Challenge 6

challenge_6
hotel_bookings
air_bnb
fed_rate
debt
usa_households
abc_poll
Visualizing Time and Relationships
Author

Prachiti Parkar

Published

May 7, 2022

library(tidyverse)
library(ggplot2)
library(readxl)
library(tidyr)

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 ⭐
  • fed_rate ⭐⭐
  • abc_poll ⭐⭐⭐
  • usa_hh ⭐⭐⭐
  • hotel_bookings ⭐⭐⭐⭐
  • AB_NYC ⭐⭐⭐⭐⭐
debt_data_og <- read_xlsx("_data/debt_in_trillions.xlsx")
debt_data <- debt_data_og
head(debt_data)
# A tibble: 6 × 8
  `Year and Quarter` Mortgage `HE Revolving` Auto …¹ Credi…² Stude…³ Other Total
  <chr>                 <dbl>          <dbl>   <dbl>   <dbl>   <dbl> <dbl> <dbl>
1 03:Q1                  4.94          0.242   0.641   0.688   0.241 0.478  7.23
2 03:Q2                  5.08          0.26    0.622   0.693   0.243 0.486  7.38
3 03:Q3                  5.18          0.269   0.684   0.693   0.249 0.477  7.56
4 03:Q4                  5.66          0.302   0.704   0.698   0.253 0.449  8.07
5 04:Q1                  5.84          0.328   0.72    0.695   0.260 0.446  8.29
6 04:Q2                  5.97          0.367   0.743   0.697   0.263 0.423  8.46
# … with abbreviated variable names ¹​`Auto Loan`, ²​`Credit Card`,
#   ³​`Student Loan`
summary(debt_data)
 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  
dim(debt_data)
[1] 74  8

Briefly describe the data

There are 8 columns and 74 rows. The data shows the debt over period of year and quarters. The debt has various types - mortgage, HE Revolving, Auto Loan, Credit Card, Student Loan, Other accumulated to total debt in the last column.

Tidy Data (as needed)

Is your data already tidy, or is there work to be done? Be sure to anticipate your end result to provide a sanity check, and document your work here.

I believe it would be easier to perform visualizations if we divide the year and quarter into 2 separate columns.

Are there any variables that require mutation to be usable in your analysis stream? For example, do you need to calculate new values in order to graph them? Can string values be represented numerically? Do you need to turn any variables into factors and reorder for ease of graphics and visualization?

Document your work here.

debt_data <- debt_data %>%
  rename(year_quarter = `Year and Quarter`)

debt_data <- debt_data %>%
  separate(year_quarter, into = c("Year", "Quarter"), sep = ":Q")

head(debt_data)
# A tibble: 6 × 9
  Year  Quarter Mortgage `HE Revolving` `Auto Loan` Credit…¹ Stude…² Other Total
  <chr> <chr>      <dbl>          <dbl>       <dbl>    <dbl>   <dbl> <dbl> <dbl>
1 03    1           4.94          0.242       0.641    0.688   0.241 0.478  7.23
2 03    2           5.08          0.26        0.622    0.693   0.243 0.486  7.38
3 03    3           5.18          0.269       0.684    0.693   0.249 0.477  7.56
4 03    4           5.66          0.302       0.704    0.698   0.253 0.449  8.07
5 04    1           5.84          0.328       0.72     0.695   0.260 0.446  8.29
6 04    2           5.97          0.367       0.743    0.697   0.263 0.423  8.46
# … with abbreviated variable names ¹​`Credit Card`, ²​`Student Loan`

Time Dependent Visualization

avg_debt <- debt_data %>%
  group_by(Year) %>%
  summarise(AvgTotal = mean(Total))

head(avg_debt)
# A tibble: 6 × 2
  Year  AvgTotal
  <chr>    <dbl>
1 03        7.56
2 04        8.66
3 05        9.62
4 06       10.9 
5 07       12.0 
6 08       12.6 
ggplot(avg_debt,aes(x=Year, y=AvgTotal)) + geom_point() + ggtitle("Evolution of Average Debt over Years")

Visualizing Part-Whole Relationships

debt_data<-debt_data%>%
  mutate(Year = as.integer(Year)+2000,Quarter = as.integer(Quarter))%>%
  gather("debt_type", "amount", Mortgage:Total)

head(debt_data)
# A tibble: 6 × 4
   Year Quarter debt_type amount
  <dbl>   <int> <chr>      <dbl>
1  2003       1 Mortgage    4.94
2  2003       2 Mortgage    5.08
3  2003       3 Mortgage    5.18
4  2003       4 Mortgage    5.66
5  2004       1 Mortgage    5.84
6  2004       2 Mortgage    5.97
debt_data %>%
  filter(debt_type != "Total") %>%
  mutate(debt_type = fct_relevel(debt_type, "Mortgage", "Auto Loan", "Credit Card", "HE Revolving", "Other", "Student Loan")) %>%
  ggplot(aes(x = Year + (Quarter-1) / 4, y = amount, fill = debt_type)) +
  geom_area() +
  labs(title = "Debt Type Breakdown by Quarter (Stacked Area Chart)",
       x = "Year",
       y = "Debt Amount (Trillions of US Dollars)",
       fill = "Debt Type")