACS

winvest_counties <- get_acs(
  geography = 'county',
  state = 'IA',
  county = c('Story', 'Grundy', 'Chickasaw', 'Buchanan'),
  variables = "B19013_001",
  year = 2021,
  survey = 'acs5' 
)

Getting data from the 2017-2021 5-year ACS

ggplot(winvest_counties, aes(y = estimate, x = NAME)) + 
  ggtitle("Median Income")+
  geom_bar(stat="identity", color = "#3182bd", fill="#9ecae1")+labs(x="county name",y="dollars")+
  scale_x_discrete(labels = function(x) str_remove(x, " County, Iowa|, Iowa"))+
  geom_errorbar(aes(ymin = estimate - moe, ymax = estimate + moe),
              width = 0.5, linewidth = 0.5)

GitHub and Blogs

We also had a GitHub workshop this week to get familiar with GitHub actions. I created my own folder and ReadMe in the DSPG2023 Repo. We also created blogs using quarto/ RStudio.

Things to Work On

Setting up everything, especially the blog posts took a while and I had trouble with getting my changes to show up on GitHub. Thankfully I was able to get help and eventually got everything to show up correctly. I am looking forward to improving my blog pages and trying new things.