Homework_2_Jyoti

Instructions

Homework 2: Reading in Data -Read in a dataset from the _data folder in the course blog repository, or choose your own data. If you decide to use one of the datasets we have provided, please use a challenging dataset - check with us if you are not sure. -Clean the data as needed using dplyr and related tidyverse packages. -Provide a narrative about the data set (look it up if you aren’t sure what you have got) and the variables in your dataset, including what type of data each variable is. The goal of this step is to communicate in a visually appealing way to non-experts - not to replicate r-code. -Identify potential research questions that your dataset can help answer

debt <- read_xlsx("_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`

Describing the data

This data set shows amount of debt people have from 2003 by the type of debt in trillions. It has 74 rows and 8 columns. The types of debts are 6 and end of the column. It shows total amount of debt.

Tidy the data

Firstly, I changed column names and added column ‘order’ to make time series line graph.(original year variables are characteristic variables so R told me that it cannot be made into graph. Maybe there are other better way to do this, but now I don’t know about it. I’ll try to find out.)

colnames(debt) = c("year_and_quarter", "mortgage","HE_revolving", "auto_loan","credit_card","student_loan","other","total")
debt<-mutate(debt, 'order'=1:74)
colnames(debt)

[1] "year_and_quarter" "mortgage"         "HE_revolving"     "auto_loan"       
[5] "credit_card"      "student_loan"     "other"            "total"           
[9] "order"           

It has many columns so cannot easily recognized. So I pivoted it.

pivot_debt<-pivot_longer(debt,col=c(mortgage, HE_revolving, auto_loan, credit_card,student_loan,other, total), names_to="types", values_to="amount_of_debt")
pivot_debt

# A tibble: 518 × 4
   year_and_quarter order types        amount_of_debt
   <chr>            <int> <chr>                 <dbl>
 1 03:Q1                1 mortgage              4.94 
 2 03:Q1                1 HE_revolving          0.242
 3 03:Q1                1 auto_loan             0.641
 4 03:Q1                1 credit_card           0.688
 5 03:Q1                1 student_loan          0.241
 6 03:Q1                1 other                 0.478
 7 03:Q1                1 total                 7.23 
 8 03:Q2                2 mortgage              5.08 
 9 03:Q2                2 HE_revolving          0.26 
10 03:Q2                2 auto_loan             0.622
# … with 508 more rows

Now, I can see it at one glance. Its column reduced 8 to 3 and row increased 74 to 518.

Narrative about the data set

I made a time series line graph of total debt by quarter.

debt_tot<-filter(pivot_debt, types=="total")
debt_tot

# A tibble: 74 × 4
   year_and_quarter order types amount_of_debt
   <chr>            <int> <chr>          <dbl>
 1 03:Q1                1 total           7.23
 2 03:Q2                2 total           7.38
 3 03:Q3                3 total           7.56
 4 03:Q4                4 total           8.07
 5 04:Q1                5 total           8.29
 6 04:Q2                6 total           8.46
 7 04:Q3                7 total           8.83
 8 04:Q4                8 total           9.04
 9 05:Q1                9 total           9.21
10 05:Q2               10 total           9.49
# … with 64 more rows

ggplot(debt_tot, aes(x=order, y=amount_of_debt)) + 
  geom_line() +
  ggtitle("Quarterly Total Debt")

In the same way, I can draw a time series graph according to the type of debt.

debt_mort<-filter(pivot_debt, types=="mortgage")

ggplot(debt_mort, aes(x=order, y=amount_of_debt)) + 
  geom_line()+
  ggtitle("Quarterly Mortgage Debt")

Using this data, changes in debt related to the financial crisis in the late 2000s can be reviewed by type. We can find out what type of debt was sensitive to the financial crisis. In addition, research problems such as identifying which of the various debt types is most closely linked to the total debt can be established.