In laurelboyd/craggy_2019: Housing Data and Eviction Data for King County, Washington, United States

This RMarkdown document gives you a head start by processing the data, and lets you visualize the data using burro.

Run this code block to install burro (Data exploration app)

install.packages("remotes")
remotes::install_github("laderast/burro")

# Install from the source repository,
# if you are not running in a fork of the craggy2019 project.
# remotes::install_github("pdxrlang/craggy_2019")

Once installed, run from here on...

knitr::opts_chunk$set(echo = TRUE)
library(burro)
library(tidyr)
library(dplyr)
library(janitor)
library(craggy2019)

Looking at the evictions dataset

evictions <- readr::read_csv(system.file("extdata", "evictions.csv", package = "craggy2019")) %>% 
  janitor::clean_names() %>% 
  mutate(low_flag = factor(low_flag), imputed=factor(imputed), subbed=factor(subbed)) %>%
  mutate(parent_location = stringr::str_replace(parent_location, pattern = ", Washington", replacement = ""))

burro::explore_data(evictions)

should_be_numeric <- c("estimated_number_foreclosures", "estimated_number_mortgages", "estimated_foreclosure_rate"      , "total_90_day_vacant_residential_addresses","total_residential_addresses","estimated_90_day_vacancy_rate", "total_hicost_2004_to_2006_hmda_loans",     
"total_2004_to_2006_hmda_loans",            
"estimated_hicost_loan_rate",               
"bls_unemployment_rate", "ofheo_price_change")


forclose_wa <- readr::read_csv(system.file("extdata", "forecloseWATract.csv", package = "craggy2019")) %>%
  janitor::clean_names() %>% mutate_at(should_be_numeric, ~na_if(., "#NULL!")) %>% mutate_at(should_be_numeric, ~stringr::str_replace(., "%", "")) %>% mutate_at(should_be_numeric, as.numeric) %>% select(-county, -state, -sta)

burro::explore_data(forclose_wa)

Explore King County Zillow Values

This one doesn't work - I will push fixes to burro.

king_zillow <- readr::read_csv(system.file("extdata", "king_zillow.csv", package = "craggy2019"))

burro::explore_data(king_zillow,outcome_var = NULL)

One Night Counts

one_night <- readr::read_csv(system.file("extdata", "oneNightCount.csv", package = "craggy2019")) %>% janitor::clean_names() %>% tidyr::gather("neighborhood", "count", -year, -location)

burro::explore_data(one_night)

# Sample code for grabbing spatial data
library(tigris)
options(tigris_use_cache = TRUE)

# Grab shape files for King county at the census tract level
king_spatial <- tracts(state = "WA", county = "King")

dat <- geo_join(spatial_data = king_spatial, evictions, by = "GEOID") 

laurelboyd/craggy_2019 documentation built on Nov. 4, 2019, 4:17 p.m.