Challenge 6

challenge_6
hotel_bookings
air_bnb
fed_rate
debt
usa_households
abc_poll
Visualizing Time and Relationships
Author

Shriya Sehgal

Published

December 2, 2022

library(tidyverse)
library(ggplot2)
library(lubridate)
library(tidyr)
library(treemap)

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

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 ⭐⭐⭐⭐⭐
data <- read_csv("_data/FedFundsRate.csv")

Briefly describe the data

summary(data)
      Year          Month             Day         Federal Funds Target Rate
 Min.   :1954   Min.   : 1.000   Min.   : 1.000   Min.   : 1.000           
 1st Qu.:1973   1st Qu.: 4.000   1st Qu.: 1.000   1st Qu.: 3.750           
 Median :1988   Median : 7.000   Median : 1.000   Median : 5.500           
 Mean   :1987   Mean   : 6.598   Mean   : 3.598   Mean   : 5.658           
 3rd Qu.:2001   3rd Qu.:10.000   3rd Qu.: 1.000   3rd Qu.: 7.750           
 Max.   :2017   Max.   :12.000   Max.   :31.000   Max.   :11.500           
                                                  NA's   :442              
 Federal Funds Upper Target Federal Funds Lower Target
 Min.   :0.2500             Min.   :0.0000            
 1st Qu.:0.2500             1st Qu.:0.0000            
 Median :0.2500             Median :0.0000            
 Mean   :0.3083             Mean   :0.0583            
 3rd Qu.:0.2500             3rd Qu.:0.0000            
 Max.   :1.0000             Max.   :0.7500            
 NA's   :801                NA's   :801               
 Effective Federal Funds Rate Real GDP (Percent Change) Unemployment Rate
 Min.   : 0.070               Min.   :-10.000           Min.   : 3.400   
 1st Qu.: 2.428               1st Qu.:  1.400           1st Qu.: 4.900   
 Median : 4.700               Median :  3.100           Median : 5.700   
 Mean   : 4.911               Mean   :  3.138           Mean   : 5.979   
 3rd Qu.: 6.580               3rd Qu.:  4.875           3rd Qu.: 7.000   
 Max.   :19.100               Max.   : 16.500           Max.   :10.800   
 NA's   :152                  NA's   :654               NA's   :152      
 Inflation Rate  
 Min.   : 0.600  
 1st Qu.: 2.000  
 Median : 2.800  
 Mean   : 3.733  
 3rd Qu.: 4.700  
 Max.   :13.600  
 NA's   :194

The data described above contains the information about the different federal fund rates from 1954-2017. It displays the information about the GDP percent change, unemployment rate,___ etc.

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.

data$Date <- as.Date(with(data,paste(Year,Month,Day,sep="-")),"%Y-%m-%d")
data
# A tibble: 904 × 11
    Year Month   Day Federal F…¹ Feder…² Feder…³ Effec…⁴ Real …⁵ Unemp…⁶ Infla…⁷
   <dbl> <dbl> <dbl>       <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
 1  1954     7     1          NA      NA      NA    0.8      4.6     5.8      NA
 2  1954     8     1          NA      NA      NA    1.22    NA       6        NA
 3  1954     9     1          NA      NA      NA    1.06    NA       6.1      NA
 4  1954    10     1          NA      NA      NA    0.85     8       5.7      NA
 5  1954    11     1          NA      NA      NA    0.83    NA       5.3      NA
 6  1954    12     1          NA      NA      NA    1.28    NA       5        NA
 7  1955     1     1          NA      NA      NA    1.39    11.9     4.9      NA
 8  1955     2     1          NA      NA      NA    1.29    NA       4.7      NA
 9  1955     3     1          NA      NA      NA    1.35    NA       4.6      NA
10  1955     4     1          NA      NA      NA    1.43     6.7     4.7      NA
# … with 894 more rows, 1 more variable: Date <date>, and abbreviated variable
#   names ¹​`Federal Funds Target Rate`, ²​`Federal Funds Upper Target`,
#   ³​`Federal Funds Lower Target`, ⁴​`Effective Federal Funds Rate`,
#   ⁵​`Real GDP (Percent Change)`, ⁶​`Unemployment Rate`, ⁷​`Inflation Rate`

The columns that contains the information about the Year, month and day are combined into a single field Date.

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?

Time Dependent Visualization

Forward filling the missing federal fun rate values to get the continuous visualization.

data_filled <- data %>% fill(`Effective Federal Funds Rate`, .direction = 'updown')
ggplot(data_filled, aes(x=Date, y=data_filled$`Effective Federal Funds Rate`)) + 
  geom_line() + 
  xlab("Date") + 
  ylab("Federal Funds Rate") + 
  ggtitle("Date vs Effective Federal Funds Rate")

Visualizing Part-Whole Relationships

Lets explore the Federal Fund Rate column to get an idea of which year the rate was high using the treemap visualization.

data_filled%>%
  treemap(index=c("Year"), vSize="Effective Federal Funds Rate", title="Effective Federal Funds Rate Comparison, 1954-2017")