Challenge 6

Visualizing Time and Relationships
Author

Tejaswini_Ketineni

Published

December 4, 2022

#loading all the required libraries
knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)
library(tidyverse)
── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
✔ ggplot2 3.4.0      ✔ purrr   0.3.5 
✔ tibble  3.1.8      ✔ dplyr   1.0.10
✔ tidyr   1.2.1      ✔ stringr 1.5.0 
✔ readr   2.1.3      ✔ forcats 0.5.2 
Warning: package 'ggplot2' was built under R version 4.2.2
Warning: package 'stringr' was built under R version 4.2.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
library(ggplot2)
library(readxl)
library(lubridate)
Warning: package 'lubridate' was built under R version 4.2.2
Loading required package: timechange
Warning: package 'timechange' was built under R version 4.2.2

Attaching package: 'lubridate'

The following objects are masked from 'package:base':

    date, intersect, setdiff, union
library(summarytools)

Attaching package: 'summarytools'

The following object is masked from 'package:tibble':

    view
library(skimr)

Challenge Overview

Today’s challenge is to:

  1. read in a data set, and describe the data set using both words and any supporting information (e.g., tables, etc)
  2. tidy data (as needed, including sanity checks)
  3. mutate variables as needed (including sanity checks)
  4. create at least one graph including time (evolution)
  • try to make them “publication” ready (optional)
  • Explain why you choose the specific graph type
  1. Create at least one graph depicting part-whole or flow relationships
  • try to make them “publication” ready (optional)
  • Explain why you choose the specific graph type

R Graph Gallery is a good starting point for thinking about what information is conveyed in standard graph types, and includes example R code.

(be sure to only include the category tags for the data you use!)

Read in data

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

  • debt ⭐
  • fed_rate ⭐⭐
  • abc_poll ⭐⭐⭐
  • usa_hh ⭐⭐⭐
  • hotel_bookings ⭐⭐⭐⭐
  • AB_NYC ⭐⭐⭐⭐⭐

{r}

library(readxl)
debt_in_trillions <- read_excel("_data/debt_in_trillions.xlsx")

Briefly describe the data

#we will use library fun modeling to get variable distribution
library(funModeling)
plot_num(debt_in_trillions)

summary(debt_in_trillions)  
 Year and Quarter      Mortgage       HE Revolving      Auto Loan     
 Length:74          Min.   : 4.942   Min.   :0.2420   Min.   :0.6220  
 Class :character   1st Qu.: 8.036   1st Qu.:0.4275   1st Qu.:0.7430  
 Mode  :character   Median : 8.412   Median :0.5165   Median :0.8145  
                    Mean   : 8.274   Mean   :0.5161   Mean   :0.9309  
                    3rd Qu.: 9.047   3rd Qu.:0.6172   3rd Qu.:1.1515  
                    Max.   :10.442   Max.   :0.7140   Max.   :1.4150  
  Credit Card      Student Loan        Other            Total       
 Min.   :0.6590   Min.   :0.2407   Min.   :0.2960   Min.   : 7.231  
 1st Qu.:0.6966   1st Qu.:0.5333   1st Qu.:0.3414   1st Qu.:11.311  
 Median :0.7375   Median :0.9088   Median :0.3921   Median :11.852  
 Mean   :0.7565   Mean   :0.9189   Mean   :0.3831   Mean   :11.779  
 3rd Qu.:0.8165   3rd Qu.:1.3022   3rd Qu.:0.4154   3rd Qu.:12.674  
 Max.   :0.9270   Max.   :1.5840   Max.   :0.4860   Max.   :14.957  
#The dataset gave us information on different types of loan through different years (2003-2021) and quarters. There are 74 rows and 8 columns, of which 1 is character type(which is Year and Quarter) and rest of the 7 are numeric. There aren’t any missing values in the columns.
#the unit ranges for these values dont look like they are on the same scale
#some are left skewed and some are right skewed except Student Loan and HE Revolving

Tidy Data (as needed)

Is your data already tidy, or is there work to be done? Be sure to anticipate your end result to provide a sanity check, and document your work here.

# assigning column names following standard convention
colnames(debt_in_trillions) <- c('year_quarter','mortgage','he_revolving','auto_loan',
'credit_card', 'student_loan','other','total')

Are there any variables that require mutation to be usable in your analysis stream? For example, do you need to calculate new values in order to graph them? Can string values be represented numerically? Do you need to turn any variables into factors and reorder for ease of graphics and visualization?

