Zillow Data

Overview

1. Use a function and loop to load and clean zillow data.
2. Summarize the zillow data and display mean & standard deviation using error bars.
3. Visualize a summary of the zillow data on a US map
4. Add ggplot details to make a poster-ready plot

First load these

library(tidyverse)
library(scales) #  (change labels & breaks for axes & legends)

Then prepare to grab some zillow data. Several datasets are available in the Week 8 folder. The script found there load_clean_zillow.R can be used to load & clean the data.

Data Import & Tidy

Call a script used to load & tidy the zillow data. (Note: this file lives here)

1. Then you have 3 zillow datasets nrin, yts & nra

• nrin: income needed to rent, on average
• yts: years to save in order to be able to buy, on average
• nra: percentage spent on average house

2. You have a function clean_it(). that cleans the data
3. You then have a function get_states() to select subsets of data

Load data as follows:

# load 3 zillow datasets, clean them and create a function -
source('~/t/dat309/week8/load_clean_zillow.R')

[1] "dataset yts loaded"

[1] "dataset nrin loaded"

[1] "dataset nra loaded"

# call the function and with 2nd parameter name the variable 
zyts <- clean_it(yts, "years_to_save")

compare “years to save” among different states

states_yts <- zyts |> group_by(state_name) |> 
          summarize(
            mean_years = mean(years_to_save, na.rm = TRUE),
            # standard deviation
            sd_years = sd(years_to_save, na.rm = TRUE),
            # what's your n?
            n = n())

# look at the beginning of the data
states_yts |> head()

# A tibble: 6 × 4
  state_name mean_years sd_years     n
  <chr>           <dbl>    <dbl> <int>
1 AK               7.22    0.611   328
2 AL               6.43    1.35   1968
3 AR               6.03    1.30    984
4 AZ               9.10    2.60    984
5 CA              12.4     3.71   4264
6 CO               9.84    2.18   1148

Plot the means & standard deviations across states.

states_yts |> ggplot(
  aes(x = state_name, y = mean_years)) + 
  geom_point() + 
  # notice the +/- in the ymin & ymax
  geom_errorbar(aes(ymin = mean_years - sd_years, ymax = mean_years + sd_years)) +
  # instead of swapping x,y aesthetics use coord_flip()
  coord_flip()

Improve by reordering the state_name

states_yts |> 
  mutate(state_name = reorder(state_name,mean_years)) |>
  ggplot(
    aes(x = state_name, y = mean_years)) + 
    geom_point() + 
    geom_errorbar(
      aes(ymin = mean_years - sd_years, 
          ymax = mean_years + sd_years)) +
    labs(x = "Number of Years of Saving before Buying") +
    coord_flip()

Exercises

1. In the plot above replace the NA with National_Average or something similar.
2. Produce this plot for a single year’s worth of data.
3. Use this data to color a map of the US.

We’ll tackle Exercise 3. here:

The usmap package

To make plots on a map of the US, use the package usmap link.

One key is the “fips code”, a simple code unique to US states & counties. For example,

# install.packages("usmap")
library(usmap)
# the fips() function lives in the package usmap
fips(state = 'MI', county = 'Marquette')

[1] "26103"

Use the fips() function to mutate a new variable (also called fips) that the mapping utility can use for plotting.

# need a fips code to plot
states_yts <- states_yts |> mutate(fips = fips(state_name))

# this is NOT a ggplot gadget, this is why you must use quotes
# when accessing the variables in your data
plot_usmap(data = states_yts, 
           values = "mean_years")

Undergo a similar analysis & exercise for different data.

Income Needed to Buy

library(scales)
# load the data
znrin <- clean_it(nrin, "income_needed")

# look at just Michigan
mi <- get_states(znrin, "MI", 200)

[1] "MI"

# examine income needed vs. region
reg_mi <- group_by(mi, region_name) |>
  summarize(mean_income_needed = mean(income_needed, na.rm = TRUE),
            sd_income_needed = sd(income_needed, na.rm = TRUE))

reg_mi |> 
  mutate(region_name = reorder(region_name,mean_income_needed)) |>
  ggplot(
    aes(x = region_name, 
        y = mean_income_needed, 
        color = region_name)) + # associate color with region name
  geom_point() + # a single point tracking the mean
  geom_errorbar(
    aes(ymin = mean_income_needed - sd_income_needed, 
         ymax = mean_income_needed + sd_income_needed)) + # for the SD
  # for poster-ready plots, create good labels
  labs(y = "Income",
       x = "Region",
       title = "Income Needed Before Buying",
       subtitle = "Michigan",
       caption = "Data from Zillow") +
  scale_y_continuous(labels = label_dollar()) +
  scale_color_viridis_d(end = .75) +  # end the spectrum at greenish
  theme(legend.position = "none",
        axis.text.y = element_text(size = 7)) + 
  coord_flip()

Exercise

1. Repeat for the nra data.