Challenge 6

Visualizing Time and Relationships

library(tidyverse)
library(ggplot2)
library(readxl)
library(lubridate)

knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)

Read in data

set.seed(42)
# read in the data using readr
debt_raw <- read_xlsx("_data/debt_in_trillions.xlsx")
head(debt_raw, 5)

Year and Quarter	Mortgage	HE Revolving	Auto Loan	Credit Card	Student Loan	Other	Total
03:Q1	4.942	0.242	0.641	0.688	0.2407	0.4776	7.2313
03:Q2	5.080	0.260	0.622	0.693	0.2429	0.4860	7.3839
03:Q3	5.183	0.269	0.684	0.693	0.2488	0.4773	7.5551
03:Q4	5.660	0.302	0.704	0.698	0.2529	0.4486	8.0655
04:Q1	5.840	0.328	0.720	0.695	0.2598	0.4465	8.2893

Briefly describe the data

This dataset accounts for debt in trillions in various categories. The categories include Mortgage, Revolving, Auto, Credit, Student, and other debt. The dataset also accounts for the total debt. The timescale of this dataset is per-quarter.

Tidy Data (as needed)

The main thing that needs to be changed is how the date is stored. Currently it is YY:QQ, where YY is the last two digits of the year, and QQ indicates the quarter.

debt <- debt_raw %>%
    mutate(date = parse_date_time(`Year and Quarter`, orders="yq"))
head(debt$date)

[1] "2003-01-01 UTC" "2003-04-01 UTC" "2003-07-01 UTC" "2003-10-01 UTC"
[5] "2004-01-01 UTC" "2004-04-01 UTC"

Time Dependent Visualization

ggplot(debt, aes(x=date, y=Total)) +
    ggtitle('Total Debt in Trillions of $') +
    geom_line()

I chose this graph because Total is a metric of change over time.

Visualizing Part-Whole Relationships

I want to create a stacked area plot to show each category of depth individually. To do this, I need to pivot the data so that one of the columns represents the type of debt.

debt_piv <- debt %>%
    pivot_longer(
        cols = c(`Mortgage`, `HE Revolving`, `Auto Loan`, `Credit Card`, `Student Loan`, `Other`),
        names_to = "Loan Type", 
        values_to = "Loan Value"
    )
head(debt_piv)

Year and Quarter	Total	date	Loan Type	Loan Value
03:Q1	7.2313	2003-01-01	Mortgage	4.9420
03:Q1	7.2313	2003-01-01	HE Revolving	0.2420
03:Q1	7.2313	2003-01-01	Auto Loan	0.6410
03:Q1	7.2313	2003-01-01	Credit Card	0.6880
03:Q1	7.2313	2003-01-01	Student Loan	0.2407
03:Q1	7.2313	2003-01-01	Other	0.4776

Now I can create the plot.

ggplot(debt_piv, aes(x=`date`, y=`Loan Value`, fill=`Loan Type`)) +
    ggtitle('Total Debt in Trillions of $') +
    geom_area()