Challenge 9 Solution

Creating a function

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

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

Create a function that reads in and cleans a dataset

I chose to create a function that changes a column of year and quarter characters to dates, and removes the old column.

#create a function called "read_debt", f is the file name and by default is false
read_debt<-function(f){
#read in the data file
  d<-read_excel(f)%>%
#change the "year and quarter" to a date format
    mutate(date_orig = parse_date_time(`Year and Quarter`, orders="yq"))%>%
#remove the old column
  select(-`Year and Quarter`)
  return(d)
}

Now we can apply this to the “Debt in Trillions” dataset:

#apply function
debt_date<-read_debt("_data/debt_in_trillions.xlsx")

#check it out!
debt_date

ABCDEFGHIJ0123456789

Mortgage <dbl>	HE Revolving <dbl>	Auto Loan <dbl>	Credit Card <dbl>	Student Loan <dbl>	Other <dbl>	Total <dbl>	date_orig <dttm>
4.942	0.2420	0.6410	0.6880	0.2407000	0.4776	7.23130	2003-01-01
5.080	0.2600	0.6220	0.6930	0.2429000	0.4860	7.38390	2003-04-01
5.183	0.2690	0.6840	0.6930	0.2488000	0.4773	7.55510	2003-07-01
5.660	0.3020	0.7040	0.6980	0.2529000	0.4486	8.06550	2003-10-01
5.840	0.3280	0.7200	0.6950	0.2598000	0.4465	8.28930	2004-01-01
5.967	0.3670	0.7430	0.6970	0.2629000	0.4231	8.46000	2004-04-01
6.210	0.4260	0.7510	0.7060	0.3300000	0.4100	8.83300	2004-07-01
6.360	0.4680	0.7280	0.7170	0.3457000	0.4229	9.04160	2004-10-01
6.512	0.5020	0.7250	0.7100	0.3636000	0.3941	9.20670	2005-01-01
6.696	0.5280	0.7740	0.7170	0.3744000	0.4024	9.49180	2005-04-01

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Et voila!