Challenge 6

challenge_6
debt
Visualizing Time and Relationships
Author

Mariia Dubyk

Published

November 19, 2022

library(tidyverse)
library(ggplot2)

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

Challenge Overview

Today’s challenge is to:

  1. read in a data set, and describe the data set using both words and any supporting information (e.g., tables, etc)
  2. tidy data (as needed, including sanity checks)
  3. mutate variables as needed (including sanity checks)
  4. create at least one graph including time (evolution)
  • try to make them “publication” ready (optional)
  • Explain why you choose the specific graph type
  1. Create at least one graph depicting part-whole or flow relationships
  • try to make them “publication” ready (optional)
  • Explain why you choose the specific graph type

R Graph Gallery is a good starting point for thinking about what information is conveyed in standard graph types, and includes example R code.

(be sure to only include the category tags for the data you use!)

Read in data

Read in one (or more) of the following datasets, using the correct R package and command.

  • debt ⭐
Debt_orig <- readxl::read_xlsx("_data/debt_in_trillions.xlsx")

Briefly describe the data

The dataset gives information about the amount of different types of dept from 2001 to 2021. We have information for each quarter for these years. We can see such types as Mortgage, HE Revolving, Auto Loan, Credit Card, Student Loan and other. Table also shows total amount. The dataset contains 74 cases and 8 variables.

summary(Debt_orig)
 Year and Quarter      Mortgage       HE Revolving      Auto Loan     
 Length:74          Min.   : 4.942   Min.   :0.2420   Min.   :0.6220  
 Class :character   1st Qu.: 8.036   1st Qu.:0.4275   1st Qu.:0.7430  
 Mode  :character   Median : 8.412   Median :0.5165   Median :0.8145  
                    Mean   : 8.274   Mean   :0.5161   Mean   :0.9309  
                    3rd Qu.: 9.047   3rd Qu.:0.6172   3rd Qu.:1.1515  
                    Max.   :10.442   Max.   :0.7140   Max.   :1.4150  
  Credit Card      Student Loan        Other            Total       
 Min.   :0.6590   Min.   :0.2407   Min.   :0.2960   Min.   : 7.231  
 1st Qu.:0.6966   1st Qu.:0.5333   1st Qu.:0.3414   1st Qu.:11.311  
 Median :0.7375   Median :0.9088   Median :0.3921   Median :11.852  
 Mean   :0.7565   Mean   :0.9189   Mean   :0.3831   Mean   :11.779  
 3rd Qu.:0.8165   3rd Qu.:1.3022   3rd Qu.:0.4154   3rd Qu.:12.674  
 Max.   :0.9270   Max.   :1.5840   Max.   :0.4860   Max.   :14.957

Tidy Data (as needed)

We need to remove column “Total”, pivot the data and change variable with information about the date.

debt <- select(Debt_orig, !contains("Total"))
debt <- debt %>%
  pivot_longer(cols = Mortgage:Other, names_to = "debt type", values_to = "amount")
debt <- debt %>%
  mutate(`Year and Quarter` = str_replace_all(`Year and Quarter`, c("20" = "2020", "21" = "2021", "03" = "2003", "04" = "2004", "05" = "2005", "06" = "2006", "07" = "2007", "08" = "2008", "09" = "2009", "10" = "2010", "11" = "2011", "12" = "2012", "13" = "2013", "14" = "2014", "15" = "2015", "16" = "2016", "17" = "2017", "18" = "2018", "19" = "2019")))
debt <- debt %>%
  mutate(`Year and Quarter` = str_replace_all(`Year and Quarter`, c("Q1" = "1", "Q2" = "2", "Q3" = "3", "Q4" = "4")))
debt <- debt %>%
  mutate(`Year and Quarter` = str_replace_all(`Year and Quarter`, c(":" = "-")))

library(date)
Error in library(date): there is no package called 'date'
library(zoo)
debt$`Year and Quarter` <- as.yearqtr(debt$`Year and Quarter`, format = "%Y-%q")
class(debt$`Year and Quarter`)
[1] "yearqtr"

Time Dependent Visualization

  1. As first visualization I chose line graph. We can see difference between debt types and changes from 2003 to 2021. It is very common choice for time dependent visualization. I think it is very easy to perceive information about dynamic in time.
ggplot(debt, aes(x = `Year and Quarter`, y = `amount`, color = `debt type`)) +
  geom_line() +
  labs(title = "Debt amount 2003-2021",
       x = "Year and Quarter",
       y = "Amount in trillions") + theme_minimal()

Visualizing Part-Whole Relationships

  1. I chose treemap chart to visualize comparison between the amount of debt types. I visualize mean for each type for 2001-2021. I am not sure that this is the proper way to work with such data. My aim was to show the difference between all types for these years.
debt <- debt %>%
  group_by(`debt type`) %>%
  select(amount, `debt type` ) %>%
  summarize_all(mean, na.rm = TRUE)
library(treemap)
treemap(debt, index="debt type", vSize="amount", type="index", title="Amount of debt by type 2003-2021")