Challenge 9

Creating a function

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

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

Challenge Overview

Today’s challenge is simple. Create a function, and use it to perform a data analysis / cleaning / visualization task:

Examples of such functions are: 1) A function that reads in and cleans a dataset.
2) A function that computes summary statistics (e.g., computes the z score for a variable).
3) A function that plots a histogram.

That’s it!

Create a function to read in and clean a dataset.

I will be reusing the dataset - debt_in_trillion as I’m already familiar with it (Challenge 6). Here using a function, I’m creating a new column called Date which parses date and quarter column into a single Date column. Q1, Q2, Q3, Q4 will be mapped to 01, 04, 07, and 10 respectively.

data <- read_xlsx("_data/debt_in_trillions.xlsx")
view(data)

debt_data <- function(x) {
  data <-read_excel("_data/debt_in_trillions.xlsx")
  data <- data%>%
         mutate(Date = parse_date_time(`Year and Quarter`, orders="yq"))
  return(data)
}
debt_data()

# 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: Date <dttm>, and abbreviated variable
#   names ¹​`HE Revolving`, ²​`Auto Loan`, ³​`Credit Card`, ⁴​`Student Loan`

Function to calculate summary statistics - z-score of a variable

Below is a new function to calculate z-score for a variable, that is a column in the data for our use case.

z_score <- function(val) {
  result <- (val - mean(val)) / sd(val)
  return(result)
}

Calculating z-score for total column in the data.

data <-read_excel("_data/debt_in_trillions.xlsx")
z_score(data$Total) 

 [1] -2.61296476 -2.52529286 -2.42693486 -2.13369932 -2.00512152 -1.90705078
 [7] -1.69275444 -1.57290935 -1.47805593 -1.31425999 -1.14173132 -1.02102445
[13] -0.80293627 -0.59306374 -0.38376573 -0.26776994 -0.16320711  0.04046059
[19]  0.20287792  0.34024566  0.43498418  0.47094920  0.51478515  0.51162529
[25]  0.43101999  0.36069861  0.28704502  0.22218159  0.19391516  0.09400595
[31]  0.03735818 -0.03801898 -0.01434871 -0.02865429 -0.06789407 -0.13936449
[37] -0.19712906 -0.22715130 -0.26966586 -0.25185571 -0.31505302 -0.35986566
[43] -0.28690149 -0.14844192 -0.07432871 -0.08467009 -0.03985745  0.02736151
[49]  0.04115002  0.04229906  0.16409752  0.19339809  0.27153295  0.29164118
[55]  0.32783601  0.45767776  0.54328139  0.60877679  0.67542123  0.78630379
[61]  0.82249861  0.86960934  0.99542944  1.01381411  1.08505472  1.19536276
[67]  1.24821869  1.35910125  1.44815201  1.42861829  1.47860162  1.59695296
[73]  1.64578724  1.82561232

Function to plot graph

data <- data %>%
mutate(Date = parse_date_time(`Year and Quarter`, orders="yq"))

 data <- data %>%
     pivot_longer(
         cols = c(`Mortgage`, `HE Revolving`, `Auto Loan`, `Credit Card`, `Student Loan`, `Other`, `Total`),
         names_to = "Type", 
         values_to = "Value"
     )

 plot_trend <- function(data) {
 ggplot(data, aes(x=`Date`, y=`Value`, fill=`Type`)) +
 geom_area()
 }

plot_trend(data)