Document your work here.

# tsibble: tidy temporal data frames and tools
#library(tsibble) --> installed
#all the numerical variables are independent in nature and need no further manipulation
#year_quarter can be further divided into year and quarter columns for ease of visualizations
debt_in_trillions$year = as.integer(substring(debt_in_trillions$year_quarter, first=1, last=2))
debt_in_trillions$quarter = substring(debt_in_trillions$year_quarter, first=4, last=5)
head(debt_in_trillions)
# A tibble: 6 × 10
  year_quarter mortg…¹ he_re…² auto_…³ credi…⁴ stude…⁵ other total  year quarter
  <chr>          <dbl>   <dbl>   <dbl>   <dbl>   <dbl> <dbl> <dbl> <int> <chr>  
1 03:Q1           4.94   0.242   0.641   0.688   0.241 0.478  7.23     3 Q1     
2 03:Q2           5.08   0.26    0.622   0.693   0.243 0.486  7.38     3 Q2     
3 03:Q3           5.18   0.269   0.684   0.693   0.249 0.477  7.56     3 Q3     
4 03:Q4           5.66   0.302   0.704   0.698   0.253 0.449  8.07     3 Q4     
5 04:Q1           5.84   0.328   0.72    0.695   0.260 0.446  8.29     4 Q1     
6 04:Q2           5.97   0.367   0.743   0.697   0.263 0.423  8.46     4 Q2     
# … with abbreviated variable names ¹​mortgage, ²​he_revolving, ³​auto_loan,
#   ⁴​credit_card, ⁵​student_loan
#we added now two new columns --> year(integer) and quarter

Time Dependent Visualization

#lets check how the total loan is spread of the years
ggplot(debt_in_trillions) +
  geom_point(aes(x=year, y=total)) + labs(title = "Debt trend with time", x = "Time", y = "Total Debt") + theme_minimal()

#it increased till 2006 and then dropped(maybe due to 2008 recession? and then increased
#after 2011

Visualizing Part-Whole Relationships

#the dataset is in wide format
#before doing any further visualizations, if we convert into long format, it will be easier

debt_in_trillions_long<-debt_in_trillions%>%pivot_longer(cols = mortgage:other, names_to = "loan_type", values_to = "totals")%>%select(-total)%>%mutate(loan_type = as.factor(loan_type))
head(debt_in_trillions_long)
# A tibble: 6 × 5
  year_quarter  year quarter loan_type    totals
  <chr>        <int> <chr>   <fct>         <dbl>
1 03:Q1            3 Q1      mortgage      4.94 
2 03:Q1            3 Q1      he_revolving  0.242
3 03:Q1            3 Q1      auto_loan     0.641
4 03:Q1            3 Q1      credit_card   0.688
5 03:Q1            3 Q1      student_loan  0.241
6 03:Q1            3 Q1      other         0.478
color_values = c("aquamarine", "azure", "cornsilk", "coral1", "darkorange", 
                 "darkgoldenrod1")
ggplot(debt_in_trillions_long, aes(x=year, y=totals, fill=loan_type)) +
  geom_bar(position="stack", stat="identity") + labs(title = "Distribution of Loan Types")+
  scale_y_continuous(labels = scales::label_number(suffix = " Trillion"))+
  theme(legend.position = "top") +
  guides(fill = guide_legend(nrow = 1)) +
  scale_fill_manual(labels=
                      str_replace(levels(debt_in_trillions_long$loan_type), " ", "\n"),
                      values=color_values)

library(dplyr)

df <- debt_in_trillions_long %>%
  select(loan_type,totals) %>%
  group_by(loan_type) %>%
  summarise(loantotal = sum(totals)) %>%
  mutate(total_perc = (loantotal/sum(loantotal))*100) %>% 
  arrange(total_perc)

#for the below graph, it totals works, labels can use the value total_perc to display #total percentage

ggplot(df, aes(x="", y=total_perc, fill=loan_type)) +
  geom_bar(stat="identity", width=1) +
  coord_polar("y", start=0) + labs(title = "Proportion of Loan Types") + theme_void() + scale_fill_brewer(palette="PiYG") + geom_text(aes(x = 1.6, label = paste0(round(total_perc), "%")), position = position_stack(vjust = 0.5))