Challenge 4

Code

library(tidyverse)

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

Read in data

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

• abc_poll.csv ⭐
• poultry_tidy.xlsx or organiceggpoultry.xls⭐⭐
• FedFundsRate.csv⭐⭐⭐
• hotel_bookings.csv⭐⭐⭐⭐
• debt_in_trillions.xlsx ⭐⭐⭐⭐⭐

Code

debt_t <- readxl::read_excel("_data/debt_in_trillions.xlsx", sheet="Sheet1")
table(select(debt_t, `Year and Quarter`))

Year and Quarter
03:Q1 03:Q2 03:Q3 03:Q4 04:Q1 04:Q2 04:Q3 04:Q4 05:Q1 05:Q2 05:Q3 05:Q4 06:Q1 
    1     1     1     1     1     1     1     1     1     1     1     1     1 
06:Q2 06:Q3 06:Q4 07:Q1 07:Q2 07:Q3 07:Q4 08:Q1 08:Q2 08:Q3 08:Q4 09:Q1 09:Q2 
    1     1     1     1     1     1     1     1     1     1     1     1     1 
09:Q3 09:Q4 10:Q1 10:Q2 10:Q3 10:Q4 11:Q1 11:Q2 11:Q3 11:Q4 12:Q1 12:Q2 12:Q3 
    1     1     1     1     1     1     1     1     1     1     1     1     1 
12:Q4 13:Q1 13:Q2 13:Q3 13:Q4 14:Q1 14:Q2 14:Q3 14:Q4 15:Q1 15:Q2 15:Q3 15:Q4 
    1     1     1     1     1     1     1     1     1     1     1     1     1 
16:Q1 16:Q2 16:Q3 16:Q4 17:Q1 17:Q2 17:Q3 17:Q4 18:Q1 18:Q2 18:Q3 18:Q4 19:Q1 
    1     1     1     1     1     1     1     1     1     1     1     1     1 
19:Q2 19:Q3 19:Q4 20:Q1 20:Q2 20:Q3 20:Q4 21:Q1 21:Q2 
    1     1     1     1     1     1     1     1     1 

Briefly describe the data

Code

view(debt_t)
ncol(debt_t)

[1] 8

Code

nrow(debt_t)

[1] 74

Code

nrow(debt_t) * (ncol(debt_t)-1)

[1] 518

Code

debt_t2 <- debt_t %>%
  pivot_longer(col = -c(`Year and Quarter`), names_to="debt_type", values_to = "debt_value")

Identify variables that need to be mutated

The Year and Quarter variable has to be mutated to make analysis more efficient. Since Year and Quarter denotes a date or time, this is not right and needs to be converted to Date object.

Code

library(lubridate)
debt_t3 <- debt_t2 %>%
  mutate(year = str_c("20", str_sub(`Year and Quarter`, 1, 2)),quarter = str_sub(`Year and Quarter`, 5, 5), year_and_quarter = quarter(as_date(str_c(year, quarter), format="%Y%q"), with_year=TRUE)) %>% 
  select(-c(`Year and Quarter`, `year`, `quarter`)) %>%
  relocate(debt_type, debt_value, year_and_quarter) %>%
  mutate(debt_value = str_remove(as.character(debt_value), ".0+$"))

Code

head(debt_t3)

# A tibble: 6 × 3
  debt_type    debt_value year_and_quarter
  <chr>        <chr>                 <dbl>
1 Mortgage     4.942                 2003.
2 HE Revolving 0.242                 2003.
3 Auto Loan    0.641                 2003.
4 Credit Card  0.688                 2003.
5 Student Loan 0.2407                2003.
6 Other        0.4776                2003.