Challenge 6 Emma Rasmussen

challenge_6

debt

Visualizing Time and Relationships

Author

Emma Rasmussen

Published

August 23, 2022

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

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

Read in data

debt<-read_excel("_data/debt_in_trillions.xlsx")
debt

# A tibble: 74 × 8
   `Year and Quarter` Mortgage HE Revolvin…¹ Auto …² Credi…³ Stude…⁴ 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
 7 04:Q3                  6.21         0.426   0.751   0.706   0.33  0.41   8.83
 8 04:Q4                  6.36         0.468   0.728   0.717   0.346 0.423  9.04
 9 05:Q1                  6.51         0.502   0.725   0.71    0.364 0.394  9.21
10 05:Q2                  6.70         0.528   0.774   0.717   0.374 0.402  9.49
# … with 64 more rows, and abbreviated variable names ¹`HE Revolving`,
#   ²`Auto Loan`, ³`Credit Card`, ⁴`Student Loan`
# ℹ Use `print(n = ...)` to see more rows

debtOrig<-debt

Briefly describe the data

This data represents debt in trillions over time, broken down by particular categories (ex: mortgage, student loans, credit card debt etc). Assume this is some amalgamation of debt owed by Americans but I could not find the original data source (again I know nothing about economics).

print(summarytools::dfSummary(debt,
                        varnumbers = FALSE,
                        plain.ascii  = FALSE, 
                        style        = "grid", 
                        graph.magnif = 0.8, 
                        valid.col    = FALSE),
      method = 'render',
      table.classes = 'table-condensed')

Data Frame Summary

debt

Dimensions: 74 x 8
Duplicates: 0

Variable

Stats / Values

Freqs (% of Valid)

Graph

Missing

Year and Quarter [character]

1. 03:Q1

2. 03:Q2

3. 03:Q3

4. 03:Q4

5. 04:Q1

6. 04:Q2

7. 04:Q3

8. 04:Q4

9. 05:Q1

10. 05:Q2

[ 64 others ]

1	(	1.4%	)
1	(	1.4%	)
1	(	1.4%	)
1	(	1.4%	)
1	(	1.4%	)
1	(	1.4%	)
1	(	1.4%	)
1	(	1.4%	)
1	(	1.4%	)
1	(	1.4%	)
64	(	86.5%	)

0 (0.0%)

Mortgage [numeric]

Mean (sd) : 8.3 (1.2)

min ≤ med ≤ max:

4.9 ≤ 8.4 ≤ 10.4

IQR (CV) : 1 (0.1)

74 distinct values

0 (0.0%)

HE Revolving [numeric]

Mean (sd) : 0.5 (0.1)

min ≤ med ≤ max:

0.2 ≤ 0.5 ≤ 0.7

IQR (CV) : 0.2 (0.2)

73 distinct values

0 (0.0%)

Auto Loan [numeric]

Mean (sd) : 0.9 (0.2)

min ≤ med ≤ max:

0.6 ≤ 0.8 ≤ 1.4

IQR (CV) : 0.4 (0.3)

71 distinct values

0 (0.0%)

Credit Card [numeric]

Mean (sd) : 0.8 (0.1)

min ≤ med ≤ max:

0.7 ≤ 0.7 ≤ 0.9

IQR (CV) : 0.1 (0.1)

69 distinct values

0 (0.0%)

Student Loan [numeric]

Mean (sd) : 0.9 (0.4)

min ≤ med ≤ max:

0.2 ≤ 0.9 ≤ 1.6

IQR (CV) : 0.8 (0.5)

73 distinct values

0 (0.0%)

Other [numeric]

Mean (sd) : 0.4 (0)

min ≤ med ≤ max:

0.3 ≤ 0.4 ≤ 0.5

IQR (CV) : 0.1 (0.1)

70 distinct values

0 (0.0%)

Total [numeric]

Mean (sd) : 11.8 (1.7)

min ≤ med ≤ max:

7.2 ≤ 11.9 ≤ 15

IQR (CV) : 1.4 (0.1)

74 distinct values

0 (0.0%)

Generated by summarytools 1.0.1 (R version 4.2.1)
2022-08-28

Tidy Data (as needed)

#fixing date variable
debtNEW<-debt%>%
  mutate(YearQuarter=parse_date_time(`Year and Quarter`, 
                                     orders="yq"))
debtNEW

# A tibble: 74 × 9
   `Year and Quarter` Mortgage HE Revolvin…¹ Auto …² Credi…³ Stude…⁴ 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
 7 04:Q3                  6.21         0.426   0.751   0.706   0.33  0.41   8.83
 8 04:Q4                  6.36         0.468   0.728   0.717   0.346 0.423  9.04
 9 05:Q1                  6.51         0.502   0.725   0.71    0.364 0.394  9.21
10 05:Q2                  6.70         0.528   0.774   0.717   0.374 0.402  9.49
# … with 64 more rows, 1 more variable: YearQuarter <dttm>, and abbreviated
#   variable names ¹`HE Revolving`, ²`Auto Loan`, ³`Credit Card`,
#   ⁴`Student Loan`
# ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names

#pivoting debt categories
debtNEWPIVOT<-pivot_longer(debtNEW, c("Mortgage", "HE Revolving", "Auto Loan", "Credit Card", "Student Loan", "Other"),
             names_to="DebtCategory", values_to="DebtByCategory")

debtNEWPIVOT

# A tibble: 444 × 5
   `Year and Quarter` Total YearQuarter         DebtCategory DebtByCategory
   <chr>              <dbl> <dttm>              <chr>                 <dbl>
 1 03:Q1               7.23 2003-01-01 00:00:00 Mortgage              4.94 
 2 03:Q1               7.23 2003-01-01 00:00:00 HE Revolving          0.242
 3 03:Q1               7.23 2003-01-01 00:00:00 Auto Loan             0.641
 4 03:Q1               7.23 2003-01-01 00:00:00 Credit Card           0.688
 5 03:Q1               7.23 2003-01-01 00:00:00 Student Loan          0.241
 6 03:Q1               7.23 2003-01-01 00:00:00 Other                 0.478
 7 03:Q2               7.38 2003-04-01 00:00:00 Mortgage              5.08 
 8 03:Q2               7.38 2003-04-01 00:00:00 HE Revolving          0.26 
 9 03:Q2               7.38 2003-04-01 00:00:00 Auto Loan             0.622
10 03:Q2               7.38 2003-04-01 00:00:00 Credit Card           0.693
# … with 434 more rows
# ℹ Use `print(n = ...)` to see more rows

#selecting for 2021 row only. I used tail() because filter was not working for me.
debt2021<-tail(debtNEWPIVOT, n=6)
debt2021

# A tibble: 6 × 5
  `Year and Quarter` Total YearQuarter         DebtCategory DebtByCategory
  <chr>              <dbl> <dttm>              <chr>                 <dbl>
1 21:Q2               15.0 2021-04-01 00:00:00 Mortgage             10.4  
2 21:Q2               15.0 2021-04-01 00:00:00 HE Revolving          0.322
3 21:Q2               15.0 2021-04-01 00:00:00 Auto Loan             1.42 
4 21:Q2               15.0 2021-04-01 00:00:00 Credit Card           0.787
5 21:Q2               15.0 2021-04-01 00:00:00 Student Loan          1.57 
6 21:Q2               15.0 2021-04-01 00:00:00 Other                 0.421

#data frame with 2 variables debt category and debt in trillions for 2021.
select(debt2021, 4, 5)

# A tibble: 6 × 2
  DebtCategory DebtByCategory
  <chr>                 <dbl>
1 Mortgage             10.4  
2 HE Revolving          0.322
3 Auto Loan             1.42 
4 Credit Card           0.787
5 Student Loan          1.57 
6 Other                 0.421

#creating a new df of my select manually. debt2021 did not work with gg plot so i created a data frame by type in the values which doesn't feel right. Then the graph worked though. I feel like this code is very inefficient
debt2021df <- data.frame(
  name=c("Mortgage","Revolving","Auto Loan","Credit Card","Student Loan", "Other") ,  
  value=c(10.442, 0.322, 1.415, 0.787, 1.570, 0.421)
  )

debt2021df

          name  value
1     Mortgage 10.442
2    Revolving  0.322
3    Auto Loan  1.415
4  Credit Card  0.787
5 Student Loan  1.570
6        Other  0.421

Time Dependent Visualization

ggplot(debtNEW, aes(YearQuarter, Total))+geom_line()+
  labs(x="Date", y="Debt in Trillions", title="Debt 2003-2021")

I chose a line graph to help visualize change over time. Easier to visualize increase and deacrease in debt over the date variable ## Visualizing Part-Whole Relationships

#Creating bar graph to transform into pie chart
ggplot(debt2021df, aes(x=name, y=value)) + 
  geom_bar(stat = "identity")+
  labs(x="Debt Category", y="Debt in Trillions", title= "2021 Quarter 2 Debt in Trillions")

#trying to create pie chart
ggplot(debt2021df, aes(x=name, y=value)) + 
  geom_bar(stat = "identity")+
  labs(x="Debt Category", y="Debt in Trillions", title= "2021 Quarter 2 Debt in Trillions")+
  coord_polar()

#I don't know what happened. I need help. I was trying to make a simple pie chart

I chose a pie chart (what was meant to be a pie chart) to help visualize proportion of debt (Bar graph was just precursor to make pie chart with ggplot). I think this paints a clearer picture than a bar graph of how debt falls proportionally by category.